Uncovering student learning profiles with a video annotation tool: reflective learning with and without instructional norms
Bibliographic record
Abstract
This study explores the types of learning profiles that evolve from student use of video annotation software for reflective learning. The data traces from student use of the software were analysed across four undergraduate courses with differing instructional conditions. That is, the use of graded or non-graded self-reflective annotations. Using hierarchical cluster analysis, four profiles of students emerged: minimalists, task-oriented, disenchanted, and intensive users. Students enrolled in one of the courses where grading of the video annotation software was present, were exposed to either another graded course (annotations graded) or non-graded course (annotations not graded) in their following semester of study. Further analysis revealed that in the presence of external factors (i.e., grading), more students fell within the task-oriented and intensive clusters. However, when the external factor is removed, most students exhibited the disenchanted and minimalist learning behaviors. The findings provide insight into how students engage with the different features of a video annotation tool when there are graded or non-graded annotations and, most importantly, that having experience with one course where there are external factors influencing students’ use of the tool is not sufficient to sustain their learning behaviour in subsequent courses where the external factor is removed.
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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 | 0.007 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".