[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122185-en":3,"doc-seo-122185-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122185,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Towards Predicting Student Learning Outcomes from Learning Management System Interactions using Machine Learning - Thesis","Advancements in classroom technology enable new data collection for educational research. This thesis investigates how students interact with course materials after the March 2020 shift to remote instruction during the COVID-19 pandemic at the University of Manitoba. Using learning management system (LMS) log timestamps, it builds a tool to generate student timelines, extract within-term behavioural features, and assess their ability to predict grade outcomes using supervised and unsupervised machine learning, plus CNNs and transformer neural networks.","Towards Predicting Student Learning Outcomes from Learning Management System Interactions using  \nMachine Learning  \nby  \nKathryn L. Marcynuk  \nA Thesis submitted to the Faculty of Graduate Studies of The University of Manitoba  \nin partial ful􀀌lment of the requirements of the degree of  \nDOCTOR OF PHILOSOPHY  \nDepartment of Electrical and Computer Engineering University of Manitoba  \nWinnipeg, Canada  \nCopyright © 2023 by Kathryn L. Marcynuk  \nTo my Dad  \nAbstract  \nAdvancements in classroom technology have resulted in new types of data collection in educational settings. Along with improvements in the 􀀌elds of arti􀀌cial intelligence and machine learning, this educational data can be used to study how we learn and create more personalised learning environments. Starting in March 2020 all in-person courses were abruptly moved to remote instruction in order to combat the COVID-19 pandemic. This in􀀍ux of students taking remote courses presented a new opportunity to study how students interact with course materials. Remote learning courses at the University of Manitoba are o􀀋ered using a learning management system (LMS) that centralizes all course activities and 􀀌les and records user-activities.  \nThe use of machine learning techniques with education-based data is an emerging discipline that o􀀋ers an opportunity to provide new insights in this area. This thesis presents a code-based tool to create student timelines from raw LMS date-time stamp data and extract features describing student behaviours within a single-term online course. The successes and limitations of these features to predict student grade outcomes were investigated using supervised and unsupervised machine learning models. The LMS data was also explored using neural network-based CNNs and transformers.  \nThe experiments presented in this thesis indicate that students predominately interact with the system at the same time on any given day relative to their previous interaction. The results further demonstrate that temporal features created from LMS interactions can predict student outcomes with greater than random accuracy. The neural network-based classi􀀌ers produced more accurate student outcome predictions than the feature-based ML models at the expense of interpretability. This thesis contributes to the body of knowledge on student modelling and prediction, as well as student behaviour within an LMS in an online course, and suggests that educators can help to reduce students' cognitive load and improve students' learning by updating the LMS at a consistent time of day.  \nAcknowledgments  \nLike all theses, this work would not have been possible without an entire network of people. To begin, I'd like to acknowledge Dr. Robert McLeod, Dr. Mark Torchia, Dr. Robert Renaud, and Dr. Laleh Behjat for your willingness to serve on my committee and for your contributions therein. Special recognition is due to my co-advisors Dr. Witold Kinsner and Dr. Jillian Seniuk-Cicek for their perspectives and encouragement.  \nThank you as well to my friends who have listened, cheered, and commiserated with me along the way. I am deeply grateful that you have supported me to repeatedly put life on hold for this degree, and pick up right back where we left o􀀋. While I won't list names here, if you are wondering whether I am referring to you-I am.  \nI would also like to thank Liz, Judy, and Les for welcoming me into your family with open arms. We didn't know the twists and turns that life was about to throw our way, but you have never wavered in your support.  \nThank you to my parents, Debbie and Don, for nurturing my curiosity and resilience (i.e. stubbornness), two necessary qualities to complete any doctoral program. I am so grateful that you taught me to love learning for its own sake, and believed in me every step of the way. Special thanks to Gracie and Edna for reminding me to face your fears, and make your own rules.  \nFinally, Matt-thank you for continuing to be my cheering s","cbCaiugLWtaIXUL4","https://ap.wps.com/l/cbCaiugLWtaIXUL4","pdf",1873450,1,292,"English","en",105,"# 1 Introduction\n## 1.1 Problem Statement\n## 1.2 Thesis Formulation\n## 1.3 Thesis Organization\n# 2 Literature Review\n## 2.1 Understanding How We Learn\n## 2.2 Evolution of Artificial Intelligence & Machine Learning\n## 2.3 Evolution of Learning Theories and their Relationship with AI","[{\"question\":\"What data source does the thesis use to study student learning outcomes?\",\"answer\":\"It uses learning management system (LMS) interaction data, including raw date-time stamps that record user activities within remote courses.\"},{\"question\":\"How are student timelines and behavioural features created?\",\"answer\":\"A code-based tool converts timestamp logs into student timelines and extracts features that describe student behaviours within a single-term online course.\"},{\"question\":\"Which models are used to predict student grade outcomes?\",\"answer\":\"The study evaluates supervised and unsupervised machine learning models based on the extracted features, and also explores neural network approaches including CNNs and transformers.\"}]","Towards Predicting Student Learning Outcomes from Learning Management System Interactions using Machine Learning - 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