Abstract
Qin Peng, Runlin Li
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Predicting science achievement scores with machine learning algorithms: a case study of OECD PISA 2015-2018 data
10.1007/s00521-023-08901-6 · 2023
Educational improvement through machine learning: strategic models for better PISA scores
10.1371/journal.pone.0326121 · 2025
What schools need to know about fostering school belonging: a meta-analysis
10.1007/s10648-016-9389-8 · 2018
School belonging: evidence, experts, and everyday gaps
10.1007/s10648-025-10055-x · 2025
Adolescent school belonging and mental health outcomes in young adulthood: findings from a multi-wave prospective cohort study
10.1007/s12310-023-09626-6 · 2024
Belonging: a review of conceptual issues, an integrative framework, and directions for future research
10.1080/00049530.2021.1883409 · 2021
Defining school belonging: a cross-cultural investigation of student-led conceptualisations
10.1111/ejed.70290 · 2025
Uncovering student profiles. an explainable cluster analysis approach to PISA 2022
10.1016/j.compedu.2024.105166 · 2024
TabNet: attentive interpretable tabular learning
10.1609/aaai.v35i8.16826 · 2021
Student well-being and mathematical literacy performance in PISA 2018: a machine-learning approach
10.1080/01443410.2024.2359104 · 2024
Multiple-group factor analysis alignment
10.1080/10705511.2014.919210 · 2014
Self-efficacy: toward a unifying theory of behavioral change
10.1037/0033-295x.84.2.191 · 1977
A meta-analysis of the relation between math anxiety and math achievement
10.1037/bul0000307 · 2021
The need to belong: desire for interpersonal attachments as a fundamental human motivation
10.1037/0033-2909.117.3.497 · 1995
An analysis of PISA 2018 mathematics assessment for asia-pacific countries using educational data mining
10.3390/math11061318 · 2023
Deep neural networks and tabular data: a survey
10.1109/tnnls.2022.3229161 · 2024
Math performance and academic anxiety forms, from sociodemographic to cognitive aspects: a meta-analysis on 906,311 participants
10.1007/s10648-021-09618-5 · 2022
“XGBoost: a scalable tree boosting system,”
10.1145/2939672.2939785 · 2016
A machine-learning model of academic resilience in the times of the COVID-19 pandemic: evidence drawn from 79 countries/economies in the PISA 2022 mathematics study
10.1111/bjep.12715 · 2024
Interventions to improve connectedness, belonging, and engagement in secondary schools: a systematic review and meta-analysis
10.3390/educsci15050582 · 2025
Testing measurement invariance of mathematics self-concept and self-efficacy in PISA using MGCFA and the alignment method
10.1007/s10212-022-00623-y · 2023
The mediating role of math self-efficacy between school-based parental involvement and math performance among students in Southeast Asia: evidence from PISA 2022
10.3389/fpsyg.2025.1594131 · 2025
Multilevel determinants of school belonging: the moderating role of national safety climate
10.3389/feduc.2025.1697010 · 2025
The psychological sense of school membership among adolescents: scale development and educational correlates
10.1002/1520-6807(199301)30:1(79::aid-pits2310300113)3.0.co;2-x · 1993
Accurate predictions on small data with a tabular foundation model
10.1038/s41586-024-08328-6 · 2025
The Abbreviated Math Anxiety Scale (AMAS): construction, validity, and reliability
10.1177/1073191103010002008 · 2003
Applying machine learning and SHAP method to identify key influences on middle-school students' mathematics literacy performance
10.3390/jintelligence12100093 · 2024
The link between perceived academic control, responsibility and the development of reading skills
10.1080/0305764x.2026.2688797 · 2026
Always read the data documentation carefully. a case study using PISA data
10.21203/rs.3.rs-8286764/v1 · 2026
10.1080/03050068.2026.2681383
10.1080/03050068.2026.2681383 · 2026
Where does culture belong at school? Exploring the role of individualism and power distance in school belonging across cultures
10.1007/s12144-023-05280-y · 2024
