{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T15:00:32Z","timestamp":1775314832708,"version":"3.50.1"},"reference-count":92,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,12,13]],"date-time":"2022-12-13T00:00:00Z","timestamp":1670889600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,12,13]],"date-time":"2022-12-13T00:00:00Z","timestamp":1670889600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Comput Soc Sc"],"published-print":{"date-parts":[[2023,4]]},"DOI":"10.1007\/s42001-022-00195-3","type":"journal-article","created":{"date-parts":[[2022,12,13]],"date-time":"2022-12-13T16:10:48Z","timestamp":1670947848000},"page":"245-287","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["School dropout prediction and feature importance exploration in Malawi using household panel data: machine learning approach"],"prefix":"10.1007","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9850-7149","authenticated-orcid":false,"given":"Hazal","family":"Colak Oz","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1939-8325","authenticated-orcid":false,"given":"\u00c7i\u00e7ek","family":"G\u00fcven","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1936-3701","authenticated-orcid":false,"given":"Gonzalo","family":"N\u00e1poles","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,12,13]]},"reference":[{"key":"195_CR1","unstructured":"UNESCO Institute for Statistics (2019). Out-of-school children, adolescents and youth: Global status and trends. Fact Sheet no. 56. UIS\/2019\/ED\/FS\/56. Retrieved September 7, 2022 from: http:\/\/uis.unesco.org\/sites\/default\/files\/documents\/new-methodology-shows-258-million-children-adolescents-and-youth-are-out-school.pdf"},{"key":"195_CR2","doi-asserted-by":"publisher","DOI":"10.1177\/2158244015609666","author":"J Huisman","year":"2015","unstructured":"Huisman, J., & Smits, J. (2015). Keeping children in school: Effects of household and context characteristics on school dropout in 363 districts of 30 developing countries. SAGE Open. https:\/\/doi.org\/10.1177\/2158244015609666","journal-title":"SAGE Open"},{"key":"195_CR3","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1016\/j.socec.2003.12.004","volume":"33","author":"TR Breton","year":"2004","unstructured":"Breton, T. R. (2004). Can institutions or education explain world poverty? An augmented Solow model provides some insights. Journal of Socio-Economics, 33, 45\u201369. https:\/\/doi.org\/10.1016\/j.socec.2003.12.004","journal-title":"Journal of Socio-Economics"},{"key":"195_CR4","unstructured":"World Bank. (2020). The human capital index 2020 update : Human capital in the time of COVID-19. Washington, DC.: World Bank Retrieved September 10, 2021 from https:\/\/openknowledge.worldbank.org\/handle\/10986\/34432"},{"issue":"5","key":"195_CR5","doi-asserted-by":"publisher","first-page":"715","DOI":"10.1177\/0022427817697441","volume":"54","author":"O Backman","year":"2017","unstructured":"Backman, O. (2017). High school dropout, resource attainment, and criminal convictions. Journal of Research in Crime and Delinquency, 54(5), 715\u2013749. https:\/\/doi.org\/10.1177\/0022427817697441","journal-title":"Journal of Research in Crime and Delinquency"},{"key":"195_CR6","doi-asserted-by":"crossref","unstructured":"Bjerk, D. (2011). Re-examining the impact of dropping out on criminal and labor outcomes in early adulthood. (No. 5995). Bonn: IZA \u2013 Institute of Labor Economics. Retrieved September 10, 2021 from: https:\/\/www.iza.org\/en\/publications\/dp\/5995\/re-examining-the-impact-of-dropping-out-on-criminal-and-labor-outcomes-in-early-adulthood","DOI":"10.2139\/ssrn.1933352"},{"key":"195_CR7","doi-asserted-by":"crossref","unstructured":"Dragone, D., Migali, G., & Zucchelli, E. (2021). High school dropout and the intergenerational transmission of crime. (No. 14129). Bonn: IZA Institute of Labour Economics. Retrieved September 10, 2021 from: https:\/\/docs.iza.org\/dp14129.pdf","DOI":"10.2139\/ssrn.3794075"},{"key":"195_CR8","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1007\/s12122-009-9074-5","volume":"31","author":"M Campolieti","year":"2010","unstructured":"Campolieti, M., Fang, T., & Gunderson, M. (2010). Labour market outcomes and skill acquisition of high-school dropouts. Journal of Labour Research, 31, 39\u201352. https:\/\/doi.org\/10.1007\/s12122-009-9074-5","journal-title":"Journal of Labour Research"},{"key":"195_CR9","doi-asserted-by":"publisher","DOI":"10.1155\/2011\/957303","author":"JS Catterall","year":"2011","unstructured":"Catterall, J. S. (2011). The societal benefits and costs of school dropout