Eurasia Journal of Mathematics, Science and Technology Education

• Acosta-Gonzaga, E., & Walet, N. R. (2018). The role of attitudinal factors in mathematical on-line assessments: A study of undergraduate STEM students. Assessment & Evaluation in Higher Education, 43(5), 710-726. https://doi.org/10.1080/02602938.2017.1401976
• Agah, J. J., & Sule, L. (2015). Determinants of students’ logical reasoning and mathematics achievement. Journal of Literature, Languages and Linguistics, 5, 40- 43.
• Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211. https://doi.org/10.1016/0749-5978(91)90020-T
• Amelia, D. P., & Harahap, A. (2021). Application of interactive multimedia-based mathematics learning media to increase students’ interest in learning. Budapest International Research and Critics Institute (BIRCI-Journal): Humanities and Social Sciences, 4(2), 3153-3161. https://doi.org/10.33258/birci.v4i2.2040
• Apkarian, N., Henderson, C., Stains, M., Raker, J., Johnson, E., & Dancy, M. (2021). What really impacts the use of active learning in undergraduate STEM education? Results from a national survey of chemistry, mathematics, and physics instructors. PLoS ONE, 16(2), Article e0247544. https://doi.org/10.1371/journal.pone.0247544
• Awang, L. A., Yusop, F. D., & Danaee, M. (2025). Current practices and future direction of artificial intelligence in mathematics education: A systematic review. International Electronic Journal of Mathematics Education, 20(2), Article em0823. https://doi.org/10.29333/iejme/16006
• Bandura, A. (1997). Self-efficacy: The exercise of control. W. H. Freeman.
• Barefield, M. A. (2013). The impact of cooperative learning on student achievement. APSU. https://aspire.apsu.edu/bitstreams/005643f1-c13d-4009-92ac-31e0afb517cd/download
• Bartlett, M. S. (1954). A note on the multiplying factors for various Chi square approximations. Journal of the Royal Statistical Society: Series B (Methodological), 16(2), 296-298. https://doi.org/10.1111/j.2517-6161.1954.tb00174.x
• Black, P., & Wiliam, D. (2009). Developing the theory of formative assessment. Educational Assessment, Evaluation and Accountability, 21, 5-31. https://doi.org/10.1007/s11092-008-9068-5
• Bonwell, C. C., & Eison, J. A. (1991). Active learning: Creating excitement in the classroom. George Washington University. https://scispace.com/pdf/active-learning-creating-excitement-in-the-classroom-1991-5gd5694owy.pdf
• Castillo, A. J., Durán, P., Fuller, E., Watson, C., Potvin, G., & Kramer, L. H. (2024). Student attitudes in an innovative active learning approach in calculus. International Journal of Mathematical Education in Science and Technology, 55(3), 730-758. https://doi.org/10.1080/0020739X.2021.2023771
• Chen, B., Bastedo, K., & Howard, W. (2018). Exploring design elements for online STEM courses: Active learning, engagement & assessment design. Online Learning, 22(2), 59-75. https://doi.org/10.24059/olj.v22i2.1369
• Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2018). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). Routledge.
• Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297-334. https://doi.org/10.1007/BF02310555
• Deci, E. L., & Ryan, R. M. (2000). The “what” and “why” of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11(4), 227-268. https://doi.org/10.1207/S15327965PLI1104_01
• Deci, E. L., & Ryan, R. M. (2013). Intrinsic motivation and self-determination in human behavior. Springer.
• Deci, E. L., Eghrari, H., Patrick, B. C., & Leone, D. R. (1994). Facilitating internalization: The self-determination theory perspective. Journal of Personality, 62(1), 119-142. https://doi.org/10.1111/j.1467-6494.1994.tb00797.x
• Deif, H., Hammoudi, M. M., & Abraham, J. A. (2026). An analysis of indicators of students’ motivation to use technology in learning mathematics: A case study of NodeBook™. Journal of Curriculum Studies Research, 8(1), 124-143. https://doi.org/10.46303/jcsr.2026.7
• Deutsch, M. (1949). A theory of cooperation and competition. Human Relations, 2(2), 129-152. https://doi.org/10.1177/001872674900200204
• Duran, M. J., Aciego, J. J., Gonzalez-Prieto, I., Carrillo-Rios, J., Gonzalez-Prieto, A., & Claros-Colome, A. (2024). A gamified active-learning proposal for higher-education heterogeneous STEM courses. Education Sciences, 15(1), Article 10. https://doi.org/10.3390/educsci15010010
• Field, A. (2018). Discovering statistics using IBM SPSS Statistics (5th ed.). SAGE.
• Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50. https://doi.org/10.2307/3151312
• Freeman, S., Eddy, S. L., McDonough, M., Smith, M. K., Okoroafor, N., Jordt, H., & Wenderoth, M. P. (2014). Active learning increases student performance in science, engineering, and mathematics. PNAS, 111(23), 8410-8415. https://doi.org/10.1073/pnas.1319030111
• Frenzel, A. C., Pekrun, R., & Goetz, T. (2009). Emotion transmission in the classroom: Exploring the relationship between teacher and student enjoyment. Journal of Educational Psychology, 101(3), 705-716. https://doi.org/10.1037/a0014695
