The Effect of Postgraduate Students’ Interaction with Video Lectures on Collaborative Note-taking
Bibliographic record
Abstract
Aim/Purpose: This paper aims to explore the effects of students’ interactions with video lectures on the levels of collaboration and completeness of their group note-taking. Background: There has been an increase in the amount of online learning over the last 20 years. With video lectures becoming an increasingly utilized instructional modality, it is essential to consider students’ interactions with videos and the subsequent effect of those interactions on collaboration. Methodology: This research used a combination of survey data about student interactions with video lectures and evidence of student-to-student interactions from a sample of 149 masters and Ph.D. students at a university in South Korea. Contribution: To date, limited research has been conducted on the effect of student interactions with online instructional videos and that interaction’s effect on collaborative note-taking. Past research has examined the effects of lecture-watching behaviors and collaborative note-taking separately, and this paper looks at their relationship with one another. Findings: This paper has two main findings. The first is that interacting more with video lectures increases the amount that students interact with each other. The second is that these higher levels of interaction with videos do not impact the completeness of student note-taking. Recommendations for Practitioners: These findings of this paper suggest that instructors should encourage students to utilize active viewing strategies, as doing so will increase interaction among students, which will subsequently benefit their levels of collaboration. Recommendation for Researchers: This research shows the value of drawing links between aspects of learner consumption of instructional media and other aspects of their learning, particularly collaboration. Impact on Society: The importance of effective instruction and increasing collaboration in online learning is of great value now, particularly so, as much instruction is being delivered in online formats. Future Research: Future research should seek further to understand the relationships between aspects of instruction and collaboration. More specifically, future research could look into clickstream data and collaboration.
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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 | 0.003 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".