Supporting student reflective practices through modelling-based learning assignments

ABSTRACT

Reflective practice is becoming a more necessary skill across all disciplines and industry sectors. This study describes the reflective processes capstone engineering students engaged with as part of modelling-based learning assignments over the entire course of a semester. Students answered multiple reflection questions asking them about planning, monitoring, and evaluating their performance during the modelling-based learning exercises. A rubric was used to quantify the student performance on the reflection assignments and clustering analysis to identify and characterise different student groups. Thematic analysis was then performed on students’ reflections within the different clusters to understand the qualitative differences in student reflective practices. Our results uncovered two groups of students with similar patterns in their reflective practices, which we called active and inactive reflectors. The implications of the study include instructional approaches that can give students repeated practice and encourage them to engage in active reflection behaviours.

KEYWORDS:

• Reflective practices
• engineering education
• models and modelling
• capstone engineering course

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This material is based upon work supported by the National Science Foundation  Graduate Research Fellowship Program under grant number (DGE-1842166) as well as the National Science Foundation under grant number (EEC-1449238). Any opinions, findings, and conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

Notes on contributors

Aparajita Jaiswal

Aparajita Jaiswal is currently a doctoral candidate at Purdue University, West Lafayette. Her research interests includes data science education, computational thinking, model based learning, teamwork, student engagement and motivation in active learning environments.

Joseph A. Lyon

Joseph A. Lyon is currently a doctoral candidate in the School of Engineering Education at Purdue University. He holds a BS in Agricultural and Biological Engineering and an MS in Industrial Engineering. In 2019 he was awarded the National Science Foundation's Graduate Research Fellowship. His research interests include models and modeling, model-based reasoning, and computational thinking in engineering education.

Yiqun Zhang

Yiqun Zhang is a graduate student in Computer and Information Technology (CIT) at Purdue University. She received her Bachelor's degree from the same institution and was on the Dean's List and Semester Honors for her academic achievement. She also worked as an undergraduate research assistant at the ROCkETEd laboratory. In addition, she worked in the Purdue Writing Lab as a teaching assistant.

Alejandra J. Magana

Alejandra J. Magana is the W.C. Furnas Professor in Enterprise Excellence 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.