Steps towards a scientific approach to a database course transformation
Bibliographic record
Abstract
Instructors modify offerings of their courses in response to changes in emphasis, curriculum, student preparation, resource limitations, and problems with previous offerings. Changes also involve assessment instruments such as assignments and exams, ensuring that students are indeed learning the material, rather than relying on "information" from previous offerings. However, instructors are seldom guided by meaningful and useful data in making these changes. Instead, most rely on their "gut feelings", and many changes are made in an ad hoc manner, with minimal supporting data to assess whether the changes contribute positively or negatively to student learning. This paper reports on our experience with the transformation of an undergraduate database course at the University of British Columbia (UBC), using best practices from education research. We provide examples of the types of data that can be obtained through various instruments, and can be used in an objective analysis to affect course changes. If such data collection methods are put into place before changes to a course are anticipated, instructors will be better prepared to assess how students are affected by those changes.
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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 | 0.097 | 0.159 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".