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Record W4282838049 · doi:10.1108/qae-01-2022-0023

Forming an academic program review learning community: description of a conceptual model

2022· article· en· W4282838049 on OpenAlexaff
Alana Hoare, Catharine Dishke Hondzel, Shannon L. Wagner

Bibliographic record

VenueQuality Assurance in Education · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicEvaluation of Teaching Practices
Canadian institutionsThompson Rivers University
Fundersnot available
KeywordsConceptual modelOriginalityConceptual frameworkCoachingKnowledge managementHigher educationQuality (philosophy)Public relationsComputer scienceSociologyPsychologyPolitical science

Abstract

fetched live from OpenAlex

Purpose Higher education institutions are required to evaluate program quality through cyclical program review processes. Despite often being considered the “gold standard” of academic review, there persists dissatisfaction with the lack of integration of program review findings into other planning processes, such as budgeting, assessment and strategic planning. As a result, the notion of program review action plans “collecting dust on the shelf” is so ubiquitous that the concept is normalized as an expected outcome. The purpose of this paper is to describe a conceptual model whereby teams of faculty members receive education and training from quality assurance practitioners and educational developers, access to institutional resources, opportunities for cross-departmental collaborations and collective advocacy to increase the capacity of faculty members to implement improvement goals resulting from program reviews. Design/methodology/approach The authors theorize that a professional learning community is a meaningful approach to program review and present a conceptual model – the Academic Program Review Learning Community (PRLC) – as an antidote to hierarchical, fragmented, compliance-oriented processes. The authors suggest that the PRLC offers a reliable institutional framework for learning through formalized structures and nested support services, including peer learning and external coaching, which can enhance the catalytic capacity of reviews. Findings The authors argue that postsecondary institutions should create formal structures for incorporating learning communities because, without a reliable infrastructure for collective learning, decision-making may be fragmented oridiosyncratic because of shifting demands, priorities or disconnected faculty. Originality/value A learning community model for program review fits well with a new way to think about program review because faculty are most engaged when they feel ownership over the process. Furthermore, few models exist for conducting program review; as a result, chairs and academics often struggle to conduct reviews without a coherent framework to draw upon.

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)0.028
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.028
Threshold uncertainty score0.150

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0280.029
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0060.005
Science and technology studies0.0120.026
Scholarly communication0.0210.024
Open science0.0060.017
Research integrity0.0070.006
Insufficient payload (model declined to judge)0.0090.002

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 head0.416
GPT teacher head0.564
Teacher spread0.148 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations12
Published2022
Admission routes1
Has abstractyes

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