Bibliographic record
Abstract
This report documents the implementation of a self-paced, mastery learning inspired CS1 course. The course was designed to increase the completion rates observed in flipped and online CS1 formats already offered at our institution. We explore the experience of students in the course and evaluate performance outcomes using grade data from all three CS1 formats, student survey responses, and exit interviews. Our evaluation identifies three main challenges in our implementation. First, the course requires significant resources and administering it is significantly more time consuming for instructors than a regular course. Second, students hesitated to treat mastery quizzes as formative. Finally, the flexibility that the course provided, with little structure and few incentives to help students stay on track, led to considerable procrastination. These factors combined to lead students to delay coursework until the end of the semester -- and beyond. As a result, while our data shows an increase in completion relative to the online format, we saw no change in completion relative to the flipped CS1 offering and saw no change in student performance as evaluated by a final exam. However, students reported more deep engagement with and understanding of the material, which encourages us to further develop the course.
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.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".