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Record W4297688891 · doi:10.54691/bcpep.v5i.1578

Expectancy Effect on Academic Success of English Language Learners

2022· article· en· W4297688891 on OpenAlexaff
Yin Liu

Bibliographic record

VenueBCP Education & Psychology · 2022
Typearticle
Languageen
FieldPsychology
TopicEducational and Psychological Assessments
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsEllExpectancy theoryPsychologyMathematics educationDemographicsConstruct (python library)PopulationAcademic achievementPedagogyTeaching methodSociologySocial psychologyComputer scienceVocabulary development

Abstract

fetched live from OpenAlex

English Language Learners (ELLs) comprise a unique student body in the demographics of the student population. However, the innate challenge of acquiring language proficiency often reveals as an additional barrier for them to succeed early enough in their schooling; therefore, pedagogical studies derived to investigate factors that limit teachers and students from achieving their goals. Among them, teachers’ expectations construct effective pedagogy that addresses both the linguistic and socio-emotional needs to succeed academically. This paper thus reviews the mechanism of expectancy effect on academic success. Specifically, this effect enacts upon intrinsic motivation, nurtured through enhanced teacher-student interaction and classroom environment, to mediate the gap of achievement due to socio-economic backgrounds. Further, professional development is implied at the end to investigate barriers of access for these students for future studies.

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)0.002
metaresearch head score (Gemma)0.009
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 score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.031
GPT teacher head0.443
Teacher spread0.412 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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