Different Audiences but Similar Engagement Goals: In-Progress Work on Two Course Transformations
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
This paper reports our experience in transforming two undergraduate Computer Science courses at the University of British Columbia (UBC). In particular, we are applying an assortment of best practices from educational research known to increase student engagement. The two courses are being transformed in different ways because their learning goals and audiences vary greatly. For example, the courses use different programming languages; they differ with respect to the number, type and frequency of labs, tutorials, and class time; and one is an elective for computer scientists, whereas the other is a required course for non-specialists. Despite these differences, both courses have a greater active learning component compared to a “traditional ” lecture, with one of them adopting a flipped classroom approach. To judge the success of these efforts, we conducted numerous surveys to determine changes in student attitudes and to identify what works—and what doesn’t—for these courses.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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