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https://doi.org/10.18502/gespr.v1i2.8458
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- Cited by 2 articles
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Xin Miao
Xin Miao
Alef Education, Abu Dhabi, UAE
-
Pawan Kumar Mishra
Pawan Kumar Mishra
Alef Education, Abu Dhabi, UAE
-
Ali Nadaf
Ali Nadaf
Alef Education, Abu Dhabi, UAE
Published Date
- Jan 31, 2021
Evidence and Promises of AI Predictions to Understand Student Approaches to Math Learning in Abu Dhabi K12 Public Schools
Abstract
Transforming the education system and building highly skilled human capital for a sustainable and competitive knowledge economy have been on the UAE’s top policy agendas for the last decade. However, in the UAE, students’ math performance on the Program for International Student Assessment (PISA) has not been promising. To improve the quality of schooling, a series of malleable predictive factors including the contributions of self-system, metacognitive skills, and instructional language skills are selected and categorized under student approaches to math learning. These factors are hypothesized as both predictors and outcomes of K12 schooling. Through the analysis using machine learning technique, XGBoost, a latent relationship between student approaches to math learning and math diagnostic test performance is uncovered and discussed for students from Grade 5 to Grade 9 in Abu Dhabi public schools. This article details how the analysis results are applied for student behavior and performance prediction, precise diagnosis, and targeted intervention design possibilities. The main purpose of this study is to diagnose challenges that hinder student math learning in Abu Dhabi public schools, uncover R&D initiatives in AI-driven prediction and EdTech interventions to bridge learning gaps, and to counsel on national education policy refinement.
Keywords:
Human capital, Predictive learning factors, AI prediction, The instructional core, Self-system, Metacognition, Instructional language
References
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[31] Winne, P. H. (2010). Bootstrapping learner’s self-regulated learning. Psychological Test and Assessment Modeling, 52, 472–490. Germany: Pabst Science Publishers.
[2] Chen, T., & Guestrin, C. (2016). XGBoost. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Retrieved from https://doi.org/10.1145/2939672. 2939785
[3] City E.A., Elmore R.F., Fiarman S.E., & Teitel, L. (2009). A Network Approach To Improving Teaching And Learning. Harvard Education Press.
[4] Coleman, C., Baker, R. S., & Stephenson, S. (2019, July 2–5). A better cold-start for early prediction of student at-risk status in new school districts (ED599170). Paper presented at the International Conference on Educational Data Mining (EDM), Montreal, Canada Retrieved from https://eric.ed.gov/ ?id=ED599170
[5] Crossley, S., Karumbaiah, S., Ocumpaugh, J., Labrum, M. J., & Baker, R. S. (2019). Predicting math success in an online tutoring system using language data and click-stream variables: A longitudinal analysis. 2nd Conference on Language, Data and Knowledge (LDK 2019). Retrieved from https://doi. org/10.4230/OASIcs.LDK.2019.25
[6] Duckworth, A. L., Peterson, C., Matthews, M. D., & Kelly, D. R. (2007). Grit: Perseverance and passion for long-term goals. Journal of Personality and Social Psychology, 92(6), 1087–1101. https://doi.org/10. 1037/0022-3514.92.6.1087
[7] Efklides, A., Schwartz, B. L., & Brown, V. (2018). Motivation and affect in self-regulated learning: Does metacognition play a role? In D. H. Schunk & J. A. Greene (Eds.), Educational Psychology Handbook Series. Handbook of self-regulation of learning and performance (pp. 64–82). Routledge.
