THE USE OF A SERIES OF ONLINE MINI-LECTURES TO DELIVER FACTS IN FIRST YEAR PROGRAMMING
Bibliographic record
Abstract
In the first year programming course given to ME and MTE students at uWaterloo, four hours of traditional classroom instruction have been replaced with a series of short online mini-lectures that deliver some of the basic facts necessary to be able to code programs. The students’ comprehension of this content is assessed online by quizzes and on the midterm exam. This approach was used in a course which was not otherwise delivered online. The goal was to front-load the course to make space for a design project later in the term. The online mini-lectures were designed to be “lecture-time neutral”. The accelerated start of term allowed threshold concepts to show up on assignments a week earlier than with the traditional approach, giving students an additional week of practice with these topics. This led to noticeable gains in understanding on the final exam. Survey data was collected, and focus groups were run, to capture student feedback on the approach; additionally, course grades were analyzed to assess impact on student knowledge of course material.
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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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".