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Record W3003066153 · doi:10.1177/0098628320901379

What Do Undergraduate Students Learn From Participating in Psychological Research?

2020· article· en· W3003066153 on OpenAlexaff
Lesley Zannella, Zahra Vahedi, Stephen C. Want

Bibliographic record

VenueTeaching of Psychology · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicEvaluation of Teaching Practices
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsPsychologyPsychological researchUndergraduate researchValue (mathematics)Educational researchEducational psychologyMedical educationHigher educationSocial psychologyPedagogyApplied psychology

Abstract

fetched live from OpenAlex

Researchers often rely on undergraduate students to participate in psychological studies and so ethical guidelines state that there must be educational value to their participation. In previous studies that have asked undergraduates whether they felt they learned something new from participating in research, students have generally said yes. However, we know relatively little about what specifically they are learning. The current study aimed to extend previous research by asking undergraduate participants ( N = 479), who had all taken part in at least one psychology study, to indicate whether and what they learned about the research process, themselves, or other people as a result of their participation. Participants were also asked to recommend ways to make participating in studies more educational. Our findings suggest that the majority of participants are learning from their participation, most often about the design or process of research but also sometimes about their own psychology or that of other people. Based on students’ feedback, we provide specific recommendations for further improving the educational potential of studies.

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)0.021
metaresearch head score (Gemma)0.096
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.979
Threshold uncertainty score0.112

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.096
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.003
Scholarly communication0.0090.006
Open science0.0010.004
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0050.002

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 head0.553
GPT teacher head0.651
Teacher spread0.097 · 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.

Study designObservational
DomainEvaluation
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

Citations8
Published2020
Admission routes1
Has abstractyes

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