A Framework for Evaluating the Impact of Production Quality on Coding Demonstration Videos
Bibliographic record
Abstract
This work reports on a pilot study that examines the impact of production quality of coding demonstration videos on student learning. With an increase in demand for asynchronous content, many instructors are finding the scripting and development of coding demonstration videos to be cumbersome and time consuming. However, it is an open question whether it is necessary to carefully script and produce videos to satisfy student desires or provide positive learning outcomes. In this study, participants watched coding demonstration videos, and the impact was measured on attitude surveys and content questions. Students were randomly assigned to watch either high production quality videos which were carefully scripted and edited for concision and accuracy, or low production quality videos which were created on-the-fly by teaching assistants with no preparation time or post video editing. In this pilot study, no statistical significance in test performance was found based on the production quality of the videos. There was an impact on responses to a single question on the CAS. This work develops a methodology for further evaluation of the impact of production quality of coding demonstration videos that will eventually provide guidance to practitioners as to the level of time and effort required to maximize student benefit.
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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 | 0.076 | 0.213 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".