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Towards Automatic Analysis of Online Discussions among Hong Kong Students
 

Towards Automatic Analysis of Online Discussions among Hong Kong Students

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HU, Xiao (University of Hong Kong) ...

HU, Xiao (University of Hong Kong)

http://citers2013.cite.hku.hk/en/paper_619.htm
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    Towards Automatic Analysis of Online Discussions among Hong Kong Students Towards Automatic Analysis of Online Discussions among Hong Kong Students Presentation Transcript

    • Xiao HuUniversity of Hong KongCITE Research Symposium 2013May 12, 2013Towards Automatic Analysis of OnlineDiscussions Among Hong KongStudents
    • Outline Goals and Purposes Data Mining and Applications to Online Discussions Classification Association Rule Mining Findings More questions to answer Bridging research and teaching
    • Goals and Purposes Online discussions are widely used in education Effective for communication and collaboration Need tools to monitor online discussions Data mining may help (semi-)automatically identifyvarious patterns in online discussions, for example: Threads that need interventions Outcome predictions Role identification (e.g., question raiser, answerprovide, etc.) Network analysis of student groups Assessment of discussion quality .....
    • This Study How effective it is to mine online discussionsof HK students? A case study on 1,965 discussion posts on the subject of global warming collected from five primary or secondary schools inHong Kong from years 2006-2009 383 discussion threads involving 1 to 21participants Two commonly used Data Mining techniques Classification Association rule mining
    • What is Data Mining? To identify patterns (or to prove no patterns) from adataset DM is NOT querying databases Where you know what you are looking for E.g., total sales in the past three years DM is NOT statistical testing Where you know the hypotheses E.g. H0: the means of two groups are equal DM is discovery-based Find out unknown patterns, generate hypotheses DM is iterative exhaustively explore very large data sets
    • Data Mining –Classification Functionality: to assign one of a number of classlabels to each instance of your data Examples of classification tasks: Predicting tumor cells as benign or malignant Classifying credit card transactions as legitimate orfraudulent Categorizing news stories as finance, weather,entertainment, sports, etc Categorizing library materials by catalogs Predicting whether a post in an online forum will getreplies or not
    • How Classification Works? Given a collection of data (training set ) Each instance contains a set of attributes, one of theattributes is the class label. Find (calculate) a model for the class label as afunction of the values of other attributes Goal: previously unseen data can then be fed tothe model and the model assigns a class labelas accurately as possible Performance measure: accuracy How many instances are correctly classified
    • An Illustrative Example (1)8TrainingDataNAME RANK YEARS TENUREDMike Assistant Prof 3 noMary Assistant Prof 7 yesBill Professor 2 yesJim Associate Prof 7 yesDave Assistant Prof 6 noAnne Associate Prof 3 noClassificationAlgorithmsIF rank = ‘professor’OR years > 6THEN tenured = ‘yes’Classifier(Model)
    • An Illustrative Example (2)9ClassificationAlgorithmsIF rank = ‘professor’OR years > 6THEN tenured = ‘yes’Classifier(Model)Unseen Data(Jeff, Professor, 4)Tenured?
    • Classifying Online Discussions(1) Task1: threads with one vs. many participants To predict whether a post belongs to a threadinvolving only one participant or a thread involvingmany (> 14) participants Attributes used to build classification model Words in the posts: individual words (unigram)two consecutive words (bigrams) Classification algorithm: Naive Bayesian Empirically effective in text categorization Performance: 79.07%
    • Classifying Online Discussions(2) Task2: initial posts with vs. without replies To predict whether an initial post are likely to getreplies or not Attributes used to build classification model Words in the posts: individual words (unigram)two consecutive words (bigrams) Classification algorithm: Naive Bayesian Empirically effective in text categorization Performance: 64%Need to look deeper: mine patterns in eachcategory
    • Data Mining – Association Rules Functionality: to find associative relationsbetween patterns frequently occurring in yourdata {Pattern A} => {Pattern B} with certain probability Examples of association rule mining tasks: Basket (shopping cart) analysis: customers buyingproduct A often also buy product B Medical diagnosis: a patient with symptoms A islikely to have disease B Protein sequences: the appearances of amino acidsA indicates a greater chance of also having aminoacids C Online discussions: a post with word or phrase A islikely to be in class B
    • Mining Association Rules fromOnline Discussions (1) Task 1: Words and phrases strongly associatedwith threads with one or many participantsRank One participant Many participants1 dioxide i agree2 carbon dioxide agree3 carbon i4 temperature greenhouse gases5 global warming i think6 global think7 warming yes8 power carbon dioxide9 air global warming10 water yeah
    • Mining Association Rules fromOnline Discussions (2) Task 2: Words and phrases strongly associatedwith initial posts with or without repliesRank Has no reply Has replies1 global warming protect2 earth’s melt3 global world4 warming warming5 earth sea6 s i7 greenhouse ice8 effect rise9 gases global warming10 greenhouse effect global
    • Findings and future work Data mining techniques were able to find patternsfrom online discussions among Hong Kongstudents It was feasible to distinguish threads and posts incontrast categories Same techniques can be applied to distinguish Shallow and deep discussions (depth of threads) Confusion level of posts (need annotations ontraining data) Speech acts of posts (need annotations on trainingdata) Emotions in the posts (need annotations on trainingdata)
    • Integrating Research andTeaching Both data mining techniques are discussed andpracticed in the Data Mining course in theBachelor of Science in Information Management(BSIM 0018) The tool used in this project is also taught in thecourse Projects like this can be students’ course projects,
    • Thank you!Questions, comments, and suggestions areappreciated!Xiao Hu: xiaoxhu@hku.hk