tag:blogger.com,1999:blog-88553653197238510932024-03-13T09:28:59.274-07:00Shibani's Learning Analytics NotesShibanihttp://www.blogger.com/profile/09318859221170338553noreply@blogger.comBlogger27125tag:blogger.com,1999:blog-8855365319723851093.post-60398265351720101792016-10-05T18:52:00.002-07:002016-10-05T18:52:55.183-07:00New blog<div dir="ltr" style="text-align: left;" trbidi="on">
I've created a new website for my blogs:<br />
<a href="http://antonetteshibani.com/">http://antonetteshibani.com</a><br />
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I'm still keeping this blog alive for my old posts, but will not be adding new posts. Do follow my new website for updates :)<br />
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Shibanihttp://www.blogger.com/profile/09318859221170338553noreply@blogger.com0tag:blogger.com,1999:blog-8855365319723851093.post-70713904662490530002014-12-18T23:23:00.001-08:002014-12-18T23:23:46.554-08:00My reflections on DALMOOC<div dir="ltr" style="text-align: left;" trbidi="on">
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<span style="font-family: Arial, Helvetica, sans-serif;">It's been a great experience working with the Data, Analytics and Learning MOOC (<b>DALMOOC</b>) from EdX at <a href="https://www.edx.org/course/data-analytics-learning-utarlingtonx-link5-10x#.VJO6xF4AA">https://www.edx.org/course/data-analytics-learning-utarlingtonx-link5-10x#.VJO6xF4AA</a>. I'm so glad my boss found it and encouraged me to take it (Although I started only in Week 5 of the 9-Week course). It was not easy, nevertheless, it was a very rewarding experience. I would think an estimated 5 hours/week would not be sufficient to gain all the competencies, especially as the topics become tougher in the later weeks. </span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;">I liked the <b>overall design </b>of the course where the contents progress smoothly, starting from a general introduction of learning analytics, getting started with data analysis to deeper topics like social network analysis, prediction modeling and text mining. Sadly, I jumped back and forth not following the flow, since I started late. But that's the best part, because the course is designed to be flexible, as in, I can directly access topics which interest me the most, without a need to follow the order. I also liked the fact there were ways to create artifacts for future use and easy sharing, like my blog which I created exclusively for this course!</span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;">The <b>contents</b> were good and aptly selected. There are deeper stuff in each of these topics, but this course provides the basic foundation, based on which we can explore further. I found Weeks 5 & 6 to be content-rich which was necessary to understand many concepts in Weeks 7 & 8. Activities with specific points allotted in Weeks 5&6 were good, I wish there were more in other topics to test our understanding (but better organized and with some tips).The <i>voices from the field</i> about different research taking place in this field are very useful bonus content.</span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;">The ways to <b>interact</b> were well-planned and executed. The discussion forum was so useful and the students were eager to jump in to answer questions and help others. Connecting with twitter helped us to connect to instructors and other course mates with ease. I din't have luck to go into the collaborative chat though. Along with the different options of connecting with social networks like Facebook and twitter (especially in Prosolo), I personally prefer to add in LinkedIn,Google groups as well.</span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;">The instructors asked us to reflect more than to see the different activities only as homework, and not to challenge ourselves to do everything, unfortunately, both of which I was doing at times :( This is what happens when we skip some initial weeks where important instructions are given. Anyways, considering the fact that this is the first MOOC I've ever taken, I give credit to myself for doing reasonable well. Yay :)</span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;">In terms of <b>improvements</b> that I would want to see in future courses, most of them are related to <u><i>accessibility</i></u>. Most available options, though explained upfront during orientation could be better organized. For example, the different <i>contents</i> in EdX that were in tabbed format were not much noticeable. I used to go to Prosolo every time to see all the contents since I thought EdX didn't have them all (how stupid of me, lol). Another big difficulty when I first started using EdX is to identify which video I should see first in a chapter. I would often see one, and go to the next as given in alphabetical order and then realize that I should have watched the second one first. It would be good if the course contents are <i>numbered</i> and ordered that way!</span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;">The option to download <i>handouts</i> was not given in all weeks, so giving that would help. Making the bazaar <i>assignments</i> more accessible would also help students. Rather than linking to en external assignment bank (which didn't have all the weeks' assignments), it would be better if it's integrated within EdX or Prosolo. </span><span style="font-family: Arial, Helvetica, sans-serif;">I discovered late that <i>Quickhelper </i>has two different options like Question and Discussion (A tip while hovering could help, maybe). And I didn't know if I should post under each week's course or the discussion forum link (I later came to know they both link to the same place). Probably, consider any one integrated place for all <i>discussion</i>? Of course, as our instructors talked about future plans, it would be good to see r<i>eal-time analysis</i> to check who stands where in the network and whom we should get connected to for better access. </span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;">Those were some suggestions I could think of to overcome the difficulties I faced while taking this course. I think these would be definitely looked after in future courses. For the construction of such a course for first time, it is so well done. I've learned so much in so little time (miles to go, yet). Big appreciation to all instructors who put in so much time and effort to bring it together. Thank you so much!</span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;">After toiling for days to learn, do assignments and acquire competencies, it's now time to relax . And don't forget to connect to peers. Happy holidays everyone!</span></div>
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Shibanihttp://www.blogger.com/profile/09318859221170338553noreply@blogger.com1tag:blogger.com,1999:blog-8855365319723851093.post-81923764231268092222014-12-18T08:12:00.000-08:002014-12-18T08:25:18.792-08:00Concept Map<div dir="ltr" style="text-align: left;" trbidi="on">
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<span style="background-attachment: initial; background-clip: initial; background-color: #fdfdfd; background-image: initial; background-origin: initial; background-position: initial; background-repeat: initial; background-size: initial; line-height: 115%;"><span style="font-family: Arial, Helvetica, sans-serif;">Competency 9.2: Integrate various course
concepts through creation of a graphical representation (concept map) of the
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<span style="font-family: Arial, Helvetica, sans-serif;"><span style="background-color: #fdfdfd; line-height: 18.3999996185303px; text-align: justify;">We've had a fruitful time learning about different topics in Learning Analytics. The next task in hand was to create a concept map by finding connections among them. With an assurance from the instructors that there is no right or wrong concept map, I started constructing it using the tool available from </span><span style="line-height: 18.3999996185303px; text-align: left;"><a href="http://cmap.ihmc.us/download/">http://cmap.ihmc.us/download/</a></span></span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;">If you are going to do it for the first time just like me, I would recommend you to read about concept maps from few articles (The links given in the course were useful). </span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;">Since the concept maps follow hierarchy, I started from "Learning", since the impact on learning is what all methods look for, though the steps differ. I noticed that text mining and prediction modeling were connected very well. All these methods were also following the same learning analytics cycle. Our course structure reminds me of an opportunity where all these methods can be implemented to get a bigger picture of our learning using analysis of social media, logs, chats, discussion forums etc. Anyways, here it goes, my concept map integrating the different course concepts. (I know it's a bit cluttered, but bear with me, coz I'm so much in a getting-ready-for-holiday mood and didn't feel like cleaning up more!)</span></div>
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Shibanihttp://www.blogger.com/profile/09318859221170338553noreply@blogger.com1tag:blogger.com,1999:blog-8855365319723851093.post-16466313776271902832014-12-16T10:39:00.003-08:002014-12-16T10:39:52.677-08:00Competency 4.2<div dir="ltr" style="text-align: left;" trbidi="on">
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<span style="background-color: #fdfdfd; font-size: 16px; line-height: 25.6000003814697px;"><span style="font-family: Arial, Helvetica, sans-serif;">Competency 4.2: Describe and interpret the results of social network analysis for the study of learning.</span></span></div>
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I'm going to use the analysis I did by extracting twitter data with hashtag #DALMOOC (explained in my post for competency 3.2) to interpret the results of modularity in the network. The density was 0.05 which means that the network is connected relatively well. There are a few sub-communities in the network, but still the overall network is connected to some extent.</div>
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<span style="font-family: Arial, Helvetica, sans-serif;">In the modularity report, we can see the formation of 13 communities. There are some disconnected nodes in the network containing lesser people/ single person which form smaller sub-communities. We should try to connect the disconnected nodes in the network to others in the bigger community to ensure good sharing of information.</span></div>
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The giant component algorithm brought 5 main communities which are color-coded. From the centrality measures, we can see that dgasevic plays the role of a network broker by bridging many nodes in the network. The outdegree from that person is also high which means that he is involved in conversations with many people and is willing to help.</div>
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<span style="font-family: Arial, Helvetica, sans-serif;">I've kept this part of "Social network analysis" short and tried to complete it as soon as possible since we are in the last week of the course. I will be doing the final week's tasks from tomorrow and hopefully I will post a reflection about the entire course soon :)</span></div>
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Shibanihttp://www.blogger.com/profile/09318859221170338553noreply@blogger.com0tag:blogger.com,1999:blog-8855365319723851093.post-76956887518827147562014-12-16T02:32:00.002-08:002014-12-16T02:32:31.393-08:00Competency 4.1<div dir="ltr" style="text-align: left;" trbidi="on">
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<span style="background: #FDFDFD; color: #3c3c3c; font-family: "Arial","sans-serif";">Competency
4.1: Describe and critically reflect on approaches to the use of social network
analysis for the study of learning.<o:p></o:p></span></div>
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<span style="background: #FDFDFD; color: #3c3c3c; font-family: "Arial","sans-serif";">I've
seen some great insights on how to use social network analysis to improve
learning. The impact of social network analysis on educational constructs like
learning design, sense of community, creative potential, social presence,
academic performance and MOOC pedagogy looks promising. The possible data
sources could be discussion boards, course enrollments, twitter and other
social networks data, self-reports or course design. Metrics like network
density, degree centrality, eccentricity, modularity etc. help us to get an
idea about the network of and individuals in a network.<o:p></o:p></span></div>
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<span style="background: #FDFDFD; color: #3c3c3c; font-family: "Arial","sans-serif";">Learning
design could affect students’ activities in a big way. Students who are
familiar with a design are generally more comfortable using it. To see if
students in a course are learning as expected, we can monitor them using SNA
and guide them as needed. We can see at what stage the instructor's role is
more important than peer-facilitation by seeing the interactions and provide
help to students.<o:p></o:p></span></div>
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<span style="background: #FDFDFD; color: #3c3c3c; font-family: "Arial","sans-serif";">Monitoring
the sense of community will be useful in identifying isolated groups/
individuals who may not receive all information. We can in such cases guide
them to be part of larger communities. We can also advise students to join new
groups for assignments to get connected to more students. These factors can
impact the creative potential, social presence and academic performance of
students if suitable help is provided. Awareness of more ways of communication
and their usefulness should be advocated to students to help them understand
the distributed structure of MOOCs and be better involved.<o:p></o:p></span></div>
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<span style="background: #FDFDFD; color: #3c3c3c; font-family: "Arial","sans-serif";">I
would think that this kind of analysis should happen along the way in any
course to see how students are doing in the course of time. Seeing the results
at the end may not be of much help to students. Rather, positive measures can
be taken like introducing new hashtags for better connection or introducing a
list or shared document of all students and their resources will help students
better in the rest of their course. It could be beneficial to connect different social networks to get the complete picture about an individual student as well.<o:p></o:p></span></div>
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Shibanihttp://www.blogger.com/profile/09318859221170338553noreply@blogger.com0tag:blogger.com,1999:blog-8855365319723851093.post-63616123506284992162014-12-15T07:06:00.000-08:002014-12-15T07:06:44.391-08:00Competency 3.2/ Assignment 79<div dir="ltr" style="text-align: left;" trbidi="on">
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visualize analysis results in Gephi.</span><o:p></o:p></span></div>
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<span style="background: rgb(253, 253, 253); color: #3c3c3c;"><span style="font-family: Arial, Helvetica, sans-serif;">I did assignment 79 in which we are asked to extract data from twitter and analyse the twitter network in Gephi. I extracted twitter data using NodeXL, a freely available add-on to excel. I searched for all tweets with the tag DALMOOC and used it as my data set. </span></span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;"><span style="background: rgb(253, 253, 253); color: #3c3c3c;">I exported </span><span style="background-color: #fdfdfd; color: #3c3c3c;">the data from NodeXL as a Graphml file which I then used to import into Gephi. I tried different visualizations and measures. I was pretty amazed to see what difference a good visualization can make in analyzing and presenting data :) The snapshots should be quite self-explanatory.</span></span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;"><span style="background-color: #fdfdfd; color: #3c3c3c;">It was interesting to see the network graph color-coded by the geographical location </span><span style="background-color: #fdfdfd; color: #3c3c3c;">(This is only a part of the whole network graph).</span></span></div>
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<span style="color: #3c3c3c; font-family: Arial, Helvetica, sans-serif;"><span style="background-color: #fdfdfd;">I've varied the degree of centrality by color (This is only a part of the whole network graph)</span></span></div>
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<span style="background: rgb(253, 253, 253); color: #3c3c3c;"><span style="font-family: Arial, Helvetica, sans-serif;">The graph density was 0.050 and the diameter of the graph was 5. </span></span></div>
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<span style="color: #3c3c3c; font-family: Arial, Helvetica, sans-serif;"><span style="background-color: #fdfdfd;">The modularity report is shown. There were 13 communities, some having single and two peers.</span></span></div>
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<span style="color: #3c3c3c; font-family: Arial, Helvetica, sans-serif;"><span style="background-color: #fdfdfd;">I applied the giant component algorithm to filter out small communities.Now five main communities that emerged are color coded and shown below:</span></span></div>
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<span style="color: #3c3c3c; font-family: Arial, Helvetica, sans-serif;"><span style="background-color: #fdfdfd;">It was very interesting to apply different visualization techniques to our course data. Hoping to use these types of visualizations on my own research data!</span></span></div>
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Shibanihttp://www.blogger.com/profile/09318859221170338553noreply@blogger.com0tag:blogger.com,1999:blog-8855365319723851093.post-20570579696240028662014-12-15T00:35:00.002-08:002014-12-15T01:06:52.024-08:00Competency 3.1 - Basics of Social Network Analysis<div dir="ltr" style="text-align: left;" trbidi="on">
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<span style="background-color: #fdfdfd; font-family: Arial, Helvetica, sans-serif; line-height: 25.6000003814697px;">I'm going back to </span><span style="background-color: #fdfdfd; font-family: Arial, Helvetica, sans-serif; line-height: 25.6000003814697px;">Weeks 3 and 4 to</span><span style="background-color: #fdfdfd; font-family: Arial, Helvetica, sans-serif; line-height: 25.6000003814697px;"> learn about Social Network Analysis since the course is nearing completion. I will go back to the final wrap up Week 9 after I finish these two weeks' lessons.</span></div>
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<span style="background-color: #fdfdfd; line-height: 25.6000003814697px;">Competency 3.1: Define social network analysis and its main analysis methods.</span></span></h3>
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<b style="line-height: 25.6000003814697px;">Social Network Analysis (SNA) </b><span style="line-height: 25.6000003814697px;">provides insights into how different social processes unfold while learning happens in any learning environment. It helps us to study the effects of interaction and social context in education. The different network elements are actors and their relations. </span></div>
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<span style="background-attachment: initial; background-clip: initial; background-image: initial; background-origin: initial; background-position: initial; background-repeat: initial; background-size: initial; font-family: Arial, sans-serif; font-weight: normal;">The nodes/ actors could be students email
addresses, tweets or any such actions. I would typically use SNA to see the
interaction between students, for example in a chatroom/ discussion forum, to see who is talking to whom, who replies to whom, who is following what
question, who voted for a question etc. Based on the interaction patterns, we
can construct the network graph. We can from here see if any measure from the
network can correlate to learning or performance. <o:p></o:p></span></div>
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<span style="line-height: 25.6000003814697px;">Diameter:</span></h4>
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<span style="line-height: 25.6000003814697px;">Diameter determines the longest distance between any pair of nodes in a network. It measures the extent to which each individual node can communicate with any other node in the network. </span></div>
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Density:</h4>
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Density determines the potential of the entire network to talk to each other. It can be used to determine the extent to which some individual nodes share the information. The spread of information is very fast in a highly dense network. </div>
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Degree Centrality:</h4>
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Degree centrality is a simple measure that indicates the overall number of connections for each actor in a network. Network measures may have specific meaning when considered in the context of directed graphs.</div>
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<b>In-Degree Centrality:</b></div>
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In-degree centrality is a measure of the number of other nodes that directly try to establish connection to a particular node. Also refers to the popularity or prestige of a node in a network.</div>
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<b>Out-Degree Centrality:</b></div>
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Out-degree centrality is the measure of the number of nodes to which particular nodes are talking. </div>
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Betweenness Centrality:</h4>
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Betweenness centrality indicates the ease of connection with anybody else in the network, in particular, to try to connect all small sub communities in the network. Brokerage role is best measured by this measure.</div>
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Closeness Centrality:</h4>
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Closeness centrality measures the ease or the shortest distance of a node to anybody else in the network. It indicates how quickly a node can get to another node in the network.</div>
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Network Modularity:</h4>
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Network modularity is used to identify common sub-groups talking to each other where a group of actors have close ties to each other. An algorithm for finding the giant component can be used to identify the largest component of all connected nodes in the network. This filters out single nodes that are not connected to the network to easily identify and analyse communities in the network.</div>
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Shibanihttp://www.blogger.com/profile/09318859221170338553noreply@blogger.com0tag:blogger.com,1999:blog-8855365319723851093.post-32274036026126559272014-12-12T00:29:00.003-08:002014-12-12T00:29:57.928-08:00Competency 8.5<div dir="ltr" style="text-align: left;" trbidi="on">
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<span style="background: rgb(253, 253, 253); font-family: Arial, sans-serif; font-size: 12pt;">Competency 8.5: Examine texts from different
categories and notice characteristics they might want to include in feature
space for models and then use this reasoning to start to make tentative
decisions about what kinds of features to include in their models.</span><span style="font-family: 'Times New Roman', serif; font-size: 12pt;"><o:p></o:p></span></div>
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<span style="background: rgb(253, 253, 253); font-family: Arial, sans-serif; font-size: 12pt;">I tried the Bazaar activity in Prosolo (but my
