Opportunities and Challenges of Using Video to Examine High School Students' Metacognition
Bibliographic record
Abstract
This article reflects on the opportunities and challenges of using digital video (DV) technology as a visual research tool in qualitative research. The ideas are derived from a multiple case study that examined ten high school students’ metacognitive thinking as they created video representations of their own. The article begins with a brief history of visual research, and an introduction to the context, problem, and definition of metacognition within the study. This is followed by a literature review that examines the use of video in qualitative research and an explanation of the research questions and methodology. As revealed by the embedded video exemplars within this paper, many instances of students’ metacognitive thinking, behavior, and feelings were inferred from video observations of students working on their video artifacts, discussing ideas with their group members, or responding to my questions. In the discussion, I explore the opportunities and challenges of drawing definitive conclusions about students’ metacognitive thinking within video imagery and the multiple possible ways of interpreting this information.
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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 | 0.079 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".