Student Video-Usage in Introductory Engineering Courses
Bibliographic record
Abstract
As videos are gaining popularity in flipped and blended Engineering classrooms, there is an increasing need to track and understand students’ use of the videos, in order to identify evidence-based practices matched to the emerging trends in video and video annotation tools. We explore students’ surveyresponses, follow-up interviews, and log data from their interaction with common video platforms as well as, ViDeX, a new experimental video annotation tool, to evaluate how, when and why students watch, rewatch, and annotate videos in two large introductory Engineering courses, with flipped, and blended formats. Our findings show that students watch thevideos with the instructors’ intended use in mind, and plan their review process accordingly. In the flipped classroom, most students summarized the short preclass screencasts in their personal notes to minimize the need to re-watch the videos before the exam. In contrast, students in the blended classroom reexamined the long tutorial videos mostly to redo the problems before the midterm and final exams. Bookmarking seemed to be useful for locating those problems of interest. Since the problems required drawings and computations, paper annotation was more beneficial than a dedicated video annotation platform.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".