From Meals to Marks: Modeling the Impact of Family Involvement on Reading Performance with Counterfactual Explainable AI
This study investigates the impact of family engagement on student reading achievement in the United Arab Emirates (UAE) using counterfactual explainable artificial intelligence (CXAI) analysis. Drawing data from 24,600 students in the UAE PISA dataset, the analysis employed Gradient Boosting, SHAP...
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MDPI AG
2025-07-01
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| Series: | Education Sciences |
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| Online Access: | https://www.mdpi.com/2227-7102/15/7/928 |
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| author | Myint Swe Khine Nagla Ali Othman Abu Khurma |
| author_facet | Myint Swe Khine Nagla Ali Othman Abu Khurma |
| author_sort | Myint Swe Khine |
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| description | This study investigates the impact of family engagement on student reading achievement in the United Arab Emirates (UAE) using counterfactual explainable artificial intelligence (CXAI) analysis. Drawing data from 24,600 students in the UAE PISA dataset, the analysis employed Gradient Boosting, SHAP (SHapley Additive exPlanations), and counterfactual simulations to model and interpret the influence of ten parental involvement variables. The results identified time spent talking with parents, frequency of family meals, and encouragement to achieve good marks as the strongest predictors of reading performance. Counterfactual analysis revealed that increasing the time spent talking with parents and frequency of family meals from their minimum (1) to maximum (5) levels, while holding other variables constant at their medians, could increase the predicted reading score from the baseline of 358.93 to as high as 448.68, marking an improvement of nearly 90 points. These findings emphasize the educational value of culturally compatible parental behaviors. The study also contributes to methodological advancement by integrating interpretable machine learning with prescriptive insights, demonstrating the potential of XAI for educational policy and intervention design. Implications for educators, policymakers, and families highlight the importance of promoting high-impact family practices to support literacy development. The approach offers a replicable model for leveraging AI to understand and enhance student learning outcomes across diverse contexts. |
| format | Article |
| id | doaj-art-b93d78c013ee4e7da4cd9717d4b78e40 |
| institution | Matheson Library |
| issn | 2227-7102 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Education Sciences |
| spelling | doaj-art-b93d78c013ee4e7da4cd9717d4b78e402025-07-25T13:20:54ZengMDPI AGEducation Sciences2227-71022025-07-0115792810.3390/educsci15070928From Meals to Marks: Modeling the Impact of Family Involvement on Reading Performance with Counterfactual Explainable AIMyint Swe Khine0Nagla Ali1Othman Abu Khurma2School of Education, Curtin University, Bentley, WA 6102, AustraliaCurriculum and Instruction Division, Emirates College for Advanced Education, Abu Dhabi SE43, United Arab EmiratesCurriculum and Instruction Division, Emirates College for Advanced Education, Abu Dhabi SE43, United Arab EmiratesThis study investigates the impact of family engagement on student reading achievement in the United Arab Emirates (UAE) using counterfactual explainable artificial intelligence (CXAI) analysis. Drawing data from 24,600 students in the UAE PISA dataset, the analysis employed Gradient Boosting, SHAP (SHapley Additive exPlanations), and counterfactual simulations to model and interpret the influence of ten parental involvement variables. The results identified time spent talking with parents, frequency of family meals, and encouragement to achieve good marks as the strongest predictors of reading performance. Counterfactual analysis revealed that increasing the time spent talking with parents and frequency of family meals from their minimum (1) to maximum (5) levels, while holding other variables constant at their medians, could increase the predicted reading score from the baseline of 358.93 to as high as 448.68, marking an improvement of nearly 90 points. These findings emphasize the educational value of culturally compatible parental behaviors. The study also contributes to methodological advancement by integrating interpretable machine learning with prescriptive insights, demonstrating the potential of XAI for educational policy and intervention design. Implications for educators, policymakers, and families highlight the importance of promoting high-impact family practices to support literacy development. The approach offers a replicable model for leveraging AI to understand and enhance student learning outcomes across diverse contexts.https://www.mdpi.com/2227-7102/15/7/928family engagementreading achievementexplainable AIcounterfactual analysisUAE education |
| spellingShingle | Myint Swe Khine Nagla Ali Othman Abu Khurma From Meals to Marks: Modeling the Impact of Family Involvement on Reading Performance with Counterfactual Explainable AI Education Sciences family engagement reading achievement explainable AI counterfactual analysis UAE education |
| title | From Meals to Marks: Modeling the Impact of Family Involvement on Reading Performance with Counterfactual Explainable AI |
| title_full | From Meals to Marks: Modeling the Impact of Family Involvement on Reading Performance with Counterfactual Explainable AI |
| title_fullStr | From Meals to Marks: Modeling the Impact of Family Involvement on Reading Performance with Counterfactual Explainable AI |
| title_full_unstemmed | From Meals to Marks: Modeling the Impact of Family Involvement on Reading Performance with Counterfactual Explainable AI |
| title_short | From Meals to Marks: Modeling the Impact of Family Involvement on Reading Performance with Counterfactual Explainable AI |
| title_sort | from meals to marks modeling the impact of family involvement on reading performance with counterfactual explainable ai |
| topic | family engagement reading achievement explainable AI counterfactual analysis UAE education |
| url | https://www.mdpi.com/2227-7102/15/7/928 |
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