Pathways to digital reading literacy among secondary school students: A multilevel analysis using data from 31 economies

Computers & Education, Год журнала: 2024, Номер 218, С. 105090 - 105090

Опубликована: Май 23, 2024

Язык: Английский

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Examining key capitals contributing to students' science‐related career expectations and their relationship patterns: A machine learning approach

Journal of Research in Science Teaching, Год журнала: 2024, Номер unknown

Опубликована: Март 31, 2024

Abstract Through the lens of science capital, this research aims to detect key factors and their main effects in identifying students with science‐related career expectations. A machine learning approach (i.e., random forest) was employed analyze a dataset 519,334 15‐year‐old from Programme for International Student Assessment (PISA) 2015. The global analysis identified 25 out 88 contextual features: (1) “how you think,” making feel is relevant, enjoyable, interesting relatively more crucial than being ambitious confident; (2) “what know,” students' math literacy, epistemological beliefs, awareness environmental matters were factors; (3) “who parents valuing science, expecting children enter providing emotional support as similar or even important economic, social, cultural status (ESCS)‐related constructs, while teachers fairness ranked top among all teaching‐related features; (4) do,” appropriate time, engagement activities, ICT use schoolwork factors. These findings indicate optimistic situation, most capitals malleable educators. Accumulated local effect plots further discriminated how these related expectations four distinct ways: “increasing,” “S‐shaped,” “inverted‐U‐shaped,” “decreasing,” shedding light on we could optimize resources enhance aspirations. comparison between Hong Kong analyses suggests by model generally effective but not necessarily essential specific region. cross‐cultural generalizability prevalence might vary forms.

Язык: Английский

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Unpacking the Dynamics of AI-Based Language Learning: Flow, Grit, and Resilience in Chinese EFL Contexts

Behavioral Sciences, Год журнала: 2024, Номер 14(9), С. 838 - 838

Опубликована: Сен. 19, 2024

Artificial intelligence and positive psychology play crucial roles in education, yet there is limited research on how these psychological factors influence learners' use of AI, particularly language education. Grounded self-determination theory, this study investigates the influencing Chinese English intention to AI for learning. Utilizing structural equation modeling, examines mediating grit, flow, resilience relationship between basic needs AI. Data were analyzed using AMOS 26 SPSS 26. The findings reveal that mediate adopt tools This provides valuable insights into educational environments can be designed fulfill needs, thereby fostering greater engagement acceptance

Язык: Английский

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Machine learning investigation of optimal psychoemotional well-being factors for students’ reading literacy

Education and Information Technologies, Год журнала: 2024, Номер 29(14), С. 18257 - 18285

Опубликована: Март 8, 2024

Язык: Английский

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Uncovering student profiles. An explainable cluster analysis approach to PISA 2022

Computers & Education, Год журнала: 2024, Номер unknown, С. 105166 - 105166

Опубликована: Сен. 1, 2024

Язык: Английский

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Unraveling the relationship between ESCS and digital reading performance: A multilevel mediation analysis of ICT-related psychological needs

Education and Information Technologies, Год журнала: 2024, Номер unknown

Опубликована: Янв. 19, 2024

Язык: Английский

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Educational Big Data Mining: Comparison of Multiple Machine Learning Algorithms in Predictive Modelling of Student Academic Performance

International Journal of Advanced Computer Science and Applications, Год журнала: 2024, Номер 15(6)

Опубликована: Янв. 1, 2024

Utilisation of Educational Data Mining (EDM) can be useful in predicting academic performance students to mitigate student attrition rate, allocation resources, and aid decision-making processes for higher education institution. This article uses a large dataset from the Programme International Student Assessment (PISA) consisting 612,004 participants 79 countries, supported by machine learning approach predict performance. Unlike most literature that is confined one geographical location or with limited datasets factors, this studies other factors contribute success data various backgrounds. The accuracy proposed model achieved 74%. It discovered Gradient Boosted Trees surpass classification models were considered (Logistic Regression, Naïve Bayes, Deep Learning, Random Forest, Fast Large Margin, Generalised Linear Model, Decision Tree Support Vector Machine). Reading skills habits are highest importance students.

Язык: Английский

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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

British Journal of Educational Psychology, Год журнала: 2024, Номер 94(4), С. 1224 - 1244

Опубликована: Сен. 22, 2024

Abstract Background Given that students from socio‐economically disadvantaged family backgrounds are more likely to suffer low academic performance, there is an interest in identifying features of resilience, which may mitigate the relationship between socio‐economic status and performance. Aims This study sought combine machine learning explainable artificial intelligence (XAI) technique identify key resilience mathematics during COVID‐19. Materials Methods Based on PISA 2022 data 79 countries/economies, random forest model coupled with Shapley additive explanations (SHAP) value not only uncovered but also examined contributions each feature. Results Findings indicated 35 were identified classification academically resilient non‐academically students, largely validated previous framework. Notably, gender differences shown distribution some features. Research findings tended have a stable emotional state, high levels self‐efficacy, truancy positive future aspirations. Discussion has established research paradigm essentially methodological nature bridge gap psychological theories big field educational psychology. Conclusion To sum up, our shed light issues education equity quality global perspective times COVID‐19 pandemic.

Язык: Английский

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Predicting the Mathematics Literacy of Resilient Students from High‐performing Economies: A Machine Learning Approach

Studies In Educational Evaluation, Год журнала: 2024, Номер 83, С. 101412 - 101412

Опубликована: Ноя. 4, 2024

Язык: Английский

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Applying a Support Vector Machine (SVM-RFE) Learning Approach to Investigate Students’ Scientific Literacy Development: Evidence from Asia, Europe, and South America

Journal of Intelligence, Год журнала: 2024, Номер 12(11), С. 111 - 111

Опубликована: Ноя. 5, 2024

Cultivating scientific literacy is a goal widely shared by educators and students around the world. Many studies have sought to enhance students' proficiency in through various approaches. However, there need explore attributes associated with advanced levels of literacy, especially influence contextual factors. In this context, our study employs machine learning technique-the SVM-RFE algorithm-to identify critical characteristics strong Asia, Europe, South America. Our research has pinpointed 30 key factors from broader set 162 that are indicative outstanding among 15-year-old secondary school students. By utilizing student samples three continents, provides comprehensive analysis these across entire dataset, along comparative examination optimal between continents. The findings highlight importance factors, which should be considered educational policymakers leaders when developing policies instructional strategies foster most effective development literacy.

Язык: Английский

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