Selecting the Right Strategy for Deep Learning

Summary of Li, H., & Zhang, Y. (2024)

Background & Existing Knowledge
Personalized teaching is vital for learner-centered instruction, but selecting the right strategy for each learner remains a challenge. Deep learning offers a way to automate and improve this decision-making.

New Insights from This Study
✔ A deep neural network model accurately matched students to optimal teaching strategies.
✔ Personalized strategies led to better performance and learner satisfaction.
✔ The model adapted in real time to changing learner behaviors.

Practical Applications: How Educators Can Use This Research

Smart Strategy Selection
📌 Use digital platforms that recommend teaching approaches per learner.
📌 Match learners with tasks based on predicted preferences and outcomes.
📌 Combine data from assessments and classroom behaviors.

Instructional Personalization
📌 Vary content delivery (videos, simulations, reading) based on learner needs.
📌 Allow choice in learning format aligned with recommended strategies.
📌 Provide differentiated support using model insights.

Continuous Feedback Loop
📌 Regularly update models with fresh student data.
📌 Collect learner feedback to validate recommendations.
📌 Train teachers in interpreting DL-generated insights.

Conclusion
Deep learning enables real-time, data-driven selection of teaching strategies, making personalized instruction more scalable and impactful.

Key Words
personalized teaching, instructional strategies, deep learning, learner modeling, adaptive education

Reference
Li, H., & Zhang, Y. (2024). Personalized Teaching Strategy Recommendation Based on Deep Learning. International Journal of Educational Technology, 29(2), 101–118.

Suggested Citation
Ross, E. M., & Malar, D. B. J. (2025). Selecting the Right Strategy for Deep Learning. Excellent Educator, 2(12), 2.