Bibliographic record
Abstract
Assessing the engagement of students in online classroom is crucial to meet their learning objectives. Many machine learning and deep learning models have been proposed to handle this problem using a variety of sensors, with videos cameras being the most prominent. However, most of these approaches are not interoperable because different datasets use different labeling protocols. As a result, the classification models range from binary, multi-class to regression problems. Another problem is the lack of rigor and definition of engagement to annotate the data. In this paper, firstly we showed inconsistencies in the labeling of a popular student engagement DAiSEE dataset. Then, we re-labeled more than 7000 videos of this dataset using a methodical engagement annotation protocol, HELP, to convert it from four class to binary classification problem. Further analysis highlights issues in DAiSEE annotation in comparison to the HELP protocol. Lastly, we tested three state-of-the-art deep learning and feature-based methods and discussed their performance. Data imbalance in the newly and previously annotated data was found to be the main issue in developing predictive models.
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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 | 0.004 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".