Analyzing the Effects of Active Learning Classrooms in CS2
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it