myself since I didn't get matched to a teammate), to explore advanced feature extraction in LightSIDE and see which
features work well giving better performance. I first used the
sentiment_sentences data set and configured stretchy patterns using the
pre-defined categories positive and negative. There was a very significant
improvement in performance from unigrams only to stretchy patterns.</span><span style="font-family: 'Times New Roman', serif; font-size: 12pt;"><o:p></o:p></span></div>
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<span style="background: rgb(253, 253, 253); font-family: Arial, sans-serif; font-size: 12pt;">To look at the details, I used Explore results pane
to analyse the results.</span><span style="font-family: 'Times New Roman', serif; font-size: 12pt;"><o:p></o:p></span></div>
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<span style="background: rgb(253, 253, 253); font-family: Arial, sans-serif; font-size: 12pt;">The m</span><span style="font-family: Arial, sans-serif; font-size: 12pt;">ore indicative words which occurred
more times had stronger weights (E.g. dull, too, enjoyable). Commonly occurring
words like a, of and punctuation had lesser or no feature weights assigned to
them. The stretchy patterns helped in predicting many positive and
negative instances correctly, by considering the position and structure of
previous and coming words. </span><span style="font-family: Arial, sans-serif; font-size: 12pt;">Examples below:</span></div>
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STRONG-POS [GAP] , but --> the movie is loaded with good intentions ,
but ---> neg<br />
one [GAP] the STRONG-POS --> one of the best of the year --> pos</span><span style="font-family: 'Times New Roman', serif; font-size: 12pt;"><o:p></o:p></span></div>
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<a href="http://4.bp.blogspot.com/-q9jmauBG-jc/VIqlgnwqDXI/AAAAAAAABZg/zpkT8JwLpNA/s1600/7.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="http://4.bp.blogspot.com/-q9jmauBG-jc/VIqlgnwqDXI/AAAAAAAABZg/zpkT8JwLpNA/s1600/7.jpg" height="212" width="400" /></a></div>
<div style="text-align: justify;">
<br /></div>
<div class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: justify;">
<span style="font-family: Arial, sans-serif; font-size: 12pt;">In the newsgroup data set, there were overlaps in some categories like
religion & atheism, forsale & windows due to some words. The context
should be captured more in such cases using stretchy patterns.</span><span style="font-family: 'Times New Roman', serif; font-size: 12pt;"><o:p></o:p></span></div>
<div class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: justify;">
<br /></div>
<div style="text-align: justify;">
<span style="font-family: Arial, sans-serif; font-size: 12pt; line-height: 115%;">In my test data set of
plants classification into fruits, vegetables and flowers, it was seen the the
unigram features were most predictive. The structure of the text was not of
importance since the unigrams feature space did a decent prediction than bigrams and trigrams included.</span></div>
<div style="text-align: justify;">
<span style="background-color: #fdfdfd; line-height: 25.6000003814697px;"><span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></span></div>
<div style="text-align: justify;">
<span style="background-color: #fdfdfd; font-size: 16px; line-height: 25.6000003814697px;"><span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></span></div>
<div style="text-align: justify;">
<span style="background-color: #fdfdfd; font-size: 16px; line-height: 25.6000003814697px;"><span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></span></div>
<div style="text-align: justify;">
<span style="background-color: #fdfdfd; color: #3c3c3c; font-family: 'Open Sans', Verdana, Geneva, sans-serif, sans-serif; font-size: 16px; line-height: 25.6000003814697px;"><br /></span></div>
</div>
Shibanihttp://www.blogger.com/profile/09318859221170338553noreply@blogger.com0tag:blogger.com,1999:blog-8855365319723851093.post-5857273991800012492014-12-11T03:20:00.000-08:002014-12-11T22:41:03.639-08:00Competency 8.3/ 8.4<div dir="ltr" style="text-align: left;" trbidi="on">
<div style="margin-bottom: .0001pt; margin: 0in; text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;"><span style="background: rgb(253, 253, 253); font-family: Arial, sans-serif; font-size: 13pt;"><b>Competency 8.3: Compare the performance of different
models.</b></span><span style="font-size: 13pt;"><o:p></o:p></span></span></div>
<div style="margin: 0in 0in 0.0001pt; text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></div>
<div style="margin-bottom: .0001pt; margin: 0in; text-align: justify;">
</div>
<div style="margin: 0in 0in 0.0001pt; text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;"><span style="background: rgb(253, 253, 253); font-family: Arial, sans-serif; font-size: 13pt;">I compared two models, one from a unigram only feature set
and the other from a unigram, bigram and trigram feature set using my test data
set. I was at first using the Newsgroup data set as suggested in the Prosolo
assignment, but some options were not working for me in the Explore results tab
of LightSIDE. I was not sure if I could make a proper analysis without Feature
weights, so I chose to use my small test data set instead. Below is the
comparison of the two models:<o:p></o:p></span><u1:p></u1:p></span></div>
<div style="margin-bottom: .0001pt; margin: 0in; text-align: justify;">
<span style="background: rgb(253, 253, 253); font-family: Arial, Helvetica, sans-serif;"><br /></span></div>
<div class="separator" style="clear: both; text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif; margin-left: 1em; margin-right: 1em;"><br /></span></div>
<div class="separator" style="clear: both; text-align: justify;">
<span style="color: black; font-family: Arial, Helvetica, sans-serif; margin-left: 1em; margin-right: 1em;"><a href="http://1.bp.blogspot.com/-6A2GTqaXtyE/VIl49A1jSvI/AAAAAAAABYo/OXv650ku-1g/s1600/1_comparison.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="http://1.bp.blogspot.com/-6A2GTqaXtyE/VIl49A1jSvI/AAAAAAAABYo/OXv650ku-1g/s1600/1_comparison.jpg" height="210" width="400" /></a></span></div>
<div class="separator" style="clear: both; text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></div>
<div style="margin-bottom: .0001pt; margin: 0in;">
<span style="font-family: Arial, sans-serif; font-size: 13.5pt;"><b>Competency 8.4: Inspect models
and interpret the weights assigned to different features as well as to reason
about what these weights signify and whether they make sense.</b></span></div>
<div class="MsoNormal">
<span style="font-size: 13.0pt; line-height: 115%;"><br />
</span><span style="font-family: Arial, sans-serif; font-size: 13pt; line-height: 115%;">I went to the Explore results tab to do some basic
error analysis. The confusion matrix of 123 grams model was better than the 1
grams model. I looked at specific features in detail that predicted wrong
categories.</span></div>
<div style="text-align: left;">
<br /></div>
<div class="separator" style="clear: both; text-align: justify;">
<a href="http://2.bp.blogspot.com/-kT8hXG_NaPc/VIl5ffgIowI/AAAAAAAABYw/pEKvJzTH-ms/s1600/3_flowering1.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><span style="color: black; font-family: Arial, Helvetica, sans-serif;"><img border="0" src="http://2.bp.blogspot.com/-kT8hXG_NaPc/VIl5ffgIowI/AAAAAAAABYw/pEKvJzTH-ms/s1600/3_flowering1.jpg" height="212" width="400" /></span></a></div>
<div class="separator" style="clear: both; text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></div>
<div style="margin-bottom: .0001pt; margin: 0in; text-align: justify;">
<span style="background: rgb(253, 253, 253); font-family: Arial, Helvetica, sans-serif; font-size: 13pt;">E.g. The term "flowering" which had a high
Feature Influence for flowers wrongly predicted a fruit which contained the
term as a flower. Few terms like "genus", "plants" did not
make a correct prediction even along with its bigram and trigrams:<u1:p></u1:p><o:p></o:p></span></div>
<div class="separator" style="clear: both; text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></div>
<div class="separator" style="clear: both; text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif; margin-left: 1em; margin-right: 1em;"><a href="http://2.bp.blogspot.com/-gPOxIkhqKXM/VIl5klXpzYI/AAAAAAAABY4/aKZbO6JJSBQ/s1600/2_genus.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="http://2.bp.blogspot.com/-gPOxIkhqKXM/VIl5klXpzYI/AAAAAAAABY4/aKZbO6JJSBQ/s1600/2_genus.jpg" height="211" width="400" /></a></span></div>
<div class="separator" style="clear: both; text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></div>
<div class="separator" style="clear: both; text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></div>
<div style="margin-bottom: .0001pt; margin: 0in; text-align: justify;">
<span style="background: rgb(253, 253, 253); font-family: Arial, Helvetica, sans-serif; font-size: 13pt;">The data set was very small, so it did not have enough
features to train the model on. There were many wrong predictions in the case
of punctuation features as well. I guess that the model would do well
when trained with more data using unigrams, bigrams and trigrams.<u1:p></u1:p><o:p></o:p></span></div>
<div style="text-align: justify;">
<span style="background-color: #fdfdfd; font-size: 16px; line-height: 25.6000003814697px;"><span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></span></div>
<div style="text-align: justify;">
<span style="background-color: #fdfdfd; font-size: 16px; line-height: 25.6000003814697px;"><span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></span></div>
<div style="text-align: justify;">
<span style="background-color: #fdfdfd; color: #3c3c3c; font-family: 'Open Sans', Verdana, Geneva, sans-serif, sans-serif; font-size: 16px; line-height: 25.6000003814697px;"><br /></span></div>
</div>
Shibanihttp://www.blogger.com/profile/09318859221170338553noreply@blogger.com0tag:blogger.com,1999:blog-8855365319723851093.post-33361092339164746552014-12-10T17:56:00.002-08:002014-12-10T17:56:59.975-08:00Competency 8.2<div dir="ltr" style="text-align: left;" trbidi="on">
<div style="text-align: justify;">
<span style="background-color: #fdfdfd; color: #3c3c3c; font-size: 16px; line-height: 25.6000003814697px;"><span style="font-family: Arial, Helvetica, sans-serif;">Competency 8.2: Build and evaluate models using alternative feature spaces.</span></span></div>
<div style="text-align: justify;">
<span style="background-color: #fdfdfd; color: #3c3c3c; font-size: 16px; line-height: 25.6000003814697px;"><span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></span></div>
<div style="text-align: justify;">
<span style="background-color: #fdfdfd; color: #3c3c3c; font-size: 16px; line-height: 25.6000003814697px;"><span style="font-family: Arial, Helvetica, sans-serif;">I used the different feature spaces that I saved in the previous exercise for building models. My data set was very small and I intended to use it just for testing. I found significant improvement in metrics while comparing the models of POS features Vs Unigrams and bigrams. I could see from my data that the n-grams were most predictive of the categories.</span></span></div>
<div style="text-align: justify;">
<span style="background-color: #fdfdfd; color: #3c3c3c; font-size: 16px; line-height: 25.6000003814697px;"><span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></span></div>
<div class="separator" style="clear: both; text-align: justify;">
<a href="http://4.bp.blogspot.com/-rI_nzmDTCK0/VIj06OA6mBI/AAAAAAAABYE/14dAD27xwfA/s1600/sig%2Bimprovement.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><span style="font-family: Arial, Helvetica, sans-serif;"><img border="0" src="http://4.bp.blogspot.com/-rI_nzmDTCK0/VIj06OA6mBI/AAAAAAAABYE/14dAD27xwfA/s1600/sig%2Bimprovement.jpg" height="212" width="400" /></span></a></div>
<div style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></div>
<div style="text-align: justify;">
<span style="background-color: #fdfdfd; color: #3c3c3c; font-family: Arial, Helvetica, sans-serif; font-size: 16px; line-height: 25.6000003814697px;"><br /></span></div>
<div style="text-align: justify;">
<span style="background-color: #fdfdfd; color: #3c3c3c; font-family: Arial, Helvetica, sans-serif; font-size: 16px; line-height: 25.6000003814697px;">I couldn't find significant improvements in model metrics for many basic features. I used Naive Bayes as the classification algorithm. I also tried other algorithms, but there was not a big difference in the metrics' values. Few feature spaces I tried along with the metrics for their models are below:</span></div>
<div style="text-align: justify;">
<span style="background-color: #fdfdfd; color: #3c3c3c; font-size: 16px; line-height: 25.6000003814697px;"><span style="font-family: Arial, Helvetica, sans-serif;"> </span></span></div>
<table border="0" cellpadding="0" cellspacing="0" class="MsoNormalTable" style="border-collapse: collapse; margin-left: 4.65pt; text-align: justify; width: 355px;">
<tbody>
<tr style="height: .3in; mso-yfti-firstrow: yes; mso-yfti-irow: 0;">
<td style="border: solid windowtext 1.0pt; height: .3in; padding: 0in 5.4pt 0in 5.4pt; width: 135.75pt;" width="181">
<div class="MsoNormal" style="margin-bottom: 0.0001pt;">
<b><span style="color: #3c3c3c;"><span style="font-family: Arial, Helvetica, sans-serif;">Feature Space<o:p></o:p></span></span></b></div>
</td>
<td style="border-left: none; border: solid windowtext 1.0pt; height: .3in; padding: 0in 5.4pt 0in 5.4pt; width: 67.5pt;" width="90">
<div class="MsoNormal" style="margin-bottom: 0.0001pt;">
<b><span style="color: #3c3c3c;"><span style="font-family: Arial, Helvetica, sans-serif;">Accuracy<o:p></o:p></span></span></b></div>
</td>
<td style="border-left: none; border: solid windowtext 1.0pt; height: .3in; padding: 0in 5.4pt 0in 5.4pt; width: 63.0pt;" width="84">
<div class="MsoNormal" style="margin-bottom: 0.0001pt;">
<b><span style="color: #3c3c3c;"><span style="font-family: Arial, Helvetica, sans-serif;">Kappa<o:p></o:p></span></span></b></div>
</td>
</tr>
<tr style="height: .3in; mso-yfti-irow: 1;">
<td style="border-top: none; border: solid windowtext 1.0pt; height: .3in; padding: 0in 5.4pt 0in 5.4pt; width: 135.75pt;" width="181">
<div class="MsoNormal" style="margin-bottom: 0.0001pt;">
<span style="color: #3c3c3c;"><span style="font-family: Arial, Helvetica, sans-serif;">POS grams<o:p></o:p></span></span></div>
</td>
<td style="border-bottom: solid windowtext 1.0pt; border-left: none; border-right: solid windowtext 1.0pt; border-top: none; height: .3in; padding: 0in 5.4pt 0in 5.4pt; width: 67.5pt;" width="90">
<div class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: right;">
<span style="color: #3c3c3c;"><span style="font-family: Arial, Helvetica, sans-serif;">42%<o:p></o:p></span></span></div>
</td>
<td style="border-bottom: solid windowtext 1.0pt; border-left: none; border-right: solid windowtext 1.0pt; border-top: none; height: .3in; padding: 0in 5.4pt 0in 5.4pt; width: 63.0pt;" width="84">
<div class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: right;">
<span style="color: #3c3c3c;"><span style="font-family: Arial, Helvetica, sans-serif;">0.12<o:p></o:p></span></span></div>
</td>
</tr>
<tr style="height: .3in; mso-yfti-irow: 2;">
<td style="border-top: none; border: solid windowtext 1.0pt; height: .3in; padding: 0in 5.4pt 0in 5.4pt; width: 135.75pt;" width="181">
<div class="MsoNormal" style="margin-bottom: 0.0001pt;">
<span style="color: #3c3c3c;"><span style="font-family: Arial, Helvetica, sans-serif;">12 grams_count<o:p></o:p></span></span></div>
</td>
<td style="border-bottom: solid windowtext 1.0pt; border-left: none; border-right: solid windowtext 1.0pt; border-top: none; height: .3in; padding: 0in 5.4pt 0in 5.4pt; width: 67.5pt;" width="90">
<div class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: right;">
<span style="color: #3c3c3c;"><span style="font-family: Arial, Helvetica, sans-serif;">58%<o:p></o:p></span></span></div>
</td>
<td style="border-bottom: solid windowtext 1.0pt; border-left: none; border-right: solid windowtext 1.0pt; border-top: none; height: .3in; padding: 0in 5.4pt 0in 5.4pt; width: 63.0pt;" width="84">
<div class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: right;">
<span style="color: #3c3c3c;"><span style="font-family: Arial, Helvetica, sans-serif;">0.36<o:p></o:p></span></span></div>
</td>
</tr>
<tr style="height: .3in; mso-yfti-irow: 3;">
<td style="border-top: none; border: solid windowtext 1.0pt; height: .3in; padding: 0in 5.4pt 0in 5.4pt; width: 135.75pt;" width="181">
<div class="MsoNormal" style="margin-bottom: 0.0001pt;">
<span style="color: #3c3c3c;"><span style="font-family: Arial, Helvetica, sans-serif;">1 grams_pairs<o:p></o:p></span></span></div>
</td>
<td style="border-bottom: solid windowtext 1.0pt; border-left: none; border-right: solid windowtext 1.0pt; border-top: none; height: .3in; padding: 0in 5.4pt 0in 5.4pt; width: 67.5pt;" width="90">
<div class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: right;">
<span style="color: #3c3c3c;"><span style="font-family: Arial, Helvetica, sans-serif;">61%<o:p></o:p></span></span></div>
</td>
<td style="border-bottom: solid windowtext 1.0pt; border-left: none; border-right: solid windowtext 1.0pt; border-top: none; height: .3in; padding: 0in 5.4pt 0in 5.4pt; width: 63.0pt;" width="84">
<div class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: right;">
<span style="color: #3c3c3c;"><span style="font-family: Arial, Helvetica, sans-serif;">0.41<o:p></o:p></span></span></div>
</td>
</tr>
<tr style="height: .3in; mso-yfti-irow: 4;">
<td style="border-top: none; border: solid windowtext 1.0pt; height: .3in; padding: 0in 5.4pt 0in 5.4pt; width: 135.75pt;" width="181">
<div class="MsoNormal" style="margin-bottom: 0.0001pt;">
<span style="color: #3c3c3c;"><span style="font-family: Arial, Helvetica, sans-serif;">12 grams_length<o:p></o:p></span></span></div>
</td>
<td style="border-bottom: solid windowtext 1.0pt; border-left: none; border-right: solid windowtext 1.0pt; border-top: none; height: .3in; padding: 0in 5.4pt 0in 5.4pt; width: 67.5pt;" width="90">
<div class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: right;">
<span style="color: #3c3c3c;"><span style="font-family: Arial, Helvetica, sans-serif;">61%<o:p></o:p></span></span></div>
</td>
<td style="border-bottom: solid windowtext 1.0pt; border-left: none; border-right: solid windowtext 1.0pt; border-top: none; height: .3in; padding: 0in 5.4pt 0in 5.4pt; width: 63.0pt;" width="84">
<div class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: right;">
<span style="color: #3c3c3c;"><span style="font-family: Arial, Helvetica, sans-serif;">0.41<o:p></o:p></span></span></div>
</td>
</tr>
<tr style="height: .3in; mso-yfti-irow: 5;">
<td style="border-top: none; border: solid windowtext 1.0pt; height: .3in; padding: 0in 5.4pt 0in 5.4pt; width: 135.75pt;" width="181">
<div class="MsoNormal" style="margin-bottom: 0.0001pt;">
<span style="color: #3c3c3c;"><span style="font-family: Arial, Helvetica, sans-serif;">12 POS grams<o:p></o:p></span></span></div>
</td>
<td style="border-bottom: solid windowtext 1.0pt; border-left: none; border-right: solid windowtext 1.0pt; border-top: none; height: .3in; padding: 0in 5.4pt 0in 5.4pt; width: 67.5pt;" width="90">
<div class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: right;">
<span style="color: #3c3c3c;"><span style="font-family: Arial, Helvetica, sans-serif;">65%<o:p></o:p></span></span></div>
</td>
<td style="border-bottom: solid windowtext 1.0pt; border-left: none; border-right: solid windowtext 1.0pt; border-top: none; height: .3in; padding: 0in 5.4pt 0in 5.4pt; width: 63.0pt;" width="84">
<div class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: right;">
<span style="color: #3c3c3c;"><span style="font-family: Arial, Helvetica, sans-serif;">0.47<o:p></o:p></span></span></div>
</td>
</tr>
<tr style="height: .3in; mso-yfti-irow: 6;">
<td style="border-top: none; border: solid windowtext 1.0pt; height: .3in; padding: 0in 5.4pt 0in 5.4pt; width: 135.75pt;" width="181">
<div class="MsoNormal" style="margin-bottom: 0.0001pt;">
<span style="color: #3c3c3c;"><span style="font-family: Arial, Helvetica, sans-serif;">12 grams_no stop<o:p></o:p></span></span></div>
</td>
<td style="border-bottom: solid windowtext 1.0pt; border-left: none; border-right: solid windowtext 1.0pt; border-top: none; height: .3in; padding: 0in 5.4pt 0in 5.4pt; width: 67.5pt;" width="90">
<div class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: right;">
<span style="color: #3c3c3c;"><span style="font-family: Arial, Helvetica, sans-serif;">69%<o:p></o:p></span></span></div>
</td>
<td style="border-bottom: solid windowtext 1.0pt; border-left: none; border-right: solid windowtext 1.0pt; border-top: none; height: .3in; padding: 0in 5.4pt 0in 5.4pt; width: 63.0pt;" width="84">
<div class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: right;">
<span style="color: #3c3c3c;"><span style="font-family: Arial, Helvetica, sans-serif;">0.52<o:p></o:p></span></span></div>
</td>
</tr>
<tr style="height: .3in; mso-yfti-irow: 7;">
<td style="border-top: none; border: solid windowtext 1.0pt; height: .3in; padding: 0in 5.4pt 0in 5.4pt; width: 135.75pt;" width="181">
<div class="MsoNormal" style="margin-bottom: 0.0001pt;">
<span style="color: #3c3c3c;"><span style="font-family: Arial, Helvetica, sans-serif;">12 grams<o:p></o:p></span></span></div>
</td>
<td style="border-bottom: solid windowtext 1.0pt; border-left: none; border-right: solid windowtext 1.0pt; border-top: none; height: .3in; padding: 0in 5.4pt 0in 5.4pt; width: 67.5pt;" width="90">
<div class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: right;">
<span style="color: #3c3c3c;"><span style="font-family: Arial, Helvetica, sans-serif;">73%<o:p></o:p></span></span></div>
</td>
<td style="border-bottom: solid windowtext 1.0pt; border-left: none; border-right: solid windowtext 1.0pt; border-top: none; height: .3in; padding: 0in 5.4pt 0in 5.4pt; width: 63.0pt;" width="84">
<div class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: right;">
<span style="color: #3c3c3c;"><span style="font-family: Arial, Helvetica, sans-serif;">0.59<o:p></o:p></span></span></div>
</td>
</tr>
<tr style="height: .3in; mso-yfti-irow: 8; mso-yfti-lastrow: yes;">
<td style="border-top: none; border: solid windowtext 1.0pt; height: .3in; padding: 0in 5.4pt 0in 5.4pt; width: 135.75pt;" width="181">
<div class="MsoNormal" style="margin-bottom: 0.0001pt;">
<span style="color: #3c3c3c;"><span style="font-family: Arial, Helvetica, sans-serif;">123 grams<o:p></o:p></span></span></div>
</td>
<td style="border-bottom: solid windowtext 1.0pt; border-left: none; border-right: solid windowtext 1.0pt; border-top: none; height: .3in; padding: 0in 5.4pt 0in 5.4pt; width: 67.5pt;" width="90">
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<span style="color: #3c3c3c;"><span style="font-family: Arial, Helvetica, sans-serif;">73%<o:p></o:p></span></span></div>
</td>
<td style="border-bottom: solid windowtext 1.0pt; border-left: none; border-right: solid windowtext 1.0pt; border-top: none; height: .3in; padding: 0in 5.4pt 0in 5.4pt; width: 63.0pt;" width="84">
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<span style="color: #3c3c3c;"><span style="font-family: Arial, Helvetica, sans-serif;">0.59<o:p></o:p></span></span></div>
</td>
</tr>
</tbody></table>
<div style="text-align: justify;">
<span style="background-color: #fdfdfd; color: #3c3c3c; font-size: 16px; line-height: 25.6000003814697px;"><span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></span></div>
<div style="text-align: justify;">
<span style="background-color: #fdfdfd; color: #3c3c3c; font-size: 16px; line-height: 25.6000003814697px;"><span style="font-family: Arial, Helvetica, sans-serif;">To test with a real data set, I tried the hands on activity of text feature extraction given in Prosolo using sentiment_sentences data set. I extracted different feature spaces from the basic feature set and used logistic regression. There was significant improvement while expanding the feature set.</span></span></div>
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<a href="http://1.bp.blogspot.com/-MXnomywvZnE/VIj5gyvvGTI/AAAAAAAABYQ/MwOTmpKLUYs/s1600/4.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="http://1.bp.blogspot.com/-MXnomywvZnE/VIj5gyvvGTI/AAAAAAAABYQ/MwOTmpKLUYs/s1600/4.jpg" height="211" width="400" /></a></div>
