USING MICRO-VIDEO PROJECTS IN LARGE ENGINEERING CLASSES TO DIFFERENTIATE ASSESSMENT
Bibliographic record
Abstract
A new assessment tool is introduced thatuses one minute long, individual micro-videopresentations to give students an opportunity todemonstrate mastery of specific engineering conceptsorally instead of in the typical written form. The shorttime-frame of the videos requires students to thinkcritically about the concept and to explain it concisely. Italso reduces assessment time, allowing teachingassistants to grade it quickly, even in a large class.Three micro-video projects were implemented in athird year and fourth year civil engineering course atCarleton University with 180 and 96 students,respectively. The format of the micro-video tool isdiscussed in detail, including the assessment rubric thatwas used. Three anonymous elective student surveys wereconducted at different stages to solicit student opinions.Students generally thought that the micro-videoassignment was valuable, enjoyable, and provided a fairevaluation of their understanding.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".