From rote learning to deep learning: Filling the gap by enhancing engineering students' reasoning skills through explanatory learning activities
- Conference
- Location
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Baltimore , Maryland
- Publication Date
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June 25, 2023
- Start Date
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June 25, 2023
- End Date
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June 28, 2023
- Conference Session
- Tagged Division
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Educational Research and Methods Division (ERM)
- Page Count
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17
- DOI
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10.18260/1-2--43756
- Permanent URL
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https://strategy.asee.org/43756
- Download Count
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109
Paper Authors
Huihui Qi University of California San Diego
Dr.Huihui Qi is an Associate Teaching Professor in the department of Mechanical and Aerospace Engineering, at the University of California San Diego.
Minju Kim
University of California San Diego
orcid.org/0000-0001-5878-7350
Minju Kim is a postdoctoral scholar at the Engaged Teaching Hub at the UCSD Teaching+Learning Commons. Minju received her Ph.D in Experimental Psychology at UC San Diego. With Engaged Teaching Hub, Minju has designed TA training materials for oral exams and have conducted quantitative analysis on the value of oral exams as early diagnostic tool (Kim et al., ASEE 2022). Minju is interested in designing assessments that can capture and motivate students' deep conceptual learning, such as oral exams and the usage of visual representations (e.g., diagrams and manual gestures).
Yu Li University of California San Diego
Brian has received his Master of Science degree in material science. He is currently continuing his education as a Material Science Ph.D. student. As a graduate student, Brian has spent the past three years as a teaching assistant in a variety of undergraduate courses. His research background focuses on medical devices and soft composite development.
Carolyn L. Sandoval University of California, San Diego
Dr. Sandoval is the Associate Director of the Teaching + Learning Commons at the University of California, San Diego. She earned a PhD in Adult Education-Human Resource Development. Her research interests include adult learning and development, faculty de
Curt Schurgers University of California San Diego
Curt Schurgers is a Teaching Professor in the UCSD Electrical and Computer Engineering Department. His research and teaching are focused on course redesign, active learning, and project-based learning. He also co-directs a hands-on undergraduate research program called Engineers for Exploration, in which students apply their engineering knowledge to problems in exploration and conservation.
Marko V. Lubarda
University of California San Diego
orcid.org/0000-0002-3755-271X
Marko V. Lubarda is an Assistant Teaching Professor in the Department of Mechanical and Aerospace Engineering at the University of California, San Diego. He teaches mechanics, materials science, design, computational analysis, and engineering mathematics courses, and has co-authored the undergraduate textbook Intermediate Solid Mechanics (Cambridge University Press, 2020). He is dedicated to engineering pedagogy and enriching students' learning experiences through teaching innovations, curriculum design, and support of undergraduate student research.
Xuan Emily Gedney
Saharnaz Baghdadchi University of California San Diego
Saharnaz Baghdadchi is an Associate Teaching Professor at UC San Diego. She is interested in scholarly teaching and employs active learning techniques to empower students to attain an expert level of critical thinking. Her expertise facilitates students' journey towards connecting facts with practical knowledge to tackle intricate engineering challenges. She excels in crafting innovative assessments and explores their impact on enhancing students' learning outcomes and fostering an inclusive educational environment.
Alex Phan
University of California San Diego
orcid.org/0000-0003-2489-2886
Dr. Phan received his Ph.D. in Mechanical Engineering from the University of California San Diego with a specialization in medical devices. He is currently an instructor for the Department of Electrical and Computer Engineering focusing on hands-on education.
Abstract
Rote learning refers to the superficial learning of concepts and procedures, typically by brute memorization and with little integration into existing cognitive schemas, resulting in poor knowledge retention and inability to apply the knowledge in new and evolving contexts. With rote learning, students usually learn declarative and procedural knowledge but usually do not pay attention to conditional knowledge (when to use what knowledge). As a result, they usually can replicate the problem-solving process in a familiar context but are unable to transfer the knowledge and use the concept for a new application.
This paper explores the use of explanatory learning activities to promote students’ deep learning. Cognitive psychology literature shows that students do not necessarily learn concepts deeply by solving problems, unless they monitor their thinking and decision-making process before and during problem solving, and reflect on the process after will help to conditionalize their knowledge, i.e., when to use what knowledge to solve the problem.
In this paper, we present a study on a multidimensional approach to enhancing students' reasoning skills by integrating a variety of explanatory learning activities, namely oral exams, written guidance prompts for homework which asks students to justify their problem-solving process, and video assignment in which students perform group-explanation on homework assignments. Oral exams, due to their adaptive diagnostic nature, provide an opportunity to probe students’ thought process behind their decision-making. In contrast, written exams are limited in this capacity: when students write down an equation, it is difficult to tell whether they understand the concept well or if they are trying to recall similar procedures from class examples and homework assignments. Oral exams also allow students to receive feedback from a content expert who can clear up misconceptions. Group explanation activities offer the benefits of feedback exchange and social learning among students. The paper will present the details of these learning activities as well as the outcomes. Mixed research methods were used to study the impact of verbal explanations of learning activities. Students' learning outcomes are mainly measured by exam performance. Students' perceptions were studied through both quantitative Likert-scale questions and free-response to open-ended questions.
Qi, H., & Kim, M., & Li, Y., & Sandoval, C. L., & Schurgers, C., & Lubarda, M. V., & Gedney, X. E., & Baghdadchi, S., & Phan, A. (2023, June), From rote learning to deep learning: Filling the gap by enhancing engineering students' reasoning skills through explanatory learning activities Paper presented at 2023 ASEE Annual Conference & Exposition, Baltimore , Maryland. 10.18260/1-2--43756
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