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Record W2084994746 · doi:10.5539/ies.v5n3p24

Experiential Learning in a Common Core Curriculum: Student Expectations, Evaluations, and the Way Forward

2012· article· en· W2084994746 on OpenAlexvenueno aff
Gavin Porter, Jessica King, Nathalie F. Goodkin, Cecilia Ka Yuk Chan

Bibliographic record

VenueInternational Education Studies · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicReflective Practices in Education
Canadian institutionsnot available
Fundersnot available
KeywordsExperiential learningCurriculumPsychologyContext (archaeology)PedagogyCritical thinkingExperiential educationActive learning (machine learning)Mathematics educationCommon coreHigher educationCore (optical fiber)Political scienceEngineering

Abstract

fetched live from OpenAlex

Universities are becoming increasingly conscious of how learning activities align with the attributes they desire in their graduates. Experiential learning is viewed by many institutions as an essential activity for students to gain attributes such as problem solving skills, observation skills, advocacy, and critical thinking. An experiential learning activity, in the form of an environmental fieldtrip, was examined in the context of a university’s new common core curriculum. Student expectations were compared to evaluation of the fieldtrip itself, and guidelines for future trip outings are made in consideration of both published works and our own experiences. The ability of departments and faculties to engage students in beneficial and enjoyable learning will be of utmost importance in attracting student enrollment. This is particularly pressing in universities that are transitioning into less differentiated first year intakes.

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)0.032
metaresearch head score (Gemma)0.079
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.168

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0320.079
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0060.003
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.084
GPT teacher head0.543
Teacher spread0.459 · 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 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

Citations8
Published2012
Admission routes1
Has abstractyes

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