A Hybrid Learning Approach for Modelling and Analyzing Clickstream Data from Learning Management Systems
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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 real-time 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.
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Copyright © 2021 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, research, scholarship, and teaching. Theses may only be shared by linking to Carleton University Institutional Repository and no part may be used without proper attribution to the author. No part may be used for commercial purposes directly or indirectly via a for-profit platform; no adaptation or derivative works are permitted without consent from the copyright owner.
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- 2021
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amezagahechavarria-ahybridlearningapproachformodellingandanalyzing.pdf | 2023-05-05 | Public | Download |