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Relationships Between Out-of-School-Time Lessons and Academic Performance Among Adolescents in Four High-Performing Education Systems

2022· book-chapter· en· W4226400407 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

VenueAdvances in educational technologies and instructional design book series · 2022
Typebook-chapter
Languageen
FieldSocial Sciences
TopicGlobal Educational Reforms and Inequalities
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsSocioeconomic statusChinaScope (computer science)Academic achievementPolitical sciencePsychologyMathematics educationSociologyDemography

Abstract

fetched live from OpenAlex

Research into the effects of out-of-school-time mathematics and science lessons on academic performance has thus far proved inconclusive. The relationship between the two requires investigation to elucidate the benefits of these lessons or lack thereof. Using data from the 2009 Program for International Student Assessment (PISA), this study examined the relationship between out-of-school-time mathematics and science lessons and academic performance among 15-year-olds in Hong Kong, China; Korea; Shanghai, China; and Singapore. In light of different cultural contexts, educational standards, and societal norms, and after accounting for gender and family socioeconomic status, which takes into consideration parents' occupational status, years of education, and home possessions, regression analyses revealed inconsistent results across these countries. The study concludes with the implications of the findings and scope for future research, underscoring the need for further investigation that addresses educational disparities in Asia and globally.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.443
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.003
Open science0.0000.000
Research integrity0.0000.001
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 head0.057
GPT teacher head0.313
Teacher spread0.255 · 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