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"CompanyBranding",ExpressionUUID->"2d6fb7b3-4a71-45ed-a5d1-595ae34d625e"] }, Open ]], Cell[CellGroupData[{ Cell["", "SlideShowNavigationBar", CellTags-> "SlideShowHeader",ExpressionUUID->"c036b14c-8d75-4ca1-955f-c15d427743d4"], Cell[CellGroupData[{ Cell["ABSTRACT", "Section", CellChangeTimes->{{3.7257199329223537`*^9, 3.725720005948655*^9}, { 3.72572008110417*^9, 3.725720088583918*^9}, {3.735906066467186*^9, 3.7359060766280823`*^9}, {3.7734191105847*^9, 3.773419111113975*^9}},ExpressionUUID->"19833cfa-da84-4cef-8557-\ b850b6cb6842"], Cell["\<\ We extended the generative Latent Dirichlet Allocation framework for topic \ modelling to include distinct author priors and allow for multiple authors to \ collaborate on a text. Revised inference methods were designed to handle the \ additional complexity and larger parameter space. Extending LDA in this way \ not only gives it more modeling power, but leads to insights about author \ interests, allowing for more accurate inference and the personalization of \ recommendations in online systems. We applied this analysis to forum data \ from two MOOCs at MIT to understand the evolution of conversation topics in \ the courses and evaluate student engagment and learning outcomes. We \ compared this analysis to hand-coded topic analysis to understand the \ benefits and drawbacks of the automated approach.\ \>", "Text", CellChangeTimes->{{3.725720132418301*^9, 3.7257201347705364`*^9}, { 3.7734191065563927`*^9, 3.773419108328516*^9}, {3.773493946357828*^9, 3.7734940366155453`*^9}, {3.773494089710732*^9, 3.773494129224236*^9}},ExpressionUUID->"827fbb58-34f7-4856-a780-\ 6a3ac202a509"] }, Open ]] }, Open ]], Cell[CellGroupData[{ Cell["", "SlideShowNavigationBar", CellTags-> "SlideShowHeader",ExpressionUUID->"a3d2f483-7319-4980-9379-564626ce8f3c"], Cell[CellGroupData[{ Cell["A look ahead", "Section", CellChangeTimes->{{3.7814293795850153`*^9, 3.781429435040153*^9}},ExpressionUUID->"cf4271ba-bfa0-40d2-b96d-\ d5936094b414"], Cell[CellGroupData[{ Cell["\<\ The motivating problem - increasing collaboration in MOOC discussion forums\ \>", "Item", CellChangeTimes->{{3.7814294553993797`*^9, 3.781429500949997*^9}},ExpressionUUID->"1fa28d1b-9b1a-449d-9210-\ 076cb69f0906"], Cell["The method of attack - applied topic modeling", "Item", CellChangeTimes->{{3.7814294553993797`*^9, 3.781429511929818*^9}},ExpressionUUID->"9bf3a8d3-1a04-4f76-8bb0-\ 5f97a0e113cd"], Cell["\<\ The theoretical context - latent Dirichlet allocation and an extension for \ author-specific priors\ \>", "Item", CellChangeTimes->{{3.7814294553993797`*^9, 3.781429575473291*^9}, { 3.7814377480842743`*^9, 3.781437772584206*^9}},ExpressionUUID->"e66515ed-b3bc-481e-b322-\ 9ee5caa74876"], Cell["Theory to computation - performing LDA in Wolfram Language", "Item", CellChangeTimes->{{3.7814294553993797`*^9, 3.7814296056203203`*^9}},ExpressionUUID->"b3f5f7c8-47ab-4d88-8f85-\ b50651bbf1cf"], Cell["\<\ Computation to application - Simulated and anticipated results\ \>", "Item", CellChangeTimes->{{3.7814294553993797`*^9, 3.781429634870954*^9}, 3.7814377882322693`*^9},ExpressionUUID->"8ad5c286-5396-475e-bf41-\ 00be289e598e"], Cell["Extensions and areas for further work", "Item", CellChangeTimes->{{3.7814294553993797`*^9, 3.781429693646935*^9}, { 3.7814351137932653`*^9, 3.781435113793387*^9}},ExpressionUUID->"5057a703-2c3f-41c4-8b7f-\ 8866af8c07b1"] }, Open ]], Cell["\<\ A quick disclaimer - I am first and foremost a math graduate student, \ studying the theoretical properties and guarantees of machine learning \ algorithms. This presentation will be more words and math than