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Record W4282050014 · doi:10.29173/isotl598

Student Engagement in Concept Mapping

2022· article· en· W4282050014 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueImagining SoTL · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Critical Thinking Development
Canadian institutionsUniversity of LethbridgeRed Deer Polytechnic
Fundersnot available
KeywordsStudent engagementConcept mapPsychologyCritical thinkingProcess (computing)Health careQualitative researchMedical educationMathematics educationPedagogySociologyMedicineComputer sciencePolitical science

Abstract

fetched live from OpenAlex

Student engagement has been an important discourse in higher education, and researchers have determined that engagement in educational activities is vital for student retention. Retaining students in learning is crucial in a stressful healthcare environment, hence the need to identify the associated engagement factors. Concept mapping offers students a realistic venue for critical thinking, clinical reasoning, and engagement in educational activities. Building on the results obtained from a basic qualitative study where, interviews and journals were analyzed, we examined the theoretical basis for students’ engagement during concept mapping in a flipped clinical learning. Accordingly, this article discusses the factors that influenced engagement while concept mapping within a stressful healthcare clinical learning environment and explains how the process of mapping patient care improved students’ engagement in learning. The impacts of concept mapping process went beyond participation to self-direction, higher-level thinking, and greater impact on clinical decision making for the student participants.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.742
Threshold uncertainty score0.998

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.055
GPT teacher head0.384
Teacher spread0.330 · 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