Profiling Student Readiness for Personalized Learning to Support Sustainable Education

¹ Department of Information Technology Education, Universitas Negeri Surabaya, 60231 Surabaya, Indonesia
² Department of Information Systems, Universitas Negeri Surabaya, 60231 Surabaya, Indonesia
³ Department of Information Systems, Universitas Pembangunan Nasional “Veteran” Jawa Timur, 60294 Surabaya, Indonesia
⁴ Department of Educational Management, Universitas Palangka Raya, 73112 Palangka Raya, Indonesia
⁵ Graduate School of Science and Technology, Kumamoto University, 860-8555 Kumamoto, Japan
⁶ Department of Business Statistics, Institut Teknologi Sepuluh Nopember, 60111 Surabaya, Indonesia
⁷ Department of Informatics, Universitas Negeri Surabaya, 63392 Magetan, Indonesia

^* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.

Abstract

In the current era of educational transformation, personalized learning has emerged as a promising strategy to enhance the effectiveness of learning processes. Its plays a crucial role in supporting the broader goals of sustainable education. This study explores student profiles as a basic element in designing personalized learning using K-means clustering techniques. This study integrates additional aspects such as motivation, learning style, future employment, and technological proficiency key factors. The study provides a framework for grouping students to improve their readiness for personalized learning. The cluster analysis identified four clusters of students that namely Cluster 0 to 3. The highest readiness identified for Cluster 2 with flexibility in adapting to various learning styles, strong motivation, and high technological proficiency. Cluster 1 showed moderate readiness with balanced learning preferences not as optimal as Cluster 2. In contrast, Clusters 0 and 3 showed lower readiness. These cluster require improvement strategy to increase interactive learning engagement, motivation, and technological proficiency. These findings show the important role of clustering techniques in optimizing personalized learning strategies to support sustainability education.