• Gaskin, J., Lim, J., & Steed, J. (2022). Model fit measures. Gaskination’s StatWiki. http://statwiki.gaskination.com/
• Gottfried, M. A. (2015). The influence of applied STEM coursetaking on advanced mathematics and science coursetaking. The Journal of Educational Research, 108(5), 382-399. https://doi.org/10.1080/00220671.2014.899959
• Grira, S., Hammoudi, M. M., & Alseksek, Y. (2025). Enhancing students’ mathematical attitudes to boost college algebra courses success. Applied Mathematics & Information Sciences, 19(3), 683-693. https://doi.org/10.18576/amis/190316
• Hammoudi, M. M. (2019). Predictive factors of students’ motivation to succeed in introductory mathematics courses: evidence from higher education in the UAE. International Journal of Mathematical Education in Science and Technology, 50(5), 647-664. https://doi.org/10.1080/0020739X.2018.1529339
• Hammoudi, M. M. (2020). Measurement of students’ mathematics motivation and self-concept at institutions of higher education: Evidence of reliability and validity. International Journal of Mathematical Education in Science and Technology, 51(1), 63-86. https://doi.org/10.1080/0020739X.2019.1670369
• Hammoudi, M. M., & Grira, S. (2023a). Improving students’ motivation in calculus courses at institutions of higher education: Evidence from graph-based visualization of two models. Eurasia Journal of Mathematics, Science and Technology Education, 19(1), Article em2209. https://doi.org/10.29333/ejmste/12771
• Hammoudi, M. M., & Grira, S. (2023b). Optimization of the motivation for success in mathematics at higher education: Evidence from novel graph-based visualization of three models. Eurasia Journal of Mathematics, Science and Technology Education, 19(11), Article em2356. https://doi.org/10.29333/ejmste/13808
• Hammoudi, M. M., Grira, S., & Alseksek, Y. (2023). Mathematics motivation success optimization in higher education: Novel graph-based visualization of three models. In Proceedings of the 2023 7^th International Conference on Education and E-Learning (pp. 138-142). https://doi.org/10.1145/3637989.3637997
• Hammoudi, M. M., Grira, S., & Alseksek, Y. (2025). Improving engineering students’ motivation for success in statistics and data science: An innovative visualization of four mathematical models in higher education in United Arab Emirates. International Journal of Engineering Pedagogy, 15(2), 122-152. https://doi.org/10.3991/ijep.v15i2.50611
• Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural modeling. Journal of the Academy of Marketing Science, 43(1), 115-135. https://doi.org/10.1007/s11747-014-0403-8
• Hidi, S., & Renninger, K. A. (2006). The four-phase model of interest development. Educational Psychologist, 41(2), 111-127. https://doi.org/10.1207/s15326985ep4102_4
• Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.
• Hwang, G. J., & Tu, Y. F. (2021). Roles and research trends of artificial intelligence in mathematics education: A bibliometric mapping analysis and systematic review. Mathematics, 9(6), Article 584. https://doi.org/10.3390/math9060584
• Johnson, D. W., & Johnson, R. T. (2009). An educational psychology success story: Social interdependence theory and cooperative learning. Educational Researcher, 38(5), 365-379. https://doi.org/10.3102/0013189X09339057
• Kaiser, H. F. (1974). An index of factorial simplicity. Psychometrika, 39(1), 31-36. https://doi.org/10.1007/BF02291575
• Kayan-Fadlelmula, F., Sellami, A., Abdelkader, N., & Umer, S. (2022). A systematic review of STEM education research in the GCC countries: Trends, gaps and barriers. International Journal of STEM Education, 9, Article 2. https://doi.org/10.1186/s40594-021-00319-7
• Kenny, D. A., & McCoach, D. B. (2003). Effect of the number of variables on measures of fit in structural equation modeling. Structural Equation Modeling, 10(3), 331-351. https://doi.org/10.1207/S15328007SEM1003_1
• Kestin, G., Miller, K., Klales, A., Milbourne, T., & Ponti, G. (2025). AI tutoring outperforms in-class active learning: An RCT introducing a novel research-based design in an authentic educational setting. Scientific Reports, 15, Article 17458. https://doi.org/10.1038/s41598-025-97652-6
• Klang, N., Karlsson, N., Kilborn, W., Eriksson, P., & Karlberg, M. (2021). Mathematical problem-solving through cooperative learning—The importance of peer acceptance and friendships. Frontiers in Education, 6. https://doi.org/10.3389/feduc.2021.710296
• Kopparlo, M. (2024). Role of mathematics in retention of undergraduate STEM majors: A meta-analysis. Journal of Mathematics Education, 12(1), 107-122.
• Krapp, A. (2002). Structural and dynamic aspects of interest development: Theoretical considerations from an ontogenetic perspective. Learning and Instruction, 12(4), 383-409. https://doi.org/10.1016/S0959-4752(01)00011-1
• Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. Cambridge University Press. https://doi.org/10.1017/CBO9780511815355
