Mathematics and Mother Tongue Academic Achievement: A Machine Learning Approach

Catarina Nunes, Ana Beatriz-Afonso, Frederico Cruz-Jesus, Tiago Oliveira, Mauro Castelli

Abstract


Academic achievement is of great interest to education researchers and practitioners. Several academic achievement determinants have been described in the literature, mostly identified by analyzing primary (sample) data with classic statistical methods. Despite their superiority, only recently have machine learning methods started to be applied systematically in this context. However, even when this is the case, the ability to draw conclusions is greatly hampered by the "black-box" effect these methods entail. We contribute to the literature by combining the efficiency of machine learning methods, trained with data from virtually every public upper-secondary student of a European country, with the ability to quantify exactly how much each driver impacts academic achievement on Mathematics and mother tongue, through the use of prototypes. Our results indicate that the most important general academic achievement inhibitor is the previous retainment. Legal guardian's education is a critical driver, especially in Mathematics; whereas gender is especially important for mother tongue, as female students perform better. Implications for research and practice are presented.

 

Doi: 10.28991/ESJ-2022-SIED-010

Full Text: PDF


Keywords


Academic Achievement; Education; Machine Learning; Networks.

References


Koch, A., Nafziger, J., & Nielsen, H. S. (2015). Behavioral economics of education. Journal of Economic Behavior and Organization, 115, 3–17. doi:10.1016/j.jebo.2014.09.005.

Spinath, B. (2012). Academic Achievement. Encyclopedia of Human Behavior(2nd Ed.), 1-8. doi:10.1016/B978-0-12-375000-6.00001-X.

OECD. (2019). OECD Employment Outlook 2019 - The future of work. Organisation for Economic Co-operation and Development Publishing. doi:10.1787/9ee00155-en.

Hattie, J., & Clarke, S. (2018). Visible learning: feedback. Routledge, London, United Kingdom. doi:10.4324/9780429485480.

Al-Khafaji, M., & Eryilmaz, M. (2021, November). Using Artificial Intelligence Methods to Predict Student Academic Achievement. In Proceedings of the Future Technologies Conference, 403-414, Springer, Cham, Switzerland. doi: 10.1007/978-3-030-89880-9_31.

Fischer, C., Pardos, Z. A., Baker, R. S., Williams, J. J., Smyth, P., Yu, R., Slater, S., Baker, R., & Warschauer, M. (2020). Mining Big Data in Education: Affordances and Challenges. Review of Research in Education, 44(1), 130–160. doi:10.3102/0091732X20903304.

Philippou, N., Ajoodha, R., & Jadhav, A. (2020). Using machine learning techniques and matric grades to predict the success of first year university students. 2nd international multidisciplinary information technology and engineering conference (IMITEC), IEEE, 1-5. doi: 10.1109/IMITEC50163.2020.9334087.

Chowa, G. A. N., Masa, R. D., Ramos, Y., & Ansong, D. (2015). How do student and school characteristics influence youth academic achievement in Ghana? A hierarchical linear modeling of Ghana YouthSave baseline data. International Journal of Educational Development, 45, 129–140. doi:10.1016/j.ijedudev.2015.09.009.

Gottfried, M. A., & Plasman, J. S. (2018). Linking the Timing of Career and Technical Education Coursetaking With High School Dropout and College-Going Behavior. American Educational Research Journal, 55(2), 325–361. doi:10.3102/0002831217734805.

Ansary, N. S., & Luthar, S. S. (2009). Distress and academic achievement among adolescents of affluence: A study of externalizing and internalizing problem behaviors and school performance. Development and Psychopathology, 21(1), 319–341. doi:10.1017/S0954579409000182.

Jahnukainen, M. (2001). Social exclusion and dropping out of education. International Perspectives on Inclusive Education, 1, 1–12. doi:10.1016/s1479-3636(01)80003-9.

Alexander, K. L., Entwisle, D. R., & Kabbani, N. S. (2001). The dropout process in life course perspective: Early risk factors at home and school. Teachers College Record, 103(5), 760–822. doi:10.1111/0161-4681.00134.

Steinmayr, R., Weidinger, A. F., & Wigfield, A. (2018). Does students’ grit predict their school achievement above and beyond their personality, motivation, and engagement? Contemporary Educational Psychology, 53, 106–122. doi:10.1016/j.cedpsych.2018.02.004.

Lee, J. S., & Bowen, N. K. (2006). Parent involvement, cultural capital, and the achievement gap among elementary school children. American Educational Research Journal, 43(2), 193–218. doi:10.3102/00028312043002193.

