A Hybrid Learning Approach for Modelling and Analyzing Clickstream Data from Learning Management Systems

Abstract

In this research, a hybrid approach combining a Hidden Markov Model (HMM) with a Long Short-Term Memory (LSTM) recurrent neural network (RNN) is introduced to model realtime online feedback to students when completing academic activities using online Learning Management Systems (LMS). The solution provided is a Smart Classifier which unravels, and processes hidden patterns in the data to train appropriate metrics to raise flags indicating outlier student behavior based on historical data from previous and ongoing sessions. This work introduces an approach that facilitates modifications of the attention mechanism in Transformer models. Using this approach, the predictor module of the proposed solution is improved. The key element of this improvement is to use a Bayesian Graph Network (BGN) coupled to a Transformer. As a novelty, this method provides a systematic customization of the attention mechanism in Transformer models that can be applied to a range of problems involving clickstream data.iii AcknowledgementsBefore anything else, I wish to express my deepest gratitude to my supervisor, Dr. M.Omair Shafiq for his unwavering support and belief in me. This thesis and the research behind it have been continuously guided by his enthusiasm, knowledge, research and teaching excellence in the field. From the early stages of my transition into the program and subsequent course work, to the completion of this thesis under the remote work and studying conditions imposed on us all by the pandemic, his remarkably constructive and effective feedback, insightful comments, and encouragement have been crucial for my success at every step of this journey. He has taught me very valuable lessons in a demanding field, always in the most professional, and yet warm and friendly, manner. This work would not have been possible without the financial support of the Carleton University COVID-19 Rapid Response Research Grant, awarded to Dr. M. Omair Shafiq in 2020; and the funding opportunity from the School of Information Technology to undertake my studies. I would like to express my sincere thanks to Dr. Audrey Girouard, Associate Director, Graduate Studies for her overall guidance, and to the faculty and staff of the School of Information Technology for providing students with a stable and highly successful academic program amidst the unprecedented COVID-19 pandemic. I would like to extend my special thanks to my professors in the program at the School of Information Technology and the School of Computer Science, especially Dr. Olga Baysal, Dr. Elio Velazquez, and PhD. Candidate Mohamed Abdelazez, for their valuable work in shaping some of the key methods and techniques applied in this research. I would like iv to express my gratitude to Dr. David Sprague for his kind help and support allowing me to contribute to his Teaching Assistance team, gaining valuable and practical knowledge while helping students through the last two years. I would like to thank all members of the Administrative Staff at the School of Information Technology, a...