Student perception of topic difficulty: Lecture capture in higher education
Bibliographic record
Abstract
<p>Perception of topic difficulty is a likely predictor of lecture capture video use, as student perception of difficulty has been shown to affect a variety of outcomes in academic settings. This study measured the relationship between perceived difficulty and the use of lecture capture technology in a second year biochemistry course while additionally taking into account student learning approaches, comfort with technology, gender and performance outcomes. In several analyses, it was found that a higher perceived level of difficulty was associated with an increased number of video accessions, although this relationship was not consistent across all topics. As well, it was found that surface learning approach score and gender were significantly associated with the number of accessions of lecture capture videos, while deep approach score, course grade, and level of comfort with technology were not. This study confirms that student use of lecture capture is related to their perception of topic difficulty, and demonstrates that student characteristics also influence lecture capture behaviour. Although the strength of our observed associations were weak, the level of content difficulty may be an important factor to consider when deciding when to use lecture videos as learning resources in higher education.</p>
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".