20. Developing a New Activity: STUDENT APPROVED
Bibliographic record
Abstract
Do you have an idea for a new activity or laboratory exercise that you would like to incorporate into your course but feel unsure as to how it will be received by your students? This was our concern when developing first-year biology labs for a biology majors’ course at University of Windsor. Through a Centred on Learning Innovation Fund (CLIF) grant at our institution, we were able to form new and revised laboratory exercises, incorporating on-line, active, and reflective components. But, would the students like the labs? Which labs should be replaced? Using student surveys and a ‘trial’ lab, we were able to collect information about the new lab, as well as the old labs. It was a revelation to witness the enthusiasm and the appreciation first-year students had for being involved in the development of the labs. The goal of this essay is to identify the benefits and costs of incorporating a new activity into a course, as well as describing the process that we developed, which includes student input as an important component in the development of the activity.
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.
How this classification was reachedexpand
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".