2025 ASEE Annual Conference & Exposition

Virtual learning in programming has gained traction as an effective method for delivering advanced education, particularly in object-oriented programming (OOP). However, challenges remain in teaching complex topics like OOP. There are opportunities to advance by utilizing online platforms that offer more specialized yet flexible and adaptive learning paths. These platforms provide comprehensive resources, interactive coding environments, and collaboration tools, enhancing the learning experience. Their flexibility allows learners to progress at their own pace while accommodating varied schedules. Moreover, virtual learning enables real-time feedback and peer interactions, essential for mastering intricate OOP concepts.
With the primary objective of designing a flexible OO programming course for engineering students that incorporates multiple learning paths based on profile characterization, this paper aims to address the following question: What are the student profiles in an OOP programming course for an online engineering career? To this end, unsupervised learning techniques, such as clustering, were employed to categorize students based on patterns of LMS use behavior and academic performance associated with an existing instructional design for an online OO Programming course. We aim to uncover patterns that predict learning outcomes by analyzing behavioral data from virtual learning platforms. This approach seeks to optimize the adaptation of educational content to individual learning styles and needs, improving engagement and success in mastering OOP concepts. The findings of this research contribute to the broader application of AI in education, demonstrating the transformative potential of adaptive learning technologies in shaping the future of programming education.