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Record W1549632266 · doi:10.22329/celt.v7i2.3981

Inquiry Guided Learning Projects for the Development of Critical Thinking in the College Classroom: a pilot study

2014· article· en· W1549632266 on OpenAlexaffvenue
Danielle C. Bentley

Bibliographic record

VenueCollected Essays on Learning and Teaching · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Critical Thinking Development
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsLikert scaleCritical thinkingContext (archaeology)PsychologyProcess (computing)Presentation (obstetrics)Mathematics educationActive learning (machine learning)PedagogyComputer science

Abstract

fetched live from OpenAlex

This paper describes the inaugural success of implementing Inquiry Guided Learning projects in a college-level human anatomy and physiology course. In this context, scientific inquiry was used as a means of developing skills required for critical thinking amoung students. The projects were designed using the Information Search Process (Kuhlthau, 1991) as a framework with emphasis placed on three of the six stages: question selection, scientific research, and presentation of results. The projects were quantitatively assessed using self-reported confidence ratings from a 10-point Likert scale. The projects were also qualitatively assessed using informal student feedback focusing on student suggestions for project improvement. Moving forward, the Inquiry Guided Learning projects will continue to be a formal course component, with the next stage of implementation to include a thorough assessment of student learning outcomes.

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)0.018
metaresearch head score (Gemma)0.037
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 score0.018
Threshold uncertainty score0.094

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.037
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0040.002
Scholarly communication0.0030.002
Open science0.0020.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.001

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 head0.093
GPT teacher head0.390
Teacher spread0.297 · 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".

Quick stats

Citations4
Published2014
Admission routes2
Has abstractyes

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