• Leon, C., Lipuma, J., & Oviedo-Torres, X. (2025). Artificial intelligence in STEM education: A transdisciplinary framework for engagement and innovation. Frontiers in Education, 10. https://doi.org/10.3389/feduc.2025.1619888
• Létourneau, A., Deslandes Martineau, M., Charland, P., Karran, J. A., Boasen, J., & Léger, P. M. (2025). A systematic review of AI-driven intelligent tutoring systems (ITS) in K-12 education. npj Science of Learning, 10, Article 29. https://doi.org/10.1038/s41539-025-00320-7
• Liu, S., Leung, F. K., & Jiang, Z. (2024). The appraisal antecedents of Shanghai students’ mathematics anxiety and the moderating effects of teacher behaviours: From the perspective of the control-value theory. Asia Pacific Journal of Education, 44(2), 251-264. https://doi.org/10.1080/02188791.2022.2049699
• Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Pearson.
• Lugosi, E., & Uribe, G. (2022). Active learning strategies with positive effects on students’ achievements in undergraduate mathematics education. International Journal of Mathematical Education in Science and Technology, 53(2), 403-424. https://doi.org/10.1080/0020739X.2020.1773555
• Maričić, M., Anđić, B., Mumcu, F., Marić, M., Gordić, S., Ranitović, M. G., & Cvjetićanin, S. (2025). Enhancing student engagement through instructional STEAM learning activities and self-explanation effect. Eurasia Journal of Mathematics, Science and Technology Education, 21(1), Article em2560. https://doi.org/10.29333/ejmste/15798
• Maulida, L., Nurossobah, P., Aura, B. A., Nengsih, E. D., & Rasilah, R. (2024). Improving the effectiveness of mathematics learning through artificial intelligence: Literature review. Journal of General Education and Humanities, 3(4), 323-338. https://doi.org/10.58421/gehu.v3i4.267
• Mazana, Y. M., Suero Montero, C., & Olifage, C. R. (2019). Investigating students’ attitude towards learning mathematics. International Electronic Journal of Mathematics Education, 14(1), 207-231. https://doi.org/10.29333/iejme/3997
• McAuley, E., Duncan, T., & Tammen, V. V. (1989). Psychometric properties of the intrinsic motivation inventory in a competitive sport setting: A confirmatory factor analysis. Research Quarterly for Exercise and Sport, 60(1), 48-58. https://doi.org/10.1080/02701367.1989.10607413
• Nguyen, D. T., & Pham, Q. V. (2025). The evolving landscape of AI integration in mathematics education: A systematic review of trends (2015-2025). Eurasia Journal of Mathematics, Science and Technology Education, 21(10), Article em2714. https://doi.org/10.29333/ejmste/17078
• Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill.
• Nzaramyimana, E., Mukandayambaje, E., Iyamuremye, L., Hakizumuremyi, V., & Ukobizaba, F. (2021). Effectiveness of GeoGebra towards students’ active learning, performance and interest to learn mathematics. International Journal of Mathematics and Computer Research, 9(10), 2423-2430. https://doi.org/10.47191/ijmcr/v9i10.05
• Osborne, J., Simon, S., & Collins, S. (2003). Attitudes towards science: A review of the literature and its implications. International Journal of Science Education, 25(9), 1049-1079. https://doi.org/10.1080/0950069032000032199
• Palacios, A., Arias, V., & Arias, B. (2013). Attitudes towards mathematics: Construction and validation of a measurement instrument. Journal of Psychodidactics, 19(1), 67-91. https://doi.org/10.1387/RevPsicodidact.8961
• Pallant, J. (2020). SPSS survival manual (7th ed.). McGraw-Hill. https://doi.org/10.4324/9781003117445
• Pearson. (2026). AI-powered study tools for higher education. Pearson. https://www.pearson.com/en-us/higher-education/products-services/aistudytool.html
• Pekrun, R. (2006). The control-value theory of achievement emotions. Educational Psychology Review, 18(4), 315-341. https://doi.org/10.1007/s10648-006-9029-9
• Pekrun, R., Goetz, T., Frenzel, A. C., Barchfeld, P., & Perry, R. P. (2011). Measuring emotions in students’ learning and performance: The achievement emotions questionnaire. Contemporary Educational Psychology, 36(1), 36-48. https://doi.org/10.1016/j.cedpsych.2010.10.002
• Pekrun, R., Goetz, T., Titz, W., & Perry, R. P. (2002). Academic emotions in students’ self-regulated learning and achievement: A program of qualitative and quantitative research. Educational Psychologist, 37(2), 91-105. https://doi.org/10.1207/S15326985EP3702_4
• Pintrich, P. R. (2003). A motivational science perspective on the role of student motivation in learning and teaching contexts. Journal of Educational Psychology, 95(4), 667-686. https://doi.org/10.1037/0022-0663.95.4.667
• Pintrich, P. R., & De Groot, E. V. (1990). Motivational and self-regulated learning components of classroom academic performance. Journal of Educational Psychology, 82(1), 33-40. https://doi.org/10.1037/0022-0663.82.1.33
• Pintrich, P. R., Smith, D. A. F., Garcia, T., & McKeachie, W. J. (1991). A manual for the use of the motivated strategies for learning questionnaire (MSLQ). National Center for Research to Improve Postsecondary Teaching and Learning.
