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Record W412688351 · doi:10.5206/eei.v25i1.7717

Self-Regulatory Efficacy and Mindset of At-Risk Students: An Exploratory Study

2015· article· en· W412688351 on OpenAlexaffvenue
Ian Matheson

Bibliographic record

VenueExceptionality Education International · 2015
Typearticle
Languageen
FieldPsychology
TopicEducation, Achievement, and Giftedness
Canadian institutionsQueen's University
Fundersnot available
KeywordsMindsetPsychologyExploratory researchMathematics educationSelf-efficacyAcademic achievementDevelopmental psychologySocial psychology

Abstract

fetched live from OpenAlex

There is a limited body of research examining how students’ beliefs about intelligence and about their abilities relate to different learning environments. As reported here, I examined secondary school students’ beliefs, goals, and expectations guided by Zimmerman’s (2000) model of self-regulated learning. In this exploratory study, 230 secondary school students reported on their beliefs about learning and intelligence, as well as on their confidence in their self-regulatory abilities. I made comparisons between groups of students on beliefs, goals, and expectations based on their school stream, achievement, learning disability status, and gender. Both self-regulatory efficacy and reading mindset were significantly different for students based on their school stream and their achievement level. The findings of this exploratory study suggest a need for further research that focuses directly on whether at-risk students demonstrate maladaptive motivation and specifically on their beliefs, goals, and expectations of themselves as learners.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.053
GPT teacher head0.417
Teacher spread0.364 · 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

Citations9
Published2015
Admission routes2
Has abstractyes

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