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Shibanihttp://www.blogger.com/profile/09318859221170338553noreply@blogger.com0tag:blogger.com,1999:blog-8855365319723851093.post-52960643016523872272014-12-10T16:05:00.001-08:002014-12-10T16:05:56.686-08:00Competency 8.1<div dir="ltr" style="text-align: left;" trbidi="on">
<span style="background-color: #fdfdfd; color: #3c3c3c; font-size: 16px; line-height: 25.6000003814697px;"><span style="font-family: Arial, Helvetica, sans-serif;">Competency 8.1: Prepare data for use in LightSIDE and use LightSIDE to extract a wide range of feature types.</span></span><br />
<span style="background-color: #fdfdfd; color: #3c3c3c; font-size: 16px; line-height: 25.6000003814697px;"><span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></span>
<span style="background-color: #fdfdfd; color: #3c3c3c; font-size: 16px; line-height: 25.6000003814697px;"><span style="font-family: Arial, Helvetica, sans-serif;">For the purpose of this exercise, I created a simple data set of three types of plants: vegetables, fruits and flowers. I classified text (taken from Wikipedia) based on the three categories. It looked like this:</span></span><br />
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<a href="http://2.bp.blogspot.com/-Ue6ntQ8Sta0/VIjcpDR0bQI/AAAAAAAABXo/qxJwd0RCfns/s1600/test%2Bdata.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="http://2.bp.blogspot.com/-Ue6ntQ8Sta0/VIjcpDR0bQI/AAAAAAAABXo/qxJwd0RCfns/s1600/test%2Bdata.jpg" height="211" width="400" /></a></div>
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<span style="background-color: #fdfdfd; color: #3c3c3c; font-size: 16px; line-height: 25.6000003814697px;"><span style="font-family: Arial, Helvetica, sans-serif;">I loaded my input csv file into LightSIDE and extracted basic features like unigrams and bigrams first. Then I checked different basic features and extracted their feature sets.</span></span><br />
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<a href="http://3.bp.blogspot.com/-5yZC5eOTG-Y/VIjfg2Ubl8I/AAAAAAAABX0/rHzYmPjW5kg/s1600/extract.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="http://3.bp.blogspot.com/-5yZC5eOTG-Y/VIjfg2Ubl8I/AAAAAAAABX0/rHzYmPjW5kg/s1600/extract.jpg" height="212" width="400" /></a></div>
<br />
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<span style="background-color: #fdfdfd; color: #3c3c3c; font-size: 16px; line-height: 25.6000003814697px;"><span style="font-family: Arial, Helvetica, sans-serif;">I saved all the feature sets for building models later using alternative feature spaces. </span></span><br />
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Shibanihttp://www.blogger.com/profile/09318859221170338553noreply@blogger.com2tag:blogger.com,1999:blog-8855365319723851093.post-21910784014812714112014-12-10T06:12:00.002-08:002014-12-10T06:12:31.071-08:00Week 8 Activity - Data preparation<div dir="ltr" style="text-align: left;" trbidi="on">
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<b><span style="font-family: Arial, sans-serif; font-size: 12pt;">Activity: Textual data pre-processing
and informal analysis</span></b><b><span style="font-family: 'Times New Roman', serif; font-size: 12pt;"><o:p></o:p></span></b></div>
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<span style="font-family: Arial, sans-serif; font-size: 12pt;">Rule 1:</span></div>
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<span style="font-family: Arial, sans-serif; font-size: 12pt;">I created a list of positive words (unigrams and bigrams) from the given
data and used them to identify positive and negative instances.</span><span style="font-family: 'Times New Roman', serif; font-size: 12pt;"><o:p></o:p></span></div>
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<i><span lang="EN-US" style="font-family: Arial, sans-serif; font-size: 12pt;">IF (effective OR intriguing OR breathtaking OR
captivated OR (NOT not)_perfect OR loved OR real_chemistry OR really_good OR
charm OR enthralled OR beautifully_done OR thoughtprovoking OR poignant OR
fabulous OR sweet OR true_chemistry OR so_well OR enjoy OR excellent OR
well_handled OR touching OR believable OR likeable OR very_successful OR enjoy
OR interesting OR good OR entertaining OR great OR believable OR
engaging) </span></i><i><span style="font-family: Arial, sans-serif; font-size: 12pt;">THEN pos ELSE neg</span></i><span style="font-family: 'Times New Roman', serif; font-size: 12pt;"><o:p></o:p></span></div>
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<br /></div>
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<span style="font-family: Arial, sans-serif; font-size: 12pt;">This rule doesn't apply correctly on all negative instances since some
of them have positive words also. </span><span style="font-family: 'Times New Roman', serif; font-size: 12pt;"><o:p></o:p></span></div>
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<span style="font-family: Arial, sans-serif; font-size: 12pt;">Rule 2:</span></div>
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<span style="font-family: Arial, sans-serif; font-size: 12pt;">This rule is based on a list of negative words from the given data</span><span style="font-family: 'Times New Roman', serif; font-size: 12pt;"><o:p></o:p></span></div>
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<i><span style="font-family: Arial, sans-serif; font-size: 12pt;">IF </span></i><i><span lang="EN-US" style="font-family: Arial, sans-serif; font-size: 12pt;">(not_perfect OR
dull OR onedimensional OR misused OR unnatural OR lack OR missmarketed OR
went_wrong OR worst OR shallow OR awful OR terrible OR really_bad OR cliché OR
waste OR unintentional_laughs OR silliness OR immaturity OR </span></i><i><span style="font-family: Arial, sans-serif; font-size: 12pt;">passionless OR
false_hope OR collapse OR annoying OR undercut OR not_so_well OR disaster OR
not_original</span></i><i><span lang="EN-US" style="font-family: Arial, sans-serif; font-size: 12pt;">) THEN neg ELSE pos</span></i><span style="font-family: 'Times New Roman', serif; font-size: 12pt;"><o:p></o:p></span></div>
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<span lang="EN-US" style="font-family: Arial, sans-serif; font-size: 12pt;">This rule predicts some positive instances wrongly
since a few of negative words occur in positive instances. </span><span style="font-family: 'Times New Roman', serif; font-size: 12pt;"><o:p></o:p></span></div>
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<span lang="EN-US" style="font-family: Arial, sans-serif; font-size: 12pt;">Rule 3:</span><span style="font-family: 'Times New Roman', serif; font-size: 12pt;"> </span></div>
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<span lang="EN-US" style="font-family: Arial, sans-serif; font-size: 12pt;">To overcome the issue of wrong predictions due to
some instances containing both positive and negative words, I used count to
see </span><span style="font-family: Arial, sans-serif; font-size: 12pt;">which dominates which.</span><span style="font-family: 'Times New Roman', serif; font-size: 12pt;"><o:p></o:p></span></div>
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<i><span lang="EN-US" style="font-family: Arial, sans-serif; font-size: 12pt;">FOR ALL(effective OR intriguing OR breathtaking OR captivated OR (NOT
not)_perfect OR loved OR real_chemistry OR really_good OR charm OR enthralled
OR beautifully_done OR thoughtprovoking OR poignant OR fabulous OR sweet OR
true_chemistry OR so_well OR enjoy OR excellent OR well_handled OR touching OR
believable OR likeable OR very_successful OR enjoy OR interesting OR good OR
entertaining OR great OR believable OR engaging) </span></i><i><span style="font-family: Arial, sans-serif; font-size: 12pt;">Add 1 to count_pos
for each occurrence</span></i><span style="font-family: 'Times New Roman', serif; font-size: 12pt;"><o:p></o:p></span></div>
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<br /></div>
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<i><span lang="EN-US" style="font-family: Arial, sans-serif; font-size: 12pt;">FOR ALL(not_perfect OR dull OR onedimensional OR misused OR unnatural OR
lack OR missmarketed OR went_wrong OR worst OR shallow OR awful OR terrible OR
really_bad OR cliché OR waste OR unintentional_laughs OR silliness OR
immaturity OR passionless OR false_hope OR collapse OR annoying OR
undercut OR not_so_well OR disaster OR not_original) </span></i><i><span style="font-family: Arial, sans-serif; font-size: 12pt;">Add 1 to count_neg
for each occurrence</span></i><span style="font-family: 'Times New Roman', serif; font-size: 12pt;"><o:p></o:p></span></div>
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<i><span lang="EN-US" style="font-family: Arial, sans-serif; font-size: 12pt;">IF count_pos> count_neg, THEN pos</span></i><span style="font-family: 'Times New Roman', serif; font-size: 12pt;"><o:p></o:p></span></div>
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<i><span lang="EN-US" style="font-family: Arial, sans-serif; font-size: 12pt;">ELSE neg</span></i><span style="font-family: 'Times New Roman', serif; font-size: 12pt;"><o:p></o:p></span></div>
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<span lang="EN-US" style="font-family: Arial, sans-serif; font-size: 12pt;">Rule 4:</span><span style="font-family: 'Times New Roman', serif; font-size: 12pt;"> </span></div>
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<span style="font-family: Arial, sans-serif; font-size: 12pt;">We can see that the list of words are hand-picked based on our sample
data, so the above rule over-fits to our data. I removed words which may have
different contexts in different occurrences and maintained only words that are
predictive at all occurrences.</span><span style="font-family: 'Times New Roman', serif; font-size: 12pt;"><o:p></o:p></span></div>
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<br /></div>
<div class="MsoNormal" style="margin: 0in 0in 0.0001pt 0.5in;">
<i><span lang="EN-US" style="font-family: Arial, sans-serif; font-size: 12pt;">FOR ALL(effective OR breathtaking OR loved OR (real OR
true_chemistry) OR really_good OR enthralled OR beautifully_done OR
thoughtprovoking OR fabulous OR excellent OR well_handled OR very_successful) </span></i><i><span style="font-family: Arial, sans-serif; font-size: 12pt;">Add 1 to count_pos
for each occurrence</span></i><span style="font-family: 'Times New Roman', serif; font-size: 12pt;"><o:p></o:p></span></div>
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<br /></div>
<div class="MsoNormal" style="margin: 0in 0in 0.0001pt 0.5in;">
<i><span lang="EN-US" style="font-family: Arial, sans-serif; font-size: 12pt;">FOR ALL(dull OR unnatural OR missmarketed OR went_wrong OR worst OR
shallow OR awful OR terrible OR really_bad OR waste OR silliness OR
annoying) </span></i><i><span style="font-family: Arial, sans-serif; font-size: 12pt;">Add 1 to count_neg for each occurrence</span></i><span style="font-family: 'Times New Roman', serif; font-size: 12pt;"><o:p></o:p></span></div>
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<br /></div>
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<i><span lang="EN-US" style="font-family: Arial, sans-serif; font-size: 12pt;">IF count_pos> count_neg, THEN pos</span></i><span style="font-family: 'Times New Roman', serif; font-size: 12pt;"><o:p></o:p></span></div>
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<i><span lang="EN-US" style="font-family: Arial, sans-serif; font-size: 12pt;">ELSE neg</span></i><span style="font-family: 'Times New Roman', serif; font-size: 12pt;"><o:p></o:p></span></div>
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<br /></div>
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<span style="font-family: Arial, sans-serif; font-size: 12pt;">Even though the above rule seems to fit okay, it may not be very
predictive of instances which contain <u>words other than the ones liste</u>d
or which contain an <u>opposite context of a word</u>. They can be
captured to some extent by complex rules involving the proximity of word
occurrence. More features can be added and tested by cross-validation until we
get a model with reasonable reliability. My take away is that it is not at all
an easy task! :)</span><span style="font-family: 'Times New Roman', serif; font-size: 12pt;"><o:p></o:p></span></div>
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Shibanihttp://www.blogger.com/profile/09318859221170338553noreply@blogger.com0tag:blogger.com,1999:blog-8855365319723851093.post-90033373093231387722014-12-09T03:33:00.000-08:002014-12-09T03:33:49.758-08:00Week 6 Activity<div dir="ltr" style="text-align: left;" trbidi="on">
<div style="margin-bottom: .0001pt; margin: 0in; text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;">In the activity for Week 6, we
were asked to calculate different metrics for assessing models which were discussed in Ryan Baker's
unit of Behavior Detection and Model Assessment. T<span style="background-attachment: initial; background-clip: initial; background-color: white; background-image: initial; background-origin: initial; background-position: initial; background-repeat: initial; background-size: initial;">wo
data sets, </span>classifier-data-asgn2.csv<span style="background-attachment: initial; background-clip: initial; background-color: white; background-image: initial; background-origin: initial; background-position: initial; background-repeat: initial; background-size: initial;"> and </span>regressor-data-asgn2.csv
were given. <o:p></o:p></span></div>
<div style="margin: 0in 0in 0.0001pt; text-align: justify;">
<br /></div>
<div style="margin: 0in 0in 0.0001pt; text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;">I used Excel for these
calculations and for the last metric (A' or AUC), I downloaded a plugin called
XLSTAT from <a href="http://www.xlstat.com/en/">http://www.xlstat.com/en/</a> since SPSS didnot give the correct
answer. I will detail out the steps which I followed to complete this
activity containing 11 questions. I urge you to save all the steps since you may need the answer of previous steps to continue the next steps. To better understand the steps I've described, refer the lecture videos :)</span></div>
<div style="margin: 0in 0in 0.0001pt; text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></div>
<h4 style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><i>Q1) Using regressor-data-asgn2.csv, what is the Pearson
correlation between data and predicted (model)? (Round to three significant
digits; e.g. 0.24675 should be written as 0.247) (Hint: this is easy to compute
in Excel)</i></span></h4>
<div style="margin: 0in 0in 0.0001pt; text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></div>
<div class="MsoNormal" style="text-align: left;">
<span style="line-height: 115%;"><span style="font-family: Arial, Helvetica, sans-serif;"> Use the excel function CORREL or PEARSON to calculate the Pearson correlation for the regressor model using the given two input arrays of data. Round the number you get, instead of truncating it to get the correct answer.</span></span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></div>
<h4 style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><i>Q2) Using regressor-data-asgn2.csv, what is the RMSE between
data and predicted (model)? (Round to three significant digits; e.g. 0.24675
should be written as 0.247) (Hint: this is easy to compute in Excel)</i></span></h4>
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<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></div>
<div class="MsoNormal" style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">Calculate the residual (difference between actual data and
predicted model) and use those values for the array in the below formula:<o:p></o:p></span></div>
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<span style="background: rgb(250, 250, 250); color: #333333; line-height: 115%;"><span style="font-family: Arial, Helvetica, sans-serif;">=SQRT(SUMSQ(A2:A1001)/COUNTA(A2:A1001))</span></span></div>
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<span style="background: rgb(250, 250, 250); color: #333333; line-height: 115%;"><span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></span></div>
<div class="MsoNormal" style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><span style="background: rgb(250, 250, 250); color: #333333; line-height: 115%;"></span></span></div>
<h4 style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><i>Q3) Using regressor-data-asgn2.csv, what is the MAD between
data and predicted (model)? (Round to three significant digits; e.g. 0.24675
should be written as 0.247) (Hint: this is easy to compute in Excel)</i></span></h4>
<div class="MsoNormal" style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></div>
<div class="MsoNormal" style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><span style="background: rgb(250, 250, 250); color: #333333; line-height: 115%;"><o:p> Calculate the </o:p></span>absolute values of the previous residual values in an array =ABS(<span style="background: rgb(250, 250, 250); color: #333333; line-height: 15.3333320617676px;">RMSE!A2:A1001) </span>and average them. </span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></div>
<h4 style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><i>Q4) Using classifier-data-asgn2.csv, what is the accuracy of
the predicted (model)? Assume a threshold of 0.5. (Just give a rounded value
rather than including the decimal; e.g. write 57.213% as 57) (Hint: this is
easy to compute in Excel)</i></span></h4>
<div class="MsoNormal" style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></div>
<div class="MsoNormal" style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><span style="line-height: 115%;">Compute
the column of </span><span style="line-height: 115%;">predicted
model values with Y based on the given threshold of 0.5 (If >0.5, then Y)</span><span style="line-height: 115%;">. Compare it with the no of
Ys in data to find the number of agreements. Calculate "= no. of agreements/ total count" for the accuracy.</span></span></div>
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<span style="line-height: 115%;"><span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></span></div>
<h4 style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><i>Q5) Using classifier-data-asgn2.csv, how well would a
detector perform, if it always picked the majority (most common) class? (Just
give a rounded value rather than including the decimal; e.g. write 57.213% as
57) (Hint: this is easy to compute in Excel)</i></span></h4>
<div class="MsoNormal" style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></div>
<div class="MsoNormal" style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">Calculate "= number of disagreements/total count". Use previous step values.</span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></div>
<h4 style="text-align: left;">
<span style="line-height: 115%;"><span style="font-family: Arial, Helvetica, sans-serif;"><i>Q6)
Is this detector’s performance better than chance, according to the accuracy
and the frequency of the most common class?</i></span></span></h4>
<div class="MsoNormal" style="text-align: left;">
<span style="line-height: 115%;"><span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></span></div>
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<span style="line-height: 115%;"><span style="font-family: Arial, Helvetica, sans-serif;">Answer Yes/No based on the previous values you got.</span></span></div>
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<h4 style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><i>Q7) What is this detector’s value for Cohen’s Kappa? Assume
a threshold of 0.5. (Just round to the first two decimal places; e.g. write
0.74821 as 0.75).</i></span></h4>
<div class="MsoNormal" style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;">I calculated the agreements between data and prediction model to form the confusion matrix of the number of True Negatives(TN), True Positives (TP), False Positives (FP), False Negatives (FN) and listed them as below from O5 to O8 and then used a formula:</span></div>
<div style="text-align: left;">
<table border="0" cellpadding="0" cellspacing="0" style="border-collapse: collapse; width: 64px;">
<colgroup><col style="width: 48pt;" width="64"></col>
</colgroup><tbody>
<tr height="24" style="height: 18.0pt;">
<td class="xl65" height="24" style="height: 18.0pt; width: 48pt;" width="64"><span class="font5" style="font-family: Arial, Helvetica, sans-serif;"><sub>00 (TN)</sub></span></td>
</tr>
<tr height="24" style="height: 18.0pt;">
<td class="xl65" height="24" style="border-top: none; height: 18.0pt;"><span class="font5" style="font-family: Arial, Helvetica, sans-serif;"><sub>11 (TP)</sub></span></td>
</tr>
<tr height="24" style="height: 18.0pt;">
<td class="xl65" height="24" style="border-top: none; height: 18.0pt;"><span class="font5" style="font-family: Arial, Helvetica, sans-serif;"><sub>01 (FP)</sub></span></td>
</tr>
<tr height="24" style="height: 18.0pt;">
<td class="xl65" height="24" style="border-top: none; height: 18.0pt;"><span class="font5" style="font-family: Arial, Helvetica, sans-serif;"><sub>10 (FN)</sub></span></td></tr>
</tbody></table>
</div>
<div class="MsoNormal" style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">=((O5+O6)-((((O6+O7)*(O6+O8))/SUM(O5:O8))+(((O5+O7)*(O5+O8))/SUM(O5:O8))))/((SUM(O5:O8))-((((O6+O7)*(O6+O8))/SUM(O5:O8))+(((O5+O7)*(O5+O8))/SUM(O5:O8))))</span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;">Alternatively, you may apply the values from your confusion matrix to any online calculator for Cohen's Kappa.</span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></div>
<h4 style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><i>Q8) What is this detector’s precision, assuming we are
trying to predict “Y” and assuming a threshold of 0.5 (Just round to the first
two decimal places; e.g. write 0.74821 as 0.75).</i></span></h4>
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<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;">Use formula Precision = TP/ (TP+FP)</span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></div>
<h4 style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><i>Q9) What is this detector’s recall, assuming we are trying
to predict “Y” and assuming a threshold of 0.5 (Just round to the first two
decimal places; e.g. write 0.74821 as 0.75).</i></span></h4>
<div class="MsoNormal" style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></div>
<div class="MsoNormal" style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">Use formula Recall = TP/ (TP+FN)</span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></div>
<h4 style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><i>Q10) Based on the precision and recall, should this detector
be used for strong interventions that have a high cost if mis-applied, or
fail-soft interventions with low benefit and a low cost if mis-applied?</i></span></h4>
<div class="MsoNormal" style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;">Select the correct option from the list of options.</span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></div>
<h4 style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><i>Q11) What is this detector's value for A'? (Hint: There are
some data points with the exact same detector confidence, so it is probably
preferable to use a tool that computes A', such as
http://www.columbia.edu/~rsb2162/computeAPrime.zip -- rather than a tool that computes
the area under the ROC curve).</i></span></h4>
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<span style="line-height: 115%;"><span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></span></div>
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<span style="line-height: 115%;"><span style="font-family: Arial, Helvetica, sans-serif;">I used ROC Curve from XLSTAT plugin to compute Area under the curve (AUC) using excel. </span></span></div>
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<span style="line-height: 115%;"><span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></span></div>
<div class="separator" style="clear: both; text-align: center;">
