Work in Progress: The Electric Circuit Concepts Diagnostic (ECCD)

Nathaniel Hunsu; Kun Yao; Adel Al Weshah; Olanrewaju Olaogun; Shiyu Wang

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Conference: 2022 ASEE Annual Conference & Exposition
Location: Minneapolis, MN
Publication Date: August 23, 2022
Start Date: June 26, 2022
End Date: June 29, 2022
Conference Session: Electrical and Computer Engineering Division Poster Session
Page Count: 9
DOI: 10.18260/1-2--40855
Permanent URL: https://peer.asee.org/40855
Download Count: 301

Paper Authors

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Nathaniel Hunsu University of Georgia

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Nathaniel Hunsu is currently an assistant professor of Engineering Education at the University of Georgia. He is affiliated with the Engineering Education Transformational Institute and the school of electrical and computer engineering at the university. He holds a PhD in Educational Psychology from Washington State University. His research interests are in learning and cognition, students’ engagement in their learning contexts, and the assessment of learning and engagement in engineering classrooms. He conducts studies that examine student engagement and academic resilience in engineering education. He is currently the principal investigator on two NSF-funded projects. The first project examines factors that influence academic resilience among engineering students, while the other involves the development of a diagnostic tool to identify students’ misconceptions in electrical engineering.

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Kun Yao University of Georgia

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Adel Al Weshah University of Georgia

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Dr. Al Weshah is a lecturer in the School of Electrical and Computer Engineering in the College of Engineering at the University of Georgia. He is also affiliated with the Engineering Education Transformational Institute (EETI). His engineering educational research interests include remote labs and developing innovative instructional materials and techniques.

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Olanrewaju Olaogun University of Georgia

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Olanrewaju Olaogun is currently a Ph.D. candidate in Electrical and Computer Engineering at the University of Georgia. He received a B.Sc. in Electrical/Electronic Engineering from the University of Benin, Nigeria, and an M.Sc. in Electrical Engineering from Florida Institute of Technology, USA. He is interested in the conceptual change research in engineering and STEM. His research emphasis at the time is on formulation and testing of models of conceptual change learning and understanding of the processes by which conceptual change occurs in engineering. He can be reached at olanrewaju.olaogun@uga.edu

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Shiyu Wang University of Georgia

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Abstract

When students show up in engineering classrooms, some come with misconceptions about the concepts covered in engineering course contents. However, instructional efforts, and being able to learn advanced engineering concepts require building on existing accurate prior knowledge to be effective. Some instructors rely on concept inventories (CIs) to identify misconceptions in students’ prior knowledge – hoping to use the information they glean from students’ CI scores to adjust their own instructions in order to be effective in their pedagogies. However, many instructors never benefit much from using CIs because they lack either the know-how, time commitment, or statistical skills required to use them effectively and efficiently. Furthermore, there sometimes are ambiguities about whether students’ CI scores should be interpreted as showing a lack of prior knowledge, or as revealing a misconception. The Electric Circuit Concepts Diagnostic (ECCD) project team will address these limitations of CIs by creating web-based electric circuit concept inventory that: (i.) provides an immediate and multi-purpose feedback system for reporting about students’ circuits and electricity prior knowledge; (ii.) differentiates, with a high probability, between a lack of prior knowledge and misconceptions; and (iii.) uses a scheme of multidimensional knowledge profiles to report on students’ prior knowledge and misconceptions. The project will integrate the affordances of cognitive diagnostic modeling (CDM), multi-tier testing frameworks, and computer-assisted testing to realize these project objectives. The ECCD project will be implemented in three overlapping phases, and provide a framework for developing value-added CIs in other STEM domains. In this first work-in-progress report, our primary goal is to introduce the ECCD inventory to the ECE research and teaching community, to highlight our rationale for using a multi-tier testing approach, and how a cognitive diagnostic modeling (CDM) technique will be used to optimize the utility of the ECCD inventories as a knowledge diagnostic tool. Our secondary goal is to seek feedback from researchers and instructors who would be the potential users of the instrument.

Citation
Format

Hunsu, N., & Yao, K., & Al Weshah, A., & Olaogun, O., & Wang, S. (2022, August), Work in Progress: The Electric Circuit Concepts Diagnostic (ECCD) Paper presented at 2022 ASEE Annual Conference & Exposition, Minneapolis, MN. 10.18260/1-2--40855

TY  - CPAPER
AB  - When students show up in engineering classrooms, some come with misconceptions about the concepts covered in engineering course contents. However, instructional efforts, and being able to learn advanced engineering concepts require building on existing accurate prior knowledge to be effective. Some instructors rely on concept inventories (CIs) to identify misconceptions in students’ prior knowledge – hoping to use the information they glean from students’ CI scores to adjust their own instructions in order to be effective in their pedagogies. However, many instructors never benefit much from using CIs because they lack either the know-how, time commitment, or statistical skills required to use them effectively and efficiently.  Furthermore, there sometimes are ambiguities about whether students’ CI scores should be interpreted as showing a lack of prior knowledge, or as revealing a misconception. The Electric Circuit Concepts Diagnostic (ECCD) project team will address these limitations of CIs by creating web-based electric circuit concept inventory that: (i.) provides an immediate and multi-purpose feedback system for reporting about students’ circuits and electricity prior knowledge; (ii.) differentiates, with a high probability, between a lack of prior knowledge and misconceptions; and (iii.) uses a scheme of multidimensional knowledge profiles to report on students’ prior knowledge and misconceptions. The project will integrate the affordances of cognitive diagnostic modeling (CDM), multi-tier testing frameworks, and computer-assisted testing to realize these project objectives. The ECCD project will be implemented in three overlapping phases, and provide a framework for developing value-added CIs in other STEM domains. 
In this first work-in-progress report, our primary goal is to introduce the ECCD inventory to the ECE research and teaching community, to highlight our rationale for using a multi-tier testing approach, and how a cognitive diagnostic modeling (CDM) technique will be used to optimize the utility of the ECCD inventories as a knowledge diagnostic tool. Our secondary goal is to seek feedback from researchers and instructors who would be the potential users of the instrument.

AU  - Nathaniel Hunsu
AU  - Kun Yao
AU  - Adel Al Weshah
AU  - Olanrewaju Olaogun
AU  - Shiyu Wang
CY  - Minneapolis, MN
DA  - 2022/08/23
PB  - ASEE Conferences
TI  - Work in Progress: The Electric Circuit Concepts Diagnostic (ECCD)
UR  - https://peer.asee.org/40855
DO  - 10.18260/1-2--40855
ER  -