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Experiential Learning Assessment in Post-secondary Education

2024· article· en· W4406886480 on OpenAlexaffvenueabout
Jay Wilson, Thomas Yates, Marc Gobeil, Alec E. Aitken, Kevin wâsakâyâsiw Lewis

Bibliographic record

VenueThe Canadian Journal for the Scholarship of Teaching and Learning · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education and Employability
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsExperiential learningPsychologyExperiential educationPedagogyMathematics education

Abstract

fetched live from OpenAlex

This article shares results from the research project “Experiential Learning Assessment in Post-Secondary Education.” The purpose of this research was to develop a further understanding of experiential learning assessment (EL) through an exploratory approach with university faculty and students. This article shares findings from the first part of a two-part research project examining the faculty experience of assessment in EL. A second research study is underway that provides the student perspective. The first phase of the research engaged University of Saskatchewan instructors with experience in experiential learning. Nine participants completed an online survey and six discussed their experiences and understanding in a focus group or an interview. Findings revealed that EL students were far more engaged with their learning than those in traditional courses. Participants applied their experience to expand upon current definitions and characteristics of EL assessment. They also shared differences with EL instruction and assessment as compared to traditional courses. Common themes identified by participants included a need for questioning and processes to integrate more EL assessment strategies into teaching and learning. Participants shared advice for those considering EL approaches in their own teachings. As EL continues to be a key area of growth for many universities and post-secondary institutions, this study contributes to the body of knowledge and appeals to faculty, designers, and others tasked with implementing effective EL.

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)0.012
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.733
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0060.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.003
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 head0.033
GPT teacher head0.388
Teacher spread0.355 · 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.

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

Citations0
Published2024
Admission routes3
Has abstractyes

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