<a href="http://4.bp.blogspot.com/-gXU7LKjL8QA/VIbboiooEtI/AAAAAAAABXI/AvStDU4blxw/s1600/AUC.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><span style="font-family: Arial, Helvetica, sans-serif;"><img border="0" src="http://4.bp.blogspot.com/-gXU7LKjL8QA/VIbboiooEtI/AAAAAAAABXI/AvStDU4blxw/s1600/AUC.jpg" height="223" width="400" /></span></a></div>
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<span style="line-height: 115%;"><span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></span></div>
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<span style="line-height: 115%;"><span style="font-family: Arial, Helvetica, sans-serif;">To compute A' without ROC curve, you may follow our co-learner's steps listed in his blog:</span></span></div>
<div class="MsoNormal" style="text-align: left;">
<span style="line-height: 16.8666667938232px;"><span style="font-family: Arial, Helvetica, sans-serif;"><a href="http://logort.com/2014/12/09/how-to-compute-auc-without-roc-curve/">http://logort.com/2014/12/09/how-to-compute-auc-without-roc-curve/</a></span></span></div>
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<span style="line-height: 115%;"><span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></span></div>
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<span style="line-height: 115%;"><span style="font-family: Arial, Helvetica, sans-serif;">Hope this helps you to reach this screen! :)</span></span></div>
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<a href="http://1.bp.blogspot.com/-SLDOTV9DJIs/VIbdvTo_PKI/AAAAAAAABXU/r4wMdXNOaAs/s1600/complete.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="http://1.bp.blogspot.com/-SLDOTV9DJIs/VIbdvTo_PKI/AAAAAAAABXU/r4wMdXNOaAs/s1600/complete.jpg" height="223" width="400" /></a></div>
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Shibanihttp://www.blogger.com/profile/09318859221170338553noreply@blogger.com2tag:blogger.com,1999:blog-8855365319723851093.post-27140257286992432512014-12-08T04:49:00.002-08:002014-12-08T04:49:35.607-08:00Competency 7.4<div dir="ltr" style="text-align: left;" trbidi="on">
<div style="text-align: justify;">
<span style="background-color: #fdfdfd; font-family: Arial, Helvetica, sans-serif; line-height: 25.6000003814697px;">Competency 7.4: Describe how models might be used in Learning Analytics research, specifically for the problem of assessing some reasons for attrition along the way in MOOCs.</span></div>
<br />
<div style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;"><span style="background-color: #fdfdfd; line-height: 25.6000003814697px;">One particular model described by Dr. Carolyn talks about how certain properties of discussion correlate to dropout in MOOC. It explores how analyses of sentiment predict attrition over time (Sentiment however was found to be the least consistent and weakest indicator for dropouts). Refer the article below:</span></span></div>
<div class="separator" style="clear: both; text-align: center;">
<span style="font-family: Arial, Helvetica, sans-serif; margin-left: 1em; margin-right: 1em;"><a href="http://1.bp.blogspot.com/-2OuUURCht50/VIWboJtYPDI/AAAAAAAABW4/3bk_gogr5Dg/s1600/paper%2Bsentiment%2Banalysis.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="http://1.bp.blogspot.com/-2OuUURCht50/VIWboJtYPDI/AAAAAAAABW4/3bk_gogr5Dg/s1600/paper%2Bsentiment%2Banalysis.jpg" height="92" width="400" /></a></span></div>
<div class="separator" style="clear: both; text-align: center;">
<br /></div>
<h4 style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;"><span style="background-color: #fdfdfd; line-height: 25.6000003814697px;">Survival Modeling:</span></span></h4>
<br />
<div style="text-align: justify;">
<span style="background-color: #fdfdfd; font-family: Arial, Helvetica, sans-serif; line-height: 25.6000003814697px;">Survival model is a regression model that captures the changes in probability of survival over time. It captures the probability at each time point and it is measured in terms of hazard ratio which indicates how much more or less likely a student is to drop out. If Hazard ratio>1, the student is significantly more likely to drop out in the next time point.</span></div>
<div style="text-align: justify;">
<span style="background-color: #fdfdfd; font-family: Arial, Helvetica, sans-serif; line-height: 25.6000003814697px;"><br /></span></div>
<div style="text-align: justify;">
<span style="background-color: #fdfdfd; font-family: Arial, Helvetica, sans-serif; line-height: 25.6000003814697px;">Sentiment analysis in MOOC forums looked at Expressed sentiment and Exposure to sentiment. The four independent variables Individual Positivity, Individual Negativity, Thread Positivity and Thread Negativity were used to calculate the dependent variable Dropout. The effects were relatively weak and inconsistent across courses.</span></div>
<div style="text-align: justify;">
<span style="background-color: #fdfdfd; font-family: Arial, Helvetica, sans-serif; line-height: 25.6000003814697px;"><br /></span></div>
<div style="text-align: justify;">
<span style="background-color: #fdfdfd; font-family: Arial, Helvetica, sans-serif; line-height: 25.6000003814697px;">Some factors that may contribute to student attrition like student's prior motivation, skill set/ knowledge in the area, previous experience in learning MOOCs are difficult to capture. We can link different analysis methods like social network analysis, text mining, predictive modeling and survey data analysis to try to get the complete picture of an individual student for more consistent results. </span></div>
</div>
Shibanihttp://www.blogger.com/profile/09318859221170338553noreply@blogger.com0tag:blogger.com,1999:blog-8855365319723851093.post-38317921870401954202014-12-08T03:49:00.001-08:002014-12-08T04:25:49.142-08:00Competency 7.3/ Assignment<div dir="ltr" style="text-align: left;" trbidi="on">
<h3 style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;">Building a simple text classification experiment - Training and evaluating a simple predictive model</span></h3>
<div>
<div style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></div>
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<div>
<div style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;">I used LightSIDE tool as explained by Dr. Carolyn to run a simple classification experiment. The tool is easy to use and straightforward if we follow the steps. </span></div>
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<div>
<div style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></div>
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<div>
<div style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;">In the Extract Features pane, I loaded the NewsgroupTopic dataset from the sample data directory in LightSIDE. I selected Unigram and Bigram features and clicked on Extract. I then saved the feature space for later use.</span></div>
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<div style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></div>
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<a href="http://4.bp.blogspot.com/-3vmPpKpsA-U/VIWPhzBN-RI/AAAAAAAABWg/WA20MtKCyoE/s1600/extraction.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="http://4.bp.blogspot.com/-3vmPpKpsA-U/VIWPhzBN-RI/AAAAAAAABWg/WA20MtKCyoE/s1600/extraction.jpg" height="223" width="400" /></a></div>
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<span style="font-family: Arial, Helvetica, sans-serif;">In the Build Models pane, I used the recently created feature table. I selected Naive Bayes as the learning plugin, set the number of folds to be 20 for cross-validation and clicked on Train. </span></div>
</div>
<div class="separator" style="clear: both; text-align: justify;">
<a href="http://3.bp.blogspot.com/-LX-xHquyJeU/VIWPn_KW6MI/AAAAAAAABWo/2RV6wTSLZCk/s1600/result.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="http://3.bp.blogspot.com/-LX-xHquyJeU/VIWPn_KW6MI/AAAAAAAABWo/2RV6wTSLZCk/s1600/result.jpg" height="223" width="400" /></a></div>
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<span style="font-family: Arial, Helvetica, sans-serif;">I got Accuracy 58% and Kappa = 0.44 for the model as given in the assignment, which means my steps were correct :)</span></div>
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Shibanihttp://www.blogger.com/profile/09318859221170338553noreply@blogger.com0tag:blogger.com,1999:blog-8855365319723851093.post-71677952482963988542014-12-08T01:03:00.000-08:002014-12-08T04:25:24.162-08:00Competency 7.1/ 7.2 Text Mining<div dir="ltr" style="text-align: left;" trbidi="on">
<div style="text-align: justify;">
<span style="background-color: white;"><span style="font-family: Arial, Helvetica, sans-serif;">Text Mining is the process of extracting and identifying useful and meaningful information, from different sources of unstructured text data.</span></span></div>
<div style="text-align: justify;">
<span style="background-color: white;"><span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></span>
</div>
<h3 style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;">Prominent Areas of Text Mining</span></h3>
<h4 style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;">Information Retrieval:</span></h4>
<div style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;">Information Retrieval is the process of searching and retrieving the required document from a collection of documents based on the given search query. The search engines we use like Google, Yahoo etc. make use of IR techniques for matching and returning documents relevant to the user's query.</span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span>
</div>
<h4 style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;">Document Classification/ Text Categorization:</span></h4>
<div style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;">Classification is the process of identifying the category a new observation belongs to, on the basis of a training set consisting of data with pre-defined categories (supervised learning). An example is the classification of email into spam/non-spam.</span></div>
<div style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span>
</div>
<h4 style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;">Clustering:</span></h4>
<div style="text-align: left;">
<div style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;">Clustering is the unsupervised procedure of classification where a set of similar objects are grouped to a cluster. An example analysis would be the summarization of common complaints based on open-ended survey responses.</span></div>
</div>
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<div style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></div>
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<h4 style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;">Trend Analysis:</span></h4>
<h4 style="text-align: left;">
<div style="text-align: left;">
<div style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif; font-weight: normal;">Trend Analysis is the process of discovering the trends of different topics over a given period of time. It is widely applied in summarizing news events and social network trends. An example would be the prediction of stock prices based on news articles.</span></div>
</div>
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<span style="font-family: Arial, Helvetica, sans-serif; font-weight: normal;"><br /></span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;"><div style="text-align: justify;">
Sentiment Analysis:</div>
</span></h4>
<div style="text-align: left;">
<div style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;">Sentiment analysis is the process of categorizing opinions based on sentiments like positive, negative or neutral. Sample applications include identifying sentiments in movie reviews and gaining real-time awareness to users' feedback.</span></div>
</div>
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<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></div>
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<h3 style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;">Sub-area of Text Mining</span></h3>
<h4 style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;">Collaborative Learning Process Analysis</span></h4>
<div style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;">It is the process of analyzing the collaborative learning process of students using text mining techniques. Different indicators and language features are used for this study. Some of them are:</span></div>
<div style="text-align: justify;">
<br /></div>
<ul style="text-align: left;">
<li style="text-align: justify;"><span style="font-family: Arial, Helvetica, sans-serif;">General indicators of interactivity</span></li>
<li style="text-align: justify;"><span style="font-family: Arial, Helvetica, sans-serif;">Turn length</span></li>
<li style="text-align: justify;"><span style="font-family: Arial, Helvetica, sans-serif;">Conversation Length</span></li>
<li style="text-align: justify;"><span style="font-family: Arial, Helvetica, sans-serif;">Number of student questions</span></li>
<li style="text-align: justify;"><span style="font-family: Arial, Helvetica, sans-serif;">Student to tutor word ratio</span></li>
<li style="text-align: justify;"><span style="font-family: Arial, Helvetica, sans-serif;">Student initiative</span></li>
<li style="text-align: justify;"><span style="font-family: Arial, Helvetica, sans-serif;">Features related to cognitive processes</span></li>
<li style="text-align: justify;"><span style="font-family: Arial, Helvetica, sans-serif;">Transactivity</span></li>
</ul>
<div style="text-align: justify;">
<br /></div>
<div style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;">Data familiarity in the domain is important to understand and develop features that are relevant.</span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></div>
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Shibanihttp://www.blogger.com/profile/09318859221170338553noreply@blogger.com0tag:blogger.com,1999:blog-8855365319723851093.post-48470687607227082632014-12-03T21:24:00.005-08:002014-12-03T21:25:15.031-08:00Competency 6.2: Key Diagnostic Metrics<div dir="ltr" style="text-align: left;" trbidi="on">
<span style="font-family: Arial, Helvetica, sans-serif;">A part of Week 6 was designed to learn about the diagnostic metrics, to see how well our model does, as either classifiers or regressors. </span><br />
<h2 style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif; font-size: large;">Metrics for Classifiers</span></h2>
<h3 style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">Accuracy:</span></h3>
<span style="font-family: Arial, Helvetica, sans-serif;">The easiest measure of model goodness is accuracy. It is also called agreement, when measuring the inter-rater reliability.</span><br />
<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span>
<span style="font-family: Arial, Helvetica, sans-serif;">Accuracy = # of agreements/ Total # of assessments</span><br />
<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span>
<span style="font-family: Arial, Helvetica, sans-serif;">It is generally not considered a good metric across fields, since it has non even assignment to categories and not useful. E.g. 92% accuracy in the Kindergarten Failure Detector Model in the extreme case always says Pass.</span><br />
<h3 style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">Kappa:</span></h3>
<span style="font-family: Arial, Helvetica, sans-serif;">Kappa = (Agreement - Expected Agreement) / (1 - Expected Agreement)</span><br />
<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span>
<span style="font-family: Arial, Helvetica, sans-serif;">If Kappa value</span><br />
<span style="font-family: Arial, Helvetica, sans-serif;">= 0, agreement is at chance</span><br />
<span style="font-family: Arial, Helvetica, sans-serif;">= 1, agreement is perfect</span><br />
<span style="font-family: Arial, Helvetica, sans-serif;">= negative infinity, agreement is perfectly inverse</span><br />
<span style="font-family: Arial, Helvetica, sans-serif;">> 1, something is wrong</span><br />
<span style="font-family: Arial, Helvetica, sans-serif;">< 0, agreement is worse than chance</span><br />
<span style="font-family: Arial, Helvetica, sans-serif;">0<Kappa<1, no absolute standard. For data-mined models, 0.3-0.5 is considered good enough for publishing.</span><br />
<span style="font-family: Arial, Helvetica, sans-serif;">Kappa is scaled by the proportion of each category, influenced by the data set. We can compare the Kappa values within the same data set, but not between two data sets.</span><br />
<h3 style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">ROC:</span></h3>
<span style="font-family: Arial, Helvetica, sans-serif;">The Receiver Operating Characteristic Curve (ROC) is used while a model predicts something having two values (E.g correct/incorrect, dropout/not dropout) and outputs a probability or other real value (E.g. Student will drop out with 73% probability). </span><br />
<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span>
<span style="font-family: Arial, Helvetica, sans-serif;">It takes any number as cut-off (threshold) and some number of predictions (maybe 0) may then be classified as 1's and the rest may be classified as 0s. There are four possibilities for a classification threshold:</span><br />
<span style="font-family: Arial, Helvetica, sans-serif;">True Positive (TP) - Model and the Data say 1</span><br />
<span style="font-family: Arial, Helvetica, sans-serif;">False Positive (FP) - Data says 0, Model says 1</span><br />
<span style="font-family: Arial, Helvetica, sans-serif;">True Negative (TN) - Model and the Data say 0</span><br />
<span style="font-family: Arial, Helvetica, sans-serif;">False Negative (FN) - Data says 1, Model says 0</span><br />
<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span>
<span style="font-family: Arial, Helvetica, sans-serif;">The ROC Curve has in its X axis Percent False Positives (Vs. True Negatives) and in Y axis Percent True Positives (Vs. False Negatives). The model is good if it is above the chance line in its diagonal.</span><br />
<h3 style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">A':</span></h3>
<span style="font-family: Arial, Helvetica, sans-serif;">A' is the probability that if the model is given an example from each category, it will accurately identify which is which. It is a close relative of ROC and mathematically equivalent to Wilcoxon statistic. It gives useful result, since we can compute statistical tests for:</span><br />
<span style="font-family: Arial, Helvetica, sans-serif;">- whether two A' values are significantly different in the same or different data sets.</span><br />
<span style="font-family: Arial, Helvetica, sans-serif;">- whether an A' value is significantly different than choice.</span><br />
<h3 style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">A' Vs Kappa:</span></h3>
<span style="font-family: Arial, Helvetica, sans-serif;">A' is more difficult to compute and works only for 2 categories. It's meaning is invariant across data sets i.e) A'=0.6 is always better than A'=0.5. It is easy to interpret statistically and has value almost always higher than Kappa values. It also takes confidence into account.</span><br />
<h3 style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">Precision and Recall:</span></h3>
<span style="font-family: Arial, Helvetica, sans-serif;">Precision is the probability that a data point classified as true is actually true.</span><br />
<span style="font-family: Arial, Helvetica, sans-serif;">Precision = TP / (TP+FP)</span><br />
<span style="font-family: Arial, Helvetica, sans-serif;">Recall is the probability that a data point that is actually true is classified as true.</span><br />
<span style="font-family: Arial, Helvetica, sans-serif;">Recall = TP / (TP+FN)</span><br />
<span style="font-family: Arial, Helvetica, sans-serif;">They don't take confidence into account.</span><br />
<h2 style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span><span style="font-family: Arial, Helvetica, sans-serif; font-size: large;">Metrics for Regressors</span></h2>
<h3 style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">Linear Correlation (Pearson correlation):</span></h3>
<span style="font-family: Arial, Helvetica, sans-serif;">In r(A,B) when A's value changes, does B change in the same direction?</span><br />
<span style="font-family: Arial, Helvetica, sans-serif;">It assumes a linear relationship.</span><br />
<span style="font-family: Arial, Helvetica, sans-serif;">If correlation value is</span><br />
<span style="font-family: Arial, Helvetica, sans-serif;">1.0 : perfect</span><br />
<span style="font-family: Arial, Helvetica, sans-serif;">0.0 : none</span><br />
<span style="font-family: Arial, Helvetica, sans-serif;">-1.0 : perfectly negatively correlated</span><br />
<span style="font-family: Arial, Helvetica, sans-serif;">In between 0 and 1 : Depends on the field</span><br />
<span style="font-family: Arial, Helvetica, sans-serif;">0.3 is good enough in education since a lot of factors contribute to just any dependent measure.</span><br />
<span style="font-family: Arial, Helvetica, sans-serif;">Different functions (outliers) may also have the same correlation.</span><br />
<h3 style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">R square:</span></h3>
<span style="font-family: Arial, Helvetica, sans-serif;">R square is correlation squared. It is the measure of what percentage of variance in dependent dependent measure is explained by a model. If predicting A with B,C,D,E, it is often used as the measure of model goodness rather than r.</span><br />
<h3 style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">MAE/MAD:</span></h3>
<span style="font-family: Arial, Helvetica, sans-serif;">Mean Absolute Error/ Deviation is the average of absolute value of actual value minus predicted value. i.e) the average of each data point's difference between actual and predicted value. It tells the average amount to which the predictions deviate from the actual value and is very interpret able.</span><br />
<h3 style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">RMSE:</span></h3>
<span style="font-family: Arial, Helvetica, sans-serif;">Root Mean Square Error (RMSE) is the square root of average of (actual value minus predicted value)^2. It can be interpreted similar to MAD but it penalizes large deviation more than small deviation. It is largely preferred to MAD. Low RMSE is good.</span><br />
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<b><span style="font-family: Arial, Helvetica, sans-serif;">RMSE/ MAD<o:p></o:p></span></b></div>
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<b><span style="font-family: Arial, Helvetica, sans-serif;">Correlation<o:p></o:p></span></b></div>
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<b><span style="font-family: Arial, Helvetica, sans-serif;">Model<o:p></o:p></span></b></div>
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<span style="font-family: Arial, Helvetica, sans-serif;">Low</span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;">High</span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;">Good</span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;">High</span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;">Low</span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;">Bad</span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;">High</span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;">High</span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;">Goes in the right direction, but systematically biased</span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;">Low</span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;">Low</span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;">Values are in the right range, but doesn’t capture relative change</span></div>
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<h3 style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">Information Criteria:</span></h3>
<h3 style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">BiC:</span></h3>
<div>
<span style="font-family: Arial, Helvetica, sans-serif;">Bayesian Information Criterion (BiC) makes trade-off between goodness of fit and flexibility of fit (number of parameters). The formula for linear regression:</span></div>
