GRADE PREDICTORS IN A SECONDARY SUMMER COURSE OFFERING: AN INVESTIGATION OF PRIOR FAILURES AND EMPLOYMENT
Bibliographic record
Abstract
A second year electrical circuits course at the author’s home institution was historically delivered in the winter term to one section of approximately 400 students. The department observed that approximately 70-100 students were losing a year of school due to the combined high fail rates and single offerings of this course and its prerequisite. To allow these students to remain on track in their programs, additional offerings of both courses were created. The secondary offering of the circuits course was offered in the summer term. This paper presents a statistical analysis of data obtained over two years of the summer offering of the circuits course. After correcting for GPA and attendance, prior failures in both courses and having a job (co-op or otherwise) are shown to have no statistical significance. These results indicate that concerns around employment workload and sufficient time to study appear to be unfounded and that the fact that a student has failed in the past does not predict their future grades. This preliminary work is part of a larger research project investigating the impact of an online, blended delivery approach on attendance, student perception of learning, and impact on grades.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".