MétaCan
Menu
Back to cohort
Record W2317555153 · doi:10.1080/0969594x.2013.844094

Students’ interpersonal trust and attitudes towards standardised tests: exploring affective variables related to student assessment

2013· article· en· W2317555153 on OpenAlexafffund
Man-Wai Chu, Qi Guo, Jacqueline P. Leighton

Bibliographic record

VenueAssessment in Education Principles Policy and Practice · 2013
Typearticle
Languageen
FieldPsychology
TopicCommunication in Education and Healthcare
Canadian institutionsUniversity of Alberta
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychologyInterpersonal communicationSocial psychologyTest (biology)Expectancy theoryScale (ratio)Value (mathematics)Structural equation modelingInterpersonal relationshipComputer science

Abstract

fetched live from OpenAlex

Cognitive and psychometric variables have directed research on student test performance. However, student learning involves a substantial affective component. The objective of this study was to explore the relationship between two kinds of affective variables – interpersonal trust and attitudes towards standardised tests – likely to underlie student test performance. We also examined the effect of a print media report on students’ trust and attitudes towards testing. Using structural equation modelling, we investigated the responses of 206 university students to a modified version of the Test Attitude Survey and Interpersonal Trust Scale. One notable result indicated that variation in students’ attitudes about the effort expended on tests was explained by their interpersonal trust and their attitudes towards the value placed on tests. Viewed through the lens of expectancy theory, these results suggest that affective variables need to be more fully considered when considering practice and generating policy to improve student test performance.

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)0.006
metaresearch head score (Gemma)0.046
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.046
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.099
GPT teacher head0.527
Teacher spread0.428 · 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

Citations14
Published2013
Admission routes2
Has abstractyes

Explore more

Same venueAssessment in Education Principles Policy and PracticeSame topicCommunication in Education and HealthcareFrench-language works237,207