Does it matter when it happens? Assessing whether formative quizzes at different timepoints in a course are predictive of final exam grades
Bibliographic record
Abstract
An important step towards promoting academic success is identifying students at risk of poor academic performance, particularly so they can be supported before they fall too far behind. In this study we investigated whether students’ grades on five quizzes that were distributed evenly throughout a course were differentially predictive of their grades on the final cumulative exam. Our focus was not on summative value of the quizzes, but rather how this information could be used to help inform more specific guidelines regarding when student performance should be a concern, and to provide insight for how to better individualize student support. The results of a regression analysis showed that students’ grades for the second, fourth, and fifth quiz were significant predictors of students’ performance on the final cumulative exam. Students’ grade on the first quiz reached borderline significance as a predictor of their grade on the final cumulative exam. Our findings suggest that students’ performance throughout various timepoints of a course is important to take into account for considerations about students' academic achievement. The implications of these findings include the use of frequent quizzing to identify students who would benefit most from additional, targeted academic support to improve the trajectory of their academic achievement. Additionally, students should be made aware of the relationship between their grades on quizzes to the final cumulative exam to help inform their decisions regarding individual academic planning and success.
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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 | 0.007 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".