Improved Student Learning in a Circuits Course with a Novel Web-Based System

Fred W. DePiero; Lynne A. Slivovsky; Dominic J. Dal Bello

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Conference: 2024 ASEE Annual Conference & Exposition
Location: Portland, Oregon
Publication Date: June 23, 2024
Start Date: June 23, 2024
End Date: June 26, 2024
Conference Session: Innovative Pedagogical Techniques in Engineering Education
Tagged Division: Electrical and Computer Engineering Division (ECE)
Page Count: 17
DOI: 10.18260/1-2--47588
Permanent URL: https://peer.asee.org/47588
Download Count: 52

Paper Authors

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Fred W. DePiero California Polytechnic State University, San Luis Obispo

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Dr. Fred DePiero received his B.S. and M.S. degrees in Electrical Engineering from Michigan State University in 1985 and 1987. He then worked as a Development Associate at Oak Ridge National Laboratory until 1993. While there he was involved in a variety

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Lynne A. Slivovsky California Polytechnic State University, San Luis Obispo orcid.org/0000-0001-8815-9312

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Dr. Lynne Slivovsky is the Inaugural Chair of Computer Engineering at California Polytechnic State University, San Luis Obispo, California, USA.

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Dominic J. Dal Bello Allan Hancock College orcid.org/0000-0001-8002-3226

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Dom Dal Bello is Professor of Engineering at Allan Hancock College (AHC), a California community college between UC Santa Barbara and Cal Poly San Luis Obispo. At AHC, he is Department Chair of Mathematical Sciences, Faculty Advisor of MESA (the Mathematics, Engineering, Science Achievement Program), and Principal/Co-Principal Investigator of several National Science Foundation projects (S-STEM, LSAMP, IUSE). In ASEE, he is chair of the Two-Year College Division, and Vice-Chair/Community Colleges of the Pacific Southwest Section. He received the Outstanding Teaching Award for the ASEE/PSW Section in 2022.

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Abstract

This paper documents improved student performance on traditional in-class exams, based on their usage of CCC [anonymized]. CCC is a custom-built system for review, practice, and quizzes. It generates circuits with random topologies and random element types. Component values are also randomized. CCC analyzes a circuit automatically and produces detailed, equation-by-equation solutions, along with numeric results. Class quizzes include problems with identical circuits for each student, as well as problems with distinct circuits (of comparable complexity). Students are strongly encouraged to collaborate on these quizzes and to compare the distinct circuits with their peers.

In terms of established best practices, CCC closely parallels the “Make It Stick” [1] paradigm, which has demonstrated benefits across multiple disciplines. In this approach, students are presented with examples that are similar, but not exactly matching, a problem of interest. Published efforts have demonstrated improvements to long-term retention, using this paradigm. The CCC implementation is similar. Students select a learning objective (e.g., mesh analysis, nodal analysis, equivalent circuits) as well as a difficulty level. A circuit and solution are then generated automatically. Thus, a student can access an unlimited number of similar (but not identical) example problems.

CCC also provides a scaffolding feature [2]. Students can select an alternative method of solution to review, for the same circuit. For example, if a student is trying to learn nodal analysis and is struggling with a particular circuit, they can opt to see the circuit analyzed via a prior, more familiar, method. This helps build connections between different techniques and to build on prior abilities.

Features of CCC make the system useful for collaborative learning. Of course, some students prefer an individual approach. And a goal of the system is to support them too. To examine benefits for both learning styles, a survey asked students to describe their level of involvement with collaborative learning. These demographics were used to examine benefits on exam performance, for both individual and collaborative learners. The full paper will include results from the survey as well as results from a regression analysis that relates usage of CCC to student exam scores. CCC usage for each student is recorded by tallying mouse clicks, associated with practice and review activity. Predictive variables include students’ cumulative GPA.

The authors consider the review and practice capabilities of CCC to be important features for students who may have a different preparation, than is typical. For example, students who changed majors, who transferred from another institution, or who may have had lengthy delays since their prerequisite coursework.

[1] Brown, Roediger and McDaniel, Make It Stick, Harvard University Press (2014). [2] Azevedo and Jacobson, Advances in scaffolding learning with hypertext and hypermedia: a summary and critical analysis, Educational Technology, Research and Development, Feb 2008, 56, 1, Research Library, p 93.

Citation
Format

DePiero, F. W., & Slivovsky, L. A., & Dal Bello, D. J. (2024, June), Improved Student Learning in a Circuits Course with a Novel Web-Based System Paper presented at 2024 ASEE Annual Conference & Exposition, Portland, Oregon. 10.18260/1-2--47588

TY - CPAPER
AB - This paper documents improved student performance on traditional in-class exams, based on their usage of CCC [anonymized]. CCC is a custom-built system for review, practice, and quizzes. It generates circuits with random topologies and random element types. Component values are also randomized. CCC analyzes a circuit automatically and produces detailed, equation-by-equation solutions, along with numeric results. Class quizzes include problems with identical circuits for each student, as well as problems with distinct circuits (of comparable complexity). Students are strongly encouraged to collaborate on these quizzes and to compare the distinct circuits with their peers.

In terms of established best practices, CCC closely parallels the “Make It Stick” [1] paradigm, which has demonstrated benefits across multiple disciplines. In this approach, students are presented with examples that are similar, but not exactly matching, a problem of interest. Published efforts have demonstrated improvements to long-term retention, using this paradigm. The CCC implementation is similar. Students select a learning objective (e.g., mesh analysis, nodal analysis, equivalent circuits) as well as a difficulty level. A circuit and solution are then generated automatically. Thus, a student can access an unlimited number of similar (but not identical) example problems.

CCC also provides a scaffolding feature [2]. Students can select an alternative method of solution to review, for the same circuit. For example, if a student is trying to learn nodal analysis and is struggling with a particular circuit, they can opt to see the circuit analyzed via a prior, more familiar, method. This helps build connections between different techniques and to build on prior abilities.

Features of CCC make the system useful for collaborative learning. Of course, some students prefer an individual approach. And a goal of the system is to support them too. To examine benefits for both learning styles, a survey asked students to describe their level of involvement with collaborative learning. These demographics were used to examine benefits on exam performance, for both individual and collaborative learners. The full paper will include results from the survey as well as results from a regression analysis that relates usage of CCC to student exam scores. CCC usage for each student is recorded by tallying mouse clicks, associated with practice and review activity. Predictive variables include students’ cumulative GPA.

The authors consider the review and practice capabilities of CCC to be important features for students who may have a different preparation, than is typical. For example, students who changed majors, who transferred from another institution, or who may have had lengthy delays since their prerequisite coursework.

[1] Brown, Roediger and McDaniel, Make It Stick, Harvard University Press (2014).
[2] Azevedo and Jacobson, Advances in scaffolding learning with hypertext and hypermedia: a summary and critical analysis, Educational Technology, Research and Development, Feb 2008, 56, 1, Research Library, p 93.

AU - Fred W. DePiero
AU - Lynne A. Slivovsky
AU - Dominic J. Dal Bello
CY - Portland, Oregon
DA - 2024/06/23
PB - ASEE Conferences
TI - Improved Student Learning in a Circuits Course with a Novel Web-Based System
UR - https://peer.asee.org/47588
DO - 10.18260/1-2--47588
ER -