Modeling and Predicting Online Learning Activities of Students: An HMM-LSTM based Hybrid Solution
Bibliographic record
Abstract
With significant increase in popularity of online education, need for educators to get to know about learning experiences of students and to provide feedback to students has also been increasing. In this paper, we propose an HMM-LSTM based hybrid solution to model and predict online learning activities of students using online learning management systems (LMS) or platforms and provide real-time feedback to students. Our solution is a smart classifier empowered by and based on the Markov-Chain (MC) approach, specifically a Hidden Markov Model (HMM), with the Long Short-Term Memory (LSTM) neural network. The novelty is in the use and treatment of hidden data and metrics to raise flags that indicate outlier online student behavior based on historical data from the same online session and other sessions in the past. We propose a design of a system in which we utilize HMM and LSTM, and the LSTM component of the model is ‘advised’ by the HMM probability used in the metrics that drive the outlier detection process. The system relies on the LSTM prediction to perform early detection of patterns.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".