Student Critical Self-Reflection and Perceptions of Video-Based Pro-Section Computer-Assisted Instruction
Bibliographic record
Abstract
Introduction The cost, high resource demands, and psychological significance of in-person cadaveric labs are barriers to their use. Computer-assisted instruction (CAI) of gross anatomy is widely available as an alternative option. However, student engagement, reflections, and expectations of learning anatomy with CAI instead of in-person labs may influence their learning experience and outcomes. Purpose To evaluate students’ critical self-reflection and perceptions of learning using online self-guided anatomy modules with video-based pro-section CAI. Methods A prospective observational cross-sectional study was conducted with first-year occupational therapy students who received anatomy education using CAI involving online self-guided anatomy modules with video-based pro-section instruction. Critical self-reflection was measured using Kember's Critical Self-Reflection Questionnaire scores and open-ended comments. Paired analysis of self-reported Kember nonreflective and reflective actions was conducted followed by quantitative (correlation, Student t-tests) and qualitative (directed content analysis) exploration of factors associated with critical self-reflection. Results Of the 126 students enrolled in the study, 97 consented and completed the study. The students’ Kember Understanding (U) subscale mean score was significantly higher than the Habitual Action (HA), Reflection, and Critical Self-Reflection subscales. The largest effect size was found between the U and HA subscales ( d s = 1.3, 95% CI [1.0, 1.5]). Academic outcomes (anatomy quiz sum score, term grade) did not correlate with the Kember scores. Overall, students felt that video-based anatomy pro-section CAI was best used in a supplementary manner and opportunities for hands-on learning of anatomy were needed. Conclusion Video-based anatomy pro-section CAI helped students understand anatomy but did not readily engage students in critical self-reflection. Strategic course and curriculum design with integrated and hands-on learning opportunities are needed to optimize student anatomy learning experience and academic outcomes while using this type of CAI.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".