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Does a Curriculum‐Targeted, Dissection‐Based Laboratory Workbook Influence Student Learning Outcomes in Undergraduate Human Anatomy?

2018· article· en· W3175121545 on OpenAlexaffabout
Jason Valencia, Sean McWatt, Lorraine Jadeski

Bibliographic record

VenueThe FASEB Journal · 2018
Typearticle
Languageen
FieldEngineering
TopicAnatomy and Medical Technology
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsWorkbookCurriculumWorkloadMedical educationPsychologyHuman anatomyDissection (medical)MedicineComputer scienceAnatomyPedagogy

Abstract

fetched live from OpenAlex

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 .

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.036
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.036
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.005
GPT teacher head0.262
Teacher spread0.258 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations1
Published2018
Admission routes2
Has abstractyes

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