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Please use this identifier to cite or link to this item: http://hdl.handle.net/10791/267

Title: Identifying student difficulty and frustration from discussion forum postings
Authors: Harris, Steven C
Supervisor(s): Kumar, Vivekanandan (Faculty of Science, School of Computing and Information Systems)
Examining Committee: Xin, Cindy (Simon Fraser University)
Fraser, Shawn (Faculty of Health Disciplines)
Kinshuk (University of North Texas)
Degree: Master of Science, Information Systems (MScIS)
Department: Faculty of Science and Technology
Keywords: sentiment analysis
opinion mining
natural language processing
Issue Date: 9-Jul-2018
Abstract: This work applies natural language processing techniques, like those used in sentiment analysis, to the data generated by students in a digital online learning environment to detect confused or frustrated students and alert instructors so that time-sensitive educational support can be provided. Utilizing a data set of 9,141 discussion posts collected from an Introduction to Java Programming course, seven types of classifiers were tested, including Support Vector Machine (SVM), Naive Bayes, and Random Forest algorithms; it was determined that the optimum results for the data set was an SVM classifier using a non-linear Gaussian kernel, combined with a custom dictionary and noun phrase POS frequency count for feature vector identification and the determination of a relevance probability. The resulting application, TutorAlert, produced a promising F1 score of 0.79 and an accuracy of 0.83. Further, agreement values of 88% were achieved during inter-rater reliability testing between the classifier and human judges.
Graduation Date: Jun-2019
URI: http://hdl.handle.net/10791/267
Appears in Collections:Theses & Dissertations

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