Ober, T. M., Coggins, M. R., Rebouças-Ju, D., Suzuki, H., & Cheng, Y. (2021). Effect of teacher support on students’ math attitudes: Measurement invariance and moderation of students’ background characteristics. Contemporary Educational Psychology, 66, 101988. doi:10.1016/j.cedpsych.2021.101988.

Kuziemko, I. (2006). Using shocks to school enrollment to estimate the effect of school size on student achievement. Economics of Education Review, 25(1), 63-75. doi: 10.1016/j.econedurev.2004.10.003.

Deary, I. J., Strand, S., Smith, P., & Fernandes, C. (2007). Intelligence and educational achievement. Intelligence, 35(1), 13–21. doi:10.1016/j.intell.2006.02.001.

Galindo-Rueda, F., & Vignoles, A. (2005). The declining relative importance of ability in predicting educational attainment. Journal of Human Resources, 40(2), 335–353. doi:10.3368/jhr.xl.2.335.

Costa-Mendes, R., Oliveira, T., Castelli, M., & Cruz-Jesus, F. (2021). A machine learning approximation of the 2015 Portuguese high school student grades: A hybrid approach. Education and Information Technologies, 26(2), 1527–1547. doi:10.1007/s10639-020-10316-y.

Phan, H. P. (2012). Prior Academic Achievement, Effort, and Achievement Goal Orientations: A Longitudinal Examination. Journal of Educational and Developmental Psychology, 2(2), 57–71,. doi:10.5539/jedp.v2n2p57.

Trautwein, U., Lüdtke, O., Marsh, H. W., Köller, O., & Baumert, J. (2006). Tracking, grading, and student motivation: Using group composition and status to predict self-concept and interest in ninth-grade mathematics. Journal of Educational Psychology, 98(4), 788–806. doi:10.1037/0022-0663.98.4.788.

Jimerson, S. R. (2001). Meta-analysis of grade retention research: Implications for practice in the 21st century. School Psychology Review, 30(3), 420–437. doi:10.1080/02796015.2001.12086124.

Martin, A. J. (2011). Holding back and holding behind: Grade retention and students’ non-academic and academic outcomes. British Educational Research Journal, 37(5), 739–763. doi:10.1080/01411926.2010.490874.

Anderson, B. G. E., Whipple, A. D., & Jimerson, S. R. (2011). Mental health outcomes. Learning Disability Practice, 14(3), 11–11. doi:10.7748/ldp.14.3.11.s7.

Portes, A., & Rumbaut, R. G. (2005). Introduction: The second generation and the children of immigrants longitudinal study. Ethnic and Racial Studies, 28(6), 983–999. doi:10.1080/01419870500224109.

Patterson, M. M., & Pahlke, E. (2011). Student Characteristics Associated with Girls’ Success in a Single-sex School. Sex Roles, 65(9–10), 737–750. doi:10.1007/s11199-010-9904-1.

Tolen, A., & Quinlin, L. (2017). The Efficacy of Student Retention: A Review of Research & Literature. Available online: https://dokumen.tips/documents/the-efficacy-of-student-retention-a-review-of-research-remediation-for-students.html?page=1 (accessed on June 2022).

Mensah, F. K., & Kiernan, K. E. (2010). Gender differences in educational attainment: Influences of the family environment. British Educational Research Journal, 36(2), 239–260. doi:10.1080/01411920902802198.

Brunner, M., Krauss, S., & Kunter, M. (2008). Gender differences in mathematics: Does the story need to be rewritten? Intelligence, 36(5), 403–421. doi:10.1016/j.intell.2007.11.002.

Brunner, M., Gogol, K. M., Sonnleitner, P., Keller, U., Krauss, S., & Preckel, F. (2013). Gender differences in the mean level, variability, and profile shape of student achievement: Results from 41 countries. Intelligence, 41(5), 378–395. doi:10.1016/j.intell.2013.05.009.

Kubey, R. (2001). Internet use and collegiate academic performance decrements: early findings. Journal of Communication, 51(2), 366–382. doi:10.1093/joc/51.2.366.

Bowers, A. J., & Berland, M. (2013). Does recreational computer use affect high school achievement? Educational Technology Research and Development, 61(1), 51–69. doi:10.1007/s11423-012-9274-1.

Torres-Díaz, J. C., Duart, J. M., Gómez-Alvarado, H. F., Marín-Gutiérrez, I., & Segarra-Faggioni, V. (2016). Internet use and academic success in university students. Comunicar, 24(48), 61–70. doi:10.3916/C48-2016-06.

