Revolutionizing University Mathematics Education: The Impact of Deep Learning and Educational Technology - Budiarti, Erna; James, Mathew Lowa (Journal of Teaching and Learning Mathematics, 2025)
The integration of deep learning into university mathematics education presents transformative possibilities. This study conducted a Systematic Literature Review (SLR), examining 80 studies from sources like IEEE Xplore, ScienceDirect, Scopus, and Google Scholar. The focus was on the application of deep learning in formal education, excluding non-educational contexts. Findings indicate that deep learning enhances learning through interactive visualization and simulation tools, with virtual reality applications improving geometry and statistics comprehension by up to 30% compared to traditional methods. Challenges such as limited infrastructure and resistance from lecturers were identified. To address these, the study advocates for regular technology-based training for lecturers and gradual curriculum integration of technology. Strengthening collaboration between universities, the technology industry, and policymakers is recommended to support implementation. Additionally, the proposal of an online evaluation platform is suggested to continuously monitor student competency improvements. A systematic approach to implementing deep learning is anticipated to optimize mathematics education effectiveness in universities, preparing students for complex workforce challenges.