recovery. Education Research International. https:\/\/doi.org\/10.1155\/2011\/957303","journal-title":"Education Research International"},{"key":"195_CR10","doi-asserted-by":"publisher","first-page":"547","DOI":"10.1016\/j.jpolmod.2018.06.005","volume":"41","author":"C Mussida","year":"2019","unstructured":"Mussida, C., Sciulli, D., & Signorelli, M. (2019). Secondary school dropout and work outcomes in ten developing countries. Journal of Policy Modeling, 41, 547\u2013567. https:\/\/doi.org\/10.1016\/j.jpolmod.2018.06.005","journal-title":"Journal of Policy Modeling"},{"issue":"1","key":"195_CR11","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1111\/j.1759-5436.2009.00003.x","volume":"40","author":"N Kabeer","year":"2009","unstructured":"Kabeer, N., & Mahmud, S. (2009). Imagining the future: Children, education and intergenerational transmission of poverty in urban Bangladesh. IDS Bulletin, 40(1), 10\u201321. https:\/\/doi.org\/10.1111\/j.1759-5436.2009.00003.x","journal-title":"IDS Bulletin"},{"issue":"8","key":"195_CR12","doi-asserted-by":"publisher","first-page":"1183","DOI":"10.1002\/jid.1754","volume":"22","author":"K Bird","year":"2010","unstructured":"Bird, K., Higgins, K., & McKay, A. (2010). Conflict, education and the intergenerational transmission of poverty in Northern Uganda. Journal of International Development, 22(8), 1183\u20131196. https:\/\/doi.org\/10.1002\/jid.1754","journal-title":"Journal of International Development"},{"key":"195_CR13","doi-asserted-by":"publisher","unstructured":"Rose, P., & Dyer, C. (2008). Chronic poverty and education: a review of the literature. Chronic Poverty Research Centre Working Paper No. 131. https:\/\/doi.org\/10.2139\/ssrn.1537105","DOI":"10.2139\/ssrn.1537105"},{"key":"195_CR14","unstructured":"Moses, E. (2011). Quality of education and the labour market: A conceptual and literature overview. Stellenbosch Economic Working Papers: 07\/11. Matieland: South Africa."},{"key":"195_CR15","doi-asserted-by":"publisher","first-page":"412","DOI":"10.1016\/j.worlddev.2015.05.007","volume":"74","author":"D Boccanfuso","year":"2015","unstructured":"Boccanfuso, D., Larouche, A., & Trandafirc, M. (2015). Quality of higher education and the labor market in developing countries: Evidence from an education reform in Senegal. World Development, 74, 412\u2013424. https:\/\/doi.org\/10.1016\/j.worlddev.2015.05.007","journal-title":"World Development"},{"key":"195_CR16","doi-asserted-by":"crossref","unstructured":"Haimovich, F., Vazquez, E., & Adelman, M. (2021). Scalable early warning systems for school dropout prevention: Evidence from a 4.000-school randomized controlled trial, Documento de Trabajo, No. 285, Universidad Nacional de La Plata, Centro de Estudios Distributivos, Laborales y Sociales (CEDLAS), La Plata.","DOI":"10.1596\/1813-9450-9685"},{"issue":"4","key":"195_CR17","doi-asserted-by":"publisher","first-page":"357","DOI":"10.5243\/jsswr.2013.22","volume":"4","author":"SJ Wilson","year":"2013","unstructured":"Wilson, S. J., & Tanner-Smith, E. E. (2013). Dropout prevention and intervention Programs for improving school completion among school-aged children and youth: A systematic review. Journal of the Society for Social Work and Research, 4(4), 357\u2013372. https:\/\/doi.org\/10.5243\/jsswr.2013.22","journal-title":"Journal of the Society for Social Work and Research"},{"issue":"1","key":"195_CR18","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1111\/j.0963-7214.2004.01301010.x","volume":"13","author":"SL Christenson","year":"2004","unstructured":"Christenson, S. L., & Thurlow, M. L. (2004). School dropouts. Current Directions in Psychological Science, 13(1), 36\u201339. https:\/\/doi.org\/10.1111\/j.0963-7214.2004.01301010.x","journal-title":"Current Directions in Psychological Science"},{"key":"195_CR19","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1016\/j.childyouth.2018.11.030","volume":"96","author":"JY Chung","year":"2018","unstructured":"Chung, J. Y., & Lee, S. (2018). Dropout early warning systems for high school students using machine learning. Children and Youth Services Review, 96, 346\u2013353. https:\/\/doi.org\/10.1016\/j.childyouth.2018.11.030","journal-title":"Children and Youth Services Review"},{"issue":"1","key":"195_CR20","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1080\/21568235.2020.1718520","volume":"10","author":"L Kemper","year":"2020","unstructured":"Kemper, L., Vorhoff, G., & Wigger, B. U. (2020). Predicting student dropout: A machine learning approach. European Journal of Higher Education, 10(1), 