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An Experiential Learning Model: Collaborative Student Creations of Multidisciplinary Community Classroom Experience

2019· article· en· W2927114445 on OpenAlexaff
Lorelei Boschman, Colleen Whidden, Jason McLester

Bibliographic record

VenuePapers on postsecondary learning and teaching. · 2019
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsMedicine Hat College
Fundersnot available
KeywordsExperiential learningMultidisciplinary approachPsychologyExperiential educationCollaborative learningMathematics educationPedagogyMedical educationSociologyMedicine

Abstract

fetched live from OpenAlex

A community classroom experience, grounded in the philosophy of experiential learning, was the gauntlet we threw down for our Education students and ourselves as instructors. We situated this experience in the pillars of community classroom and experiential learning. Goals for our students became twofold: goals as a current post-secondary student and goals as a future educator. To activate this experience, groups of students engaged both collaboratively and individually with exploratory learning at a local community classroom site. Student reflections showed deep value and learning through this experience and of this experience. There were challenges including navigating collaborative group work and the necessity of becoming vulnerable alongside the successes of connecting exploratory learning to the real world and witnessing authentic interdisciplinary work. Further questions arising from this research centre on authentic assessment practices and the idea of giving back to the community through these real world experiences.

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)0.007
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0040.012
Scholarly communication0.0090.009
Open science0.0040.010
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0100.002

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 head0.020
GPT teacher head0.389
Teacher spread0.368 · 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 designNot applicable
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

Citations2
Published2019
Admission routes1
Has abstractyes

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