Promotion and prevention orientations in the choice to attend lectures or watch them online
Bibliographic record
Abstract
Abstract When presented with the option to use a new instructional technology, students often face an approach–avoidance conflict. This study explored promotion and prevention orientations, concepts linked to approach and avoidance in Higgins's regulatory focus theory, in the choice to attend lectures or watch them online. Openness, a core disposition in the Big Five Model of personality, and positive attitudes towards the utility of the Internet, reflect promotion orientations that are potentially related to the choice to watch lectures online. By contrast, neuroticism, another core disposition in the Big Five Model, and anxiety about the Internet as a computer technology, reflect a prevention orientation that is potentially related to the choice of attending lectures in class. The results illustrate that both promotion and prevention are at work in the choice to attend lectures or to watch them online. Neuroticism and anxiety about the Internet as a computer technology were related to the choice to attend lectures in class, whereas the perceived utility of the Internet was related to the choice to watch lectures online. Instructional mode choice was not related to examination performance, suggesting that the choice to attend lectures or watch them online has more to do with individual differences in promotion and prevention orientations than with pedagogical characteristics that impact learning.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".