"In Our Own Words": Creating Videos as Teaching and Learning Tools
Bibliographic record
Abstract
Online videos, particularly those on YouTube, have proliferated on the internet; watching them has become part of our everyday activity. While libraries have often harnessed the power of videos to create their own promotional and informational videos, few have created their own teaching and learning tools beyond screencasting videos. In the summer of 2010, the authors, two librarians at York University, decided to work on a video project which culminated in a series of instructional videos entitled “Learning: In Our Own Words.” The purpose of the video project was twofold: to trace the “real” experience of incoming students and their development of academic literacies skills (research, writing and learning) throughout their first year, and to create videos that librarians and other instructors could use as instructional tools to engage students in critical thinking and discussion. This paper outlines the authors’ experience filming the videos, creating a teaching guide, and screening the videos in the classroom. Lessons learned during this initiative are discussed in the hope that more libraries will develop videos as teaching and learning tools.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".