"A Video of Myself Helps Me Learn": A Scoping Review of the Evidence of Video-Making for Situated Learning
Bibliographic record
Abstract
Nursing, dance and studio-based arts, engineering, and athletic therapy are viewed as practice-oriented professions in which the teaching and situated learning of practical skills are central. In order to succeed, students must perform a series of performance-based assessments, which seemingly require an “able” body to enact complex tasks in situated and/or simulation-based contexts (for example, “safe nursing practice”). Our interdisciplinary research seeks to intervene within the culture of professional learning by investigating what we know about the use of smartphone video recording for situated, practice-based learning, and for supporting interactive video-based assessment as a means of accommodation and extending access for students, including students with performance anxiety, mature students, ESL learners, students with disabilities, and students in remote communities. In this paper we employ a scoping review methodology to present our findings related to students’ and instructors’ perspectives on the use of smartphone video to demonstrate and document practical knowledge and practice-oriented competencies across fields in the arts and sciences. We also examine broader research, as well as the ethical and design implications for the development of our technology-based toolbox project – an online resource created to advance pedagogies deploying smartphones as tools for practical skills acquisition - and for accommodation - within multidisciplinary practical learning environments.
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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 | 0.014 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".