[8] Farah, S. (2012). Education Quality & Competitiveness in the UAE (Sheikh Saud bin Saqr Al Qasimi Foundation for Policy Research Policy Paper No. 2). doi:10.18502/aqf.0011
[9] Friedman, J.H. (2001) Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232. https://doi.org/10.1214/aos/1013203451
[10] Gallagher, K. (2011). Bilingual education in the UAE: Factors, variables and critical questions. Education, Business and Society: Contemporary Middle Eastern Issues, 4(1), 62–79. https://doi.org/10.1108/ 17537981111111274
[11] The government of Abu Dhabi. (2008, November). The Abu Dhabi Economic Vision 2030. Retrieved from https://www.actvet.gov.ae/en/Media/Lists/ELibraryLD/economic-vision-2030-full-versionEn.pdf
[12] Hanushek, E. A., & Kimko, D. D. (2000). Schooling, labor-force quality, and the growth of nations. American Economic Review, 90(5), 1184–1208. https://doi.org/10.1257/aer.90.5.1184
[13] Harvey, J. L., & Kumar, S. A. (2019). A practical model for educators to predict student performance in K-12 education using machine learning. 2019 IEEE Symposium Series on Computational Intelligence (SSCI). Retrieved from https://doi.org/10.1109/ssci44817.2019.9003147
[14] Kippels, S., & Ridge, N. (2019). The growth and transformation of K–12 education in the UAE. In Gallagher K. (Eds.), Education in the United Arab Emirates (pp. 37–55). Singapore: Springer. https://doi.org/10. 1007/978-981-13-7736-5_3
[15] Kostyuk, V., Almeda, M. V., & Baker, R. S. (2018). Correlating affect and behavior in reasoning mind with state test achievement. Proceedings of the 8th International Conference on Learning Analytics and Knowledge, 26–30. Retrieved from https://doi.org/10.1145/3170358.3170378
[16] Kumari, P., Jain, P. K., & Pamula, R. (2018). An efficient use of ensemble methods to predict students’ academic performance. 2018 4th International Conference on Recent Advances in Information Technology (RAIT). Retrieved from https://doi.org/10.1109/rait.2018.8389056
[17] Lundberg, S. M., & Lee, S. I. (2017). A Unified Approach to Interpreting Model Predictions. Advances in Neural Information Processing Systems. Retrieved from https://papers.nips.cc/paper/ 7062-a-unified-approach-to-interpreting-model-predictions
[18] McCombs, B. L. (1986). The role of the self-system in self-regulated learning. Contemporary Educational Psychology, 11(4), 314–332. https://doi.org/10.1016/0361-476x(86)90028-7
[19] Mokhtari, K., Dimitrov, D. M., & Reichard, C. A. (2018). Revising the Metacognitive Awareness of Reading Strategies Inventory (MARSI) and testing for factorial invariance. Studies in Second Language Learning and Teaching, 8(2), 219–246. https://doi.org/10.14746/ssllt.2018.8.2.3
[20] OECD. (2010). Mathematics Teaching and Learning Strategies in PISA. Retrieved from https: //www.oecd-ilibrary.org/docserver/9789264039520-en.pdf?expires=1586113622&id=id&accname= guest&checksum=81842A52D802FE8FEBB18E513DB02268
[21] OECD. (2018). PISA Results 2018: UAE Country Note. Retrieved from http://www.oecd.org/pisa/ publications/PISA2018_CN_ARE.pdf
[22] Pardos, Z. A., Baker, R. S., San Pedro, M., Gowda, S. M., & Gowda, S. M. (2014). Affective states and state tests: Investigating how affect and engagement during the school year predict end-of-Year learning outcomes. Journal of Learning Analytics, 1(1), 107–128. https://doi.org/10.18608/jla.2014.11.6
[23] Shapley, L. S. (1953). A value for n-person games. In H. W. Kuhn & A. W. Tucker (Eds.), Contributions To the Theory of Games (AM-28), Volume II (pp. 307–318). Princeton University Press. https://doi.org/10. 1515/9781400881970-018
[24] UAE Ministry of Education. (2010). The Ministry of Education Strategy 2010–2020: Aiming in Accomplishing a Score of 10/10 in All Its Initiatives. Retrieved from https://www.moe.gov.ae/Arabic/ Docs/MOE%20_Strategy.pdf
[25] UAE Ministry of Finance. (2018). Federal Budget 2018. Retrieved from https://www.mof.gov.ae/en/ resourcesAndBudget/fedralBudget/Pages/budget2018.aspx
[26] UAE Ministry of Finance. (2019). Federal Budget 2019. Retrieved from https://www.mof.gov.ae/en/ resourcesAndBudget/fedralBudget/Pages/budget2019.aspx
[27] Vision 2021. (2005, December). UAE Vision 2021. Retrieved March 16, 2020 from http://www.vision2021. ae
[28] UNESCO. (n.d.). Sustainable Goals for UAE. Retrieved from http://uis.unesco.org/en/country/ae
[29] Winne, P. H. (1985). Steps toward promoting cognitive achievements. The Elementary School Journal, 85(5), 673–693. https://doi.org/10.1086/461429
[30] Winne, P. H. (1997). Experimenting to bootstrap self-regulated learning. Journal of Educational Psychology, 89(3), 397–410. https://doi.org/10.1037/0022-0663.89.3.397
[31] Winne, P. H. (2010). Bootstrapping learner’s self-regulated learning. Psychological Test and Assessment Modeling, 52, 472–490. Germany: Pabst Science Publishers.