Differentiated instruction on undergraduate students based on classification and prediction of students performance using PSO-BP neural network

Abstract

Background

Differentiated instruction (DI) is increasingly recognized as a strategy to meet the diverse learning needs of students by adapting teaching methods to individual characteristics. However, accurate student classification remains a challenge, limiting DI's effectiveness.

Aims

This study aims to develop a more precise, data-driven classification method to enhance DI implementation and improve learning outcomes.

Samples

The study utilized a dataset of 1386 second-year Mechanical and Automotive Engineering undergraduates for model training and validation. An independent external test set of 150 students from the same discipline, not included in the initial dataset, was employed to empirically evaluate the implementation of DI.

Methods

We integrated educational data mining with a Particle Swarm Optimization-Backpropagation (PSO-BP) neural network to classify and predict student performance. Key factors influencing academic performance, including past grades, student engagement, and teacher-student interaction, were analyzed. Pearson correlation and multicollinearity testing were employed to ensure model robustness, and K-fold cross-validation ensured accuracy.

Results

The PSO-BP model significantly improved classification accuracy, particularly in 3-Classes and 5-Classes groupings. Students receiving DI based on these refined classifications demonstrated notable improvements in academic performance compared to those classified using traditional methods.

Conclusion

This study offers a scalable, data-driven solution for student classification in DI, addressing the limitations of current models and providing a practical framework for educational institutions to tailor learning experiences, ultimately enhancing student outcomes and teaching efficacy.