Analysis on Course Scores of Learners of Online Teaching Platforms Based on Data Mining
Bibliographic record
Abstract
After years of development, online teaching platforms (OLPs) have accumulated a huge amount of data on student scores. To effectively mine out the useful knowledge and information behind the massive data, this paper puts forward a course score analysis model for OLP learners based on data mining. Firstly, the score features of OLP learners were classified, and the calculation method of computational features was presented. Then, the score features were clustered through expectation maximization (EM) clustering, which has the advantage of unsupervised learning. Moreover, the salient features were obtained through principal component analysis (PCA). Finally, the support vector machine (SVM) prediction algorithm, a supervised learning method, was constructed, and merged with the clustering algorithm to realize accurate classification of the course scores of OLP learners. The effectiveness of the proposed method was proved through experiments. Based on the correlation between learner scores and courses, this research enables teachers to improve current teaching models and methods.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".