An Effective One-Hour Differentiated Instruction Model for Enhancing Student Engagement and Learning Outcome

Abstract

This paper presents a framework for teaching a one-hour computer networks class using adaptive learning. The goal is to meet the different learning needs of students by offering personalized instruction and varied content. The class covers basic network topologies and models, using an adaptive platform to personalize instruction based on student understanding. Through interactive lectures, group activities, and adaptive quizzes, students receive a customized learning experience that helps them grasp the fundamentals of computer networks effectively. Results showed that students who received personalized instruction were more engaged and performed better on assessments. The discussion emphasizes the benefits of differentiated instruction and adaptive learning technologies in improving learning outcomes, despite initial setup challenges. In conclusion, this framework effectively enhances student understanding of computer networks by providing customized and interactive learning experiences.