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Learning Factors for TIMSS Math Performance Evidenced Through Machine Learning in the UAE

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Artificial Intelligence in Education Technologies: New Development and Innovative Practices (AIET 2022)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 154))

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Abstract

Understanding how the UAE K12 education system performs with data-driven evidence is key to inform better policy making to support UAE vision to upskill human capital growth for its economic transformation. In this study, we investigate the potential of using machine learning techniques to understand key learning factors contributing to UAE student math performance on the TIMSS 2019 assessment. Due to the fact that learning factors co-exist and interact with one another, we explore the SHapley Additive exPlanations (SHAP) approach to explain the complexity of the model. The results highlight the importance and contributions of each learning factor and uncover the relationships between the learning factors. Understanding key learning factors and identifying evidence-based intervention opportunities will help policymakers with informed education intervention designs to improve student mathematics learning, in order to improve UAE student TIMSS math performance over the long run.

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Notes

  1. 1.

    Retrieved from the TIMSS 2019 International Database Downloads, https://timss2019.org/international-database/.

References

  1. Hanushek, E.A., Woessmann, L.: Education, knowledge capital, and economic growth. Econ. Educ. 171–182 (2020). https://doi.org/10.1016/b978-0-12-815391-8.00014-8

  2. Hanushek, E.A., Woessmann, L.: How much do educational outcomes matter in OECD countries? Econ. Policy 26(67), 427–491 (2011)

    Article  Google Scholar 

  3. Ibourk, A.: Determinants of educational achievement in Morocco: a micro-econometric analysis applied to the TIMSS study. Int. Educ. Stud. 6(12), 25–36 (2013)

    Article  Google Scholar 

  4. Sandoval-Hernández, A., Białowolski, P.: Factors and conditions promoting academic resilience: a TIMSS-based analysis of five Asian education systems. Asia Pac. Educ. Rev. 17(3), 511–520 (2016)

    Article  Google Scholar 

  5. Sulku, S.N., Abdioglu, Z.: Public and private school distinction, regional development differences, and other factors influencing the success of primary school students in Turkey. Educ. Sci.: Theory Pract. 15(2), 419–31 (2015)

    Google Scholar 

  6. Suri, T., Boozer, M.A., Ranis, G., Stewart, F.: Paths to success: the relationship between human development and economic growth. World Dev. 39(4), 506–522 (2011)

    Article  Google Scholar 

  7. Drent, M., Meelissen, M.R.M., van der Kleij, F.M.: The contribution of TIMSS to the link between school and classroom factors and student achievement. J. Curric. Stud. 45(2), 198–224 (2013)

    Google Scholar 

  8. Bofah, E.A., Hannula, M.S.: TIMSS data in an African comparative perspective: investigating the factors influencing achievement in mathematics and their psychometric properties. Large-Scale Assess. Educ. 3(1), 1–36 (2015)

    Google Scholar 

  9. Filiz, E., Enes, Öz.: Educational data mining methods for TIMSS 2015 mathematics success: Turkey case. Sigma J. Eng. Nat. Sci. 38(2), 963–77 (2020)

    Google Scholar 

  10. Kwak, Y.: An analysis of the Korean science education environment for 20 years of TIMSS. J. Korean Earth Sci. Soc. 39(4), 378–387 (2018)

    Article  Google Scholar 

  11. Cardoso, A.P., Ferreira, M., Abrantes, J.L., Seabra, C., Costa, C.: Personal and pedagogical interaction factors as determinants of academic achievement. Procedia-Soc. Behav. Sci. 29, 1596–1605 (2011)

    Google Scholar 

  12. DeFreitas, K., Bernard, M.: Comparative performance analysis of clustering techniques in educational data mining. IADIS Int. J. Comput. Sci. Inf. Syst. 10(2) (2015)

    Google Scholar 

  13. Martinez Abad, F., Chaparro Caso López, A.A.: Data-mining techniques in detecting factors linked to academic achievement. School Eff. School Improv. 28(1), 39–55 (2017)

    Google Scholar 

  14. UAE Vision 2021. First-Rate Education System (2019). https://www.vision2021.ae/en/national-agenda-2021/list/first-rate-circle

  15. Mullis, I.V.S., Martin, M.O. (eds.): TIMSS 2019 Assessment Frameworks. Boston College, TIMSS & PIRLS International Study Center (2017). http://timssandpirls.bc.edu/timss2019/frameworks/

