AI-Powered Tutoring Quality and Perceived Mathematics Learning Outcomes among Higher Secondary School Students
DOI:
https://doi.org/10.54536/ajiri.v5i4.8896Keywords:
Artificial Intelligence, Learning Engagement, Mathematics Education, Partial Least Squares Structural Equation Modelling, Perceived Learning Outcomes, Self-Efficacy, Tutoring QualityAbstract
AI Tutoring has the potential to facilitate learning in mathematics, including through the quality of instruction, perceived usefulness, engagement, and confidence. This paper analyses these associations on the basis of provided partial least squares structural equation modelling result of 470 records. Perceived AI tutoring quality, perceived usefulness, mathematics learning engagement, mathematics self-efficacy and perceived mathematics learning outcomes are reflected in five reflective constructs and twenty-five indicators. The analysis records 5,000 bootstrap samples and tenfold repeated prediction. The suggested seven direct relationships are all positive with confidence intervals not equal to zero. The quality of tutoring is linked with usefulness, engagement and self-efficacy. The engagement is linked to self-efficacy and perceived outcomes and self-efficacy is linked to perceived outcomes independent of engagement. The model is used to explain 39.5 percent of the perceived outcomes. The indicators loading, internal consistency, convergent, and discriminant validity are reported to fulfill the assessment criteria as stated in the workbook. However, prediction is above the linear standard of nineteen of twenty endogenous indicators, but the differences are minor. These results set engagement and self-efficacy as complementary explanatory paths. Nevertheless, an empirical sample cannot be verified due to hypothetical platform labels and missing fieldwork documentation. The results thus detail the provided records of analysis and are not able to determine causal achievement gains. Substantive educational conclusions will still require independent evaluation and validated recruitment documentation.
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Akın, A. (2022). The effectiveness of web-based Mathematics instruction (WBMI) on K-16 students’ mathematics learning: a meta-analytic research. Education and Information Technologies, 27(6), 8015–8040. https://doi.org/10.1007/s10639-022-10931-x
Almusaed, A., Almssad, A., Yitmen, I., & Homod, R. Z. (2023). Enhancing student engagement: harnessing “aied”’s power in hybrid education—a review analysis. Education Sciences, 13(7), 632. https://doi.org/10.3390/educsci13070632
Alomari, M. A., & Jabr, M. O. (2020). The effect of the use of an educational software based on the strategy of artificial intelligence on students’ achievement and their attitudes towards it. Management Science Letters, 2951–2960. https://doi.org/10.5267/j.msl.2020.5.030
Astuti, N. (2022). Pengaruh Metode Pembelajaran Tutor Sebaya Terhadap Hasil Belajar Matematika Siswa Kelas Iii Sdn Simpang Warga 1 Kecamatan Aluh-Aluh. Jurnal Terapung : Ilmu - Ilmu Sosial, 4(1), 33. https://doi.org/10.31602/jt.v4i1.7357
Azevedo, O. B., & Jaques, P. A. (2020). Umstudent engagement: harnessing “aied”’s power in hybrid education—a review analysis. Anais Do XXXI Simpósio Brasileiro de Informática Na Educação (SBIE 2020), 762–771. https://doi.org/10.5753/cbie.sbie.2020.762
Çeti̇N, İ., Erümi̇T, A. K., Nabi̇Yev, V., Karal, H., Kösa, T., & Kokoç, M. (2023). Theeffect of gamified adaptive intelligent tutoring system artibos on problem-solving skills. Participatory Educational Research, 10(1), 344–374. https://doi.org/10.17275/per.23.19.10.1
Chai, C. S., Chiu, T. K. F., Wang, X., Jiang, F., & Lin, X.-F. (2022). Modeling chinese secondary school students’ behavioral intentions to learn Artificial intelligence with the theory of planned behavior and self-determination theory. Sustainability, 15(1), 605. https://doi.org/10.3390/su15010605
Cunska, A. (2021). Important Factors For More Effective Learning of Mathematics. Society. Integration. Education. Proceedings of the International Scientific Conference, 2, 139–150. https://doi.org/10.17770/sie2021vol2.6466
Cunska, A. (2022). Prototype of project AI4Math: interdisciplinary and innovative technology for accelerated learning of mathematics. Wseas Transactions on Business and Economics, 19, 1839–1848. https://doi.org/10.37394/23207.2022.19.165
Fitzmaurice, O., O’Meara, N., & Johnson, P. (2021). Highlighting therelevance of mathematics to secondary school students – why and how. European Journal of STEM Education, 6(1), 07. https://doi.org/10.20897/ejsteme/10895
Fornons, V., Palau, R., & Santiago, R. (2021). Secondary school students’ perception according to their learning style of a mathematics Flipped Classroom. Journal of Technology and Science Education, 11(2), 227. https://doi.org/10.3926/jotse.1092