Archibald, S. (2006). Narrowing in on educational resources that do affect student achievement. Peabody Journal of Education, 81(4), 23–42. doi:10.1207/s15327930pje8104_2.

Arranz, C. F., Sena, V., & Kwong, C. (2022). Institutional pressures as drivers of circular economy in firms: A machine learning approach. Journal of Cleaner Production, 355, 131738. doi:10.1016/j.jclepro.2022.131738.

Steinmayr, R., Dinger, F. C., & Spinath, B. (2010). Parents’ education and children's achievement: The role of personality. European Journal of Personality, 24(6), 535-550. doi: 10.1002/per.755.

Tomul, E., & Savasci, H. S. (2012). Socioeconomic determinants of academic achievement. Educational Assessment, Evaluation and Accountability, 24(3), 175–187. doi:10.1007/s11092-012-9149-3.

Chesters, J., & Daly, A. (2017). Do peer effects mediate the association between family socio-economic status and educational achievement? Australian Journal of Social Issues, 52(1), 65–77. doi:10.1002/ajs4.3.

Coleman, J. S. (1968). Equality of Educational Opportunity. Equity and Excellence in Education, 6(5), 19–28. doi:10.1080/0020486680060504.

Sirin, S. R. (2005). Socioeconomic status and academic achievement: A meta-analytic review of research. Review of Educational Research, 75(3), 417–453. doi:10.3102/00346543075003417.

Fan, X., & Chen, M. (2001). Parental involvement and students' academic achievement: A meta-analysis. Educational psychology review, 13(1), 1-22. doi:10.1023/A:1009048817385.

Castro, M., Expósito-Casas, E., López-Martín, E., Lizasoain, L., Navarro-Asencio, E., & Gaviria, J. L. (2015). Parental involvement on student academic achievement: A meta-analysis. Educational research review, 14, 33-46. doi:10.1016/j.edurev.2015.01.002.

Hill, N. E., & Taylor, L. C. (2004). Parental school involvement and children’s academic achievement pragmatics and issues. Current Directions in Psychological Science, 13(4), 161–164. doi:10.1111/j.0963-7214.2004.00298.x.

Benner, A. D., Boyle, A. E., & Sadler, S. (2016). Parental Involvement and Adolescents’ Educational Success: The Roles of Prior Achievement and Socioeconomic Status. Journal of Youth and Adolescence, 45(6), 1053–1064. doi:10.1007/s10964-016-0431-4.

Poon, K. (2020). The impact of socioeconomic status on parental factors in promoting academic achievement in Chinese children. International Journal of Educational Development, 75. doi:10.1016/j.ijedudev.2020.102175.

Ali, N., Ullah, A., Shah, M., Ali, A., Khan, S. A., Shakoor, A., Begum, A., & Ahmad, S. (2020). School role in improving parenting skills and academic performance of secondary schools students in Pakistan. Heliyon, 6(11). doi:10.1016/j.heliyon.2020.e05443.

Leithwood, K., & Jantzi, D. (2009). A review of empirical evidence about school size effects: A policy perspective. Review of Educational Research, 79(1), 464–490. doi:10.3102/0034654308326158.

Rivkin, S. G., Hanushek, E. A., & Kain, J. F. (2005). Teachers, schools, and academic achievement. Econometrica, 73(2), 417–458. doi:10.1111/j.1468-0262.2005.00584.x.

Wößmann, L., & West, M. (2006). Class-size effects in school systems around the world: Evidence from between-grade variation in TIMSS. European Economic Review, 50(3), 695–736. doi:10.1016/j.euroecorev.2004.11.005.

Damuluri, S., Islam, K., Ahmadi, P., & Qureshi, N. S. (2020). Analyzing Navigational Data and Predicting Student Grades Using Support Vector Machine. Emerging Science Journal, 4(4), 243–252. doi:10.28991/esj-2020-01227.

Rockoff, J. E. (2004). The impact of individual teachers on student achievement: Evidence from panel data. American Economic Review, 94(2), 247–252. doi:10.1257/0002828041302244.

Gregory, A., & Weinstein, R. S. (2004). Connection and regulation at home and in school: Predicting growth in achievement for adolescents. Journal of Adolescent Research, 19(4), 405–427. doi:10.1177/0743558403258859.

Wong, L. P. W., Yuen, M., & Chen, G. (2021). Career-related teacher support: A review of roles that teachers play in supporting students’ career planning. Journal of Psychologists and Counsellors in Schools, 31(1), 130–141. doi:10.1017/jgc.2020.30.