“LightGBM: a highly efficient gradient boosting decision tree,”
2017
“Plausible values: principles of item response theory and multiple imputations,”
10.1007/978-3-030-47515-4_3 · 2020
Identifying the top predictors of student well-being across cultures using machine learning and conventional statistics
10.1038/s41598-024-55461-3 · 2024
The relationships between school belonging and students' motivational, social-emotional, behavioural, and academic outcomes in secondary education: a meta-analytic review
10.1080/02671522.2019.1615116 · 2020
Disentangling the individual and contextual effects of math anxiety: a global perspective
10.1073/pnas.2115855119 · 2022
Investigating adolescent psychological wellbeing in an educational context using PISA 2018 Canadian data
10.3389/fpsyg.2024.1416631 · 2024
10.48550/arxiv.1705.07874
10.48550/arxiv.1705.07874 · 2017
From local explanations to global understanding with explainable AI for trees
10.1038/s42256-019-0138-9 · 2020
Do growth mindset interventions impact students' academic achievement? A systematic review and meta-analysis with recommendations for best practices
10.1037/bul0000352 · 2023
Predictive insights into U.S. students' mathematics performance on PISA 2022 using ensemble tree-based machine learning models
10.1016/j.ijer.2025.102537 · doi-reference
Predicting the mathematics literacy of resilient students from high-performing economies: a machine learning approach
10.1016/j.stueduc.2024.101412 · doi-reference
School belonging mediates the association between negative school climate and depressive symptoms among Chinese adolescents: a national population-based longitudinal study
10.3389/fpsyg.2024.1368451 · doi-reference
Is math anxiety always bad for math learning? The role of math motivation
10.1177/0956797615602471 · doi-reference
How does digital learning resource accessibility affects math learning anxiety in high school–an empirical analysis based on PISA 2022
10.3389/fpsyg.2025.1646854 · doi-reference
A review and synthesis of the measurement invariance literature: suggestions, practices, and recommendations for organizational research
10.1177/109442810031002 · doi-reference
Evaluating measurement invariance of students' practices regarding online information questionnaire in PISA 2022: a comparative study using MGCFA and alignment method
10.1007/s10639-024-12921-7 · doi-reference
Developing effective interventions for math anxiety
10.1177/09637214241300111 · doi-reference
Tabular data: deep learning is not all you need
10.1016/j.inffus.2021.11.011 · doi-reference
Combining statistical and machine learning methods to identify predictors of brazilian students' proficiency in PISA 2018
10.1177/07342829241262861 · doi-reference
The impact of the pandemic and school closures on non-cognitive characteristics: evidence from PISA
10.1016/j.ijedro.2025.100489 · doi-reference
The control-value theory of achievement emotions: assumptions, corollaries, and implications for educational research and practice
10.1007/s10648-006-9029-9 · doi-reference
Individual and contextual factors determining school belonging of adolescents in the UK: evidence from PISA
10.1007/s12310-024-09725-y · doi-reference
Stacking: an ensemble learning approach to predict student performance in PISA 2022
10.1007/s10639-024-13110-2 · doi-reference
Cooperative and competitive school climate: their impact on sense of belonging across cultures
10.3389/feduc.2023.1113996 · doi-reference
10.1787/01820d6d-en
10.1787/01820d6d-en · doi-reference
The relation between mathematics anxiety and mathematics performance among school-aged students: a meta-analysis
10.3102/0034654319843494 · doi-reference
Do dimensions of culture exist objectively? a validation of the revised Minkov-Hofstede model of culture with World Values Survey items and scores for 102 countries
10.1016/j.intman.2022.100971 · doi-reference
Sampling weights in multilevel modelling: an investigation using PISA sampling structures
10.1186/s40536-021-00099-0 · doi-reference