28\u201347. https:\/\/doi.org\/10.1080\/21568235.2020.1718520","journal-title":"European Journal of Higher Education"},{"key":"195_CR21","doi-asserted-by":"publisher","DOI":"10.1177\/1521025120963821","author":"H Huo","year":"2020","unstructured":"Huo, H., Cui, J., Hein, S., Padgett, Z., Ossolinski, M., Raim, R., & Zhang, J. (2020). Predicting dropout for nontraditional undergraduate students: A machine learning approach. Journal of College Student Retention: Research, Theory & Practice. https:\/\/doi.org\/10.1177\/1521025120963821","journal-title":"Journal of College Student Retention: Research, Theory & Practice"},{"key":"195_CR22","unstructured":"World Health Organization (WHO). (2020). Population below international poverty line. Retrieved September 15, 2021 from: http:\/\/uis.unesco.org\/sites\/default\/files\/documents\/new-methodology-shows-258-million-children-adolescents-and-youth-are-out-school.pdf"},{"key":"195_CR23","unstructured":"Malawi National Statistical Office. (2020). The third integrated household panel survey 2019 report. Zomba, Malawi: Malawi National Statistical Office."},{"issue":"14","key":"195_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.5334\/dsj-2019-014","volume":"18","author":"N Mduma","year":"2019","unstructured":"Mduma, N., Kalegele, K., & Machuve, D. (2019). A survey of machine learning approaches and techniques for student dropout prediction. Data Science Journal, 18(14), 1\u201310. https:\/\/doi.org\/10.5334\/dsj-2019-014","journal-title":"Data Science Journal"},{"key":"195_CR25","unstructured":"Lundberg, S., M., & Lee, S. (2017). A unified approach to interpreting model predictions. In Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS'17). Curran Associates Inc., Red Hook, NY, USA, 4768\u20134777."},{"key":"195_CR26","doi-asserted-by":"publisher","first-page":"375","DOI":"10.1146\/annurev-statistics-011516-012958","volume":"3","author":"KA Bollen","year":"2016","unstructured":"Bollen, K. A., Biemer, P. P., Karr, A. F., Tueller, S., & Berzofsky, M. E. (2016). Are survey weights needed? A review of diagnostic tests in regression analysis. Annual Review of Statistics and Its Application, 3, 375\u2013392. https:\/\/doi.org\/10.1146\/annurev-statistics-011516-012958","journal-title":"Annual Review of Statistics and Its Application"},{"issue":"2","key":"195_CR27","doi-asserted-by":"publisher","first-page":"205","DOI":"10.3102\/0034654314554431","volume":"85","author":"J Freeman","year":"2015","unstructured":"Freeman, J., & Simonsen, B. (2015). Examining the impact of policy and practice interventions on high school dropout and school completion rates: A systematic review of the literature. Review of Educational Research, 85(2), 205\u2013248. https:\/\/doi.org\/10.3102\/0034654314554431","journal-title":"Review of Educational Research"},{"issue":"15","key":"195_CR28","doi-asserted-by":"publisher","first-page":"4","DOI":"10.3991\/ijim.v15i15.20019","volume":"15","author":"IM Khan","year":"2021","unstructured":"Khan, I. M., Ahmad, A. R., Jabeur, N., & Mahdi, M. N. (2021). A conceptual framework to aid attribute selection in machine learning student performance prediction models. International Journal of Interactive Mobile Technologies (iJIM), 15(15), 4\u201319. https:\/\/doi.org\/10.3991\/ijim.v15i15.20019","journal-title":"International Journal of Interactive Mobile Technologies (iJIM)"},{"key":"195_CR29","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0180176.t002","author":"K Sekine","year":"2017","unstructured":"Sekine, K., & Hodgkin, M. E. (2017). Effect of child marriage on girls\u2019 school dropout in Nepal: Analysis of data from the Multiple Indicator Cluster Survey 2014. PLoS ONE. https:\/\/doi.org\/10.1371\/journal.pone.0180176.t002","journal-title":"PLoS ONE"},{"issue":"4","key":"195_CR30","doi-asserted-by":"publisher","first-page":"670","DOI":"10.1086\/710778","volume":"64","author":"F Zahra","year":"2020","unstructured":"Zahra, F. (2020). High hopes, low dropout: gender differences in aspirations for education and marriage, and educational outcomes in rural Malawi. Comparative Education Review, 64(4), 670\u2013702.","journal-title":"Comparative Education Review"},{"key":"195_CR31","doi-asserted-by":"publisher","unstructured":"Orooji, M., & Che, J. (2019). Predicting Louisiana public high school dropout through imbalanced learning techniques. arXiv:1910.13018 [cs.LG]. https:\/\/doi.org\/10.48550\/arXiv.1910.13018","DOI":"10.48550\/arXiv.1910.13018"},{"key":"195_CR32","doi-asserted-by":"publisher","DOI":"10.1111\/obes.12277","author":"D Sansone","year":"2019","unstructured":"Sansone, D. (2019). Beyond early warning