Does a Curriculum‐Targeted, Dissection‐Based Laboratory Workbook Influence Student Learning Outcomes in Undergraduate Human Anatomy?
Bibliographic record
Abstract
Human anatomy is an information‐dense field that often has institutional‐ and curricular‐imposed restraints. The resulting time and resource limitations cultivate a need to develop and implement tools that promote efficiency through self‐directed student learning opportunities. Furthermore, such tools require testing to ensure that they positively contribute to meaningful student learning. We recently created an educational tool to promote student efficiency in the cadaver‐based laboratory at the University of Guelph. The Human Anatomy Laboratory Companion (HALC) is a paper‐based, take‐home workbook for undergraduate students enrolled in our dissection‐ based, third‐year human anatomy course. The overall goal of the HALC was to provide students with a curriculum‐targeted, cadaver‐based resource for pre‐laboratory preparation and post‐laboratory review. This pilot study examined students' reported use of the HALC, and determined if usage influenced their course experience, contextual learning approaches, or course performance outcomes. Course experience was measured using the Course Experience Questionnaire (CEQ), while the students' preferred and contextual approaches to learning were measured using the Revised Two Factor Study Process Questionnaire (RSPQ‐2F). Student grades on written and laboratory tests were examined as performance outcomes. Students who used the HALC ‘Very Frequently’ (370.83 ± 21.476) reported statistically significantly higher scores on the Learning Resources subscale of the CEQ than students who only used it ‘Occasionally’ (260.53 ± 23.865, p = 0.03). Furthermore, students who ‘Very Frequently’ (47.92 ± 32.061) used the HALC reported statistically significantly higher scores on the Appropriate Workload subscale of the CEQ than students who ‘Never” used it (−126.92 ± 34.722, p = 0.003). Multiple linear regression (MLR) analyses found no significant correlation between HALC usage frequency and contextual learning approaches when student demographic data, preferred learning approaches, GPA, and CEQ subscale scores were included as covariates ( p > 0.05). Further MLR analysis revealed that HALC usage frequency was a statistically significant predictor of laboratory test performance when controlling for GPA, and contextual learning approaches ( β = 1.585 ± 0.678, p = 0.021). Together these data suggest that students who reported more frequent use of the HALC had: higher satisfaction with the courses learning resources, a stronger ability to cope with the amount of work that was expected of them throughout the course and, better preparation for laboratory‐based examinations. While the HALC was not found to influence the students' approach to learning in human anatomy, this study demonstrates the potential for curriculum‐targeted, dissection‐based resources that use real cadaveric images to improve laboratory‐based performance outcomes and student satisfaction in dissection‐based undergraduate human anatomy courses. This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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 | 0.001 |
| 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".