The impacts of providing novice computer science students with a second chance on their midterm exams
Bibliographic record
Abstract
Over the years, we have been approached by numerous students seeking a after performing poorly on a midterm exam in an introductory computer science course. While many students have provided creative justifications for why they should be treated differently from everyone else, some students have articulated reasons why all students in the class should receive a second chance. These reasons have included lack of experience writing university level exams, the cumulative nature of the final exam, and lack of prior experience writing exams in this subject area. After considering the arguments, the instructor for the course adjusted the grading scheme in a subsequent year so that students would receive a second chance on the midterm exam. Specifically, students who achieved a higher grade on the final exam than on the midterm exam would have their midterm exam grade replaced with their final exam grade. Making this change resulted in improved student perception of the fairness of the grading scheme while having little impact on the class average grade for the course. While these positive outcomes were observed, some negative impacts also occurred including lower midterm exam participation rates and a higher failure rate for the course.
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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 | 0.008 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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".