Personalized and Collaborative Learning Approach for Enhanced Student Engagement

Abstract

This paper examines the implementation of personalized learning strategies in the "Theory of Machines" course for mechanical engineering students, aimed at addressing diverse learning preferences often overlooked in traditional teaching methods. A survey conducted at the beginning of the semester identified students’ preferred learning styles, including Visual, Aural, Kinesthetic, and Read/Write. Tailored activities, designed using freely available online tools, were introduced to align with these preferences, ensuring students engaged with the content in a manner that best supported their individual learning styles. Engagement metrics and pre- and post-activity surveys indicated improved participation and self-assessed understanding across all learner types. To further extend learning beyond individual preferences, a collaborative hackathon was organized at the end of the course. Diverse teams, composed of students with different learning styles, worked together to simulate real-world engineering collaboration. This interdisciplinary teamwork allowed students to contribute based on their strengths while learning new approaches from their peers. The hackathon results demonstrated that peer learning significantly enhanced students’ problem-solving abilities and communication skills. This study underscores the effectiveness of combining personalized learning with collaborative projects in engineering education. By addressing individual learning needs and fostering teamwork, students were better prepared for the demands of professional engineering environments. The findings suggest that this model is scalable to other technical courses and has the potential to reshape engineering curricula to be more inclusive and dynamic. Future research should explore the long-term impacts on student outcomes and the integration of emerging technologies, such as virtual reality, to further enrich the learning experience.