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The Impact of a Novel Computer‐assisted Learning Resource on Student Learning in Undergraduate Dissection‐ and Prosection‐based Laboratory Environments

2019· article· en· W3176646641 on OpenAlexaffabout
Sean McWatt, Genevieve Newton, Lorraine Jadeski

Bibliographic record

VenueThe FASEB Journal · 2019
Typearticle
Languageen
FieldEngineering
TopicAnatomy and Medical Technology
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsResource (disambiguation)Thematic analysisMedical educationDissection (medical)PerceptionPsychologyMedicineComputer scienceAnatomyQualitative researchSociology

Abstract

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Human anatomy is an essential subject for the medical and health sciences. Teaching and learning human anatomy each require large investments of time and resources; however, many institutions are challenged by limitations to both. As a result, the approaches used to teach human anatomy are constantly evolving to overcome these limitations and deliver meaningful learning opportunities. One common method for enhancing anatomy education is through computer‐assisted learning (CAL). The rapid growth and advancement of technology over recent decades has made the creation of CAL resources cheaper, easier, and more accessible, facilitating their rise as popular supplements to traditional approaches such as dissection (DI) and prosection (PRO). Accordingly, this study evaluated a novel CAL resource that was created for and introduced into an undergraduate DI and PRO human anatomy course at the University of Guelph between the Fall 2015 and 2016 semesters. The objective was to determine the influence of the resource on the students' academic experiences through evaluations of their course satisfaction (CS), contextual approaches to learning (SAL, characterized by deep [DA] and surface [SA] approach scores), and overall course performance. Participants reported their demographic information, CS, SAL, and use of the CAL resource through a combination of online and written surveys. Written feedback regarding their perceptions of the CAL resource was also collected and thematic analysis was performed to extract common themes. CS was compared between the Fall 2015 and Fall 2016 academic cohorts using the Mann‐Whitney U test. Comparisons of contextual SAL and performance were made using analyses of covariance with preferred SAL scores and cumulative grade averages as covariates, respectively. CAL resource use by students in both DI and PRO was then characterized and multiple linear regression analyses were used to determine correlations between their use of the resource and DA scores, SA scores, and course performance. Although the students' mean (± SEM) CS improved between the 2015 and 2016 cohorts in both DI (2015 = 74.15 ± 3.29, 2016 = 86.54 ± 1.94, p = 0.002) and PRO (2015 = 78.41 ± 5.95, 2016 = 93.55 ± 3.06, p = 0.051), the mean differences (± SEM) in DA (DI = −2.11 ± 0.53, p < 0.0005; PRO = −2.72 ± 1.03, p = 0.010) and SA (DI = 1.95 ± 0.55, p < 0.0005; PRO = 4.16 ± 1.10, p < 0.0005) scores suggested that the course presented a more surface‐oriented environment in 2016 than in 2015. Contextual SAL and course performance were not directly influenced by CAL resource use ( p > 0.05); however, students in both DI ( p = 0.001) and PRO ( p = 0.025) who reported higher positive perceptions of the resource had higher DA scores. These findings indicated that using the CAL resource did not significantly enhance the students' learning experience. However, those who saw value in the resource and reported more positive perceptions toward it used deeper approaches to learning, which are representative of meaningful learning. Alongside the analyses of written student feedback, this suggests that the context in which CAL resources are to be disseminated may merit strong consideration before incorporating such resources into a course. This abstract is from the Experimental Biology 2019 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 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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.444
Threshold uncertainty score0.413

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.007
GPT teacher head0.238
Teacher spread0.231 · 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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Citations1
Published2019
Admission routes2
Has abstractyes

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