<div>
<span style="font-family: Arial, Helvetica, sans-serif;">BiC' = n log (1-r^2) + p log n </span></div>
<div>
<span style="font-family: Arial, Helvetica, sans-serif;">where n - number of students, p - number of variables</span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;">If value > 0, worse than expected, given number of variables</span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;"> value <0, better than expected, given number of variables</span></div>
<div>
<span style="font-family: Arial, Helvetica, sans-serif;">It can be used to understand the significance of difference between models. (E.g. 6 implies statistically significant difference)</span></div>
<h3 style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">AiC:</span></h3>
<div>
<span style="font-family: Arial, Helvetica, sans-serif;">An Information Criterion/ Akaike's Information Criterion (AiC) is an alternative to BiC. It has slightly different trade-off between goodness and flexibility of fit.</span></div>
<div>
<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></div>
<div>
<span style="font-family: Arial, Helvetica, sans-serif;"><b>Note: </b>There is no single measure to choose between classifiers. We have to understand multiple dimensions and use multiple metrics.</span></div>
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<div>
<h2 style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif; font-size: large;">Types of Validity</span></h2>
<h3 style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">Generalizability:</span></h3>
<span style="font-family: Arial, Helvetica, sans-serif;">Does your model remain predictive when used in a new data set?</span><br />
<span style="font-family: Arial, Helvetica, sans-serif;">Generalizability underlies the cross-validation paradigm that is common in data mining. Knowing the context of the model where it will be used in, drives the kind of generalization to be studied.</span><br />
<span style="font-family: Arial, Helvetica, sans-serif;"><u>Fail:</u> Model of boredom built on data from 3 students fails when applied to new students</span><br />
<h3 style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">Ecological Validity:</span></h3>
<span style="font-family: Arial, Helvetica, sans-serif;">Do your findings apply to real-life situations outside of research settings?</span><br />
<span style="font-family: Arial, Helvetica, sans-serif;">E.g. If a behavior detector built in lab settings work in real classrooms.</span><br />
<h3 style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">Construct Validity:</span></h3>
<span style="font-family: Arial, Helvetica, sans-serif;">Does your model actually measure what it was intended to measure?</span><br />
<span style="font-family: Arial, Helvetica, sans-serif;">Does your model fir the training data? (provided the training data is correct)</span><br />
<h3 style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">Predictive Validity:</span></h3>
<span style="font-family: Arial, Helvetica, sans-serif;">Does your model predict not just the present, but the future as well?</span><br />
<h3 style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">Substantive Validity:</span></h3>
<span style="font-family: Arial, Helvetica, sans-serif;">Does your results matter?</span><br />
<h3 style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">Content Validity:</span></h3>
<span style="font-family: Arial, Helvetica, sans-serif;">From testing; Does your test cover the full domain it is meant to cover?</span><br />
<span style="font-family: Arial, Helvetica, sans-serif;">For behavior modeling, does the model cover the full range of behavior it is intended to?</span><br />
<h3 style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">Conclusion Validity:</span></h3>
<span style="font-family: Arial, Helvetica, sans-serif;">Are your conclusions justified based on evidence?</span><br />
<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span>
<span style="font-family: Arial, Helvetica, sans-serif;">I think that the lessons in Week 5 and 6 are very useful, especially when we want to get our hands deep into predictive modeling and diagnosing its usefulness. I hope to use them in my predictive modeling work :)</span><br />
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Shibanihttp://www.blogger.com/profile/09318859221170338553noreply@blogger.com0tag:blogger.com,1999:blog-8855365319723851093.post-15044608255127503272014-12-02T01:37:00.001-08:002014-12-02T01:37:34.029-08:00Competency 6.1: Engineer both feature and training labels<div dir="ltr" style="text-align: left;" trbidi="on">
<h2 style="text-align: left;">
<br /><span style="font-family: Arial, Helvetica, sans-serif;">My notes/ learning</span></h2>
<h3 style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">Behavior Detectors</span></h3>
<div style="text-align: justify;">
<span style="background-color: #fdfdfd; line-height: 25.6000003814697px;"><span style="font-family: Arial, Helvetica, sans-serif;">Behavior detectors are automated (predictive) models that can infer from log files whether a student is behaving in a certain way. </span></span></div>
<div style="text-align: justify;">
<span style="background-color: #fdfdfd; line-height: 25.6000003814697px;"><b><span style="font-family: Arial, Helvetica, sans-serif;">Behaviors:</span></b></span></div>
<div style="text-align: justify;">
<span style="background-color: #fdfdfd; line-height: 25.6000003814697px;"><span style="font-family: Arial, Helvetica, sans-serif;">Disengaged behaviors </span></span></div>
<div style="text-align: justify;">
<span style="background-color: #fdfdfd; line-height: 25.6000003814697px;"><span style="font-family: Arial, Helvetica, sans-serif;">-gaming the system by trying to succeed without learning</span></span></div>
<div style="text-align: justify;">
<span style="background-color: #fdfdfd; line-height: 25.6000003814697px;"><span style="font-family: Arial, Helvetica, sans-serif;">-off-task behavior</span></span></div>
<div style="text-align: justify;">
<span style="background-color: #fdfdfd; line-height: 25.6000003814697px;"><span style="font-family: Arial, Helvetica, sans-serif;">-carelessness by giving wrong answer even when having the required skills</span></span></div>
<div style="text-align: justify;">
<span style="background-color: #fdfdfd; line-height: 25.6000003814697px;"><span style="font-family: Arial, Helvetica, sans-serif;">-WTF behavior - Without Thinking Fastidiously (by doing unrelated tasks while using the system)</span></span></div>
<div style="text-align: justify;">
<span style="background-color: #fdfdfd; line-height: 25.6000003814697px;"><span style="font-family: Arial, Helvetica, sans-serif;">Metacognitive behaviors</span></span></div>
<div style="text-align: justify;">
<span style="background-color: #fdfdfd; line-height: 25.6000003814697px;"><span style="font-family: Arial, Helvetica, sans-serif;">-help-avoidance</span></span></div>
<div style="text-align: justify;">
<span style="background-color: #fdfdfd; line-height: 25.6000003814697px;"><span style="font-family: Arial, Helvetica, sans-serif;">-unscaffolded self-exploration</span></span></div>
<div style="text-align: justify;">
<span style="background-color: #fdfdfd; line-height: 25.6000003814697px;"><span style="font-family: Arial, Helvetica, sans-serif;">-exploration behaviors</span></span></div>
<div style="text-align: justify;">
<b><span style="font-family: Arial, Helvetica, sans-serif;">Related Problem:</span></b></div>
<div style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;">-sensor-free affect detection (without the use of video-capture, gesture capture etc.)</span></div>
<div style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;">- detecting boredom, frustration, engaged concentration, delight</span></div>
<div style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></div>
<h3 style="text-align: justify;">
<b><span style="font-family: Arial, Helvetica, sans-serif;">Ground Truth</span></b></h3>
<div style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;">Ground truth is the <i>accuracy of classification</i> in supervised learning/ machine learning.</span></div>
<div style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;">Where to get the prediction labels from is the big issue in developing behavior detectors.</span></div>
<div style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;">E.g. How to identify when a student is off-task/ gaming the system?</span></div>
<div style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;">Behavior labels are noisy; there is no perfect way to get indicators of student behavior.</span></div>
<div style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;">Sources of ground truth:</span></div>
<br />
<ul style="text-align: left;">
<li style="text-align: justify;"><span style="font-family: Arial, Helvetica, sans-serif;">Self-Report: -common for affect, self-efficacy; not common for labeling behavior (students may not admit gaming)</span></li>
<li style="text-align: justify;"><span style="font-family: Arial, Helvetica, sans-serif;"> Field observations</span></li>
<li style="text-align: justify;"><span style="font-family: Arial, Helvetica, sans-serif;">Text replays</span></li>
<li style="text-align: justify;"><span style="font-family: Arial, Helvetica, sans-serif;">Video coding</span></li>
</ul>
<br />
<div style="text-align: justify;">
<b><span style="font-family: Arial, Helvetica, sans-serif;">Field observations:</span></b></div>
<div style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;">One or more observers watch students and take notes</span></div>
<div style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;">- requires training to do it right</span></div>
<div style="text-align: justify;">
<b><span style="font-family: Arial, Helvetica, sans-serif;">Text Replays:</span></b></div>
<div style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;">Analyzing student interaction behavior from log files based on their input in the system.</span></div>
<div style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;">- Fast to conduct</span></div>
<div style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;">- Decent inter-rater reliability</span></div>
<div style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;">- Agrees with other measures of constructs</span></div>
<div style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;">- Can be used to train behavior detectors</span></div>
<div style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;">- Only limited constructs can be coded</span></div>
<div style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;">- Lower precision than field observation due to lower bandwidth</span></div>
<div style="text-align: justify;">
<b><span style="font-family: Arial, Helvetica, sans-serif;">Video Coding:</span></b></div>
<div style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;">Videos of live behavior in the classrooms or screen replay videos analyzed</span></div>
<div style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;">- slowest, but replicable and precise</span></div>
<div style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;">- challenges in camera positioning</span></div>
<div style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></div>
<div style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;">Kappa= 0.6 or higher expected for expert coding</span></div>
<div style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;">However, 1000 data points with kappa= 0.5 > 100 data points with kappa= 0.7</span></div>
<div style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></div>
<div style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;">Once we have ground truth, we can build the detector.</span></div>
<h4 style="text-align: left;">
<div style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></div>
</h4>
<h3 style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;">Feature Engineering</span></h3>
<div style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;">Feature engineering is the <i>art of creating predictor variables</i>. The model will not be good if our features (predictors) are not good. It involves lore rather than well-known and validated principles.</span></div>
<div style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></div>
<div style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;">The big idea is how we can take the <i>voluminous, ill-formed and yet under-specified data</i> that we now have in education and <i>shape it into a reasonable set of variables</i> in an efficient and predictive way.</span></div>
<div style="text-align: justify;">
<b><span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></b></div>
<div style="text-align: justify;">
<b><span style="font-family: Arial, Helvetica, sans-serif;">Process:</span></b></div>
<br />
<ol style="text-align: left;">
<li style="text-align: justify;"><span style="font-family: Arial, Helvetica, sans-serif;">Brainstorming features - IDEO tips for brainstorming</span></li>
<li style="text-align: justify;"><span style="font-family: Arial, Helvetica, sans-serif;">Deciding what features to create - trade-off between effort and usefulness of feature</span></li>
<li style="text-align: justify;"><span style="font-family: Arial, Helvetica, sans-serif;">Creating the features - Excel, OpenRefine, Distillation code</span></li>
<li style="text-align: justify;"><span style="font-family: Arial, Helvetica, sans-serif;">Studying the impact of features on model goodness</span></li>
<li style="text-align: justify;"><span style="font-family: Arial, Helvetica, sans-serif;">Iterating on features if useful - try close variants and test</span></li>
<li style="text-align: justify;"><span style="font-family: Arial, Helvetica, sans-serif;">Go to 3 (or 1)</span></li>
</ol>
<br />
<div style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></div>
<div style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;">Feature engineering can over-fit --> Iterate and use cross-validation, test on held-out data or newly collected data.</span></div>
<div style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></div>
<div style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;">Thinking about our variables is likely to yield better results than using pre-existing variables from a standard set.</span></div>
<h3 style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span><span style="font-family: Arial, Helvetica, sans-serif;">Knowledge Engineering and Data Mining:</span></h3>
<div style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;">Knowledge engineering is where the model is created by a smart human being, rather than an exhaustive computer (that searches through all possibilities). It is also called rational modeling or cognitive modeling.</span></div>
<div style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></div>
<div style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;"><b>At its best:</b></span></div>
<div style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;">Knowledge engineering is the art of a human being becoming deeply familiar with the target construct by carefully studying the data, including possible process data, understanding the relevant theory and thoughtfully crafting an excellent model.</span></div>
<div style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;">-achieves higher construct validity and comparable performance than data mining</span></div>
<div style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;">-may even transfer better to new data (while data-mined model may get trapped at finding specific features to the population)</span></div>
<div style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></div>
<div style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;">E.g. Alevan et al.'s (2004, 2006) Help-seeking model</span></div>
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<a href="http://1.bp.blogspot.com/-XMd-uG1TB98/VH2A4ZftXaI/AAAAAAAABWI/fmOEmuEaFe4/s1600/eg.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><span style="font-family: Arial, Helvetica, sans-serif;"><img border="0" src="http://1.bp.blogspot.com/-XMd-uG1TB98/VH2A4ZftXaI/AAAAAAAABWI/fmOEmuEaFe4/s1600/eg.jpg" height="268" width="320" /></span></a></div>
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<span style="font-family: Arial, Helvetica, sans-serif;">It was developed based on scientific articles, experience in designing learning environments, log files of student interaction and experience watching students using educational software in classes.</span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></div>
<div style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;"><b>At its worst:</b></span></div>
<div style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;">If it refers to making up a simple model very quickly and calling the resultant construct by a well-known name, not testing on data or has no evidence.</span></div>
<div style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;">- poorer construct validity than data mining</span></div>
<div style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;">- predicts desired constructs poorly </span></div>
<div style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;">- can slow scientific progress by false results<br />- can hurt student outcomes by wrong intervention</span></div>
<div style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></div>
<div style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;">It is easier to identify if a data mining model is bad, from the features, validation procedure or goodness metrics; but difficult for knowledge engineering since the hard-work process in researcher's brain is invisible.</span></div>
<div style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></div>
<div style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;">To Do's for both methods:</span></div>
<div style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;">- Test the models</span></div>
<div style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;">- Use direct measures (Training labels) or Indirect measures (E.g. predicting student learning). </span></div>
<div style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;">- Careful study of construct leads to better features and better models</span></div>
<h3 style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span><span style="font-family: Arial, Helvetica, sans-serif;">Assignment - Critical Reflection:</span></h3>
<div style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><b>Possible uses in education:</b></span></div>
<div style="text-align: left;">
</div>
<div style="text-align: justify;">
<span style="font-family: Arial, Helvetica, sans-serif;">Behavior detection can be used to create automated learning management systems that will give hints to users/ comment on their performances by detecting their behavior. It can be used in places where the tutor is not available to help all students. The online automated tutor can jump in to give suggestions. If the behavior is still detected to be disengaged, an available tutor can be mapped to the student.</span></div>
<br />
<div style="text-align: justify;">
<br /></div>
</div>
Shibanihttp://www.blogger.com/profile/09318859221170338553noreply@blogger.com2tag:blogger.com,1999:blog-8855365319723851093.post-45546013436903062372014-11-26T01:33:00.001-08:002014-11-26T01:33:36.396-08:00Competency 5.1/ Week 5 Activity<div dir="ltr" style="text-align: left;" trbidi="on">
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<span style="background-color: #fdfdfd; color: #3c3c3c; line-height: 25.6000003814697px;"><span style="font-family: Arial, Helvetica, sans-serif;">Competency 5.1: Learn to conduct prediction modeling effectively and appropriately.</span></span></div>
<div style="text-align: left;">
<span style="background-color: #fdfdfd; color: #3c3c3c; line-height: 25.6000003814697px;"><span style="font-family: Arial, Helvetica, sans-serif;">I think this competency can be achieved if we are able to complete the given activity in RapidMiner. It is quite difficult for a newbie, but its well-described in the course and definitely doable :)</span></span></div>
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<span style="background-color: #fdfdfd; color: #3c3c3c; line-height: 25.6000003814697px;"><span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;"><span style="background-color: #fdfdfd; color: #3c3c3c; line-height: 25.6000003814697px;">We were asked to build a set of detectors</span><span style="color: #3c3c3c; line-height: 115%;"> predicting the variable ONTASK for the given data file using RapidMiner 5.3. I had previously installed Rapidminer 6.1, so I was using it. The question progresses to the next one only if you answer it correctly and there were 13 questions,most of which require</span><span style="color: #3c3c3c; line-height: 115%;"> you to enter the Kappa of the model you executed. There were a few difficulties along the way (for which I will try to give some useful tips) and I had a huge relief when I finally got this screen ;)</span></span></div>
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<span style="color: #3c3c3c; line-height: 115%;"><span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></span></div>
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<a href="http://3.bp.blogspot.com/-1IHGD7na3lg/VHWUVRzvfVI/AAAAAAAABV4/c9JH9TGSnOY/s1600/Activity%2Bcompletion.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><span style="font-family: Arial, Helvetica, sans-serif;"><img border="0" src="http://3.bp.blogspot.com/-1IHGD7na3lg/VHWUVRzvfVI/AAAAAAAABV4/c9JH9TGSnOY/s1600/Activity%2Bcompletion.jpg" height="223" width="400" /></span></a></div>
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<div class="MsoNormal" style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><i>1) Build
a decision tree (using operator W-J48 from the Weka Extension Pack) on the
entire data set. What is the non-cross-validated kappa?</i></span></div>
<div class="MsoNormal" style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></div>
<div class="MsoNormal" style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">You can follow the Rapidminer walkthrough video to answer this question. It's almost the same steps, but in the last stage of data import from excel, you need to change the variable types if they are not correctly guessed. My Rapid Miner 6.1 version guessed the data types correctly, but if you are using 5.3, you should probably change the types of polynomial variables which were incorrectly guessed as integer. (You can open and check the input excel file to see what kind of values are there)</span></div>
<div class="MsoNormal" style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></div>
<div class="MsoNormal" style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">You should set <span style="line-height: 115%;">attribute
name as “ONTASK” and target role = label (the value to be predicted in the
given exercise) and add operators W-J48, Apply model and Performance (Binary Classification). The rest should be fine.</span></span></div>
<div class="MsoNormal" style="text-align: left;">
<span style="line-height: 115%;"><span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></span></div>
<div class="MsoNormal" style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><i><span style="line-height: 115%;">2) </span>The kappa value you just obtained is artificially high -
the model is over-fitting to which student it is. What is the
non-cross-validated kappa, if you build the model (using the same operator),
excluding student?</i></span></div>
<div class="MsoNormal" style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></div>
<div class="MsoNormal" style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">Two ways to exclude a field - to delete the field or use Select attributes operator. The latter is better for obvious reasons. For this question you need to add "Select Attributes" operator and set Attribute filter type = single, attribute=StudentID and check invert
selection (since we are asked to exclude student). </span></div>
<div class="MsoNormal" style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><o:p></o:p></span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></div>
