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Evaluating the Impact of the Laboratory Learning Environment and Use of a Computer‐assisted Learning Resource on Anatomical Knowledge Recall among Undergraduate University Students

2020· article· en· W3016825195 on OpenAlexaffabout
Sean McWatt, Genevieve Newton, Lorraine Jadeski

Bibliographic record

VenueThe FASEB Journal · 2020
Typearticle
Languageen
FieldEngineering
TopicAnatomy and Medical Technology
Canadian institutionsUniversity of GuelphMcGill University
Fundersnot available
KeywordsPsychological interventionRecallCurriculumTest (biology)Gross anatomyLearning environmentResource (disambiguation)Computer scienceMedical educationPsychologyMedicineMathematics educationPathologyPedagogyBiology

Abstract

fetched live from OpenAlex

Developing lasting knowledge of human anatomy is foundational for students in the health sciences. Anatomy teaching methods are constantly adapting to achieve this goal, despite time and resource limitations. Interventions that target the laboratory environment, such as moving from dissection‐ to prosection‐based teaching or adding computer‐assisted learning resources, are widespread. However, the long‐term effect of such interventions on knowledge retention is not well understood. Accordingly, this study evaluated 1) the influence of the laboratory learning environment (dissection‐ versus prosection‐based) and 2) the impact of a curriculum‐targeted computer‐assisted learning resource on long‐term knowledge recall among non‐medical undergraduate human anatomy students at the University of Guelph. Participants reported their demographic information, approaches to learning in the course, and use of the computer‐assisted learning resource through a combination of online and written surveys. Knowledge recall was assessed through a written test of short‐answer questions administered three months after the course ended. The test included two low‐order questions and two high‐order questions (based on the Blooming Anatomy Tool), which were evaluated using the Structure of the Observed Learning Outcome Taxonomy to yield performance scores. The performance scores were compared between dissection‐ and prosection‐based groups, as well as between low‐ and high‐frequency users of the computer‐assisted learning resource with the Mann‐Whitney U test. Furthermore, multiple linear regression analyses were used to investigate the relationships between 1) the laboratory learning environment and performance and 2) use of the computer‐assisted learning resource and performance, while controlling for students’ approach to learning scores, final grades, and how recently they studied the material. The laboratory environment was not found to influence knowledge recall ( p > 0.05). However, high‐frequency users of the computer‐assisted learning resource demonstrated stronger knowledge recall than low‐frequency users ( p = 0.003) and use of the resource was strongly positively correlated with performance on high‐order questions ( p = 0.014) when controlling for the aforementioned variables. Deep approaches to learning were positively associated with overall knowledge recall ability in both comparisons ( p < 0.05). These findings suggest that the dissection‐ and prosection‐based laboratory teaching approaches at the University of Guelph offered equal opportunities for long‐term knowledge retention; however, using the resource more frequently and pursuing a deeper approach to learning in the course may help students develop stronger long‐term knowledge recall abilities. Therefore, supplemental computer‐assisted learning resources can be used as a low‐risk intervention to support cadaver‐based human anatomy education and promote long‐term knowledge retention.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.083
Threshold uncertainty score0.504

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.030
GPT teacher head0.272
Teacher spread0.242 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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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Citations0
Published2020
Admission routes2
Has abstractyes

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