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Record W3097575064 · doi:10.5267/j.msl.2020.10.032

The role of parents on their children’s academic performance

2020· article· en· W3097575064 on OpenAlexvenueno aff
Nguyễn Thị Ngọc Lan, Nga Duong, Huyen Dinh, Mai Nguyen, Thoa Nguyen

Bibliographic record

VenueManagement Science Letters · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Educational Reforms and Inequalities
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyTest (biology)Minor (academic)Family incomeDevelopmental psychologyAcademic achievementSocial psychologyMathematics educationPolitical scienceEconomicsEconomic growth

Abstract

fetched live from OpenAlex

This study aims to investigate the impact of the family factors on scholastic achievements of students in high schools and universities in Vietnam. The primary data is collected from the 538 students through an online questionnaire. We apply one empirical model with the support of SPSS and STATA to test the family determinants of academic attainments-GPA. We find that there are three factors which impact on GPA of these: Age gap, Income of the families, Negative reactions of parents to their child’s low-performed results in academic performance. The research results show that all three factors can explain for 8% with the changes in GPA of learners. While the age gap between parents and their children has minor positive effects on academic performances, family income is significantly and positively correlated to GPA. This is the reason why students from opulent families often perform better than those of more meager families.

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 score0.010
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.019
GPT teacher head0.278
Teacher spread0.258 · 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

Citations4
Published2020
Admission routes1
Has abstractyes

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