indicators: High school dropout and machine learning. Oxford Bulletin of Economics and Statistics. https:\/\/doi.org\/10.1111\/obes.12277","journal-title":"Oxford Bulletin of Economics and Statistics"},{"key":"195_CR33","unstructured":"DHS (2022). Measures DHS. Retrieved September 13, 2022 from:http:\/\/www.measuredhs.com\/Measure [Accessed Date:"},{"key":"195_CR34","unstructured":"World Bank. (20201). Integrated household panel survey 2010\u20132013\u20132016\u20132019 (Long-term panel,102 EAs). Retrieved June 12, 2022 from: https:\/\/microdata.worldbank.org\/index.php\/catalog\/3819"},{"key":"195_CR35","doi-asserted-by":"publisher","first-page":"625","DOI":"10.1007\/s42081-020-00093-w","volume":"3","author":"S Yang","year":"2020","unstructured":"Yang, S., & Kim, J. K. (2020). Statistical data integration in survey sampling: A review. Japanese Journal of Statistics and Data Science, 3, 625\u2013650. https:\/\/doi.org\/10.1007\/s42081-020-00093-w","journal-title":"Japanese Journal of Statistics and Data Science"},{"issue":"3","key":"195_CR36","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1177\/096228029600500303","volume":"5","author":"D Pfeffermann","year":"1996","unstructured":"Pfeffermann, D. (1996). The use of sampling weights for survey data analysis. Statistical Methods in Medical Research, 5(3), 239\u2013261. https:\/\/doi.org\/10.1177\/096228029600500303","journal-title":"Statistical Methods in Medical Research"},{"issue":"2","key":"195_CR37","doi-asserted-by":"publisher","first-page":"183","DOI":"10.2307\/2345174","volume":"139","author":"TMF Smith","year":"1976","unstructured":"Smith, T. M. F. (1976). The foundations of survey sampling: A Review. Journal of the Royal Statistical Society, 139(2), 183\u2013195. https:\/\/doi.org\/10.2307\/2345174","journal-title":"Journal of the Royal Statistical Society"},{"issue":"4","key":"195_CR38","first-page":"567","volume":"46","author":"ND Nguyen","year":"2015","unstructured":"Nguyen, N. D., & Murphy, P. (2015). To weight or not to weight? A statistical analysis of how weights affect the reliability of the quarterly national household survey for immigration research in Ireland. The Economic and Social Review, 46(4), 567\u2013603.","journal-title":"The Economic and Social Review"},{"key":"195_CR39","unstructured":"Bertolet, M. (2008). To weight or not to weight? Incorporating sampling designs into model-based analyses. [PhD thesis, Carnegie Mellon University]. Arnegie Mellon University\u2009ProQuest Dissertations Publishing. Retrieved September 14, 2021 from: To weight or not to weight? Incorporating sampling designs into model-based analyses \u2013 ProQuest"},{"key":"195_CR40","doi-asserted-by":"publisher","DOI":"10.3390\/su12156002","author":"C Gao","year":"2020","unstructured":"Gao, C., Fei, C., McCarl, B., & Leatham, D. (2020). Identifying vulnerable households using machine-learning. Sustainability. https:\/\/doi.org\/10.3390\/su12156002","journal-title":"Sustainability"},{"issue":"1","key":"195_CR41","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1353\/rhe.2003.0044","volume":"27","author":"M Walpole","year":"2003","unstructured":"Walpole, M. (2003). Socio-economic status and college: How SES affects college experiences and outcomes. The Review of Higher Education, 27(1), 45\u201373. https:\/\/doi.org\/10.1353\/rhe.2003.0044","journal-title":"The Review of Higher Education"},{"key":"195_CR42","doi-asserted-by":"publisher","first-page":"1053","DOI":"10.1007\/s10964-016-0431-4","volume":"45","author":"AD Benner","year":"2016","unstructured":"Benner, A. D., Boyle, A. E., & Sadler, S. (2016). Parental involvement and adolescents\u2019 educational success: The roles of prior achievement and socioeconomic status. Journal of Youth Adolescence, 45, 1053\u20131064. https:\/\/doi.org\/10.1007\/s10964-016-0431-4","journal-title":"Journal of Youth Adolescence"},{"key":"195_CR43","unstructured":"Molnar, C. (2022). Interpretable machine learning: A guide for making black box models explainable (2nd ed.). Retrieved June 18, 2022 from: https:\/\/christophm.github.io\/interpretable-ml-book\/"},{"key":"195_CR44","doi-asserted-by":"crossref","unstructured":"Komatsu, M., Takada, C., Neshi, C., Unoki, T., & Shikida, M. (2020). Feature extraction with SHAP value analysis for student performance evaluation in remote collaboration. Conference Presentation at the 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP). Bangkok, Thailand.","DOI":"10.1109\/iSAI-NLP51646.2020.9376830"},{"key":"195_CR45","doi-asserted-by":"publisher","DOI":"10.3390\/bdcc6010006","author":"G