Teaching CS1 with a Mastery Learning Framework: Impact on Students' Learning and Engagement
Why this work is in the frame
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Bibliographic record
Abstract
Mastery Learning, a pedagogical strategy in which students are allowed to prove mastery of the skills acquired in a course over multiple attempts (and used failed attempts as feedback) is becoming increasingly popular in higher education. Large introductory programming courses can use it to strengthen students' preparation for later courses, but some challenges to its adoption remain, such as how to scale this format to hundreds of students, or how to ensure that students do not fall behind on the material. In Spring 2021, the instructors at the Anonymous University transformed the structure of their CS1 course using a Mastery Learning format, reorganizing the material in units focused on the different course topics. Students were allowed to prove mastery of each unit separately and over multiple times, without penalties for missed or failed attempts. In this experience report, we will describe the strategies adopted to cater to a large cohort of novice students. We will compare the students' learning experience with a cohort of students who took the course in a more traditional format, and show that the students benefited from the new format in terms of quantity of skills mastered. Students also exhibited signs of increased motivation to practice and complete tests without grade incentives. Finally, we will discuss some pitfalls in our design and address some of the concerns of instructors interested in trying a Mastery Learning approach in their CS1 courses.
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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 | 0.003 | 0.000 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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