Stage Predicting Student Stay Time Length on Webpages of Online Courses Based on Grey Models
Bibliographic record
Abstract
The paper, entitled “Stage Predicting Student Stay Time Length on Webpages of Online Courses Based on Grey Models”, has been presented at the main conference. By predicting student’s stay time length, an e-learning system has potential to dynamically tailor the learning content to the students. A literature review has been conducted on power law of learning and knowledge components. Accordingly, an initial prediction method is proposed based on two learning curve modes and grey models. Based on the experimental result of this initial prediction method, the construction method of grey models has been modified. The results produced by the grey models based on the two construction methods are then compared and analyzed. The results show that prediction of stay time length is possible to a certain degree while the students learn on web pages. \nBoth presentations were received very well and leaded to many questions after the presentation, further discussions during the conference, and two potential collaborations with international research groups.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".