Ramaswami","year":"2022","unstructured":"Ramaswami, G., Susnjak, T., & Mathrani, A. (2022). On developing generic models for predicting student outcomes in educational data mining. Big Data and Cognitive Computing. https:\/\/doi.org\/10.3390\/bdcc6010006","journal-title":"Big Data and Cognitive Computing"},{"key":"195_CR46","doi-asserted-by":"publisher","first-page":"152688","DOI":"10.1109\/ACCESS.2021.3124270","volume":"9","author":"H Sahlaoui","year":"2021","unstructured":"Sahlaoui, H., Alaoui, E. A. A., Nayyar, A., Agoujil, S., & Jaber, M. M. (2021). Predicting and interpreting student performance using ensemble models and Shapley Additive Explanations. IEEE Access, 9, 152688\u2013152703.","journal-title":"IEEE Access"},{"key":"195_CR47","unstructured":"Aulck, L., Velagapudi,\u00a0N., Blumenstock,\u00a0J., & J.\u00a0West, J. (2016). Predicting student dropout in higher education. arXiv preprint arXiv:1606.06364. Retrieved June 18, 2022 from: https:\/\/arxiv.org\/abs\/1606.06364"},{"key":"195_CR48","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/IWOBI.2018.8464191","volume":"2018","author":"M Solis","year":"2018","unstructured":"Solis, M., Moreira, T., Gonzalez, R., Fernandez, T., & Hernandez, M. (2018). Perspectives to predict dropout in university students with machine learning. IEEE International Work Conference on Bioinspired Intelligence (IWOBI), 2018, 1\u20136. https:\/\/doi.org\/10.1109\/IWOBI.2018.8464191","journal-title":"IEEE International Work Conference on Bioinspired Intelligence (IWOBI)"},{"key":"195_CR49","doi-asserted-by":"publisher","DOI":"10.1016\/j.caeai.2022.100066","author":"J Niyogisubizo","year":"2022","unstructured":"Niyogisubizo, J., Liao, L., Nziyumva, E., Murwanashyaka, E., & Nshimyumukiza, P. C. (2022). Predicting student\u2019s dropout in university classes using two-layer ensemble machine learning approach: A novel stacked generalisation. Computers and Education: Artificial Intelligence. https:\/\/doi.org\/10.1016\/j.caeai.2022.100066","journal-title":"Computers and Education: Artificial Intelligence"},{"key":"195_CR50","doi-asserted-by":"crossref","unstructured":"Baranyi, M., Nagy, M., & Molontay, R. (2020) Interpretable deep learning for university dropout prediction. In Proceedings of the 21st Annual Conference on Information Technology Education. Odesa, Ukraine, 13\u201319.","DOI":"10.1145\/3368308.3415382"},{"key":"195_CR51","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-0716-1418-1","volume-title":"An introduction to statistical learning with applications in R (2nd","author":"G James","year":"2021","unstructured":"James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). An introduction to statistical learning with applications in R (2nd (Edition). New York: Springer Nature.","edition":"Edition"},{"issue":"5","key":"195_CR52","doi-asserted-by":"publisher","first-page":"491","DOI":"10.1257\/aer.p20151023","volume":"105","author":"J Kleinberg","year":"2015","unstructured":"Kleinberg, J., Ludwig, J., Mullainathan, S., & Obermeyer, Z. (2015). Prediction policy problems. American Economic Review: Papers & Proceedings, 105(5), 491\u2013495. https:\/\/doi.org\/10.1257\/aer.p20151023","journal-title":"American Economic Review: Papers & Proceedings"},{"issue":"2","key":"195_CR53","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1257\/jep.31.2.87","volume":"31","author":"S Mullainathan","year":"2017","unstructured":"Mullainathan, S., & Spiess, J. (2017). Machine learning: An applied econometric approach. Journal of Economic Perspectives, 31(2), 87\u2013106. https:\/\/doi.org\/10.1257\/jep.31.2.87","journal-title":"Journal of Economic Perspectives"},{"key":"195_CR54","unstructured":"Crespo, R. C. (2019). Two become one: improving the targeting of conditional cash transfers with a predictive model of school dropout. London: London School of Economics and Political Science. Retrieved October 8, 2021 from: http:\/\/eprints.lse.ac.uk\/101013\/1\/05_19_Cristian_Crespo.pdf"},{"key":"195_CR55","volume-title":"Python machine learning: Machine learning and deep learning with Python, scikit-learn, and TensorFlow (2nd","author":"S Raschka","year":"2017","unstructured":"Raschka, S., & Mirjalili, V. (2017). Python machine learning: Machine learning and deep learning with Python, scikit-learn, and TensorFlow (2nd (Edition). Packt Publishing Ltd.","edition":"Edition"},{"key":"195_CR56","volume-title":"The elements of statistical learning: Data mining, inference, and prediction (2nd","author":"T Hastie","year":"2017","unstructured":"Hastie, T., Tibshirani, R., & Friedman, J. (2017). The elements of statistical learning: Data mining, inference, and