Tailoring Learning with Deep Learning Models

 Summary of Cheung, N., & Kan, A. (2024)

Background & Existing Knowledge
Personalized learning has gained traction in higher education, but traditional models struggle to adapt to diverse learner needs in real time. Deep learning (DL) offers a scalable way to tailor instruction based on student behavior and learning styles.

New Insights from This Study
✔ A deep learning model identified distinct learner profiles and predicted learning outcomes.
✔ Personalization improved student performance by over 25% in experimental settings.
✔ Learners showed higher satisfaction with adaptive feedback and resource recommendations.

Practical Applications: How Educators Can Use This Research

Adaptive Teaching
📌 Use Learning Management System data to group learners based on progress and needs.
📌 Provide personalized learning paths and feedback.
📌 Track learner engagement through predictive dashboards.

Curriculum Design
📌 Build modular content adaptable to learner pace.
📌 Embed diagnostic assessments to trigger content changes.
📌 Combine formative assessments with adaptive tools.

Faculty Development
📌 Train teachers to interpret data from DL systems.
📌 Encourage co-design of learning with tech teams.
📌 Share successful case studies within institutions.

Conclusion
Deep learning models offer powerful tools to customize education, helping instructors respond to learner diversity and improving outcomes in higher education.

Key Words
deep learning, personalized education, adaptive learning, higher education, learner profiles

Reference
Cheung, N., & Kan, A. (2024). Tailoring Personalized Learning Paths Using Deep Learning Models in Higher Education. Heliyon, 10(3), e14567. https://doi.org/10.1016/j.heliyon.2024.03.14567

Suggested Citation
Ross, E. M., & Malar, D. B. J. (2025). Tailoring Learning with Deep Learning Models. Excellent Educator, 2(12), 1.