• Prince, M. (2004). Does active learning work? A review of the research. Journal of Engineering Education, 93(3), 223-231. https://doi.org/10.1002/j.2168-9830.2004.tb00809.x
• Retnowati, E., Ayres, P., & Sweller, J. (2018). Collaborative learning effects when students have complete or incomplete knowledge. Applied Cognitive Psychology, 32(6), 681-692. https://doi.org/10.1002/acp.3444
• Ribeirinha, T., Baptista, M., & Correia, M. (2024). The impact of STEM activities on the interest and aspirations in STEM careers of 12th-grade Portuguese students in science and technology curriculum. European Journal of STEM Education, 9(1), Article 21. https://doi.org/10.20897/ejsteme/15830
• Saadeghvaziri, M., Eskandari, M., & Bandelt, M. (2025). Advancing active learning: An in-depth review and an innovative model (M3). Journal of STEM Education: Innovations and Research, 26(3), 48-58. https://doi.org/10.63504/jstem.v26i3.2739
• Schukajlow, S., & Rakoczy, K. (2016). The power of emotions: Can enjoyment and boredom explain the impact of individual preconditions and teaching methods on interest and performance in mathematics? Learning and Instruction, 44, 117-127. https://doi.org/10.1016/j.learninstruc.2016.05.001
• Shank, E., Tang, H., & Morris, W. (2025). Motivation in online course design using self-determination theory: An action research study in a secondary mathematics course. Educational Technology Research and Development, 73(1), 415-441. https://doi.org/10.1007/s11423-024-10410-9
• Slavin, R. E. (2019). Cooperative learning and academic achievement: Why does groupwork work? Annals of Psychology, 35(3), 785-791.
• Soesanto, R. H., Dirgantoro, K. P. S., & Priyantim, N. (2022). Indonesian students’ perceptions towards AI-based learning in mathematics. Journal on Mathematics Education, 13(3), 531-548. http://doi.org/10.22342/jme.v13i3.pp531-548
• Sweller, J. (1988). Cognitive load during problem-solving. Cognitive Science, 12(2), 257-285. https://doi.org/10.1016/0364-0213(88)90023-7
• Tabachnick, B. G., & Fidell, L. S. (2019). Using multivariate statistics (7th ed.). Pearson.
• Tapia, M., & Marsh, G. E. (2002). Confirmatory factor analysis of the attitudes toward mathematics inventory [Paper presentation]. The Annual Meeting of the Mid-South Educational Research Association.
• Thongprasit, J., & Wannapiroon, P. (2022). Framework of artificial intelligence learning platform for education. International Education Studies, 15(1), 76-86. https://doi.org/10.5539/ies.v15n1p76
• Ting, F. S. T., Lam, W. H., & Shroff, R. H. (2019). Active learning via problem-based collaborative games in a large mathematics university course in Hong Kong. Education Sciences, 9(3), Article 172. https://doi.org/10.3390/educsci9030172
• Tran, V. D. (2012). Effects of cooperative learning on students at Angiang University in Vietnam [PhD thesis, La Trobe University]. https://doi.org/10.26181/21845394.v1
• Tsigilis, N., & Theodosiou, A. (2003). Temporal stability of the intrinsic motivation inventory. Perceptual and Motor Skills, 97(1), 271-280. https://doi.org/10.2466/pms.2003.97.1.271
• Turgut, S., & Gülşen Turgut, İ. (2018). The effects of cooperative learning on mathematics achievement in turkey a meta-analysis study. International Journal of Instruction, 11(3), 663-680. https://doi.org/10.12973/iji.2018.11345a
• Tyson, W. (2011). Modeling engineering degree attainment using high school and college academic preparation. Journal of Engineering Education, 100(4), 760-777. https://doi.org/10.1002/j.2168-9830.2011.tb00035.x
• Wagner, L. (2024). AI support can prevent college students from failing STEM classes, study shows. The 74 Million. https://www.the74million.org/article/ai-support-can-prevent-college-students-from-failing-stem-classes-study-shows/
• Wang, R. E., Ribeiro, A. T., Robinson, C. D., Loeb, S., & Demszky, D. (2024). Tutor CoPilot: A human-AI approach for scaling real-time expertise. arXiv https://doi.org/10.48550/arXiv.2410.03017
• Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education – Where are the educators?. International Journal of Educational Technology in Higher Education, 16, Article 39. https://doi.org/10.1186/s41239-019-0171-0
• Zhai, X., & Krajcik, J. (2024). Artificial intelligence-based STEM education. In X. Zhai, & J. Krajcik (Eds.), Uses of artificial intelligence in STEM education (chapter 1). Oxford University Press. https://doi.org/10.1093/oso/9780198882077.003.0001
• Zhao, D., Playfoot, J., De Nicola, C., Guarino, G., Bratu, M., Di Salvadore, F., & Muntean, G. M. (2021). An innovative multi-layer gamification framework for improved STEM learning experience. IEEE Access, 10, 3879-3889. https://doi.org/10.1109/ACCESS.2021.3139729
• Zimmerman, B. J. (2000). Attaining self-regulation. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 13-39). Academic Press. https://doi.org/10.1016/B978-012109890-2/50031-7