prediction (2nd (Edition). Springer.","edition":"Edition"},{"key":"195_CR57","doi-asserted-by":"publisher","DOI":"10.3390\/app10031042","author":"JL Rastrollo-Guerrero","year":"2020","unstructured":"Rastrollo-Guerrero, J. L., G\u00f3mez-Pulido, J. A., & Dur\u00e1n-Dom\u00ednguez, A. (2020). Analysing and predicting students\u2019 performance by means of machine learning: A review. Applied Sciences. https:\/\/doi.org\/10.3390\/app10031042","journal-title":"Applied Sciences"},{"key":"195_CR58","unstructured":"Malawi National Statistical Office. (2010). Third integrated household survey (IHS3) 2010\u20132011 basic information document. Zomba, Malawi: Malawi National Statistical Office."},{"key":"195_CR59","doi-asserted-by":"publisher","first-page":"497","DOI":"10.19139\/soic.v6i4.479","volume":"6","author":"M Hashemi","year":"2018","unstructured":"Hashemi, M., & Karimi, H. A. (2018). Weighted machine learning. Statistics, Optimisation and Information Computing, 6, 497\u2013525. https:\/\/doi.org\/10.19139\/soic.v6i4.479","journal-title":"Statistics, Optimisation and Information Computing"},{"key":"195_CR60","unstructured":"Malawi National Statistical Office. (2020). The third integrated household panel survey basic information document 2019. Zomba, Malawi: Malawi National Statistical Office."},{"key":"195_CR61","unstructured":"International Monetary Fund. (2021). IMF macroeconomic and financial data. Retrieved September 19, 2021 from https:\/\/data.imf.org\/?sk=4FFB52B2-3653-409A-B471-D47B46D904B5&sId=1485878855236"},{"key":"195_CR62","unstructured":"World Bank. (2021). Inflation, consumer prices (annual %) - Malawi. World Bank Open Data. Retrieved September 19, 2021 from https:\/\/data.worldbank.org\/indicator\/FP.CPI.TOTL.ZG?locations=MW"},{"key":"195_CR63","unstructured":"OECD. (2012). What are equivalence scales? OECD project on income distribution and poverty. Retrieved September 19, 2021 from https:\/\/www.oecd.org\/economy\/growth\/OECD-Note-EquivalenceScales.pdf"},{"key":"195_CR64","unstructured":"INDDEX Project. (2018). Data4Diets: Building blocks for diet-related food security analysis. Tufts University. Retrieved September 20, 2021 from https:\/\/inddex.nutrition.tufts.edu\/data4diets"},{"issue":"9","key":"195_CR65","doi-asserted-by":"publisher","first-page":"1029","DOI":"10.3390\/ijerph16091557","volume":"16","author":"MA Nyangasa","year":"2014","unstructured":"Nyangasa, M. A., Buck, C., Kelm, S., Sheikh, M., & Hebestreit, A. (2014). Exploring food access and sociodemographic correlates of food consumption and food insecurity in Zanzibari households. International Journal of Environmental Research and Public Health, 16(9), 1029\u20131049. https:\/\/doi.org\/10.3390\/ijerph16091557","journal-title":"International Journal of Environmental Research and Public Health"},{"issue":"6","key":"195_CR66","doi-asserted-by":"publisher","first-page":"459","DOI":"10.1093\/heapol\/czl029","volume":"21","author":"S Vyas","year":"2006","unstructured":"Vyas, S., & Kumaranayake, L. (2006). Constructing socio-economic status indices: How to use principal components analysis. Health Policy and Planning, 21(6), 459\u2013468. https:\/\/doi.org\/10.1093\/heapol\/czl029","journal-title":"Health Policy and Planning"},{"key":"195_CR67","doi-asserted-by":"publisher","DOI":"10.1186\/1475-9276-2-8","author":"TAJ Houweling","year":"2003","unstructured":"Houweling, T. A. J., Kunst, A. E., & Mackenbach, J. P. (2003). Measuring health inequality among children in developing countries: does the choice of the indicator of economic status matter? International Journal for Equity in Health. https:\/\/doi.org\/10.1186\/1475-9276-2-8","journal-title":"International Journal for Equity in Health"},{"key":"195_CR68","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1007\/s11205-020-02594-3","volume":"155","author":"TA Naveed","year":"2021","unstructured":"Naveed, T. A., Gordon, D., Ullah, S., & Zhang, M. (2021). The construction of an asset index at household level and measurement of economic disparities in Punjab (Pakistan) by using MICS-Micro Data. Social Indicators Research, 155, 73\u201395. https:\/\/doi.org\/10.1007\/s11205-020-02594-3","journal-title":"Social Indicators Research"},{"key":"195_CR69","doi-asserted-by":"publisher","DOI":"10.1080\/01973533.2016.1277529","author":"CG Thompson","year":"2017","unstructured":"Thompson, C. G., Kim, R. S., Aloe, A. M., & Becker, B. J. (2017). Extracting the variance inflation factor and other multicollinearity diagnostics from typical regression results. Basic and Applied Social