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Record W2988767436 · doi:10.1080/13598139.2019.1622224

Self-regulated learning in research with gifted learners

2019· article· en· W2988767436 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueHigh Ability Studies · 2019
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsPsychologyMathematics educationSelf-regulated learningPedagogy

Abstract

fetched live from OpenAlex

This special issue presents a sample of modern work on self-regulated learning (SRL) among high ability and gifted students. It includes diverse views about the construct per se, and gifted students’ and their teachers’ accounts about SRL and factors they believe moderate it. Zeidner and Stroeger (this issue) set the stage with a sketch of an extensive literature about SRL that has deep roots in North American educational philosophy and practice. The menu of work here is fundamentally well done and, in varying ways and degrees, slightly provocative.A trite observation would be these articles don’t fully represent the multiple facets and complex articulation among them comprising SRL, especially given relatively less research with participants identified as academically talented or gifted. In this situation, I would be pedantic to point out such-and-such is omitted or this-or-that is underrepresented. Rather, using admittedly using idiosyncratic standards, I select a few matters for discussion and, hopefully, constructive critique. Other commentators would likely apply different filters.Abbreviation SRL = Self-regulated learning

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.032
Threshold uncertainty score0.862

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.171
GPT teacher head0.478
Teacher spread0.306 · 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