Evaluating the Effect of Follow-up Questions in an Online Exercise
Bibliographic record
Abstract
Studies in other disciplines demonstrate that writing summaries of videos or readings is an effective strategy for increasing student understanding. We explore a related strategy in computer science where we ask students to write explanations of program behaviour. Our experiments are conducted through an online application used by students to prepare for class by watching videos and solving follow-up exercises. With students randomly assigned to treatment groups, we evaluate the effect of different kinds of video follow-up questions by analyzing pre- and post-performance on subsequent select-all-that-apply multiple-choice questions. In experiment one, we found no difference in post-performance between students who wrote explanations and those who were not asked to do so. When the same prompt for explanations was accompanied by a motivational sentence, the quality of the student answers increased, but post-performance was unchanged. In a second experiment, students received either no experimental questions, questions requiring a written explanation or short-answer questions with a single correct response. Again, we found no difference in post-performance across the different treatments. This non-significant result might be explained by the post-performance question difficulty -- the question might not be hard enough to reveal differences in understanding. The question timing might also be a factor, since students complete all the questions soon after watching the video and there may not be enough time for students to need the extra retention benefit gained by doing the experimental questions.
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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 | 0.005 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".