Evaluating student understanding of core concepts in computer architecture
Bibliographic record
Abstract
Many studies have demonstrated that students tend to learn less than instructors expect in CS1. In light of these studies, a natural question is: to what extent do these results hold for subsequent, upper-division computer science courses? In this paper we describe our work in creating high-level concept questions for an upper-division computer architecture course. The questions were designed and agreed upon by subject-matter and teaching experts to measure desired minimum proficiency of students post-course. These questions were administered to four separate computer architecture courses at two different institutions: a large public university and a small liberal arts college. Our results show that students in these courses were indeed not learning as much as the instructors expected, performing poorly overall: the per-question average was only 56%, with many questions showing no statistically significant improvement from pre-course to post-course. While these results follow the trend from CS1 courses, they are still somewhat surprising given that the courses studied were taught using research-based pedagogy that is known to be effective across the CS curriculum. We discuss implications of our findings and offer possible future directions of this work.
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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 | 0.022 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".