The Relationship between Prerequisite Proficiency and Student Performance in an Upper-Division Computing Course
Bibliographic record
Abstract
While it is widely believed that taking a class's prerequisites is critical for success, less is known about how proficiency with the prerequisite knowledge from those courses affects performance in later courses. Specifically, it is unclear how well students understand material from prerequisite courses and whether that understanding may impact their outcomes in the subsequent course. Additionally, in subsequent courses, do students strengthen their knowledge from prerequisite courses and, if they do, does that improvement matter for the subsequent course? This study examines the prerequisite knowledge of 208 students in an upper-division data structures class at a large North American research university. Prerequisite proficiency on entry to the course was surprisingly low, with nearly a third of students demonstrating low proficiency and only a quarter high proficiency. Students modestly improved their proficiency during the term, lifting a third of those with low proficiency to at least medium proficiency. Overall, final exam performance was significantly correlated with prerequisite knowledge. For those with low initial proficiency, improvement in proficiency was significantly correlated with performance on the final. These results suggest that more attention needs to be placed on reinforcing prerequisite knowledge for those with low proficiency.
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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 | 0.001 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".