Developing Metacognition in First-Year Students through Interactive Online Videos
Bibliographic record
Abstract
Abstract This complete research paper examines the use and impact of a series of optional interactive online videos (“screencasts”) to develop metacognition and learning perspectives in first-year engineering students. In 2018, eight screencasts were distributed once per week at the start of an introduction to engineering course; this was expanded to nine screencasts in 2019. The effectiveness of the screencasts was assessed using a mixed methods approach, including pre- and post-interviews coded using a threshold concepts framework, pre and post deployment of the Metacognitive Awareness Inventory, an online survey, and general course observations. Student utilization of these optional resources was strong, particularly in 2019, with 70% of students on average viewing each weekly screencast, 98% viewing at least one of the nine screencasts, and 48% of students viewing at least eight. Viewership was found to be sensitive to incentivization. Analysis of interview responses, survey responses, and course grades revealed a statistically significant benefit to metacognitive awareness and academic performance of completing the screencasts. Students generally perceived the screencasts as helpful and impactful towards their learning, independent of their self-reported wellbeing or GPA.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".