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Record W2900717729 · doi:10.11575/prism/34227

Contract Cheating: An Inter-Institutional Collaborative SoTL Project from Alberta

2018· article· en· W2900717729 on OpenAlexaboutno aff
Sarah Elaine Eaton, Margaret A. Toye, Silvia Luisa Rossi, Nancy Chibry

Bibliographic record

VenueOpen MIND · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicArtificial Intelligence in Law
Canadian institutionsnot available
Fundersnot available
KeywordsCheatingBusinessPsychologySocial psychology

Abstract

fetched live from OpenAlex

Our project showcases perspectives from three Alberta post-secondary institutions, using a collaborative action research approach to reflect upon and then develop interventions to advance awareness of, and responses to, contract cheating. Contract cheating includes, but is not limited to essay mills, custom writing services, assignment completion services and professional exam takers. Contract cheating also occurs when parents, partners or another student do the work for a learner. In short, contract cheating happens when students have someone else complete academic work on their behalf, but submit their work as if they had done it themselves. Our project is framed as an action research project that extends SoTL beyond the individual classroom to a broader institutional context. Using narratives, observations and reflection-on-action as data sources, we use informal interventions such as hallway conversations and in-class discussions, designed to help both faculty members and students develop greater awareness about what contract cheating is and why it deserves attention from a teaching and learning perspective. We also discuss institutional action, such as taking part in the International Day of Action Against Contract Cheating, as a formal way to raise awareness about contract cheating in higher education more broadly. We conclude with preliminary practical and evidence-informed recommendations for practitioners, educational developers and decision-makers.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.935
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0150.002

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 head0.135
GPT teacher head0.451
Teacher spread0.316 · 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; both teacher heads agree on what is shown here.

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
Published2018
Admission routes1
Has abstractyes

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