García-Martínez, I., Fernández-Batanero, J. M., Fernández-Cerero, J., & León, S. P. (2023). Analysing the impact of artificial intelligence and computational sciences on student performance: systematic review and meta-analysis. Journal of New Approaches in Educational Research, 12(1), 171–197. https://doi.org/10.7821/naer.2023.1.1240
Hossein-Mohand, H., & Hossein-Mohand, H. (2023). Influence of motivation on the perception of mathematics by secondary school students. Frontiers in Psychology, 13, 1111600. https://doi.org/10.3389/fpsyg.2022.1111600
Kochmar, E., Vu, D. D., Belfer, R., Gupta, V., Serban, I. V., & Pineau, J. (2020). Automated impact of artificial intelligence and computational sciences on student performance: systematic review and meta-analysis. In I. I. Bittencourt, M. Cukurova, K. Muldner, R. Luckin, & E. Millán (Eds.), Artificial Intelligence in Education (Vol. 12164, pp. 140–146). Springer International Publishing. https://doi.org/10.1007/978-3-030-52240-7_26
Kshirsagar, P. R., Jagannadham, D. B. V., Alqahtani, H., Noorulhasan Naveed, Q., Islam, S., Thangamani, M., & Dejene, M. (2022). Human intelligence analysis through perception of ai in teaching and learning. Computational Intelligence and Neuroscience, 2022, 1–9. https://doi.org/10.1155/2022/9160727
Kumar, D., Haque, A., Mishra, K., Islam, F., Kumar Mishra, B., & Ahmad, S. (2023). Exploring the transformative role of artificial intelligence and metaverse in education: a comprehensive review. Metaverse Basic and Applied Research, 2, 55. https://doi.org/10.56294/mr202355
Lopes, P. C. D. C., Abreu, V. R. P. D., Streit, J. A. C., Gameiro, S., & Nunes, P. H. S. (2023). Machine learning na aprendizagem das ciências exatas: análise qualitativa para ensino fundamental e médio. Revista de Tecnologia & Gestão Sustentável, 2(5). https://doi.org/10.17271/rtgs.v2i5.3710
Marrone, R., Taddeo, V., & Hill, G. (2022). Creativity and artificial intelligence—a student perspective. Journal of Intelligence, 10(3), 65. https://doi.org/10.3390/jintelligence10030065
Moliner, L., Alegre, F., & Lorenzo-Valentín, G. (2022). Peer tutoring and math digital tools: a promising combination in middle school. Mathematics, 10(13), 2360. https://doi.org/10.3390/math10132360
Neo, M., Lee, C. P., Tan, H. Y.-J., Neo, T. K., Tan, Y. X., Mahendru, N., & Ismat, Z. (2022). Enhancing students’ online learning experiences with artificial intelligence (AI): the merlin project. International Journal of Technology, 13(5), 1023. https://doi.org/10.14716/ijtech.v13i5.5843
Radović, S. (2024). Is it only about technology? the interplay between educational technology for mathematics homework, teaching practice, and students’ activities. Journal of Computers in Education, 11(3), 743–762. https://doi.org/10.1007/s40692-023-00277-9
Samuelsson, J. (2023). Arithmetic fact fluency supported by Artificial Intelligence. Frontiers in Education Technology, 6(1), p13. https://doi.org/10.22158/fet.v6n1p13
Soesanto, R. H., Dirgantoro, K. P. S., & Priyanti, N. (2022). Indonesian students’ perceptions towards AI-based learning in mathematics. Journal on Mathematics Education, 13(3), 531–548. https://doi.org/10.22342/jme.v13i3.pp531-548
Taha, T. B., & Abdulrahman, M. S. (2023). Theimpact of technology on students’ Psychological and Educational Performance. JISA(Jurnal Informatika Dan Sains), 6(1), 91–95. https://doi.org/10.31326/jisa.v6i1.1661
Tiwari, R. (2023). The integration of AI and machine learning in education and its potential to personalize and improve student learning experiences. Interantional Journal Of Scientific Research In Engineering And Management, 07(02). https://doi.org/10.55041/IJSREM17645
Utterberg Modén, M., Tallvid, M., Lundin, J., & Lindström, B. (2021). Intelligent Tutoring Systems: Why Teachers Abandoned a Technology Aimed at Automating Teaching Processes. Hawaii International Conference on System Sciences. https://doi.org/10.24251/HICSS.2021.186
Wang, H., Tlili, A., Huang, R., Cai, Z., Li, M., Cheng, Z., Yang, D., Li, M., Zhu, X., & Fei, C. (2023). Examining the applications of intelligent tutoring systems in real educational contexts: a systematic literature review from the social experiment perspective. Education and Information Technologies, 28(7), 9113–9148. https://doi.org/10.1007/s10639-022-11555-x
Weinhandl, R., Mayerhofer, M., Houghton, T., Lavicza, Z., Eichmair, M., & Hohenwarter, M. (2022). Personas characterising secondary school mathematics students: development and applications to educational technology. Education Sciences, 12(7), 447. https://doi.org/10.3390/educsci12070447
Yue, M., Jong, M. S.-Y., & Dai, Y. (2022). Pedagogical design of K-12 artificial intelligence education: a systematic review. Sustainability, 14(23), 15620. https://doi.org/10.3390/su142315620
Zhang, W., Xiao, S., & Fu, W. (2023). Can “Smart Homework” achieve the goal of chinese “Double Reduction” policy to reduce burden and improve quality?—the positive and negative effects of “Smart Homework” on students. Sustainability, 15(12), 9759. https://doi.org/10.3390/su15129759
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