Student Video-Usage in Introductory Engineering Courses
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it