Using and sharing programming exercises to improve introductory courses (abstract only)
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
Short, automatically-assessed programming exercises, and other types of short practice problems, are a useful way to introduce and reinforce concepts and techniques in introductory programming courses. When delivered over the web, they allow students to learn and practice, with immediate feedback, at any time and place where they have access to a web browser. However, such exercises do not seem to be as widely used as they could be. Similarly, there is not a lot of literature on the effectiveness of these types of problems. The purpose of this BOF is to bring together users (and potential users) of programming exercises with developers of programming exercise systems to discuss how exercises could be used more widely and effectively. Possible discussion topics include: What features are absolutely essential for faculty to consider adoption? What are the major obstacles preventing more widespread adoption? Are faculty willing to share their exercises under an open/non-commercial license? Should exercises best used for extra practice, as graded assignments, or both?
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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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