An Empirical Analysis of Data-driven Intelligent Teaching Based on Cloud Class Platform
Bibliographic record
Abstract
At present, with the vigorous development of education platform, it is necessary to evaluate the effect of teaching process based on the data on the platform, but the evaluation method is relatively simple, lacking the data-driven evaluation based on the learning process. In order to achieve data-driven learning evaluation and improve teaching efficiency, the cloud class platform is used to collect the data of learners' learning process and the analysis and research of data visualization are carried out based on the platform. First, the students are divided into four categories by harmonic curve and cluster analysis. Second, the advantages and disadvantages of all kinds of students are pointed out by correspondence analysis. These results can guide teachers to use education data to mine the relevance between achievements and knowledge points, and help teachers to implement the talent training strategy of individualized teaching, improve teaching quality, provide timely feedback and control for students' learning, facilitate students' independent learning and improve learning effect.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".