Logistic Regression Analysis of College Students’ Learning Behavior Considering MOOC Data in Online Learning Environment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
MOOC as a new teaching mode is developing in full swing, however, MOOC courses face the thorny problems of high dropout rate and low completion rate. Therefore, this paper selects 12 learning behaviors and uses logistic regression model, decision tree and other methods to predict the withdrawal behavior according to the MOOC data on 365 University platform. The logistic regression prediction is analyzed for prediction accuracy, and its AUC value is 0.83 and 0.75, which proves that the logistic regression analysis can achieve the prediction of MOOC withdrawal behavior more stably and accurately, and helps to provide scientific guidelines for improving MOOC learning mode and learning efficiency. From the case study, it is obtained that among all the learning behaviors, the weight of online rate is 0.7582, which has the highest weight, indicating that the online rate of college students is an important index for judging whether they will produce withdrawal behaviors, which deserves the attention of MOOC platforms and educators.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it