Practical Applications of Deep Learning in Mathematics to Enhance Student Engagement and Conceptual Mastery

Aty Nurdiana; Hajjah Zulianti; Deri Ciciria; Nur Fitria; Arinta Rara Kirana

doi:10.31764/jtam.v8i4.33102

Practical Applications of Deep Learning in Mathematics to Enhance Student Engagement and Conceptual Mastery

Aty Nurdiana, Hajjah Zulianti, Deri Ciciria, Nur Fitria, Arinta Rara Kirana

Abstract

This study examines the application of deep learning strategies in mathematics education to enhance student engagement and conceptual mastery at a higher education institution in Lampung, Indonesia. Traditional teaching methods, which often focus on rote memorization and procedural fluency, are limited in fostering critical problem-solving skills and deeper conceptual understanding. This research investigates how deep learning strategies such as active learning, collaborative problem-solving, and self-regulated learning can bridge these gaps. A mixed-methods approach was used, combining quantitative data from the Deep Learning Engagement Questionnaire (DLEQ) with qualitative insights from focus group discussions, reflective journals, and interviews with lecturers. Interactive tools like GeoGebra were also incorporated to support the learning process. The findings indicate that deep learning strategies significantly improved student engagement, motivation, conceptual understanding, and problem-solving abilities. Students demonstrated better application of mathematical concepts in practical settings, and lecturers observed improved student performance. This study concludes that the integration of deep learning principles into mathematics education significantly enhances learning outcomes and equips students with the skills needed to navigate real-world challenges. These findings provide meaningful implications for curriculum developers, educators, and policymakers in fostering sustainable, student-centered learning environments within higher education.

Keywords

Conceptual Mastery; Deep Learning; Mathematics Education; Student Engagement; Problem-Solving.

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References

Azevedo, R., & Gašević, D. (2019). Analyzing Multimodal Multichannel Data about Self-Regulated Learning with Advanced Learning Technologies: Issues and Challenges. Computers in Human Behavior, 96, 207–210. https://doi.org/10.1016/J.CHB.2019.03.025

Banihashem, S. K., Farrokhnia, M., Badali, M., & Noroozi, O. (2022). The impacts of constructivist learning design and learning analytics on students’ engagement and self-regulation. Innovations in Education and Teaching International, 59(4), 442–452. https://doi.org/10.1080/14703297.2021.1890634

Bernard, J. S. (2015). Student Engagement: A Principle-Based Concept Analysis. International Journal of Nursing Education Scholarship, 12(1), 111–121. https://doi.org/10.1515/IJNES-2014-0058,

Dai, W., Li, Z., & Jia, N. (2022). Self-regulated learning, online mathematics learning engagement, and perceived academic control among Chinese junior high school students during the COVID-19 pandemic: A latent profile analysis and mediation analysis. Frontiers in Psychology, 13, 1042843. https://doi.org/10.3389/FPSYG.2022.1042843/BIBTEX

Fredricks, J. A., Wang, M. Te, Schall Linn, J., Hofkens, T. L., Sung, H., Parr, A., & Allerton, J. (2016). Using qualitative methods to develop a survey measure of math and science engagement. Learning and Instruction, 43, 5–15. https://doi.org/10.1016/J.LEARNINSTRUC.2016.01.009

González-Calatayud, V., Prendes-Espinosa, P., & Roig-Vila, R. (2021). Artificial Intelligence for Student Assessment: A Systematic Review. Applied Sciences 2021, Vol. 11, Page 5467, 11(12), 5467. https://doi.org/10.3390/APP11125467

Ishartono, N., Nurcahyo, A., Waluyo, M., Prayitno, H. J., & Hanifah, M. (2022). Integrating GeoGebra into the flipped learning approach to improve students’ self-regulated learning during the covid-19 pandemic. Journal on Mathematics Education, 13(1), 69–86. https://doi.org/10.22342/JME.V13I1.PP69-86

Kiger, M. E., & Varpio, L. (2020). Thematic analysis of qualitative data: AMEE Guide No. 131. Medical Teacher, 42(8), 846–854. https://doi.org/10.1080/0142159X.2020.1755030

Luo, R. Z., & Zhou, Y. L. (2024). The effectiveness of self-regulated learning strategies in higher education blended learning: A five years systematic review. Journal of Computer Assisted Learning, 40(6), 3005–3029. https://doi.org/10.1111/JCAL.13052

Maizora, S., Juandi, D., Muchlis, E. E., & Aziza, M. (2023). Profile of Self-regulated Learning in Web-Based Learning for Mathematics Education Students of Bengkulu University. 325–331. https://doi.org/10.2991/978-2-38476-012-1_40