APA

Hammoudi, M. M., Grira, S., & Aldabak, T. (2026). AI-designed active learning and STEM students’ perceived success in higher education mathematics and statistics courses. Eurasia Journal of Mathematics, Science and Technology Education, 22(12), em2966. https://doi.org/10.29333/ejmste/19952

Vancouver

Hammoudi MM, Grira S, Aldabak T. AI-designed active learning and STEM students’ perceived success in higher education mathematics and statistics courses. EURASIA J Math Sci Tech Ed. 2026;22(12):em2966. https://doi.org/10.29333/ejmste/19952

AMA

Hammoudi MM, Grira S, Aldabak T. AI-designed active learning and STEM students’ perceived success in higher education mathematics and statistics courses. EURASIA J Math Sci Tech Ed. 2026;22(12), em2966. https://doi.org/10.29333/ejmste/19952

Chicago

Hammoudi, Mohamad Mustafa, Sofiane Grira, and Tala Aldabak. "AI-designed active learning and STEM students’ perceived success in higher education mathematics and statistics courses". Eurasia Journal of Mathematics, Science and Technology Education 2026 22 no. 12 (2026): em2966. https://doi.org/10.29333/ejmste/19952

Harvard

Hammoudi, M. M., Grira, S., and Aldabak, T. (2026). AI-designed active learning and STEM students’ perceived success in higher education mathematics and statistics courses. Eurasia Journal of Mathematics, Science and Technology Education, 22(12), em2966. https://doi.org/10.29333/ejmste/19952

MLA

Hammoudi, Mohamad Mustafa et al. "AI-designed active learning and STEM students’ perceived success in higher education mathematics and statistics courses". Eurasia Journal of Mathematics, Science and Technology Education, vol. 22, no. 12, 2026, em2966. https://doi.org/10.29333/ejmste/19952