A Hybrid Learning Approach for Modelling and Analyzing Clickstream Data from Learning Management Systems
Bibliographic record
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.Before 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".