  16. Baradwaj, B.K., Pal, S.: Mining Educational Data to Analyze Students’ Performance (2012). ArXiv:1201.3417

    Google Scholar 

  17. Ifenthaler, D., Yau, J.-K.: Utilizing learning analytics to support study success in higher education: a systematic review. Educ. Tech. Res. Dev. 68(4), 1961–1990 (2020)

    Article  Google Scholar 

  18. Kiray, S.A., Gok, B., Selman Bozkir, A.: Identifying the factors affecting science and mathematics achievement using data mining methods. J. Educ. Sci. Environ. Health 1(1), 28–48 (2015)

    Google Scholar 

  19. Lee, J., Shute, V.J.: Personal and social-contextual factors in K–12 academic performance: an integrative perspective on student learning. Educ. Psychol. 45(3), 185–202 (2010)

    Article  Google Scholar 

  20. Akessa, G.M., Dhufera, A.G.: Factors that influences students’ academic performance: a case of Rift Valley University, Jimma, Ethiopia. J. Educ. Pract. 6(22), 55–63 (2015)

    Google Scholar 

  21. Kabakchieva, D.: Predicting student performance by using data mining methods for classification. Cybern. Inf. Technol. 13(1), 61–72 (2013)

    MathSciNet  Google Scholar 

  22. Lau, E.T., Sun, L., Yang, Q.: Modeling, prediction and classification of student academic performance using artificial neural networks. SN Appl. Sci. 1(9), 1–10 (2019)

    Google Scholar 

  23. Liem, G.A.D., Martin, A.J., Porter, A.L., Colmar, S.: Sociocultural antecedents of academic motivation and achievement: role of values and achievement motives in achievement goals and academic performance. Asian J. Soc. Psychol. 15(1), 1–13 (2012)

    Google Scholar 

  24. Schumacher, P., Olinsky, A., Quinn, J., Smith, R.: A comparison of logistic regression, neural networks, and classification trees predicting success of actuarial students. J. Educ. Bus. 85(5), 258–263 (2010)

    Article  Google Scholar 

  25. Bahadır, E.: Using neural network and logistic regression analysis to predict prospective mathematics teachers’ academic success upon entering graduate education. Kuram ve Uygulamada Egitim Bilimleri 16(3), 943–964 (2016). https://doi.org/10.12738/estp.2016.3.0214

    Article  Google Scholar 

  26. De Witte, K., Kortelainen, M.: What explains the performance of students in a heterogeneous environment? Conditional efficiency estimation with continuous and discrete environmental variables. Appl. Econ. 45(17), 2401–2412 (2013)

    Google Scholar 

  27. Nath, S.R.: Factors influencing primary students’ learning achievement in Bangladesh. Res. Educ. 88(1), 50–63 (2012)

    Article  Google Scholar 

  28. Mohtar, L.E., Halim, L., Samsudin, M.A., Ismail, M.E.: Non-cognitive factors influencing science achievement in Malaysia and Japan: an analysis of TIMSS 2015. EURASIA J. Math. Sci. Technol. Educ. 15(4), 1697 (2019)

    Google Scholar 

  29. Pérez, P.M., Castejón Costa, J.-L., Corbi, R.G.: An explanatory model of academic achievement based on aptitudes, goal orientations, self-concept and learning strategies. Span. J. Psychol. 15(1), 48–60 (2012)

    Google Scholar 

  30. Yoo, J.E., Rho, M.: TIMSS 2015 Korean student, teacher, and school predictor exploration and identification via random forests. SNU J. Educ. Res. 26(4), 43–61 (2017). https://s-space.snu.ac.kr/bitstream/10371/168474/1/26(4)_03.pdf. Accessed 30 March 2022

  31. Mohammadpour, E., Shekarchizadeh, A., Kalantarrashidi, S.A.: Multilevel modeling of science achievement in the TIMSS participating countries. J. Educ. Res. 108(6), 449–464 (2015). https://doi.org/10.1080/00220671.2014.917254

    Article  Google Scholar 

  32. Bernardo, A.B., Cordel, M.O., Lucas, R.I., Teves, J.M., Yap, S.A., Chua, U.C.: Using machine learning approaches to explore non-cognitive variables influencing reading proficiency in English among Filipino learners. Educ. Sci. 11(10), 628 (2021). https://doi.org/10.3390/educsci11100628

