Bibliographic record
Abstract
Fundamental engineering courses provide learning opportunities for students to develop problem solving and creativity skills, connect theoretical course material to the real-world, and solve complex, abstract problems such as those found in the workplace. Through a mixed methods study of students in a statics course in a small Canadian university, we explored student motivation and perception of composing and publishing their own course-relevant problems in an open educational resource (OER) textbook. We found that generating and solving their own problems for each of the six homework assignments helped students to anchor theory in the real-world, be creative, and understand the material more fully. In total, 93% of students in the course created at least one student-generated homework problem, and after the semester ended, 58% of students submitted a combined total of 59 high-quality, interesting, real-world examples to be included in the OER textbook. Of the 28 study participants, 26 students (93%) felt the activity should be repeated in future years. Students were motivated to publishing examples in the OER textbook by a desire to help future students and gain understanding of the material. Students found generating problems time-consuming, but enjoyed expressing their creativity.
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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 | 0.006 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".