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Evaluation of Undergraduate Anatomy Flipped Laboratory Sessions

2019· article· en· W3174742490 on OpenAlexaff
Paige Eansor, Michele Barbeau

Bibliographic record

VenueThe FASEB Journal · 2019
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsWestern University
Fundersnot available
KeywordsSession (web analytics)ModalitiesModality (human–computer interaction)Flipped classroomClass (philosophy)Medical educationPsychologyComputer scienceMedicineMultimediaMedical physicsMathematics educationArtificial intelligence

Abstract

fetched live from OpenAlex

Introduction The flipped classroom approach (FCA) is a growing pedagogy in higher education. With this approach, students' first exposure to content is prior to class, commonly in the form of two learning modalities: textbook readings, or video recordings. Class time is then focused on the application of knowledge using methods such as discussions, or problem solving. The FCA has been implemented in many lecture‐based courses, but has yet to be fully evaluated for anatomical laboratory sessions. Thus, this cross‐over study sought to: i) compare student outcomes between flipped laboratory sessions (FLS), utilizing different learning modalities, and traditional didactic laboratory sessions (DLS), ii) evaluate student perceptions of both the flipped and didactic laboratory sessions, and iii) determine if students have a preferred learning modality for laboratory sessions. Methodology Students (n=92) were recruited from an undergraduate human anatomy course and placed in one of five study groups, with Western REB approval. Groups 1–4 were exposed to three FLS and one DLS, each focused on a different musculoskeletal region. The three FLS utilized one of the following learning modalities to provide content exposure prior to the laboratory session: a video recording, a 3D anatomy app, or a textbook reading. The DLS were not given content exposure prior to the laboratory session. Group 5 was only exposed to DLS with no prior content exposure for each session. A case‐assessment evaluated student outcomes following each flipped and/or didactic laboratory session. Pre‐ and post‐assessments, utilizing questions with a variety of cognition levels, evaluated the impact of the specific learning modality on student outcomes, and a questionnaire assessed student perceptions of the learning modalities following each session. Results There were no differences between the FLS and DLS case‐assessment final scores. With the exception of the DLS, there were no significant changes between pre‐ and post‐assessment scores. The DLS resulted in significant improvements between pre‐ and post‐assessment scores (Difference 22.1%±2.4 p<0.05). Further analysis of performance on different cognitive level questions will determine if a specific learning modality better prepares students to correctly answer higher‐order thinking questions. Based on the student questionnaire, 41.9% of respondents ranked the didactic laboratory talk as their highest preferred learning modality; while 51.6% ranked the textbook reading as their least preferred learning modality. Further analysis of the questionnaire responses will provide insights into potential reasons for these preferences. Conclusions Undergraduate anatomy students performed significantly better on assessments following a DLS, and preferred the DLS in comparison to the FLS. Didactic lectures, common in anatomy undergraduate courses, may be preferred due to students' familiarity with them; however, as the FCA gains popularity in higher education, student performance and perceptions may change with increased exposure. Furthermore, information collected from the assessments and student questionnaires will provide insight to course designers on methods to more effectively structure the content delivery in laboratory sessions for undergraduate anatomy students. 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 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)0.010
metaresearch head score (Gemma)0.033
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.033
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.027
GPT teacher head0.365
Teacher spread0.338 · 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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Citations0
Published2019
Admission routes1
Has abstractyes

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