This full paper presents an implementation of a technology-enabled learning environment such as MATLAB Live, used to enhance student experience when engaging with computational modeling activities within a capstone engineering course. Computational modeling and simulation are key aspects within engineering education. As such, continued progress towards understanding and improving student experiences within computational modeling activities is a paramount for engineering educators. MATLAB Live, along with other similar technologies such as Jupyter Notebooks, allow for users to mix together data visualizations, working code, explanations, and other forms of media. This form of discipline-based technology-enabled learning environment allows for maximizing scaffolding and potentially lessening the cognitive load experienced during the learning activity.
The paper describes how a computational modeling intervention based on educational theories and frameworks such as the model-eliciting activity and productive failure, were scaffolded and delivered through MATLAB Live. The paper quantitatively and qualitatively identified different ways in which students engaged with MATLAB Live and how those differed between student programmers comfort levels. Additionally, quantitative analysis was used to understand the effects the intervention had on student self-efficacy. The guiding research questions were: (1) How did such technology-enabled scaffolded (MATLAB Live) modeling activity experiences impact student self-efficacy regarding programming and computational modeling? (2) Based on student comfort level with programming (self-efficacy), how did students vary in their reported experiences of MATLAB Live? The results of this analysis show that the MATLAB Live scaffolding proved beneficial to both novice and experienced programmers, yet student-reported benefits differed in key areas. The paper concludes with recommendations for using MATLAB Live and similar programs derived from the results of the study.
Joseph A. Lyon is a Lecturer for the College of Engineering Honors Program at Purdue University. He holds a Ph.D. in Engineering Education. His research interests are computational thinking and mathematical modeling.
Aparajita Jaiswal is a Ph.D. student in Purdue Polytechnic at Purdue University, West Lafayette. Her research interests are in datascience education, computational thinking, student engagement and motivation in active learning environments.
Alejandra Magana is a Professor in the Department of Computer and Information Technology and an affiliated faculty at the School of Engineering Education at Purdue University. She holds a B.E. in Information Systems, a M.S. in Technology, both from Tec de Monterrey; and a M.S. in Educational Technology and a Ph.D. in Engineering Education from Purdue University. Her research is focused on identifying how model-based cognition in STEM can be better supported by means of expert technological and computing tools such as cyber-physical systems, visualizations, and modeling and simulation tools.
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