Hybrid Learning Predictions on Learning Quality Using Multiple Linear Regression
Bibliographic record
Abstract
Indonesia declares COVID-19 Pandemic by the World Health Organization (WHO) from March 2020.This has a very impact, one of which is on the continuity of the world of education.This research aims to foretell the impact of hybrid teaching methods used at SMK Cendikia Cirebon City during the COVID-19 period on student achievement.Teachers' materials, honesty, enthusiasm, and IT backing are all factors in determining the quality of education.Multiple linear regression with root-mean-squared error as the dependent variable is used in this study (RMSE).Experiments conducted on 122 participants yielded an RMSE of 0.375 and a correlation level of 0.440 for each attribute, with test samples comprising 10% and training samples comprising 90%.As a result, the use of this multiple linear regression model can be suggested for foreseeing the introduction of a hybrid learning model to enhance educational quality.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".