Engaging the YouTube learner: Promoting home economics through videos
Bibliographic record
Abstract
Youth are intensely engaged as viewers and producers in the self-directed, participatory, and informal online learning culture of YouTube. These YouTube learners have developed learning styles that are commensurate with technology; therefore, to remain relevant, it is important for Baby Boomer-aged university instructors to adapt teaching methods accordingly. Students (N=211) enrolled in four classes of the undergraduate course entitled Professional Perspectives during the 2012-13 academic year at Brescia University College in Canada were asked to produce an informative and persuasive video for a Canadian online audience that addressed either the value and importance of Home Economics, or the aims of the United Nations' 20th Anniversary of the International Year of the Family. Professional Perspectives is taken by students in the academic programs Food and Nutrition, Family Studies, and Nutrition and Families. The students' online videos included 'rap' music, animations, whiteboard story presentations, interviews, puppet shows, pet actors, and time lapse photography. The purpose of this paper is to report on an in-practice example where an online video project enhanced students' engagement in their learning. Other educators may also feel supported, encouraged, and more confident to use digital technology in their classrooms.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.007 | 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".