<div class="MsoNormal" style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><i>3) Some other features in the data set may make your model
overly specific to the current data set. Which data features would not apply
outside of the population sampled in the current data set? Select all that
apply.</i><o:p></o:p></span></div>
<div class="MsoNormal" style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></div>
<div class="MsoNormal" style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">For this question, you need to select the options which will not generalise outside your population. The system will assist you if you are wrong.</span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></div>
<div class="MsoNormal" style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><i>4) What is the non-cross-validated kappa, if you build the W-J48
decision tree model (using the same operator), excluding student and the
variables from Question 3?</i></span></div>
<div class="MsoNormal" style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></div>
<div class="MsoNormal" style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">For this, we need to exclude all variables in Q3 which do not
apply to the population outside our sample data in addition to the studentID we already excluded. You can change the attributes by selecting<span style="line-height: 115%;"> filter type= subset and Select the attributes to be excluded in the
next window. Check invert selection. </span></span></div>
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<span style="line-height: 115%;"><span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></span></div>
<div class="MsoNormal" style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><i><span style="line-height: 115%;">5) </span>What is the non-cross-validated kappa, for the same set of
variables you used for question 4, if you use Naive Bayes?</i></span></div>
<div class="MsoNormal" style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><o:p></o:p></span></div>
<div class="MsoNormal" style="text-align: left;">
<span style="line-height: 115%;"><span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></span></div>
<div class="MsoNormal" style="text-align: left;">
<span style="line-height: 115%;"><span style="font-family: Arial, Helvetica, sans-serif;">Replace the W-J48 operator for Weka’s decision tree by Naïve bayes operator.</span></span></div>
<div class="MsoNormal" style="text-align: left;">
<span style="line-height: 115%;"><span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></span></div>
<div class="MsoNormal" style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><i><span style="line-height: 115%;">6) </span><span style="line-height: 115%;">What
is the non-cross-validated kappa, for the same set of variables you used for
question 4, if you use W-JRip?</span></i></span></div>
<div class="MsoNormal" style="text-align: left;">
<span style="line-height: 115%;"><span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></span></div>
<div class="MsoNormal" style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><span style="line-height: 115%;">Replace</span><span style="line-height: 115%;"> NaĂŻve Bayes operator by W-Jrip operator.</span></span></div>
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<span style="line-height: 115%;"><span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></span></div>
<div class="MsoNormal" style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><i><span style="line-height: 115%;">7) </span>What is the non-cross-validated kappa, for the same set of
variables you used for question 4, if you use Logistic Regression? (Hint: You
will need to transform some variables to make this work; RapidMiner will tell
you what to do)</i></span></div>
<div class="MsoNormal" style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></div>
<div class="MsoNormal" style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">Add
operators "Nominal to Numerical" and "Logistic Regression" because <span style="line-height: 115%;">Logistic
Regression cannot handle polynominal attributes/ label</span>. </span></div>
<div class="MsoNormal" style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><o:p></o:p></span></div>
<div class="MsoNormal" style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><o:p></o:p></span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;"><o:p></o:p></span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;">This was the one for which I spent maximum time, but still couldn't go through. I'm not sure if the Kappa I got was wrong or what the system expects itself was wrong. Anyways, since I couldn't afford more than a day on that issue and I was almost on the verge of quitting the activity, I had to trespass this question with Ryan's answer in the discussion forum of Quickhelper. That's very unfortunate, but I hardly had a choice :(</span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></div>
<div class="MsoNormal" style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><i>8) <span style="line-height: 115%;">What
is the non-cross-validated kappa, for the same set of variables you used for
question 4, if you use Step Regression (called Linear Regression)?</span></i></span></div>
<div class="MsoNormal" style="text-align: left;">
<span style="line-height: 115%;"><span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></span></div>
<div class="MsoNormal" style="text-align: left;">
<span style="line-height: 115%;"><span style="font-family: Arial, Helvetica, sans-serif;">Just replace Logistic Regression by Linear Regression operator.</span></span></div>
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<span style="line-height: 115%;"><span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></span></div>
<div class="MsoNormal" style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><i><span style="line-height: 115%;">9) </span>What is the non-cross-validated kappa, for the same set of
variables you used for question 4, if you use k-NN instead of W-J48? (We will
discuss the results of this test later).</i></span></div>
<div class="MsoNormal" style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><o:p></o:p></span></div>
<div class="MsoNormal" style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></div>
<div class="MsoNormal" style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">Replace Linear Regression by K-NN operator.</span></div>
<div class="MsoNormal" style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></div>
<div class="MsoNormal" style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><i>10) What is the kappa, for the same set of variables you used
for question 4, if you use W-J48, and conduct 10-fold stratified-sample
cross-validation?</i></span></div>
<div class="MsoNormal" style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><i><o:p></o:p></i></span></div>
<div class="MsoNormal" style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></div>
<div class="MsoNormal" style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">For cross-validating our model, you can refer the Rapidminer walkthrough. You need to add X-Validation operator. Remove the W-J48, Apply model and Performance operators from the process and add it inside the training and test set of X-Validation operator.</span></div>
<div class="MsoNormal" style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></div>
<div class="MsoNormal" style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><i>11) Why is the kappa lower for question 11 (cross-validation)
than question 4 (no cross-validation?)</i></span></div>
<div class="MsoNormal" style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><o:p></o:p></span></div>
<div class="MsoNormal" style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">K-NN predicts a point using itself when cross-validation is turned
off, and that’s bad.<o:p></o:p></span></div>
<div class="MsoNormal" style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></div>
<div class="MsoNormal" style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">You should be able to answer this question, otherwise the system will help.</span></div>
<div class="MsoNormal" style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></div>
<div class="MsoNormal" style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><i>12) <span style="line-height: 115%;">What
is the kappa, for the same set of variables you used for question 4, if you use
k-NN, and conduct 10-fold stratified-sample cross-validation?</span></i></span></div>
<div class="MsoNormal" style="text-align: left;">
<span style="line-height: 115%;"><span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></span></div>
<div class="MsoNormal" style="text-align: left;">
<span style="line-height: 115%;"><span style="font-family: Arial, Helvetica, sans-serif;">Replace W-J48 by K-NN inside the X-Validation training set.</span></span></div>
<div class="MsoNormal" style="text-align: left;">
<br /></div>
<div class="MsoNormal" style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><i>13) k-NN and W-J48 got almost the same Kappa when compared using
cross-validation. But the kappa for k-NN was much higher (1.000) when
cross-validation was not used. Why is that?</i></span></div>
<div class="MsoNormal" style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></div>
<div class="MsoNormal" style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">You should be able to answer this question as well, else the system will help.</span></div>
<div class="MsoNormal" style="text-align: left;">
<br /></div>
<div class="MsoNormal" style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></div>
<div class="MsoNormal" style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">I wanted to post a tutorial with the pictures so it can help new comers, but I didn't have time for that since I haven't started Week6 yet, which is running now. Hope my tips help. All the best!</span></div>
<div class="separator" style="clear: both; text-align: center;">
<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></div>
<span style="color: #3c3c3c; font-family: Verdana, sans-serif; font-size: 12pt; line-height: 115%;"><br /></span></div>
Shibanihttp://www.blogger.com/profile/09318859221170338553noreply@blogger.com0tag:blogger.com,1999:blog-8855365319723851093.post-62187259809740212192014-11-26T00:36:00.000-08:002014-11-26T00:36:12.364-08:00Competency 5.2/ Week 5 Reflection<div dir="ltr" style="text-align: left;" trbidi="on">
<div style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">A quick intro - I skipped weeks 3 & 4 for the time being since I was very much behind schedule coz of starting late. I jumped to Week 5 - Prediction modeling so that I can participate in the discussion and bazaar but sadly I was still lagging to participate. I'm aiming to complete weeks 3 and 4 later when I'm back on track :)</span></div>
<div style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></div>
<div style="text-align: left;">
<b><span style="font-family: Arial, Helvetica, sans-serif;">My Notes/ Learning:</span></b></div>
<div style="text-align: left;">
<b><span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></b></div>
<span style="font-family: Arial, Helvetica, sans-serif;"><b>Prediction </b></span><br />
<span style="font-family: Arial, Helvetica, sans-serif;">- Developing a model that can infer an aspect of data (predicted variable) from a combination of other data (predictor variables)</span><br />
<span style="font-family: Arial, Helvetica, sans-serif;">- Inferences about the future/ present </span><br />
<span style="font-family: Arial, Helvetica, sans-serif;">Two categories of prediction model:</span><br />
<div style="text-align: left;">
</div>
<ol style="text-align: left;">
<li><span style="font-family: Arial, Helvetica, sans-serif;">Regr</span><span style="font-family: Arial, Helvetica, sans-serif;">essers</span></li>
<li><span style="font-family: Arial, Helvetica, sans-serif;">Classifiers</span></li>
</ol>
<h3 style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><b>Regression</b></span></h3>
<div style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">- A model that predicts a number in data mining </span><span style="font-family: Arial, Helvetica, sans-serif;">(E.g. How long student takes to answer)</span></div>
<div style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">- Label -->.something we want to predict</span></div>
<div style="text-align: left;">
<br /></div>
<div style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><b>Building a Regression model:</b></span></div>
<div style="text-align: left;">
</div>
<ol style="text-align: left;">
<li><span style="font-family: Arial, Helvetica, sans-serif;">Training label - a data set where we already know the answer, to train the model for prediction</span></li>
<li><span style="font-family: Arial, Helvetica, sans-serif;">Test data - the data set for testing our model</span></li>
</ol>
<br />
<div style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">- The basic idea of regression is to determine which features, in which combination can predict the label's value.</span></div>
<div style="text-align: left;">
<b><br /></b></div>
<div style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><b>Linear Regression</b></span></div>
<div style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">E.g. No. of papers = 2+ 2* # of grad students - 0.1*(# of grad students)^2</span></div>
<div style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">- only for linear functions </span></div>
<div style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">- flexible, fast</span></div>
<div style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">- more accurate than complex models, once cross-validated.</span></div>
<div style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">- feasible to understand our model</span></div>
<div style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><b><br /></b></span></div>
<div style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><b>Regression Trees</b></span></div>
<div style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">E.g. If x>3 </span><span style="font-family: Arial, Helvetica, sans-serif;">y = 2A+ 3B</span></div>
<div style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"> else if x< -7 y = 2A- 3B</span></div>
<div style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"> else y = 2A+ 0.5B+ C</span></div>
<div style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">- Non-linear</span></div>
<div style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">- Different linear relationships between some variables, depending on other variables.</span></div>
<h3 style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></h3>
<h3 style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">Classification</span></h3>
<div style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">- what we want to predict is categorical</span></div>
<div style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">E.g. Correct/ Wrong (0/1), Category A/B/C/D/E/F</span></div>
<div style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">- Each label is associated with a set of "features" - to help in predicting the label</span></div>
<div style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></div>
<div style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">Classifier</span></div>
<div style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">- Determine which features, in what combination can predict the model.</span></div>
<div style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">- Many classification algorithms available </span></div>
<div style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">- Data mining packages that include them:</span></div>
<div style="text-align: left;">
</div>
<ul style="text-align: left;">
<li><span style="font-family: Arial, Helvetica, sans-serif;">Rapidminer</span></li>
<li><span style="font-family: Arial, Helvetica, sans-serif;">SAS Enterprise miner</span></li>
<li><span style="font-family: Arial, Helvetica, sans-serif;">Weka</span></li>
<li><span style="font-family: Arial, Helvetica, sans-serif;">KEEL</span></li>
</ul>
<br />
<div style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">- Some algorithms useful for educational data mining:</span></div>
<div style="text-align: left;">
</div>
<ul style="text-align: left;">
<li><span style="font-family: Arial, Helvetica, sans-serif;">Step regression</span></li>
<li><span style="font-family: Arial, Helvetica, sans-serif;">logistic regression</span></li>
<li><span style="font-family: Arial, Helvetica, sans-serif;">J48/ C4.5 Decision Trees</span></li>
<li><span style="font-family: Arial, Helvetica, sans-serif;">JRip Decision rules</span></li>
<li><span style="font-family: Arial, Helvetica, sans-serif;">K* Instance-based classifiers</span></li>
</ul>
<br />
<div style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><b>Step Regression:</b></span></div>
<div style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">- fits linear regression function, has arbitrary cut-off</span></div>
<div style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">- for binary classification (0/1)</span></div>
<div style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">- selects parameters, assigns weight to each parameter and computes numerical values</span></div>
<div style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">E.g. Y = 0.5 a + 0.7 b - 0.2 c +0.4 d + 0.3</span></div>
<div style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">If cut-off = 0.5, all values below 0.5 treated as 0 and all values >= 0.5 treated as 1.</span></div>
<div style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">- lack of closed-form expression</span></div>
<div style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">- often better in EDM, due to over-fitting (conservative)</span></div>
<div style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><b><br /></b></span></div>
<div style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><b>Logistic Regression:</b></span></div>
<div style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">- also for binary classification</span></div>
<div style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">-given a specific set of values of predictor variables, fits logistic function to data to find out the frequency/ odds of a specific value of the dependent variable.</span></div>
<div style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">E.g. m = a0+a1v1+a2v2+a2v3...</span></div>
<div style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"> p(m) = 1/ (1+e^-m)</span></div>
<div style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">- relatively conservative algorithm due to its simple functional form.</span></div>
<div style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">- useful for cases where changes in predictor variables have predictable effects on probability of predicted variable class (without interaction effects)</span></div>
<div style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">E.g. A= Bad, B= Bad, but A+B = Good</span></div>
<div style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">- Automated feature distillation available but it is not optimal.</span></div>
<div style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><b><br /></b></span></div>
<div style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><b>Decision Trees:</b></span></div>
<div style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">- Deals with interaction effects</span></div>
<div style="text-align: left;">
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<span style='font-size:11.0pt;
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<div class="separator" style="clear: both; text-align: center;">
<a href="http://4.bp.blogspot.com/-6wwVMZGy9_w/VHWG6zRZdvI/AAAAAAAABVo/yIakVQxdVhA/s1600/note.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="http://4.bp.blogspot.com/-6wwVMZGy9_w/VHWG6zRZdvI/AAAAAAAABVo/yIakVQxdVhA/s1600/note.png" height="211" width="400" /></a></div>
<div class="separator" style="clear: both; text-align: center;">
<b><br /></b></div>
<div class="separator" style="clear: both; text-align: left;">
<b><span style="font-family: Arial, Helvetica, sans-serif;">J 48/ C4.5:</span></b></div>
<div class="separator" style="clear: both; text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"> - J48 is the open source re-implementation in Weka/Rapidminer of C4.5</span></div>
<div class="separator" style="clear: both; text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">- both numerical and categorical predictor variables can be used</span></div>
<div class="separator" style="clear: both; text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">- tries to find optimal split in numerical variables</span></div>
<div class="separator" style="clear: both; text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">- relatively conservative</span></div>
<div class="separator" style="clear: both; text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">- good when the data has natural splits and multi-level</span></div>
<div class="separator" style="clear: both; text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">-good for data when same construct can be arrived at in multiple ways.</span></div>
<div class="separator" style="clear: both; text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><b><br /></b></span></div>
<div class="separator" style="clear: both; text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><b>Decision Rules:</b></span></div>
<div class="separator" style="clear: both; text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">- set of if-then rules to check in order</span></div>
<div class="separator" style="clear: both; text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">- many different algorithms available with difference in how rules are generated and selected. </span></div>
<div class="separator" style="clear: both; text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">-most popular sub-category (JRip, PART) repeatedly create decision trees and distills best values.</span></div>
<div class="separator" style="clear: both; text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">- relatively conservative - simpler than most decision trees.</span></div>
<div class="separator" style="clear: both; text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">- very interpretable models unlike other apporaches</span></div>
<div class="separator" style="clear: both; text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">- good when multi-level interactions are common</span></div>
<div class="separator" style="clear: both; text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><b><br /></b></span></div>
<div class="separator" style="clear: both; text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><b>K*</b></span></div>
<div class="separator" style="clear: both; text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">- instance based classifier</span></div>
<div class="separator" style="clear: both; text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">-predicts a data point from neighboring data points (stronger weights when points are nearby)</span></div>
<div class="separator" style="clear: both; text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">- good when data is very divergent with:</span></div>
<div class="separator" style="clear: both; text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">no easy patterns but there are clusters</span></div>
<div class="separator" style="clear: both; text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">different processes can lead to same result</span></div>
<div class="separator" style="clear: both; text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">interactable to find general rules</span></div>
<div class="separator" style="clear: both; text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">- sometimes works when nothing else works</span></div>
<div class="separator" style="clear: both; text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">Drawback - whole data set is needed --> useful for offline analysis</span></div>
<div class="separator" style="clear: both; text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><b><br /></b></span></div>