code. \ \>", "Text", CellChangeTimes->{{3.7814294553993797`*^9, 3.781429693646935*^9}, { 3.781435124105858*^9, 3.781435182466097*^9}, {3.781439036043911*^9, 3.781439036644195*^9}},ExpressionUUID->"9fbc5a5e-6c2a-435b-855f-\ 7be9ac550c89"] }, Open ]] }, Open ]], Cell[CellGroupData[{ Cell["", "SlideShowNavigationBar", CellTags-> "SlideShowHeader",ExpressionUUID->"00617b4e-d4b4-4100-b631-c6a340de41f2"], Cell[CellGroupData[{ Cell["The motivating problem", "Section", CellChangeTimes->{{3.781429762185663*^9, 3.781429764320116*^9}},ExpressionUUID->"c03773b1-d276-424f-a7dc-\ 81e78d80209e"], Cell["\<\ Growth of Massive Open Online Courses (MOOCs) has exploded in the last \ decade. The largest providers of MOOCs - Coursera, Udacity, and edX - have a \ combined 50 million users. And yet, MOOCs have failed to live up to their \ promise of providing inexpensive education to all of these members. \ Participation in courses declides steadily week by week, completion rates \ between 5% and 15% are the norm, and students with wearker educational \ backgrounds are at even higher risk, showing completion rates as low as 3%. Community forums make up a common and impactful element of modern MOOCs. The \ forums restore the collaborative element to dis- tance learning that is \ typically absent. On these forums, students can ask questions, discuss \ projects, and submit short assignments for peer viewing and revision. \ Studies have shown that interpersonal interaction and peer encouragement can \ go a long way to improving course retention and completion rates. While these interactions are important to student learning, these forums can \ be difficult for students to navigate, leaving them with the impression that \ the forums are not relevant to them, and discouraging them from contributing \ to the conversa- tion and learning from others. \ \>", "Text", CellChangeTimes->{{3.781432554818406*^9, 3.781432650858223*^9}, { 3.781432747167758*^9, 3.7814329941498327`*^9}, {3.781433830675734*^9, 3.781434072320073*^9}, 3.781439281376285*^9},ExpressionUUID->"11122882-39b6-4b12-b5fb-\ 4fbbd3289eae"] }, Open ]] }, Open ]], Cell[CellGroupData[{ Cell["", "SlideShowNavigationBar", CellTags-> "SlideShowHeader",ExpressionUUID->"fd02ff90-a8d6-4d32-bb6c-2c1f7f905e71"], Cell[CellGroupData[{ Cell["A case study - two MIT online courses", "Subsection", CellChangeTimes->{{3.7814392870548763`*^9, 3.781439291573279*^9}},ExpressionUUID->"37da920f-fd38-49ad-b57d-\ 5c1e4844ba1e"], Cell["\<\ Our research focused on two MOOCs run out of MIT. The first was Launching Innovation in Schools, a course for educators which \ provided us with data from its 2017 and 2018 runs. The second course us still being prepared, and will introduct students to \ community-centric engineering projects to provide personzlied aid to people \ with disabilities. In both courses, communication is a key skill and learning outcome, and so \ the forum components of these courses are especially important.\ \>", "Text", CellChangeTimes->{{3.781439300931712*^9, 3.781439309367038*^9}},ExpressionUUID->"e6bffef5-6dda-4d20-b85b-\ 99e182a8ab8d"] }, Open ]] }, Open ]], Cell[CellGroupData[{ Cell["", "SlideShowNavigationBar", CellTags-> "SlideShowHeader",ExpressionUUID->"a8178c1c-9b52-4009-ab63-da99ed6acf8a"], Cell[CellGroupData[{ Cell["Isolating the problem of thread naming", "Subsection", CellChangeTimes->{{3.781433000110682*^9, 3.7814330166524487`*^9}, { 3.7814332570107327`*^9, 3.781433268258326*^9}},ExpressionUUID->"e7960e95-9697-4eeb-87ad-\ 908501a12e81"], Cell["\<\ One trend we identied in student behavior was that students create new \ \[OpenCurlyDoubleQuote]threads\[CloseCurlyDoubleQuote] or