Improved Student Learning Experience in Large Programming Classes Using Pseudo-Flipped Method
Bibliographic record
Abstract
In an effort to improve student engagement in large programming classes, this study proposes a pseudo-flipped (PF) method of teaching that combines the core principles of two popular teaching methods, traditional and flipped (or inverted), thereby mitigating the drawbacks of these methods. In traditional teaching, class time is mostly used by instructors to teach a class using pre-prepared lecture slides and smartboards or similar alternatives, whereas students, mostly passively, listen to the lecture and take notes. In a purely flipped class, all resources traditionally taught in classroom are moved outside the classroom, either as text, video, audio, students are expected to read or view lectures before class, and the instructor uses class time in solving problems. In the proposed PF method, students are taught in a traditional way for half the allocated time. For the other half, students solve problems in class with the instructor’s assistance. Similar to the flipped method, in PF, students learn concepts on their own outside the classroom using an interactive textbook. To fill gaps in their knowledge, instructors spend time teaching those core concepts in class by solving problems. PF promotes active learning by engaging students towards solving problems on learnt concepts. A survey is done in a programming class to find student opinion on how useful this pseudo-flipped method is on student engagement as opposed to traditional teaching. Both quantitative and qualitative analysis of the survey responses strongly favour the proposed method, with more than 70% of students in favour of it.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".