Psychology. https:\/\/doi.org\/10.1080\/01973533.2016.1277529","journal-title":"Basic and Applied Social Psychology"},{"key":"195_CR70","doi-asserted-by":"crossref","unstructured":"Franke G. R. (2010). \u201cMulticollinearity,\u201d in\u00a0Wiley International Encyclopedia of Marketing. eds. Sheth J. N., Malhotra N. K. (New Jersey, USA: John Wiley & Sons Ltd.).","DOI":"10.1002\/9781444316568.wiem02066"},{"key":"195_CR71","unstructured":"Brownlee, J. (February 2021). A gentle introduction to threshold-moving for imbalanced classification. Machine Learning Mastery. Retrieved October 23, 2021 from https:\/\/machinelearningmastery.com\/threshold-moving-for-imbalanced-classification\/"},{"key":"195_CR72","doi-asserted-by":"publisher","first-page":"2825","DOI":"10.5555\/1953048.2078195","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., & Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825\u20132830. https:\/\/doi.org\/10.5555\/1953048.2078195","journal-title":"Journal of Machine Learning Research"},{"key":"195_CR73","doi-asserted-by":"publisher","first-page":"1929","DOI":"10.5555\/2627435.2670313","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15, 1929\u20131958. https:\/\/doi.org\/10.5555\/2627435.2670313","journal-title":"Journal of Machine Learning Research"},{"key":"195_CR74","volume-title":"Stata Statistical Software: Release 14","author":"StataCorp,","year":"2015","unstructured":"StataCorp,. (2015). Stata Statistical Software: Release 14. StataCorp LP."},{"key":"195_CR75","unstructured":"Van Rossum, G., & Drake Jr, F. L. (1995). Python reference manual. Centrum voor Wiskunde en Informatica Amsterdam."},{"key":"195_CR76","unstructured":"Chollet, F., & Others. (2015). Keras. Retrieved November 15, 2021 from https:\/\/github.com\/fchollet\/keras."},{"key":"195_CR77","unstructured":"Abadi, M., & Others. (2015). TensorFlow: Large-scale machine learning on heterogeneous systems. Retrieved November 21, 2021 from https:\/\/www.tensorflow.org\/"},{"key":"195_CR78","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1038\/s41586-020-2649-2","volume":"585","author":"CR Harris","year":"2020","unstructured":"Harris, C. R., Millman, K. J., van der Walt, S. J., et al. (2020). Array programming with NumPy. Nature, 585, 357\u2013362. https:\/\/doi.org\/10.1038\/s41586-020-2649-2","journal-title":"Nature"},{"key":"195_CR79","doi-asserted-by":"crossref","unstructured":"McKinney, W., & Others. (2010). Data structures for statistical computing in python. Paper presented at the he 9th Python in Science Conference.","DOI":"10.25080\/Majora-92bf1922-00a"},{"key":"195_CR80","doi-asserted-by":"publisher","unstructured":"Waskom, M.L. (2021). Seaborn: statistical data visualisation. Journal of Open Source Software https:\/\/doi.org\/10.21105\/joss.03021","DOI":"10.21105\/joss.03021"},{"issue":"3","key":"195_CR81","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1109\/MCSE.2007.55","volume":"9","author":"JD Hunter","year":"2007","unstructured":"Hunter, J. D. (2007). Matplotlib: A 2D graphics environment. Computing in Science & Engineering, 9(3), 90\u201395. https:\/\/doi.org\/10.1109\/MCSE.2007.55","journal-title":"Computing in Science & Engineering"},{"key":"195_CR82","unstructured":"Overseas Development Institute (2000). Ganyu labour in Malawi and its implications for livelihood security interventions - an analysis of recent literature and implications for poverty alleviation. Retrieved November 25, 2021 from: https:\/\/odi.org\/en\/publications\/ganyu-labour-in-malawi-and-its-implications-for-livelihood-security-interventions-an-analysis-of-recent-literature-and-implications-for-poverty-alleviation"},{"issue":"4","key":"195_CR83","doi-asserted-by":"publisher","first-page":"853","DOI":"10.1086\/452435","volume":"47","author":"B Wydick","year":"1999","unstructured":"Wydick, B. (1999). The effect of microenterprise lending on child schooling in Guatemala. Economic Development and Cultural Change, 47(4), 853\u2013869. https:\/\/doi.org\/10.1086\/452435","journal-title":"Economic Development and Cultural Change"},{"issue":"4","key":"195_CR84","doi-asserted-by":"publisher","first-page":"567","DOI":"10.1016\/j.worlddev.2009.11.005","volume":"38","author":"Y Shimamura","year":"2010","unstructured":"Shimamura, Y., & Lastarria-Cornhiel, S. (2010). Credit program participation and child schooling in Rural Malawi. World Development, 38(4), 567\u2013580. https:\/\/doi.org\/10.1016\/j.worlddev.2009.11.005","journal-title":"World Development"},{"key":"195_CR85","doi-asserted-by":"publisher","first-page":"9296","DOI":"10.3390\/app11199296","volume":"11","author":"TM