Practical Applications of Deep Learning in Mathematics to Enhance Student Engagement and Conceptual Mastery - Nurdiana, Aty; Zulianti, Hajjah; Ciciria, Deri; Fitria, Nur; Kirana, Arinta Rara (Universitas Muhammadiyah Mataram, 2024)
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.