    Article  Google Scholar 

  33. Nadaf, A., Eliëns, S., & Miao, X.: Interpretable-machine-learning evidence for importance and optimum of learning time. Int. J. Inf. Educ. Technol. 11(10), 444–449 (2021). https://doi.org/10.18178/ijiet.2021.11.10.1548

  34. Lundberg, S., Lee, S.-I.: A unified approach to interpreting model predictions. Adv. Neural Inf. Process. Syst. 30, 4765–4774 (2017)

    Google Scholar 

  35. Lundberg, S.M., Erion, G.G., Lee, S.-I.: Consistent Individualized Feature Attribution for Tree Ensembles (2019). ArXiv:180203888 Cs Stat

    Google Scholar 

  36. Lundberg, S.M., Erion, G., Chen, H., DeGrave, A., Prutkin, J.M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., Lee, S.-I.: From local explanations to global understanding with explainable AI for trees. Nat. Mach. Intell. 2 (2020). https://doi.org/10.1038/s42256-019-0138-9. https://par.nsf.gov/biblio/10167481

  37. Fishbein, B., Foy, P., Yin, L.: TIMSS 2019 User Guide for the International Database, 2nd ed. Boston College, TIMSS & PIRLS International Study Center (2021). https://timssandpirls.bc.edu/timss2019/international-database/

  38. Martin, M.O., von Davier, M., Mullis, I.V.: Methods and Procedures: TIMSS 2019 Technical Report. International Association for the Evaluation of Educational Achievement (2020)

    Google Scholar 

  39. CatBoost.: github.com/catboost/catboost (2020). [Online]. https://github.com/catboost/catboost

  40. Hair, J.F., Ringle, C.M., Sarstedt, M.: PLS-SEM: indeed a silver bullet. J. Mark. Theory and Pract. 19(2), 139–152 (2011). https://ssrn.com/abstract=1954735

  41. Teo, T.W., Choy, B.H.: In: Tan, O.S., Low, E.L., Tay, E.G., Yan, Y.K. (eds.) Singapore Math and Science Education Innovation. ETLPPSIP, vol. 1, pp. 43–59. Springer, Singapore (2021). https://doi.org/10.1007/978-981-16-1357-9_3

  42. OECD: Early learning matters, the international early learning and child well-being study (2018)

    Google Scholar 

  43. OECD: Better Skills, Better Jobs, Better Lives: A Strategic Approach to Education and Skills Policies for the United Arab Emirates (2015). https://www.oecd.org/education/A-Strategic-Approach-to-Education-and%20Skills-Policies-for-the-United-Arab-Emirates.pdf

  44. City, E.A., Elmore, R.F., Fiarman, S.E., Teitel, L.: A Network Approach to Improving Teaching and Learning. Harvard Education Press, Cambridge (2009)

    Google Scholar 

  45. McCombs, B.L.: The role of the self-system in self-regulated learning. Contemp. Educ. Psychol. 11, 314–332 (1986)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Alef Education, Abu Dhabi, UAE

    Ali Nadaf, Samantha Monroe, Sarath Chandran & Xin Miao

Authors
  1. Ali Nadaf
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  2. Samantha Monroe
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  3. Sarath Chandran
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  4. Xin Miao
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Corresponding author

Correspondence to Samantha Monroe .

Editor information

Editors and Affiliations

  1. Department of Curriculum and Instruction, The Education University of Hong Kong, Tai Po, Hong Kong

    Eric C. K. Cheng

  2. Swinburne University of Technology, Melbourne, VIC, Australia

    Tianchong Wang

  3. IU International University of Applied Sciences, Erfurt, Germany

    Tim Schlippe

  4. Department of Food Science & Technology, University of Patras, Agrinio, Greece

    Grigorios N. Beligiannis

Appendix

Appendix

The full list of learning factors used to predict grades 4 and 8 students in this analysis are displayed in Tables 1 and 2, respectively.

Table 1. List of features used for grade 4 prediction
Full size table
Table 2. List of features used for grade 8 prediction
Full size table

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Nadaf, A., Monroe, S., Chandran, S., Miao, X. (2023). Learning Factors for TIMSS Math Performance Evidenced Through Machine Learning in the UAE. In: Cheng, E.C.K., Wang, T., Schlippe, T., Beligiannis, G.N. (eds) Artificial Intelligence in Education Technologies: New Development and Innovative Practices. AIET 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 154. Springer, Singapore. https://doi.org/10.1007/978-981-19-8040-4_4

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  • DOI: https://doi.org/10.1007/978-981-19-8040-4_4

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