<div class="separator" style="clear: both; text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><b>Bagged Stumps:</b></span></div>
<div class="separator" style="clear: both; text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">- related to decision trees</span></div>
<div class="separator" style="clear: both; text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">- lot of trees with only the first feature, later we aggregate them</span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;">- relatively conservative</span></div>
<div class="separator" style="clear: both; text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">- close variant is Random forest (building a lot of trees and aggregating across the trees)</span></div>
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<br /></div>
<div class="separator" style="clear: both; text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><b>Some less-conservative algorithms:</b></span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;">- complex</span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;"><b>Support Vector Machines SVM:</b></span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;">- conducts dimensionality reduction on data space and then fits hyper plane which splits classes.</span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;">- creates sophisticated models, great for text mining</span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;">- not optimal for other educational data (logs, grades, interactions with software)</span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;"><b>Genetic Algorithms:</b></span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;">- uses mutation, combination and natural selection to search space of possible models.</span></div>
<div class="separator" style="clear: both; text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">- it can produce inconsistent answers - randomness</span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;"><b>Neural networks:</b></span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;">- composes of extremely complex relationships through combining "perceptrons" in different fashions</span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;">- complicated</span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;"><b>Over-fitting:</b></span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;">Fitting to noise as well as signal. </span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;">Over-fit model will be less good for new data.</span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;">Every model is over fit - we can try to reduce it but cannot completely eliminate over-fitting</span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;"><b>Assessment:</b></span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;">Check if the model transfers to new contexts or it is over-fit to a specific context</span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;">Test model on unseen data</span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;">Training set > Test set</span></div>
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<b><br /></b></div>
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<span style="font-family: Arial, Helvetica, sans-serif;"><b>Cross-validation:</b></span></div>
<div style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">- split data points into N equal sized groups</span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;">- Train on all groups except one and test on the last group.</span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;">- Repeat for all groups changing the training ans test data groups for all possible combinations.</span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;">K-fold: pick a number K, to split into this number of groups</span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;">Leave-out-one: Every data point is a fold (avoids stratification issues)</span></div>
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<b><span style="font-family: Arial, Helvetica, sans-serif;">Variants:</span></b></div>
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<span style="font-family: Arial, Helvetica, sans-serif;">Flat-Cross validation -each point has equal chance of being placed in a fold</span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;">Student-level cross validation - minimum requirement (testing generalization to new students)</span></div>
<div style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">Other levels like school, lesson, demography, software package etc.</span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></div>
<h3 style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">Uses of Prediction Modeling</span></h3>
<div style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><b>My ideas in using prediction modeling for education:</b></span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;"><b><br /></b></span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;"><u>1. Predict future career path and train accordingly:</u></span></div>
<div style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">If we are able to predict the future career path of students based on their interests in subjects, we can give more field-level training. That kind of education will be more meaningful to students to gain the skills required in the industry. Students will also be more interested to learn what they like, rather than being forced upon to learn something they don't prefer.</span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></div>
<div style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><u>2. Provide help for weak students:</u></span></div>
<div style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">Not all students require the same amount of help to understand the subject. Some students may learn easier than others. If we predict the different possible points where students may find difficulties, we can provide help in the specific areas.</span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></div>
<div style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><u>3. Identify competencies:</u></span></div>
<div style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">If we can identify the competencies of students and what they lack, we can provide more guidance in that area. For example, a student does his work perfectly and exhibits good leadership, but doesn't practice teamwork, we can guide him to learn teamwork competency better.</span></div>
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Shibanihttp://www.blogger.com/profile/09318859221170338553noreply@blogger.com0tag:blogger.com,1999:blog-8855365319723851093.post-32409063124734099932014-11-20T04:54:00.001-08:002014-11-20T04:54:51.439-08:00Competency 2.3<div dir="ltr" style="text-align: left;" trbidi="on">
<div style="text-align: left;">
<span style="background-color: #fdfdfd; line-height: 25.6000003814697px;"><span style="font-family: Arial, Helvetica, sans-serif;">Competency 2.3: Evaluate the impact of policy and strategic planning on systems-level deployment of learning analytics.</span></span></div>
<div style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></div>
<div style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;">#Assignment 68</span></div>
<div style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></div>
<div style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><span style="background-color: #fdfdfd; line-height: 25.6000003814697px;">Learning analytics could create a bigger impact on learning if implemented top-down than bottom up due to the availability of big data.However, t</span><span style="background-color: #fdfdfd; line-height: 25.6000003814697px;">he deployment of learning analytics faces many challenges at the institutional level:</span></span></div>
<span style="background-color: #fdfdfd; font-family: Arial, Helvetica, sans-serif; line-height: 25.6000003814697px;"><br /></span>
<span style="font-family: Arial, Helvetica, sans-serif;">1. Acceptance:</span><br />
<span style="font-family: Arial, Helvetica, sans-serif;">To work big on big data, big support is needed from the top management. The top management should foresee the future and possibilities of learning analytics as to what it can achieve. Only with promised outcomes, they can be expected to support it at a big level. It is not a small change to bring about in a day.</span><br />
<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span>
<span style="font-family: Arial, Helvetica, sans-serif;">2. Management:</span><br />
<span style="font-family: Arial, Helvetica, sans-serif;">A new department may be needed to manage what should be done in learning analytics. This will require funding, responsible experts, manpower and technical training. Do the institutions have what it takes to commit to this new venture?</span><br />
<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span>
<span style="font-family: Arial, Helvetica, sans-serif;">3. Ethics:</span><br />
<span style="font-family: Arial, Helvetica, sans-serif;">Personal Data Protection is a growing concern these days. When data is analyzed, it has to pass through humans and systems. How safe can our data be? Could there be a possible breach in security and what could be its implication?</span><br />
<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span>
<span style="font-family: Arial, Helvetica, sans-serif;">When we have answers for all these questions, we could probably move forward to the next era of data analytics!</span><br />
<br />
<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></div>
Shibanihttp://www.blogger.com/profile/09318859221170338553noreply@blogger.com0tag:blogger.com,1999:blog-8855365319723851093.post-22699776549138522392014-11-20T04:13:00.000-08:002014-11-20T04:30:00.979-08:00Competency 2.2<div dir="ltr" style="text-align: left;" trbidi="on">
<div style="text-align: left;">
<span style="background-color: #fdfdfd; color: #3c3c3c; line-height: 25.6000003814697px;"><span style="font-family: Arial, Helvetica, sans-serif;">Competency 2.2: Download, install, and conduct basic analytics using Tableau software.</span></span></div>
<div style="text-align: left;">
<span style="background-color: #fdfdfd; color: #3c3c3c; line-height: 25.6000003814697px;"><span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></span></div>
<div style="text-align: left;">
<span style="background-color: #fdfdfd; color: #3c3c3c; line-height: 25.6000003814697px;"><span style="font-family: Arial, Helvetica, sans-serif;">For the purpose of this competency, I've used a sample excel sheet with simple sales data. I've installed Tableau for this exercise and used a two sheet excel. My sample data below:</span></span></div>
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<a href="http://3.bp.blogspot.com/-MdIGFJwxSOw/VG3aCW21hsI/AAAAAAAABVM/xo8KvVDT8PI/s1600/Sales_sample%2Bdata_shib1.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="http://3.bp.blogspot.com/-MdIGFJwxSOw/VG3aCW21hsI/AAAAAAAABVM/xo8KvVDT8PI/s1600/Sales_sample%2Bdata_shib1.jpg" height="252" width="400" /></a></div>
<div style="text-align: left;">
<span style="background-color: #fdfdfd; color: #3c3c3c; line-height: 25.6000003814697px;"><span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></span></div>
<div style="text-align: left;">
<span style="background-color: #fdfdfd; color: #3c3c3c; line-height: 25.6000003814697px;"><span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></span></div>
<div style="text-align: left;">
<span style="background-color: #fdfdfd; color: #3c3c3c; line-height: 25.6000003814697px;"><span style="font-family: Arial, Helvetica, sans-serif;">I've then added rows and columns to create visualizations. Common tools from the Show me list are used to visualize my analysis of Sales by Rep and Sales by Region.</span></span></div>
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<span style="background-color: #fdfdfd; color: #3c3c3c; line-height: 25.6000003814697px;"><span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></span></div>
<div style="text-align: left;">
<span style="background-color: #fdfdfd; color: #3c3c3c; line-height: 25.6000003814697px;"><span style="font-family: Arial, Helvetica, sans-serif;">I've then created a dashboard with my charts.</span></span></div>
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<a href="http://4.bp.blogspot.com/-uw7JMOFhWDI/VG3auFkbdhI/AAAAAAAABVU/Egw37XjHr3w/s1600/sales%2Bdashboard_shib.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="http://4.bp.blogspot.com/-uw7JMOFhWDI/VG3auFkbdhI/AAAAAAAABVU/Egw37XjHr3w/s1600/sales%2Bdashboard_shib.jpg" height="380" width="400" /></a></div>
<div style="text-align: left;">
<br />
<br />
#Assignment 47</div>
</div>
Shibanihttp://www.blogger.com/profile/09318859221170338553noreply@blogger.com0tag:blogger.com,1999:blog-8855365319723851093.post-1056138520086294842014-11-20T03:55:00.000-08:002014-11-20T03:55:21.253-08:00Competency 2.1<div dir="ltr" style="text-align: left;" trbidi="on">
<div style="text-align: left;">
<span style="background-color: #fdfdfd; color: #3c3c3c; line-height: 25.6000003814697px;"><span style="font-family: Arial, Helvetica, sans-serif;">Competency 2.1: Describe the learning analytics data cycle.</span></span></div>
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<span style="background-color: #fdfdfd; color: #3c3c3c; line-height: 25.6000003814697px;"><span style="font-family: Arial, Helvetica, sans-serif;"><b><br /></b></span></span></div>
<div style="text-align: left;">
<span style="background-color: #fdfdfd; color: #3c3c3c; line-height: 25.6000003814697px;"><span style="font-family: Arial, Helvetica, sans-serif;"><b>Learning Analytics Data Cycle</b></span></span></div>
<div style="text-align: left;">
<span style="background-color: #fdfdfd; color: #3c3c3c; line-height: 25.6000003814697px;"><span style="font-family: Arial, Helvetica, sans-serif;">The process of Learning Analytics is a cycle and not linear due to the fact that we need to revisit the steps according to the data and results.</span></span></div>
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<span style="background-color: #fdfdfd; color: #3c3c3c; line-height: 25.6000003814697px;"><span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></span></div>
<div style="text-align: left;">
<span style="background-color: #fdfdfd; color: #3c3c3c; line-height: 25.6000003814697px;"><span style="font-family: Arial, Helvetica, sans-serif;">I see the data cycle as follows:</span></span></div>
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<span style='font-size:26.0pt;line-height:115%;
font-family:"Calibri","sans-serif";mso-fareast-font-family:Calibri'>Learning
Analytics Data Cycle</span></p>
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<div style="text-align: left;">
<span style="font-family: Arial, Helvetica, sans-serif;"><a href="http://4.bp.blogspot.com/-hcTp_LphLMM/VG3RLlBQeXI/AAAAAAAABUk/Ma4TqLZdsxM/s1600/cycle.jpg" imageanchor="1" style="clear: left; display: inline !important; margin-bottom: 1em; margin-left: 1em;"><br /></a></span><span style="font-family: Arial, Helvetica, sans-serif;"><a href="http://4.bp.blogspot.com/-hcTp_LphLMM/VG3RLlBQeXI/AAAAAAAABUk/Ma4TqLZdsxM/s1600/cycle.jpg" imageanchor="1" style="clear: left; display: inline !important; margin-bottom: 1em; margin-left: 1em;"><img border="0" src="http://4.bp.blogspot.com/-hcTp_LphLMM/VG3RLlBQeXI/AAAAAAAABUk/Ma4TqLZdsxM/s1600/cycle.jpg" height="252" width="400" /></a></span></div>
<span style="font-family: Arial, Helvetica, sans-serif;"><span style="background-color: #fdfdfd; color: #3c3c3c; line-height: 25.6000003814697px;"><div style="text-align: left;">
<span style="background-color: #fdfdfd; color: #3c3c3c; line-height: 25.6000003814697px;"><br /></span></div>
<div style="text-align: left;">
<span style="line-height: 25.6000003814697px;">Cleaning the data could be time consuming and challenging depending on the nature of data. If the data is not cleaned thoroughly, it will not produce accurate results. For example, a data set where duplicates are not removed when processed will not yield expected results.</span></div>
</span></span><div style="text-align: left;">
<span style="background-color: #fdfdfd; color: #3c3c3c; line-height: 25.6000003814697px;"><span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></span></div>
<div style="text-align: left;">
<span style="background-color: #fdfdfd; color: #3c3c3c; line-height: 25.6000003814697px;"><span style="font-family: Arial, Helvetica, sans-serif;">Manipulating the data will help us make data easier to work with. We can change the set of data to deal with, reshape it to another form, reorder the data etc. according to our requirements.</span></span></div>
<div style="text-align: left;">
<span style="background-color: #fdfdfd; color: #3c3c3c; line-height: 25.6000003814697px;"><span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></span></div>
<div style="text-align: left;">
<span style="background-color: #fdfdfd; color: #3c3c3c; line-height: 25.6000003814697px;"><span style="font-family: Arial, Helvetica, sans-serif;">Analyzing the data is to make sense of the data using different methods. It requires adding in possible transformations to process the data. Based on the analysis, prediction models can also be built for future data.</span></span></div>
<div style="text-align: left;">
<span style="background-color: #fdfdfd; color: #3c3c3c; line-height: 25.6000003814697px;"><span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></span></div>
<div style="text-align: left;">
<span style="background-color: #fdfdfd; color: #3c3c3c; line-height: 25.6000003814697px;"><span style="font-family: Arial, Helvetica, sans-serif;">Viewing the data involves looking into the processes data/ results using various visualization techniques. A lot of visualization options are available to try and understand data.</span></span></div>
<div style="text-align: left;">
<span style="background-color: #fdfdfd; color: #3c3c3c; line-height: 25.6000003814697px;"><span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></span></div>
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<span style="background-color: #fdfdfd; color: #3c3c3c; line-height: 25.6000003814697px;"><span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></span></div>
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<span style="background-color: #fdfdfd; color: #3c3c3c; line-height: 25.6000003814697px;"><span style="font-family: Arial, Helvetica, sans-serif;"><br /></span></span></div>
</div>
Shibanihttp://www.blogger.com/profile/09318859221170338553noreply@blogger.com0tag:blogger.com,1999:blog-8855365319723851093.post-72828642201862077192014-11-19T05:08:00.002-08:002014-11-19T05:15:27.585-08:00Competency 1.2<div dir="ltr" style="text-align: left;" trbidi="on">
<span style="background-color: #fdfdfd; color: #3c3c3c; font-family: Arial, Helvetica, sans-serif; line-height: 25.6000003814697px;">Competency 1.2: Define learning analytics and detail types of insight they can provide to educators and learners.</span><br />
<span style="font-family: Arial, Helvetica, sans-serif;"><span style="background-color: #fdfdfd; color: #3c3c3c; line-height: 25.6000003814697px;"><br /></span>
<span style="background-color: #fdfdfd; color: #3c3c3c; line-height: 25.6000003814697px;">My definition of Learning Analytics (based on previous definitions and my understanding):</span></span><br />
<span style="font-family: Arial, Helvetica, sans-serif;"><span style="background-color: #fdfdfd; color: #3c3c3c; line-height: 25.6000003814697px;"><br /></span>
<span style="background-color: #fdfdfd; color: #3c3c3c; line-height: 25.6000003814697px;">Learning Analytics is something which will help us to process simple <b>data</b> into useful <b>information</b> using different methods. A lot of minute, but useful information may be lost in large amounts of data, which can be extracted using learning analytics. The different methods can help us to convert raw data to a readable form, analyze using required processing and view results using possible visualizations. </span></span><br />
<span style="font-family: Arial, Helvetica, sans-serif;"><span style="background-color: #fdfdfd; color: #3c3c3c; line-height: 25.6000003814697px;"><br /></span>
<span style="background-color: #fdfdfd; color: #3c3c3c; line-height: 25.6000003814697px;">Educators and learners can get a variety of insights using learning analytics such as:</span></span><br />
<span style="background-color: #fdfdfd; color: #3c3c3c; font-family: Arial, Helvetica, sans-serif; line-height: 25.6000003814697px;">Statistical Analysis</span><br />
<span style="background-color: #fdfdfd; color: #3c3c3c; font-family: Arial, Helvetica, sans-serif; line-height: 25.6000003814697px;">Discourse Analysis</span><br />
<span style="background-color: #fdfdfd; color: #3c3c3c; font-family: Arial, Helvetica, sans-serif; line-height: 25.6000003814697px;">Text processing</span><br />
<span style="background-color: #fdfdfd; color: #3c3c3c; font-family: Arial, Helvetica, sans-serif; line-height: 25.6000003814697px;">Sentiment Analysis</span><br />
<span style="background-color: #fdfdfd; color: #3c3c3c; font-family: Arial, Helvetica, sans-serif; line-height: 25.6000003814697px;">Network Analysis</span><br />
<span style="background-color: #fdfdfd; color: #3c3c3c; font-family: Arial, Helvetica, sans-serif; line-height: 25.6000003814697px;">Prediction models</span><br />
<span style="background-color: #fdfdfd; color: #3c3c3c; font-family: Arial, Helvetica, sans-serif; line-height: 25.6000003814697px;">Machine Learning</span><br />
<span style="background-color: #fdfdfd; color: #3c3c3c; font-family: Arial, Helvetica, sans-serif; line-height: 25.6000003814697px;">Visualization techniques</span><br />
<span style="font-family: Arial, Helvetica, sans-serif;"><span style="background-color: #fdfdfd; color: #3c3c3c; line-height: 25.6000003814697px;"><br /></span>
<span style="background-color: #fdfdfd; color: #3c3c3c; line-height: 25.6000003814697px;">The list is not exhaustive. We could also add specific examples to the list.</span></span></div>
Shibanihttp://www.blogger.com/profile/09318859221170338553noreply@blogger.com0tag:blogger.com,1999:blog-8855365319723851093.post-84577194827798960772014-11-19T04:32:00.001-08:002014-11-19T05:15:40.039-08:00Competency 1.1<div dir="ltr" style="text-align: left;" trbidi="on">
<div dir="ltr" style="text-align: left;" trbidi="on">
<span style="background-color: #fdfdfd; color: #3c3c3c; font-family: Arial, Helvetica, sans-serif; font-size: 16px; line-height: 25.6000003814697px;">Competency 1.1: Identify proprietary and open source tools commonly used in learning analytics.</span><br />
<span style="font-family: Arial, Helvetica, sans-serif;"><span style="background-color: #fdfdfd; color: #3c3c3c; font-size: 16px; line-height: 25.6000003814697px;"><br />
</span> <span style="background-color: #fdfdfd;"><span style="color: #3c3c3c;"><span style="line-height: 25.6000003814697px;">I have specifically focused on text mining while searching for tools because I've been working on it, but some of these also include other options. From my usage, the most powerful ones I've seen are Rapidminer and R, both available for free. </span></span></span></span><br />
<span style="font-family: Arial, Helvetica, sans-serif;"><span style="background-color: #fdfdfd;"><span style="color: #3c3c3c;"><span style="line-height: 25.6000003814697px;"><br /></span></span></span>
<span style="background-color: #fdfdfd;"></span></span><br />
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<b><span style="font-family: Arial, Helvetica, sans-serif;">Tool<o:p></o:p></span></b></div>
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<b><span style="font-family: Arial, Helvetica, sans-serif;">Publisher<o:p></o:p></span></b></div>
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<b><span style="font-family: Arial, Helvetica, sans-serif;">Reference<o:p></o:p></span></b></div>
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<b><span style="font-family: Arial, Helvetica, sans-serif;">Website<o:p></o:p></span></b></div>
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<b><span style="font-family: Arial, Helvetica, sans-serif;">Text Analysis
Methods<o:p></o:p></span></b></div>
