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Adaptive Learning Platform for Engineering Dynamics

Year: 2025
Type: conference-paper
Abstract: Track: Student Success & Wellness Engineering Dynamics is a foundational junior‑level mechanical engineering course that focuses on the mathematical modeling and dynamic analysis of a broad spectrum of systems—from translational and rotational mechanics to thermal processes, multi‑degree‑of‑freedom mechanisms, and electromechanical and electrical subsystems—using differential equations, Laplace transforms, and transfer functions. To address both the rigor of this mathematical content and the diversity of student preparedness, we developed an adaptive learning framework with the Realizeit platform and seamlessly integrated it into UNC Charlotte’s Canvas environment. Key concepts in Dynamics 2 were mapped into a hierarchical prerequisite network of modules and nodes, each containing instructional materials, randomized practice problems, and auto‑graded assessments, including MATLAB Grader for coding tasks. Standalone exam modules provided secure, timed evaluations of mastery. Realizeit’s analytics dashboard enabled real‑time monitoring of student progress and classification of learners as high achievers, struggling students, or inconsistent performers. Based on these insights, we deployed targeted interventions—including personalized messages, supplemental videos, and concise lecture notes—which students reported made them feel more confident and supported. Preliminary outcomes indicate increased engagement, higher assignment completion rates, deeper conceptual mastery, and an overall positive learning experience. Challenges included reduced in‑class attendance and the resource demands of content development. Future directions involve refining intervention strategies, incentivizing attendance with challenge problems, and extending the adaptive framework to other STEM courses. This work demonstrates a scalable approach for integrating adaptive learning technologies to personalize instruction and enhance student success in mathematically intensive engineering curricula.
Authors: Ghasemi, Amirhossein, Demeter, Elise, Budhrani, Kiran
Language: English
Open Access status: green
OpenAlex ID: W7111485449
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