Sousa, S., Portela, M., & C. Sá, C. (2016). Teacher characteristics and student progress. School of Economics and Management, University of Minho, Braga, Portugal.

Musso, M. F., Hernández, C. F. R., & Cascallar, E. C. (2020). Predicting key educational outcomes in academic trajectories: a machine-learning approach. Higher Education, 80(5), 875–894. doi:10.1007/s10734-020-00520-7.

Şen, B., Uçar, E., & Delen, D. (2012). Predicting and analyzing secondary education placement-test scores: A data mining approach. Expert Systems with Applications, 39(10), 9468–9476. doi:10.1016/j.eswa.2012.02.112.

Martínez Abad, F., & Chaparro Caso López, A. A. (2017). Data-mining techniques in detecting factors linked to academic achievement. School Effectiveness and School Improvement, 28(1), 39–55. doi:10.1080/09243453.2016.1235591.

Asif, R., Merceron, A., Ali, S. A., & Haider, N. G. (2017). Analyzing undergraduate students’ performance using educational data mining. Computers and Education, 113, 177–194. doi:10.1016/j.compedu.2017.05.007.

Delen, D. (2010). A comparative analysis of machine learning techniques for student retention management. Decision Support Systems, 49(4), 498–506. doi:10.1016/j.dss.2010.06.003.

Miguéis, V. L., Freitas, A., Garcia, P. J. V., & Silva, A. (2018). Early segmentation of students according to their academic performance: A predictive modelling approach. Decision Support Systems, 115, 36–51. doi:10.1016/j.dss.2018.09.001.

Costa-Mendes, R., Cruz-Jesus, F., Oliveira, T., & Castelli, M. (2021). Machine learning bias in predicting high school grades: A knowledge perspective. Emerging Science Journal, 5(5), 576–597. doi:10.28991/esj-2021-01298.

Cruz-Jesus, F., Castelli, M., Oliveira, T., Mendes, R., Nunes, C., Sa-Velho, M., & Rosa-Louro, A. (2020). Using artificial intelligence methods to assess academic achievement in public high schools of a European Union country. Heliyon, 6(6), 4081. doi:10.1016/j.heliyon.2020.e04081.

Vanneschi, L., & Castelli, M. (2018). Multilayer perceptrons. Encyclopedia of Bioinformatics and Computational Biology: ABC of Bioinformatics, 612–620, Elsevier, Amsterdam, netherlands. doi:10.1016/B978-0-12-809633-8.20339-7.

Fonti, V., & Belitser, E. (2017). Feature selection using lasso. Research paper in business analytics, Vrije Universiteit Amsterdam, Amsterdam, Netherlands.

CEDEFOP. (2018). Insights into skill shortages and skill mismatch: Learning from Cedefop’s European skills and jobs survey. European Centre for the Development of Vocational Training, Publications Office of the European Union, Luxembourg.

OECD (2022). Mathematics performance (PISA) (indicator). Organisation for Economic Co-operation and Development Publishing. doi: 10.1787/04711c74-en.

Egalite, A. J., & Kisida, B. (2016). School size and student achievement: a longitudinal analysis. School Effectiveness and School Improvement, 27(3), 406–417. doi:10.1080/09243453.2016.1190385.

Rozgonjuk, D., Täht, K., & Vassil, K. (2021). Internet use at and outside of school in relation to low- and high-stakes mathematics test scores across 3 years. International Journal of STEM Education, 8(1). doi:10.1186/s40594-021-00287-y.

Andrew, M. (2014). The Scarring Effects of Primary-Grade Retention? A Study of Cumulative Advantage in the Educational Career. Social Forces, 93(2), 653–685. doi:10.1093/sf/sou074.

Tan, C. Y., Peng, B., & Lyu, M. (2019). What types of cultural capital benefit students’ academic achievement at different educational stages? Interrogating the meta-analytic evidence. Educational Research Review, 28, 100289. doi:10.1016/j.edurev.2019.100289.

Wang, T. (2021). Classroom Composition and Student Academic Achievement: The Impact of Peers’ Parental Education. The B.E. Journal of Economic Analysis and Policy, 21(1), 273–305. doi:10.1515/bejeap-2020-0109.

European Comission. (2011). Grade retention during compulsory education in Europe : regulations and statistics., Publications Office of the European Union, Luxembourg.


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DOI: 10.28991/ESJ-2022-SIED-010

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