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Record W2604914902 · doi:10.1002/ase.1690

“Applying anatomy to something I care about”: Authentic inquiry learning and student experiences of an inquiry project

2017· article· en· W2604914902 on OpenAlexaff
Lauren Anstey

Bibliographic record

VenueAnatomical Sciences Education · 2017
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsWestern University
Fundersnot available
KeywordsContext (archaeology)Artifact (error)DisciplinePedagogyPsychologyMeaningful learningTeaching methodMathematics educationSociology

Abstract

fetched live from OpenAlex

Despite advances to move anatomy education away from its didactic history, there is a continued need for students to contextualize their studies to make learning more meaningful. This article investigates authentic learning in the context of an inquiry-based approach to learning human gross anatomy. Utilizing a case-study design with three groups of students (n = 18) and their facilitators (n = 3), methods of classroom observations, interviews, and artifact collection were utilized to investigate students' experiences of learning through an inquiry project. Qualitative data analysis through open and selective coding produced common meaningful themes of group and student experiences. Overall results demonstrate how the project served as a unique learning experience where learners engaged in the opportunity to make sense of anatomy in context of their interests and wider interdisciplinary considerations through collaborative, group-based investigation. Results were further considered in context of theoretical frameworks of inquiry-based and authentic learning. Results from this study demonstrate how students can engage anatomical understandings to inquire and apply disciplinary considerations to their personal lives and the world around them. Anat Sci Educ 10: 538-548. © 2017 American Association of Anatomists.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.449
Threshold uncertainty score0.734

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.044
GPT teacher head0.467
Teacher spread0.424 · 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 designQualitative
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".

Quick stats

Citations21
Published2017
Admission routes1
Has abstractyes

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