Examining the first-person perspective as appropriate prelaboratory preparation
Bibliographic record
Abstract
Prelaboratory tasks are used to facilitate learning and introduce and provide context for laboratory work. The application of first-person perspective (FPP) technology may provide interesting new approaches to providing prelaboratory preparation. However, there is limited knowledge as to whether this perspective is useful or enjoyable for students preparing for laboratory tasks. The purpose of this study was to examine whether prelaboratory preparation, utilizing the FPP technique, was enjoyable and led to improvements in laboratory task-specific self-efficacy in comparison to the traditional text-only (TO) style. We observed that the FPP group found the style to be generally more enjoyable, entertaining, and generally fun compared with the TO group (5.3 ± 0.2 and 2.7 ± 0.3, respectively, P < 0.05). Furthermore, we found that the FPP group had a greater laboratory task-specific self-efficacy than their counterparts in the TO group, following the prelaboratory preparation (93.6 ± 1.6 and 83.5 ± 3.2, respectively, P < 0.05). We did not find any differences in scenario-based self-efficacy between the FPP and the TO group. Taken together, our data support the use of FPP videos as a novel, refreshing approach to prelaboratory preparation that builds self-efficacy in students performing laboratory tasks.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".