Personalized Digital Learning Environment with Differentiated Instruction to Foster Computational Thinking in Robotics Education

摘要

Aim/Purpose: This study aims to implement and evaluate a personalized digital learning environment (PDLE) that delivers differentiated instruction for enhancing computational thinking competencies through robotics education. Background: The background emphasizes the growing demand for computational thinking skills in the modern workforce and the need for flexible learning approaches that accommodate diverse student needs. Methodology: A mixed-methods research approach was employed, utilizing a pre-experimental design with One-Group Pretest-Posttest assessments to evaluate students’ computational thinking development, complemented by classroom observations and instructor feedback to provide deeper insights into the learning process within the PDLE system. Contribution: The key contribution of this study is the integration of PDLE with DI to provide personalized learning pathways that promote deeper engagement and skill development in computational thinking. Findings: Findings suggest that the PDLE significantly enhances students’ computational thinking abilities, particularly in problem-solving and algorithmic thinking, while also improving abstraction, pattern recognition, and fostering greater student independence through self-regulated learning. Recommendations for Practitioners: Recommendations for practitioners include adopting personalized and differentiated learning environments to accommodate diverse learners. Recommendation for Researchers: Recommendations for researchers suggest further exploration of adaptive learning technologies in robotics education. Impact on Society: The impact on society lies in equipping students with essential computational skills that are crucial for success in the digital economy. Future Research: Should explore the scalability of PDLE in other STEM disciplines and investigate long-term impacts on students’ cognitive and career development.