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Record W7065775817

Enhancing Experiential Learning Opportunities Through the Integration of Continuous Improvement Practices

2021· article· en· W7065775817 on OpenAlexaboutno aff

Bibliographic record

VenueScholarship@Western (Western University) · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Marketing Education
Canadian institutionsnot available
Fundersnot available
KeywordsExperiential learningScope (computer science)CurriculumHigher educationPlan (archaeology)PopulationOrganizational learningQuality management
DOInot available

Abstract

fetched live from OpenAlex

Experiential learning (EL) in higher education has become a prominent academic curriculum component. Recent provincial guidelines emphasize that post-secondary institutions provide students EL opportunities, outlining criteria as to what counts as an EL activity. While these guidelines provide instruction on what an EL opportunity should contain, it does not detail how post-secondary institutions should develop and implement these activities responsive to their unique student population needs. This Organizational Improvement Plan (OIP) aims to determine how an Ontario university can provide meaningful, student-focused EL opportunities through a centralized, theoretically-informed EL implementation framework. It centers around a Problem of Practice (PoP) at Gordon University (GU), where the absence of formal internal practices on how to develop and implement EL has created an imbalance in current offerings. Throughout the OIP, a distributed-adaptive hybrid leadership approach combined with the change path model (Cawsey et al., 2016) creates a pathway to propel identified change practices forward. An organizational analysis identifies key change areas, determining the scope and type of change needed. Using Starratt’s (1991, 1996) ethics of care, justice and critique reveals the ethical considerations and challenges a chosen solution needs to address. The result is a proposed solution to the PoP that focuses on organizational learning using Kolb’s (2015) EL theory. Embedding this organizational learning in GU’s existing quality assurance academic review framework will formalize the process. The model for improvement (Langley et al., 1994; Langley et al., 2009; Moen & Norman, 2009) guides the implementation, monitoring and evaluation, and communication plans to ensure continuous improvement of the chosen solution.\nKeywords: Experiential learning, Continuous improvement, Quality assurance, Distributed-adaptive leadership

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)0.020
metaresearch head score (Gemma)0.031
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 score0.020
Threshold uncertainty score0.107

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.031
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0020.004
Scholarly communication0.0080.006
Open science0.0030.011
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.001

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 head0.125
GPT teacher head0.321
Teacher spread0.196 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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