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Record W2764116483 · doi:10.15173/ijsap.v1i2.3063

We Want to be More Involved: Student Perceptions of Students as Partners Across the Degree Program Curriculum

2017· article· en· W2764116483 on OpenAlexvenueno aff
Kelly Matthews, Lauren Groenendijk, Prasad Chunduri

Bibliographic record

VenueInternational Journal for Students as Partners · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Practises and Engagement
Canadian institutionsnot available
Fundersnot available
KeywordsGeneral partnershipCurriculumInclusion (mineral)PerceptionPedagogyRhetoricPsychologyDegree programMedical educationMathematics educationPolitical scienceMedicineSocial psychology

Abstract

fetched live from OpenAlex

Engaging students-as-partners is gaining momentum in the higher education sector. This study explores undergraduate students’ perceptions of how involved they were in partnership activities across their degree programs, and whether this matched their desired level of involvement in such practices. Analysis of a quantitative study of 268 students showed statistically significant differences between perceived levels of importance and involvement for all the partnership practices (n=18) investigated in our survey. These results highlight that the students in this study want to be more substantially involved in partnership practices across their degree program. We argue against the consumerist rhetoric about the role of students as passive learners and advocate for greater inclusion of partnership activities that foster active student participation in shaping the university curricula. We discuss implications for Students as Partners in relation to the progressive development of university curricula and assessment practices along with future research directions.

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)0.007
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0030.003
Scholarly communication0.0060.002
Open science0.0010.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.181
GPT teacher head0.637
Teacher spread0.455 · 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 designQualitative
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

Citations24
Published2017
Admission routes1
Has abstractyes

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