Bibliographic record
Abstract
Academic performance (GPA) is a significant index for high school students in North America. The research aims to develop and validate the best predictive regression model to evaluate the GPA of high school students. In addition, the prediction numeric data of GPA can correspond to specific classification data of GPA (Grade Level) to calculate and compare the models’ accuracy for predicting Grade Level. The research subjects are high school students from different backgrounds in North America, including their background, study habits, participation in extracurricular activities, etc. The experiment explores the impact of different factors on student GPA and finds that the number of absences from lectures is a key factor in predicting student GPA. Multiple linear regression analysis is used as the main model in the experiment, which may be improved by the stepwise regression methods. The generalization ability of the model is evaluated through cross-validation (CV) methods. Also, the boosting or random forest model is used to be the comparing model for predicting GPA. The experimental result shows that the multiple linear regression model has high accuracy (84%) and reliability (R^2 value is 0.95) in predicting student GPA. The conclusion of the research emphasizes the importance of predicting student GPA in high school education and the potential for guiding educational practice through data analysis. Future work will consider introducing more subjects and variables, such as different subject learning abilities, mental health, and social support, to further improve the predictive accuracy of the model.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".