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Record W2524676770 · doi:10.5539/ies.v9n10p1

Students’ Perceptions of Teacher Support, Numeracy, and Assessment for Learning: Relations with Motivational Responses and Mastery Experiences

2016· article· en· W2524676770 on OpenAlexvenueno aff
Roger André Federici, Joakim Caspersen, Christian Wendelborg

Bibliographic record

VenueInternational Education Studies · 2016
Typearticle
Languageen
FieldPsychology
TopicEducation, Achievement, and Giftedness
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyMastery learningNumeracyStructural equation modelingPerceptionRelevance (law)Mathematics educationLikert scalePersistence (discontinuity)Class (philosophy)Social psychologyDevelopmental psychologyPedagogyLiteracy

Abstract

fetched live from OpenAlex

We explored a theoretical model of relations between students’ perceptions of emotional support, numeracy, and assessment for learning and their perceptions of relevance of schoolwork, motivation, persistence, and mastery experiences. We also investigated possible differences between students in lower- and upper secondary school. Participants were 44 702 students in 8th to 13th grade in Norway. The data was analyzed by means of structural equation modeling. The results revealed moderate to strong correlations between the three aspects of the learning environment. In general, they related positively to the outcome variables. However, the relations were almost entirely mediated through relevance and motivation. The present study adds to our understanding of how to work with the learning environment.

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)0.005
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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0020.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.073
GPT teacher head0.477
Teacher spread0.404 · 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

Citations18
Published2016
Admission routes1
Has abstractyes

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