A Data-Driven Digital Twin for Student Engagement Prediction in e-Learning Systems
Bibliographic record
Abstract
Machine Learning (ML) models are increasingly applied to Learning Management System (LMS) data to predict student engagement and performance. LMS data often contain missing values that can be informative. However, existing modeling approaches in education remove or impute missing values, which can lead to inaccurate or biased models. In this paper, we propose the use of digital twins to model students’ engagement based on their learning activities on LMS while preserving the missingness patterns. We leveraged synthetic data generators such as Conditional Tabular Generative Adversarial Network (CTGAN), Tabular Variational Autoencoder (TVAE), and RealTabFormer with reversible data transformations to create a virtual replica of students’ data. The CTGAN and TVAE generated balanced synthetic data that accurately captured the meaningful patterns of the real data. Moreover, XGBoost trained on a balanced virtual replica of the students’ learning activities data obtained an F1-score of above 80% in predicting the students’ engagement levels when evaluated on real data with both complete and incomplete entries. Our findings demonstrate how digital twins can be used to address the complexities of data in the education sector, improve the generalization of models, and reduce bias in real-world performance.
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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 | 0.002 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".