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Engagement in Assessment Change and the Role of SoTL

2023· article· en· W4389314126 on OpenAlexaffvenueabout
Natalie Simper, Amanda Berry, Katarina Mårtensson, Nicoleta Maynard

Bibliographic record

VenueThe Canadian Journal for the Scholarship of Teaching and Learning · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicStudent Assessment and Feedback
Canadian institutionsQueen's University
Fundersnot available
KeywordsValuation (finance)PsychologyPedagogySociologyBusiness

Abstract

fetched live from OpenAlex

This study follows a network-based Assessment Redesign Project at a Canadian university to investigate engagement and sustained implementation. The following strategies were employed in the project: mini-grants, embedded support, a community of practice, and social networks. Assessment facilitators worked in discipline clusters to achieve mutual goals for assessment reform targeted at the authentic assessment of critical thinking and problem-solving. Interviews were conducted with nine of the 25 project members one-year post-implementation. The study adopted a motivational theoretical lens to investigate how the experience of the Assessment Redesign Project affected motivation and the continued adoption or propagation of assessment strategies. Participants commented on how helpful the embedded support had been in building their assessment skills or knowledge. The mini-grants were used (in some cases) to fulfil scholarship of teaching and learning (SoTL) goals. All of those engaged in SoTL demonstrated intrinsic motivation for assessment change and had propagated assessment techniques or activities into other courses. In the few cases where motivation was purely extrinsic, there was no SoTL or continuation of assessment activities. This study highlights the links between SoTL and the longer-term impact of the Assessment Redesign Project. Suggestions are provided for institutions wishing to replicate outcomes from the project.

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)0.027
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.152
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0270.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0050.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.096
GPT teacher head0.382
Teacher spread0.287 · 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 designObservational
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

Citations1
Published2023
Admission routes3
Has abstractyes

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