Teaching CS1 with a Mastery Learning Framework: Changes in CS2 Results and Students' Satisfaction
Bibliographic record
Abstract
Mastery Learning is a pedagogical strategy that allows students to demonstrate mastery of the skills acquired in a course over multiple attempts. Failed attempts are used to provide feedback and are not factored in the final grade. Since its introduction, Mastery Learning has been applied with positive results at all levels of education. We previously presented a report detailing how we redesigned the CS1 course at the University of Houston using a Mastery Learning format, and provided evidence that students in this course mastered more skills than previous students had done in traditional settings. In this new study, we invited students who took the CS1 course in the traditional or new format to answer a survey about their attitude and habits toward the course, and saw that students in the Mastery Learning format felt more motivated to complete the required coursework and more rewarded for their efforts. They were less discouraged by initial challenges, and found learning the material overall easier. They also found it easier to assess their performance, and were more confident they could obtain a good final grade. Additionally, we recorded the performance of 385 students (193 from the traditional and 192 from the Mastery Learning format) in the subsequent CS2 course, and noticed an improvement in grade distribution. We conclude that a Mastery Learning framework can improve students' attitude and satisfaction, as well as their success in later courses.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 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".