discussion topics \ when they need to post on the forum for a class assignment, and the titles \ they give to these threads give little insight into the posts\ \[CloseCurlyQuote] contents. Below are two responses to the assignment asking students to define what \ makes a powerful learning environment \ \>", "Text", CellChangeTimes->{{3.781433026755103*^9, 3.781433029331024*^9}, { 3.781433075455868*^9, 3.78143309467883*^9}},ExpressionUUID->"018b7b81-a12a-445d-80d1-\ 71238364291a"], Cell[CellGroupData[{ Cell[GraphicsData["PDF", "\<\ 9E14ARda;S<:9LCUl^G[Yo>Pd`S0fQZCD`6M8:EE23Ck[l_oGjTNYb;bjJbV^:A544CZ:cZ[DT NU4fMfRF?4mm23Wf2VE^I7=dLVEQK@YUKVA_HVX:=B0`86mRJPXa=c@:IFiTKf9Z 2S8P<21_HVX:?3`P;eAiL6DP;e1QIfDP;e1QLVE^M20c830PDR0_DVEcKgEbHfEc 83HP<21B82m3KfidIFidLb0d830PDR0_CFETJF52KgPPFc0P<20c=3LP20_AVU/M6Eb82m6K65dIDAUHfmTIB0n?PYcM79UHFd:N07/_HOO 9DFI]ono_1^=Jl24^ZiiGGD15e7OInYDEeNhZ[Y?ekO_^^_ooUlo 4Y2012@P0@U8@08BT80492012DQ00Q:@P0@T8049B4024Y2012@P0@U8@08BT804 92012DQ00Q:@P0@T8049B4024Y2012@P0@U8@08BT80492012DQ00Q:@P0@T8049 B4024Y2012@P0@U8@08BT80492012DQ00Q:@P0@T8049B4024Y2012@P0@U8@08B T80492012DQ00Q:@P0@T8049B4024Y2012@P0@U8@08BT80492012DQ00Q:@P0@T 8049B4024Y2012@P0@U8@08BT80492012DQ00Q:@P0@T8049B4024Y2012@P0@U8 @08BT80492012DQ00Q:@P0@T8049B4024Y2012@P0@U8@08BT80492012DQ00Q:@ P0@T8049B4024Y2012@P0@U8@08BT80492012DQ00Q:@P0@T8049B4024Y2012@P 0@U8@08BT80492012DQ00Q:@P0@T8049B4024Y2012@P0@U8@08BT80492012DQ0 0Q:@P0@T8049B4024Y2012@P0@U8@08BT80492012DQ00Q:@P0@T8049B4024Y20 12@P0@U8@08BT80492012DQ00Q:@P0@T8049B4024Y2012@P0@U8@08BT8049201 2DQ00Q:@P0@T8049B4024Y2012@P0@U8@08BT80492012DQ00Q:@P0@T8049B402 4Y2012@P0@U8@09;Q<0SScab@NoWjZ^_7];DYiijZSNK2jjjjZXQnLaaVP/__;2o f_UA4]mhhhe??ogd75OBhXHCF;]fkIEGGWWTTDMnic_OfGGGGKOOO_^MMmiikkgg O_;99hMWkkKKK;KOL9`lilFH`oGGGeoCV2B6TKlH>NAeMXCT=0a;@08BT804 92012DQPNPAVE;NHVmafffdcE^2kgoe^obaVbJQKNC?onIoo6Hg[`@LOW96?2JI= H9]]]/VkYPR[KTfKooZI_mY5g^nZFcV=57J4e4bCXXhnN4I49YTMP nO;UaGP[_ZY^C@onnYbcfTGNnjYK>HdDMXCDC8bIW03FhlE_75n_^njjbG@7E[LXKV8049B4024Y30dR<` A=ejfm_Ne]m`?O i^W[dcVji=D]f_RU;gfYQi:7YTB0SBcchAOQ?OOLTd=ggGGG3COLP7WQU4X_/]F[ O05TJGo5[_EOZ/o11anl]5_MeC[E[IZ<8jAVH/cT15Bg9VMX3Q:@P0@T8049;3d2 CGG[_ook_mTI<5@20Va[g]?f[KON>TnlbBJKi5mCN16YFjPRceBOYimnnXhkk_S3 7oj`nnjkc0nn^RS?J0l=0d25ea`@MdM[g_MjmQ5LA[5mNNY^]G?aj=; 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When a \ student logs in and sees hundreds of posts that are all titled \ \[OpenCurlyDoubleQuote]Assignment 1.1\[CloseCurlyDoubleQuote], they might get \ overwhelmed, and even if they wanted to engage with other students, the \ prospect of clicking into and reading every post to decide what might \ interest them can be prohibitive. Question - how might you rewrite the titles for these posts to be more \ helpful?\ \>", "Text", CellChangeTimes->{{3.7814331104793463`*^9, 3.781433239068843*^9}, { 3.781433645657634*^9, 3.781433663333371*^9}},ExpressionUUID->"81d1af78-7f42-406a-9a50-\ e0ac0de37345"] }, Open ]] }, Open ]], Cell[CellGroupData[{ Cell["", "SlideShowNavigationBar", CellTags-> "SlideShowHeader",ExpressionUUID->"836ae06c-5d8b-43ad-a3cf-59abecbdf969"], Cell[CellGroupData[{ Cell["The method of attack - applied topic modeling", "Section", CellChangeTimes->{{3.781433286779397*^9, 3.781433291126635*^9}, { 3.781434910897984*^9, 3.781434913288766*^9}},ExpressionUUID->"497b30c1-3ed0-47c9-82da-\ e4792cc60e88"], Cell["", "Text", CellChangeTimes->{{3.7814333080069733`*^9, 3.781433312989341*^9}},ExpressionUUID->"bff86c12-bd9e-49b3-8f69-\ 3a9456101612"], Cell["\<\ Topic modeling is a branch of natural langauge processing that focuses on \ automated inference of the theme and context of individual documents as a \ part of