Alam","year":"2021","unstructured":"Alam, T. M., Mushtaq, M., Shaukat, K., Hameed, I. A., Umer Sarwar, M., & Luo, S. A. (2021). Novel method for performance measurement of public educational institutions using machine learning models. Applied Sciences, 11, 9296. https:\/\/doi.org\/10.3390\/app11199296","journal-title":"Applied Sciences"},{"issue":"January","key":"195_CR86","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1016\/j.ijedudev.2016.10.004","volume":"52","author":"BS Sunny","year":"2017","unstructured":"Sunny, B. S., Elze, M., Chihana, M., Gondwe, L., Crampin, A. C., Munkhondya, M., Kondowe, S., & Glynn, J. R. (2017). Failing to progress or progressing to fail? Age-for-grade heterogeneity and grade repetition in primary schools in Karonga District, Northern Malawi. International Journal of Educational Development, 52(January), 68\u201380. https:\/\/doi.org\/10.1016\/j.ijedudev.2016.10.004","journal-title":"International Journal of Educational Development"},{"key":"195_CR87","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijer.2020.101578","author":"L Chikhungu","year":"2020","unstructured":"Chikhungu, L., Kadzamira, E., Chiwaula, L., & Meke, E. (2020). Tackling girls dropping out of school in Malawi: Is improving household socio-economic status the solution? International Journal of Educational Research. https:\/\/doi.org\/10.1016\/j.ijer.2020.101578","journal-title":"International Journal of Educational Research."},{"key":"195_CR88","unstructured":"Cannistr\u00e0, M., Masci, C., Ieva, F., Agasisti, T., & Paganoni, A. M. (2020). Not the magic algorithm: modelling and early-predicting students dropout through machine learning and multilevel approach. Milano, Italy: Dipartimento di Matematica, Politecnico di Milano"},{"issue":"3","key":"195_CR89","doi-asserted-by":"publisher","first-page":"404","DOI":"10.1177\/0013161X18799439","volume":"55","author":"LC Sorensen","year":"2019","unstructured":"Sorensen, L. C. (2019). \u201cBig Data\u201d in educational administration: An application for predicting school dropout risk. Educational Administration Quarterly, 55(3), 404\u2013446. https:\/\/doi.org\/10.1177\/0013161X18799439","journal-title":"Educational Administration Quarterly"},{"key":"195_CR90","unstructured":"Cannistr\u00e0, M., Masci, C., Ieva, F., Agasisti, T., & Paganoni, A. M. (2020). Not the magic algorithm: modelling and early-predicting students dropout through machine learning and multilevel approach. Milano, Italy: Dipartimento di Matematica, Politecnico di Milano."},{"key":"195_CR91","doi-asserted-by":"publisher","DOI":"10.1080\/09645292.2018.1433127","author":"M Adelman","year":"2018","unstructured":"Adelman, M., Haimovich, F., Ham, A., & Vazquez, E. (2018). Predicting school dropout with administrative data: New evidence from Guatemala and Honduras. Education Economics. https:\/\/doi.org\/10.1080\/09645292.2018.1433127","journal-title":"Education Economics"},{"issue":"1","key":"195_CR92","doi-asserted-by":"publisher","first-page":"17","DOI":"10.2190\/CS.13.1.b","volume":"13","author":"D Delen","year":"2011","unstructured":"Delen, D. (2011). Predicting student attrition with data mining methods. Journal of College Student Retention, 13(1), 17\u201335. https:\/\/doi.org\/10.2190\/CS.13.1.b","journal-title":"Journal of College Student Retention"}],"container-title":["Journal of Computational Social Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42001-022-00195-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42001-022-00195-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42001-022-00195-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,3]],"date-time":"2023-05-03T16:29:49Z","timestamp":1683131389000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42001-022-00195-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,13]]},"references-count":92,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2023,4]]}},"alternative-id":["195"],"URL":"https:\/\/doi.org\/10.1007\/s42001-022-00195-3","relation":{},"ISSN":["2432-2717","2432-2725"],"issn-type":[{"value":"2432-2717","type":"print"},{"value":"2432-2725","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,13]]},"assertion":[{"value":"6 July 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 November 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 December 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"On behalf of all authors, the corresponding author declares that they have no known conflict of interests or personal relationships that could have appeared to influence the work reported in this paper. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}