EXPERIENTIAL LEARNING FOR COMPLEMENTARY CREDIT: A COURSE TO EARN CREDIT FOR EXTRACURRICULAR INVOLVEMENT
Bibliographic record
Abstract
Students often have significant learning experiences outside of the classroom, and in particular through their involvement in extracurricular activities. McMaster University has a strong student culture rooted in this type of involvement, and wanted to recognize this experiential learning. These students are often learning a variety of durable skills such as leadership, teamwork, conflict management, and communication. This paper describes the development of a course for students to earn complementary credit for a variety of diverse roles in extracurricular settings across campus. The development approach was informed by principles of student ownership and self-directed learning, and implemented by a diverse team including the instructor, staff, and students. Several focus groups conducted with actively involved students provided insight on both the structure and content of the course: workshop-style classes, with active learning modules, and opportunities for students to learn from each other. Critical reflection was the primary assessment to encourage students to derive learning from their extracurricular experiences. Preliminary observations from the first offering of the course are promising, in that students are deriving significant value from the course, and their related extracurricular experiential learning.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.097 | 0.032 |
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".