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Record W2793934261 · doi:10.1177/0098628318762905

A Program to Improve Student Engagement at Research-Focused Universities

2018· article· en· W2793934261 on OpenAlexaff
Ashley V. Whillans, Sally E. Hope, Lauren Wylie, Bob Zhao, Michael J. Souza

Bibliographic record

VenueTeaching of Psychology · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicEvaluation of Teaching Practices
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPsychologyStudent engagementFeelingMedical educationUndergraduate researchHigher educationPedagogyMathematics educationSocial psychology

Abstract

fetched live from OpenAlex

Promoting undergraduate engagement is an important and challenging obstacle at large research-focused universities. Thus, the current study evaluated whether a peer-led program of student-geared events could improve engagement among a diverse group of psychology students early on in their degrees. We randomly assigned interested second-year psychology students to participate in the program or to a wait-list. As compared to students who were randomly assigned to the wait-list, students who participated in the program attended more extracurricular events during the year, had higher grades, and reported greater feelings of engagement and enhanced skill learning. These data provide initial evidence that a peer-led program of student events can improve student life for psychology students attending large research-focused institutions.

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)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.341
GPT teacher head0.622
Teacher spread0.281 · 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 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

Citations7
Published2018
Admission routes1
Has abstractyes

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