a larger corpus. One example might be - if you were given a pile of articles cut out of \ newpapers, separated from their section headings, could you figure out what \ section each article belongs to? Online news websites add an additional complexity to the problem, since \ articles can be listed under multiple topics. An article about taking a knee \ for the pledge of allegiance at football games might fall under both sports \ and politics, or maybe culture. A key aspect to the problem is that you don\[CloseCurlyQuote]t know the \ categories in advance - you need to infer what sections the newspaper has \ from the articles themselves. \ \>", "Text", CellChangeTimes->{{3.7814332969095173`*^9, 3.781433462495638*^9}, { 3.781433493543128*^9, 3.7814337379833*^9}, {3.78143494851097*^9, 3.781435088598201*^9}, 3.781439343238585*^9},ExpressionUUID->"9ba9c450-d0de-4daa-9785-\ c7384795456f"] }, Open ]] }, Open ]], Cell[CellGroupData[{ Cell["", "SlideShowNavigationBar", CellTags-> "SlideShowHeader",ExpressionUUID->"18dd6abf-1494-4846-a43a-4d240f96a24e"], Cell[CellGroupData[{ Cell["Topic modeling and discussion forums", "Subsection", CellChangeTimes->{{3.781439349401764*^9, 3.781439357013579*^9}},ExpressionUUID->"7047d188-a038-46f0-8dda-\ f913a27e3b53"], Cell["\<\ The goal of topic modeling can be put this way: Given a collection (corpus) \ of documents, discover which topics are being covered, the proportion of each \ document that comes from each topic, and what defines each topic (what words \ are markers of a topic) How will that help us solve our original problem of MOOC forums? If we can \ have the computer \[OpenCurlyDoubleQuote]read\[CloseCurlyDoubleQuote] each \ student\[CloseCurlyQuote]s post, understand what topics are being discussed \ across the form, and apply those topic labels back to the individual posts, \ we might get somethign close to what we imagined for new thread titles. More ambitiously, if we also infer student interests from what they post, we \ can begin to recommend discussions to students that might match their \ interest, or even connect peers together who share similar interests.\ \>", "Text", CellChangeTimes->{{3.781439362825451*^9, 3.781439364721609*^9}},ExpressionUUID->"353c2a2c-1577-42f0-a074-\ 804c7a5f035c"] }, Open ]] }, Open ]], Cell[CellGroupData[{ Cell["", "SlideShowNavigationBar", CellTags-> "SlideShowHeader",ExpressionUUID->"2b33207d-7e2e-4d12-855b-0a546417bcf9"], Cell[CellGroupData[{ Cell["Theoretical context - generative models of text", "Section", CellChangeTimes->{{3.781433746211175*^9, 3.7814337638596563`*^9}, { 3.7814349283582563`*^9, 3.781434939175322*^9}},ExpressionUUID->"dac4798d-f931-42b1-b581-\ 8353fd0a3201"], Cell[TextData[{ "Below we see a generative model of text using \[OpenCurlyDoubleQuote]plate \ notation\[CloseCurlyDoubleQuote]. The rectangles or \ \[OpenCurlyDoubleQuote]plates\[CloseCurlyDoubleQuote] represent one of many \ objects at a given level of specificity. I.e. there are many documents in \ the corpus, and many words in each document. \n\nThe circles represent \ parameters and variables which are indexed at the level of the plate the \ object sits on. Looking at this diagram, we see that the prior on topics and \ word distribution per topic are fixed at the corpus level, there is a \ document topic vector for each document, and there are topic and word \ variables for each document\[CloseCurlyQuote]s word.\n\n\n", Cell[GraphicsData["PDF", "\<\ 9E14ARda;S<:9LCUl^G[Yo>Pd;BPYCLaA:20`858P;eAiL6DP;eQ? 