{"id":{"repo_id":"athabasca","oai_identifier":"oai:dt.athabascau.ca:10791/267"},"canonical_url":"https://search.dev.ndltd.org/etd/athabasca/oai:dt.athabascau.ca:10791/267","repository":{"repo_id":"athabasca","name":"Athabasca University","base_url":"https://dt.athabascau.ca/oai/request"},"display":{"title":"Identifying student difficulty and frustration from discussion forum postings","abstract":"2019-06","abstract_html":"2019-06","abstract_has_math":false,"creators":["Harris, Steven C"],"institution":"Athabasca University","degree_name":"Master of Science, Information Systems (MScIS)","degree_level":"master's","degree_discipline":"Faculty of Science and Technology","degree_department":null,"school":null,"contributors":["Kumar, Vivekanandan (Faculty of Science, School of Computing and Information Systems)","Xin, Cindy (Simon Fraser University)","Fraser, Shawn (Faculty of Health Disciplines)","Kinshuk (University of North Texas)"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-07-09","date_published":"2018-07-09","updated_at":"2026-08-21T16:41:56Z","subjects":["sentiment analysis","e-learning","opinion mining","natural language processing"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["TC-AEAU-267"],"render_values":[{"text":"TC-AEAU-267","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/10791/267","outbound_label":"Handle","outbound_source":"dc:identifier"},"source_record":{"url":"https://dt.athabascau.ca/oai/request?verb=GetRecord&metadataPrefix=oai_etdms&identifier=oai%3Adt.athabascau.ca%3A10791%2F267","prefix":"oai_etdms"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Kumar, Vivekanandan (Faculty of Science, School of Computing and Information Systems)","Xin, Cindy (Simon Fraser University)","Fraser, Shawn (Faculty of Health Disciplines)","Kinshuk (University of North Texas)"]},{"key":"dc:creator","label":"Author","values":["Harris, Steven C"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2018-07-09"]},{"key":"dc:publisher","label":"Institution","values":["Athabasca University"]},{"key":"dc:type","label":"Dc Type","values":["Electronic Thesis or Dissertation"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Faculty of Science and Technology"]},{"key":"thesis:degree_level","label":"Degree Level","values":["master's"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science, Information Systems (MScIS)"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Athabasca University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["sentiment analysis","e-learning","opinion mining","natural language processing"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/10791/267","https://dt.athabascau.ca/jspui/bitstream/10791/267/5/ThesisFinal.pdf","TC-AEAU-267"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["2019-06","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."]},{"key":"dc:title","label":"Title","values":["Identifying student difficulty and frustration from discussion forum postings"]}]}],"canonical_facts":{"dc:contributor":["Kumar, Vivekanandan (Faculty of Science, School of Computing and Information Systems)","Xin, Cindy (Simon Fraser University)","Fraser, Shawn (Faculty of Health Disciplines)","Kinshuk (University of North Texas)"],"dc:creator":["Harris, Steven C"],"dc:date":["2018-07-09"],"dc:description":["2019-06","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."],"dc:identifier":["http://hdl.handle.net/10791/267","https://dt.athabascau.ca/jspui/bitstream/10791/267/5/ThesisFinal.pdf","TC-AEAU-267"],"dc:publisher":["Athabasca University"],"dc:subject":["sentiment analysis","e-learning","opinion mining","natural language processing"],"dc:title":["Identifying student difficulty and frustration from discussion forum postings"],"dc:type":["Electronic Thesis or Dissertation"],"thesis:degree_discipline":["Faculty of Science and Technology"],"thesis:degree_level":["master's"],"thesis:degree_name":["Master of Science, Information Systems (MScIS)"],"thesis:institution_name":["Athabasca University"]},"updated_at":"2026-08-21T16:41:56Z"}