Analyzing the Effects of Active Learning Classrooms in CS2
Bibliographic record
Abstract
Active learning environments have only recently started to be analyzed in the CS discipline, in terms of their effect on student performance. Recent studies in CS1 found contradictory results, in part due to different control on the learning pedagogy used, and issued a call for further investigation. This study evaluates the effects of the learning space on student performance in CS2, as measured by their grades. We use a quasi-experimental setup with 529 participants across five lecture sections over one academic term. All sections employ the same active learning method (inverted classroom), identical lecture materials, and the same number of TAs for in-class support, but differ in terms of classroom type (active learning classroom vs traditional lecture hall), instructor, and lecture time of day. Similarly to a recent study in CS1, we find no significant impact of the learning space in CS2. We also inspect factors not analyzed in previous studies, such as student prior preparation (as measured by prerequisite CS1 grades), course drop rates, and exam failure rates, and find that the CS2 sections are statistically similar. This work also examines student survey responses, to assess student perception differences on properties of the learning space which may impact their learning experience, such as the use of technology, ability to hear the instructor, ability to get help during lectures, and conduciveness of desk types to group 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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".