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Say you want to write a news article using this generative process. You pick \ a topic vector according to the topic prior. That topic vector might say \ your article should be 30% about sports and 70% about politics. Then to write your article, you step word by word through it. Before you \ write each word, you flip a weighted coin that will give you sports 30% of \ the time and politics 70% of the time, which will tell you what topic that \ word should come from. Once you know that, say, the first word in your article should be about \ sports, you look up how words tend to be distributed in sports articles, and \ pick a word according to that distribution. Your first word might be \ \[OpenCurlyDoubleQuote]Baseball\[CloseCurlyDoubleQuote]. Continuing this way, you might get a document like: \ \>", "Text", CellChangeTimes->{{3.781439409412224*^9, 3.7814394257889223`*^9}, 3.781439473423819*^9},ExpressionUUID->"19adc2c3-c110-404c-b8fe-\ 114b77059467"], Cell["\<\ Baseball senator the is win election election these did she ballot national...\ \>", "Code", CellChangeTimes->{{3.7814394781650953`*^9, 3.781439570932281*^9}},ExpressionUUID->"6759d175-b4c9-4a9b-acc0-\ f5ccb66137c5"], Cell["\<\ Of course, this approach will result in grammatical nonsense, and no one \ actually generates text this way. What is important is this this framework \ gives us, explicitly, what the probability of getting specific documents is \ given the topic and word distributions. For example, we would be able to \ tell that the sentence\ \>", "Text", CellChangeTimes->{{3.781439482476779*^9, 3.7814394846720963`*^9}, { 3.7814396044073257`*^9, 3.7814396260417223`*^9}, {3.7814396961548243`*^9, 3.7814396965029716`*^9}},ExpressionUUID->"feca3aea-1e18-4f38-b1ed-\ feaca39b2c9f"], Cell["\<\ Tomorrow's mayoral election will coincide with the final game of the world \ series.\ \>", "Code", CellChangeTimes->{{3.781439642900695*^9, 3.781439694857336*^9}},ExpressionUUID->"2ee223f1-c0fa-43d2-9d02-\ fcaca84d9709"], Cell["\<\ has high likelihood in the model. And with these probabilities, we can \ compute the likelihood of the corpus as a function of the parameters, and \ tune the parameters to maximize this likelihood, which will tell us what \ topics exist in the corpus.\ \>", "Text", CellChangeTimes->{{3.781439698789716*^9, 3.781439763655274*^9}},ExpressionUUID->"4e2988d7-f735-4968-bea9-\ 11c193e6a816"] }, Open ]] }, Open ]], Cell[CellGroupData[{ Cell["", "SlideShowNavigationBar", CellTags-> "SlideShowHeader",ExpressionUUID->"49d73b61-aef1-4b49-845f-a5db125293c4"], Cell[CellGroupData[{ Cell["Getting specific - Latent Dirichlet Allocation", "Subsection", CellChangeTimes->{{3.781433795245248*^9, 3.781433799137108*^9}, { 3.78143471953001*^9, 3.781434722716497*^9}, {3.78143976871725*^9, 3.781439772098001*^9}},ExpressionUUID->"000942f5-9f3c-4601-bee9-\ d7e00356efa5"], Cell[BoxData[ GraphicsBox[ TagBox[RasterBox[CompressedData[" 1:eJzs3Wd3Fde2//kzunv06B7j/6DfQr+LftoP+97/vbbJkgDlhEBCQgSTTc5g 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If \ you found out the three of them were co-authoring a paper, you could probably \ guess a lot about the contents of the paper without seeing a single word of \ it.\n\nOne novel contribution of this work is allowing authors to have \ personalzied prior distributions over topics that reflect their individual \ interests. 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Instead, there are a number of \ methods in the literature for evaluating topic quality. \n\nOne of these is \ \[OpenCurlyDoubleQuote]perplexity\[CloseCurlyDoubleQuote] which measures the \ distance of the word counts in the corpus from the expected words given the \ estimated parameters. 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