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Record W3173606147 · doi:10.24908/pceea.vi0.14931

EXPLORING STUDENTS’ INTERPRETATIONS OF SUCCESS: A RESEARCH INSTRUMENT

2021· article· en· W3173606147 on OpenAlexafffundvenue
Max Ullrich, David S. Strong

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2021
Typearticle
Languageen
FieldPsychology
TopicPsychological and Temporal Perspectives Research
Canadian institutionsQueen's University
FundersQueen's University
KeywordsReliability (semiconductor)Exploratory factor analysisPsychologyScale (ratio)Variance (accounting)Variety (cybernetics)Survey instrumentExploratory researchSurvey researchMathematics educationApplied psychologyMedical educationComputer sciencePsychometricsSociologyMedicineClinical psychology

Abstract

fetched live from OpenAlex

The nebulous term ‘student success’, and other similar terms such as ‘academic success’, are widelyused in education research papers as all-encompassing phrases representing a wide variety of student outcomes, typically academic achievement and persistence. There is limited research addressing how success during an undergraduate engineering program is defined from thestudent point of view. A 60-question research survey with 266 responses, a response rate of 8%, was conducted at Queen’s University to explore this gap. The survey was validated with a mixed-methods pilot study. Instrument validity, reliability, and fairness were established duringthe full study. Scale reliability for a modified version of a Future Time Perspectives survey was established using an exploratory factor analysis with a “meritorious” KMO of0.81 with five factors explaining 49.60% of the variance.

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)0.032
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.979
Threshold uncertainty score0.110

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.032
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0020.004
Scholarly communication0.0040.003
Open science0.0010.006
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.001

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 head0.155
GPT teacher head0.395
Teacher spread0.241 · 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 designNot applicable
DomainMethods
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
Published2021
Admission routes3
Has abstractyes

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