Molenaar, I., Mooij, S. de, Azevedo, R., Bannert, M., Järvelä, S., & Gašević, D. (2023). Measuring self-regulated learning and the role of AI: Five years of research using multimodal multichannel data. Computers in Human Behavior, 139, 107540. https://doi.org/10.1016/J.CHB.2022.107540

Nezami, O. M., Dras, M., Hamey, L., Richards, D., Wan, S., & Paris, C. (2018). Automatic Recognition of Student Engagement using Deep Learning and Facial Expression. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11908 LNAI, 273–289. https://doi.org/10.1007/978-3-030-46133-1_17

Nieto, A., Sánchez-Rosas, J., & Gómez-Iñiguez, C. (2024). Identifying the role of emotional intelligence in achievement emotions and their effects on deep learning strategies in university students. Revista de Psicodidáctica (English Ed.), 29(1), 47–56. https://doi.org/10.1016/J.PSICOE.2023.11.003

Panadero, E., Alonso-Tapia, J., García-Pérez, D., Fraile, J., Sánchez Galán, J. M., & Pardo, R. (2021). Deep learning self-regulation strategies: Validation of a situational model and its questionnaire. Revista de Psicodidáctica (English Ed.), 26(1), 10–19. https://doi.org/10.1016/J.PSICOE.2020.11.003

Saint, J., Fan, Y., Gašević, D., & Pardo, A. (2022). Temporally-focused analytics of self-regulated learning: A systematic review of literature. Computers and Education: Artificial Intelligence, 3, 100060. https://doi.org/10.1016/J.CAEAI.2022.100060

Saleem, A., Kausar, H., & Deeba, F. (2021). Social Constructivism: A New Paradigm in Teaching and Learning Environment. Perennial Journal of History, 2(2), 403–421. https://doi.org/10.52700/PJH.V2I2.86

Sannathimmappa, M. B., Nambiar, V., & Aravindakshan, R. (2022). Encouraging students’ engagement and promoting deep learning in immunology through an interactive clinical case problem-solving (CCPS) approach: Perception among cohort of medical students in Oman. Journal of Education and Health Promotion, 11(1). https://doi.org/10.4103/JEHP.JEHP_162_22,

Santana, O. A., Sousa, B. A. De, Monte, S. R. S. Do, Lima, M. L. D. F., & Silva, C. F. E. (2020). Deep learning practice for high school student engagement in STEM careers. IEEE Global Engineering Education Conference, EDUCON, 2020-April, 164–169. https://doi.org/10.1109/EDUCON45650.2020.9125281

Scager, K., Boonstra, J., Peeters, T., Vulperhorst, J., & Wiegant, F. (2017). Collaborative Learning in Higher Education: Evoking Positive Interdependence. CBE—Life Sciences Education, 15(4). https://doi.org/10.1187/CBE.16-07-0219

Tee, K. N., Leong, K. E., & Abdul Rahim, S. S. (2021). A Self-Regulation Model of Mathematics Achievement for Eleventh-Grade Students. International Journal of Science and Mathematics Education, 19(3), 619–637. https://doi.org/10.1007/S10763-020-10076-8/METRICS

Tian, X., Park, K. H., & Liu, Q. (2023). Deep Learning Influences on Higher Education Students’ Digital Literacy: The Meditating Role of Higher-order Thinking. International Journal of Engineering Pedagogy (IJEP), 13(6), 33–49. https://doi.org/10.3991/IJEP.V13I6.38177

Alten, D. C., Phielix, C., Janssen, J., & Kester, L. (2019). Effects of flipping the classroom on learning outcomes and satisfaction: A meta-analysis. Educational Research Review, 28, 100281. https://doi.org/10.1016/J.EDUREV.2019.05.003

Wang, C. Y., & Lin, J. J. H. (2023). Utilizing artificial intelligence to support analyzing self-regulated learning: A preliminary mixed-methods evaluation from a human-centered perspective. Computers in Human Behavior, 144. https://doi.org/10.1016/J.CHB.2023.107721

Wang, S., Wang, F., Zhu, Z., Wang, J., Tran, T., & Du, Z. (2024). Artificial intelligence in education: A systematic literature review. Expert Systems with Applications, 252, 124167. https://doi.org/10.1016/J.ESWA.2024.124167

Zetriuslita, Nofriyandi, & Istikomah, E. (2021). The Increasing Self-Efficacy and Self-Regulated through GeoGebra Based Teaching Reviewed from Initial Mathematical Ability (IMA) Level. International Journal of Instruction, 14(1), 587–598. https://doi.org/10.29333/iji.2021.14135a

DOI: https://doi.org/10.31764/jtam.v8i4.33102