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<td nowrap="" style="border-top: none; border: solid windowtext 1.0pt; height: 144.05pt; mso-border-alt: solid windowtext .75pt; mso-border-top-alt: solid windowtext .75pt; padding: 0in 5.4pt 0in 5.4pt; width: 76.65pt;" width="102"><div align="center" class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center;">
<b><span style="font-family: Arial, Helvetica, sans-serif;">WORDSTAT<o:p></o:p></span></b></div>
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<td nowrap="" style="border-bottom: solid windowtext 1.0pt; border-left: none; border-right: solid windowtext 1.0pt; border-top: none; height: 144.05pt; mso-border-alt: solid windowtext .75pt; mso-border-left-alt: solid windowtext .75pt; mso-border-top-alt: solid windowtext .75pt; padding: 0in 5.4pt 0in 5.4pt; width: 63.15pt;" width="84"><div align="center" class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center;">
<span style="font-family: Arial, Helvetica, sans-serif; font-size: 11pt;">Provalis Research<o:p></o:p></span></div>
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<td style="border-bottom: solid windowtext 1.0pt; border-left: none; border-right: solid windowtext 1.0pt; border-top: none; height: 144.05pt; mso-border-alt: solid windowtext .75pt; mso-border-left-alt: solid windowtext .75pt; mso-border-top-alt: solid windowtext .75pt; padding: 0in 5.4pt 0in 5.4pt; width: 49.95pt;" width="67"><div align="center" class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center;">
<u><span style="color: blue; font-family: "Arial","sans-serif"; mso-ansi-language: EN-SG; mso-bidi-font-size: 10.0pt; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: EN-SG;"><a href="http://provalisresearch.com/products/content-analysis-software/"><span style="color: blue; font-family: Arial, Helvetica, sans-serif;">http://provalisresearch.com/</span></a></span></u></div>
<div align="center" class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center;">
<u><span style="color: blue; font-family: Arial, Helvetica, sans-serif; mso-ansi-language: EN-SG; mso-bidi-font-size: 10.0pt; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: EN-SG;"><a href="http://provalisresearch.com/products/content-analysis-software/"><span style="color: blue;">products/content-analysis-software/</span></a><o:p></o:p></span></u></div>
</td>
<td style="border-bottom: solid windowtext 1.0pt; border-left: none; border-right: solid windowtext 1.0pt; border-top: none; height: 144.05pt; mso-border-alt: solid windowtext .75pt; mso-border-left-alt: solid windowtext .75pt; mso-border-top-alt: solid windowtext .75pt; padding: 0in 5.4pt 0in 5.4pt; width: 148.5pt;" width="198"><div align="center" class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center;">
<span style="font-family: Arial, Helvetica, sans-serif; font-size: 11pt;">Word Categorization,<br />
Frequency Analysis,<br />
Keyword retrieval,<br />
Automated text classification<o:p></o:p></span></div>
</td>
</tr>
<tr style="height: 105.0pt; mso-yfti-irow: 2;">
<td nowrap="" style="border-top: none; border: solid windowtext 1.0pt; height: 105.0pt; mso-border-alt: solid windowtext .75pt; mso-border-top-alt: solid windowtext .75pt; padding: 0in 5.4pt 0in 5.4pt; width: 76.65pt;" width="102"><div align="center" class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center;">
<b><span style="font-family: Arial, Helvetica, sans-serif;">TagHelper<o:p></o:p></span></b></div>
</td>
<td nowrap="" style="border-bottom: solid windowtext 1.0pt; border-left: none; border-right: solid windowtext 1.0pt; border-top: none; height: 105.0pt; mso-border-alt: solid windowtext .75pt; mso-border-left-alt: solid windowtext .75pt; mso-border-top-alt: solid windowtext .75pt; padding: 0in 5.4pt 0in 5.4pt; width: 63.15pt;" width="84"><div align="center" class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center;">
<span style="font-family: Arial, Helvetica, sans-serif; font-size: 11pt;">Carolyn Rose<o:p></o:p></span></div>
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<td style="border-bottom: solid windowtext 1.0pt; border-left: none; border-right: solid windowtext 1.0pt; border-top: none; height: 105.0pt; mso-border-alt: solid windowtext .75pt; mso-border-left-alt: solid windowtext .75pt; mso-border-top-alt: solid windowtext .75pt; padding: 0in 5.4pt 0in 5.4pt; width: 49.95pt;" width="67"><div align="center" class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center;">
<u><span style="color: blue; font-family: "Arial","sans-serif"; mso-ansi-language: EN-SG; mso-bidi-font-size: 10.0pt; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: EN-SG;"><a href="http://www.cs.cmu.edu/~cprose/TagHelper.html"><span style="color: blue; font-family: Arial, Helvetica, sans-serif;">http://www.cs.cmu.edu/~cprose/</span></a></span></u></div>
<div align="center" class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center;">
<u><span style="color: blue; font-family: Arial, Helvetica, sans-serif; mso-ansi-language: EN-SG; mso-bidi-font-size: 10.0pt; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: EN-SG;"><a href="http://www.cs.cmu.edu/~cprose/TagHelper.html"><span style="color: blue;">TagHelper.html</span></a><o:p></o:p></span></u></div>
</td>
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<span style="font-family: Arial, Helvetica, sans-serif; font-size: 11pt;">Automatic coding based upon the written coding
rules<o:p></o:p></span></div>
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<b><span style="font-family: Arial, Helvetica, sans-serif;">MEPA<o:p></o:p></span></b></div>
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<td nowrap="" style="border-bottom: solid windowtext 1.0pt; border-left: none; border-right: solid windowtext 1.0pt; border-top: none; height: 152.6pt; mso-border-alt: solid windowtext .75pt; mso-border-left-alt: solid windowtext .75pt; mso-border-top-alt: solid windowtext .75pt; padding: 0in 5.4pt 0in 5.4pt; width: 63.15pt;" width="84"><div align="center" class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center;">
<span style="font-family: Arial, Helvetica, sans-serif; font-size: 11pt;">Gijsbert Erkens<o:p></o:p></span></div>
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<u><span style="color: blue; font-family: "Arial","sans-serif"; mso-ansi-language: EN-SG; mso-bidi-font-size: 10.0pt; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: EN-SG;"><a href="http://edugate.fss.uu.nl/mepa/index.htm"><span style="color: blue; font-family: Arial, Helvetica, sans-serif;">http://edugate.fss.uu.nl/mepa/</span></a></span></u></div>
<div align="center" class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center;">
<u><span style="color: blue; font-family: Arial, Helvetica, sans-serif; mso-ansi-language: EN-SG; mso-bidi-font-size: 10.0pt; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: EN-SG;"><a href="http://edugate.fss.uu.nl/mepa/index.htm"><span style="color: blue;">index.htm<br />
</span></a><o:p></o:p></span></u></div>
</td>
<td style="border-bottom: solid windowtext 1.0pt; border-left: none; border-right: solid windowtext 1.0pt; border-top: none; height: 152.6pt; mso-border-alt: solid windowtext .75pt; mso-border-left-alt: solid windowtext .75pt; mso-border-top-alt: solid windowtext .75pt; padding: 0in 5.4pt 0in 5.4pt; width: 148.5pt;" width="198"><div align="center" class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center;">
<span style="font-family: Arial, Helvetica, sans-serif; font-size: 11pt;">frequency, time-interval analysis, <br />
word-frequency,<br />
word-context analysis, sorting and
searching<o:p></o:p></span></div>
</td>
</tr>
<tr style="height: 80.6pt; mso-yfti-irow: 4;">
<td nowrap="" style="border-top: none; border: solid windowtext 1.0pt; height: 80.6pt; mso-border-alt: solid windowtext .75pt; mso-border-top-alt: solid windowtext .75pt; padding: 0in 5.4pt 0in 5.4pt; width: 76.65pt;" width="102"><div align="center" class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center;">
<b><span style="font-family: Arial, Helvetica, sans-serif;">SPSS Text
Analytics<o:p></o:p></span></b></div>
</td>
<td style="border-bottom: solid windowtext 1.0pt; border-left: none; border-right: solid windowtext 1.0pt; border-top: none; height: 80.6pt; mso-border-alt: solid windowtext .75pt; mso-border-left-alt: solid windowtext .75pt; mso-border-top-alt: solid windowtext .75pt; padding: 0in 5.4pt 0in 5.4pt; width: 63.15pt;" width="84"><div align="center" class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center;">
<span style="font-family: Arial, Helvetica, sans-serif; font-size: 11pt;">IBM<o:p></o:p></span></div>
</td>
<td style="border-bottom: solid windowtext 1.0pt; border-left: none; border-right: solid windowtext 1.0pt; border-top: none; height: 80.6pt; mso-border-alt: solid windowtext .75pt; mso-border-left-alt: solid windowtext .75pt; mso-border-top-alt: solid windowtext .75pt; padding: 0in 5.4pt 0in 5.4pt; width: 49.95pt;" width="67"><div align="center" class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center;">
<u><span style="color: blue; font-family: "Arial","sans-serif"; mso-ansi-language: EN-SG; mso-bidi-font-size: 10.0pt; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: EN-SG;"><a href="http://www-03.ibm.com/software/products/us/en/spss-text-analytics-surveys"><span style="color: blue; font-family: Arial, Helvetica, sans-serif;">http://www-03.ibm.com/software/products/</span></a></span></u></div>
<div align="center" class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center;">
<u><span style="color: blue; font-family: Arial, Helvetica, sans-serif; mso-ansi-language: EN-SG; mso-bidi-font-size: 10.0pt; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: EN-SG;"><a href="http://www-03.ibm.com/software/products/us/en/spss-text-analytics-surveys"><span style="color: blue;">us/en/spss-text-analytics-surveys</span></a><o:p></o:p></span></u></div>
</td>
<td style="border-bottom: solid windowtext 1.0pt; border-left: none; border-right: solid windowtext 1.0pt; border-top: none; height: 80.6pt; mso-border-alt: solid windowtext .75pt; mso-border-left-alt: solid windowtext .75pt; mso-border-top-alt: solid windowtext .75pt; padding: 0in 5.4pt 0in 5.4pt; width: 148.5pt;" width="198"><div align="center" class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center;">
<span style="font-family: Arial, Helvetica, sans-serif; font-size: 11pt;">Automatic categorization and grouping of terms<o:p></o:p></span></div>
</td>
</tr>
<tr style="height: 96.8pt; mso-yfti-irow: 5;">
<td nowrap="" style="border-top: none; border: solid windowtext 1.0pt; height: 96.8pt; mso-border-alt: solid windowtext .75pt; mso-border-top-alt: solid windowtext .75pt; padding: 0in 5.4pt 0in 5.4pt; width: 76.65pt;" width="102"><div align="center" class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center;">
<b><span style="font-family: Arial, Helvetica, sans-serif;">Wmatrix<o:p></o:p></span></b></div>
</td>
<td style="border-bottom: solid windowtext 1.0pt; border-left: none; border-right: solid windowtext 1.0pt; border-top: none; height: 96.8pt; mso-border-alt: solid windowtext .75pt; mso-border-left-alt: solid windowtext .75pt; mso-border-top-alt: solid windowtext .75pt; padding: 0in 5.4pt 0in 5.4pt; width: 63.15pt;" width="84"><div align="center" class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center;">
<span style="font-family: Arial, Helvetica, sans-serif; font-size: 11pt;">Paul Rayson<o:p></o:p></span></div>
</td>
<td style="border-bottom: solid windowtext 1.0pt; border-left: none; border-right: solid windowtext 1.0pt; border-top: none; height: 96.8pt; mso-border-alt: solid windowtext .75pt; mso-border-left-alt: solid windowtext .75pt; mso-border-top-alt: solid windowtext .75pt; padding: 0in 5.4pt 0in 5.4pt; width: 49.95pt;" width="67"><div align="center" class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center;">
<u><span style="color: blue; font-family: Arial, Helvetica, sans-serif; mso-ansi-language: EN-SG; mso-bidi-font-size: 10.0pt; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: EN-SG;"><a href="http://ucrel.lancs.ac.uk/wmatrix/"><span style="color: blue;">http://ucrel.lancs.ac.uk/wmatrix/</span></a><o:p></o:p></span></u></div>
</td>
<td style="border-bottom: solid windowtext 1.0pt; border-left: none; border-right: solid windowtext 1.0pt; border-top: none; height: 96.8pt; mso-border-alt: solid windowtext .75pt; mso-border-left-alt: solid windowtext .75pt; mso-border-top-alt: solid windowtext .75pt; padding: 0in 5.4pt 0in 5.4pt; width: 148.5pt;" width="198"><div align="center" class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center;">
<span style="font-family: Arial, Helvetica, sans-serif; font-size: 11pt;">Word frequencies, word search, word clouds, pos
tag frequencies, MWEs, n-grams, automatic tagging<o:p></o:p></span></div>
</td>
</tr>
<tr style="height: 86.9pt; mso-yfti-irow: 6;">
<td nowrap="" style="border-top: none; border: solid windowtext 1.0pt; height: 86.9pt; mso-border-alt: solid windowtext .75pt; mso-border-top-alt: solid windowtext .75pt; padding: 0in 5.4pt 0in 5.4pt; width: 76.65pt;" width="102"><div align="center" class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center;">
<b><span style="font-family: Arial, Helvetica, sans-serif;">KNIME<o:p></o:p></span></b></div>
</td>
<td style="border-bottom: solid windowtext 1.0pt; border-left: none; border-right: solid windowtext 1.0pt; border-top: none; height: 86.9pt; mso-border-alt: solid windowtext .75pt; mso-border-left-alt: solid windowtext .75pt; mso-border-top-alt: solid windowtext .75pt; padding: 0in 5.4pt 0in 5.4pt; width: 63.15pt;" width="84"><div align="center" class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center;">
<span style="font-family: Arial, Helvetica, sans-serif; font-size: 11pt;">Kilian Thiel<o:p></o:p></span></div>
</td>
<td style="border-bottom: solid windowtext 1.0pt; border-left: none; border-right: solid windowtext 1.0pt; border-top: none; height: 86.9pt; mso-border-alt: solid windowtext .75pt; mso-border-left-alt: solid windowtext .75pt; mso-border-top-alt: solid windowtext .75pt; padding: 0in 5.4pt 0in 5.4pt; width: 49.95pt;" width="67"><div align="center" class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center;">
<u><span style="color: blue; font-family: "Arial","sans-serif"; mso-ansi-language: EN-SG; mso-bidi-font-size: 10.0pt; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: EN-SG;"><a href="http://tech.knime.org/knime-text-processing"><span style="color: blue; font-family: Arial, Helvetica, sans-serif;">http://tech.knime.org/</span></a></span></u></div>
<div align="center" class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center;">
<u><span style="color: blue; font-family: Arial, Helvetica, sans-serif; mso-ansi-language: EN-SG; mso-bidi-font-size: 10.0pt; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: EN-SG;"><a href="http://tech.knime.org/knime-text-processing"><span style="color: blue;">knime-text-processing<br />
</span></a><o:p></o:p></span></u></div>
</td>
<td style="border-bottom: solid windowtext 1.0pt; border-left: none; border-right: solid windowtext 1.0pt; border-top: none; height: 86.9pt; mso-border-alt: solid windowtext .75pt; mso-border-left-alt: solid windowtext .75pt; mso-border-top-alt: solid windowtext .75pt; padding: 0in 5.4pt 0in 5.4pt; width: 148.5pt;" width="198"><div align="center" class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center;">
<span style="font-family: Arial, Helvetica, sans-serif; font-size: 11pt;">classification and clustering of documents, named
entity recognition, tag clouds<o:p></o:p></span></div>
</td>
</tr>
<tr style="height: 89.6pt; mso-yfti-irow: 7;">
<td nowrap="" style="border-top: none; border: solid windowtext 1.0pt; height: 89.6pt; mso-border-alt: solid windowtext .75pt; mso-border-top-alt: solid windowtext .75pt; padding: 0in 5.4pt 0in 5.4pt; width: 76.65pt;" width="102"><div align="center" class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center;">
<b><span style="font-family: Arial, Helvetica, sans-serif;">Weka<o:p></o:p></span></b></div>
</td>
<td style="border-bottom: solid windowtext 1.0pt; border-left: none; border-right: solid windowtext 1.0pt; border-top: none; height: 89.6pt; mso-border-alt: solid windowtext .75pt; mso-border-left-alt: solid windowtext .75pt; mso-border-top-alt: solid windowtext .75pt; padding: 0in 5.4pt 0in 5.4pt; width: 63.15pt;" width="84"><div align="center" class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center;">
<span style="font-family: Arial, Helvetica, sans-serif; font-size: 11pt;">The University of Waikato<o:p></o:p></span></div>
</td>
<td style="border-bottom: solid windowtext 1.0pt; border-left: none; border-right: solid windowtext 1.0pt; border-top: none; height: 89.6pt; mso-border-alt: solid windowtext .75pt; mso-border-left-alt: solid windowtext .75pt; mso-border-top-alt: solid windowtext .75pt; padding: 0in 5.4pt 0in 5.4pt; width: 49.95pt;" width="67"><div align="center" class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center;">
<u><span style="font-family: Arial, Helvetica, sans-serif;"><a href="http://www.cs.waikato.ac.nz/ml/weka/"><span style="color: blue;">http://www.cs.waikato.ac.nz/ml/weka/</span></a><span style="color: blue;"><o:p></o:p></span></span></u></div>
</td>
<td style="border-bottom: solid windowtext 1.0pt; border-left: none; border-right: solid windowtext 1.0pt; border-top: none; height: 89.6pt; mso-border-alt: solid windowtext .75pt; mso-border-left-alt: solid windowtext .75pt; mso-border-top-alt: solid windowtext .75pt; padding: 0in 5.4pt 0in 5.4pt; width: 148.5pt;" width="198"><div align="center" class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center;">
<span style="font-family: Arial, Helvetica, sans-serif; font-size: 11pt;">Text classification, clustering, association<o:p></o:p></span></div>
</td>
</tr>
<tr style="height: 71.6pt; mso-yfti-irow: 8;">
<td nowrap="" style="border-top: none; border: solid windowtext 1.0pt; height: 71.6pt; mso-border-alt: solid windowtext .75pt; mso-border-top-alt: solid windowtext .75pt; padding: 0in 5.4pt 0in 5.4pt; width: 76.65pt;" width="102"><div align="center" class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center;">
<b><span style="font-family: Arial, Helvetica, sans-serif;">TADA-Ed<o:p></o:p></span></b></div>
</td>
<td style="border-bottom: solid windowtext 1.0pt; border-left: none; border-right: solid windowtext 1.0pt; border-top: none; height: 71.6pt; mso-border-alt: solid windowtext .75pt; mso-border-left-alt: solid windowtext .75pt; mso-border-top-alt: solid windowtext .75pt; padding: 0in 5.4pt 0in 5.4pt; width: 63.15pt;" width="84"><div align="center" class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center;">
<span style="font-family: Arial, Helvetica, sans-serif; font-size: 11pt;">Agathe Merceron, <br />
Kalina Yacef<o:p></o:p></span></div>
</td>
<td style="border-bottom: solid windowtext 1.0pt; border-left: none; border-right: solid windowtext 1.0pt; border-top: none; height: 71.6pt; mso-border-alt: solid windowtext .75pt; mso-border-left-alt: solid windowtext .75pt; mso-border-top-alt: solid windowtext .75pt; padding: 0in 5.4pt 0in 5.4pt; width: 49.95pt;" width="67"><div align="center" class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center;">
<span style="color: #0000ee; font-family: Arial, Helvetica, sans-serif;"><u>http://imej.wfu.edu/articles/2005/</u></span></div>
<div align="center" class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center;">
<span style="color: #0000ee; font-family: Arial, Helvetica, sans-serif;"><u>1/03/index.asp#2</u></span></div>
</td>
<td style="border-bottom: solid windowtext 1.0pt; border-left: none; border-right: solid windowtext 1.0pt; border-top: none; height: 71.6pt; mso-border-alt: solid windowtext .75pt; mso-border-left-alt: solid windowtext .75pt; mso-border-top-alt: solid windowtext .75pt; padding: 0in 5.4pt 0in 5.4pt; width: 148.5pt;" width="198"><div align="center" class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center;">
<span style="font-family: Arial, Helvetica, sans-serif; font-size: 11pt;">Classification, clustering, association<o:p></o:p></span></div>
</td>
</tr>
<tr style="height: 130.1pt; mso-yfti-irow: 9;">
<td nowrap="" style="border-top: none; border: solid windowtext 1.0pt; height: 130.1pt; mso-border-alt: solid windowtext .75pt; mso-border-top-alt: solid windowtext .75pt; padding: 0in 5.4pt 0in 5.4pt; width: 76.65pt;" width="102"><div align="center" class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center;">
<b><span style="font-family: Arial, Helvetica, sans-serif;">RapidMiner<o:p></o:p></span></b></div>
</td>
<td style="border-bottom: solid windowtext 1.0pt; border-left: none; border-right: solid windowtext 1.0pt; border-top: none; height: 130.1pt; mso-border-alt: solid windowtext .75pt; mso-border-left-alt: solid windowtext .75pt; mso-border-top-alt: solid windowtext .75pt; padding: 0in 5.4pt 0in 5.4pt; width: 63.15pt;" width="84"><div align="center" class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center;">
<span style="font-family: Arial, Helvetica, sans-serif; font-size: 11pt;">RapidMiner Inc<o:p></o:p></span></div>
</td>
<td style="border-bottom: solid windowtext 1.0pt; border-left: none; border-right: solid windowtext 1.0pt; border-top: none; height: 130.1pt; mso-border-alt: solid windowtext .75pt; mso-border-left-alt: solid windowtext .75pt; mso-border-top-alt: solid windowtext .75pt; padding: 0in 5.4pt 0in 5.4pt; width: 49.95pt;" width="67"><div align="center" class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center;">
<u><span style="color: blue; font-family: Arial, Helvetica, sans-serif; mso-ansi-language: EN-SG; mso-bidi-font-size: 10.0pt; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: EN-SG;"><a href="http://rapidminer.com/"><span style="color: blue;">http://rapidminer.com/</span></a><o:p></o:p></span></u></div>
</td>
<td style="border-bottom: solid windowtext 1.0pt; border-left: none; border-right: solid windowtext 1.0pt; border-top: none; height: 130.1pt; mso-border-alt: solid windowtext .75pt; mso-border-left-alt: solid windowtext .75pt; mso-border-top-alt: solid windowtext .75pt; padding: 0in 5.4pt 0in 5.4pt; width: 148.5pt;" width="198"><div align="center" class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center;">
<span style="font-family: Arial, Helvetica, sans-serif; font-size: 11pt;">Operator pane --> Text Processing folder.
There are several more folders such as "Tokenization",
"Extraction", "Filtering", "Stemming",
"Transformation", and "Utility"<o:p></o:p></span></div>
</td>
</tr>
<tr style="height: 126.05pt; mso-yfti-irow: 10; mso-yfti-lastrow: yes;">
<td nowrap="" style="border-top: none; border: solid windowtext 1.0pt; height: 126.05pt; mso-border-alt: solid windowtext .75pt; mso-border-top-alt: solid windowtext .75pt; padding: 0in 5.4pt 0in 5.4pt; width: 76.65pt;" width="102"><div align="center" class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center;">
<b><span style="font-family: Arial, Helvetica, sans-serif;">R<o:p></o:p></span></b></div>
</td>
<td style="border-bottom: solid windowtext 1.0pt; border-left: none; border-right: solid windowtext 1.0pt; border-top: none; height: 126.05pt; mso-border-alt: solid windowtext .75pt; mso-border-left-alt: solid windowtext .75pt; mso-border-top-alt: solid windowtext .75pt; padding: 0in 5.4pt 0in 5.4pt; width: 63.15pt;" width="84"><div align="center" class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center;">
<span style="font-family: Arial, Helvetica, sans-serif; font-size: 11pt;">The R Foundation for Statistical Computing<o:p></o:p></span></div>
</td>
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<u><span style="font-family: Arial, Helvetica, sans-serif;"><a href="http://www.rdatamining.com/r"><span style="color: blue;">http://www.rdatamining.com/r</span></a><span style="color: blue;"><o:p></o:p></span></span></u></div>
</td>
<td style="border-bottom: solid windowtext 1.0pt; border-left: none; border-right: solid windowtext 1.0pt; border-top: none; height: 126.05pt; mso-border-alt: solid windowtext .75pt; mso-border-left-alt: solid windowtext .75pt; mso-border-top-alt: solid windowtext .75pt; padding: 0in 5.4pt 0in 5.4pt; width: 148.5pt;" width="198"><div align="center" class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center;">
<span style="font-family: Arial, Helvetica, sans-serif; font-size: 11pt;">frequent terms, clustering, classification,
association analysis<o:p></o:p></span></div>
</td>
</tr>
</tbody></table>
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