2026 ASEE Annual Conference & Exposition

Using CATE, the “Circuit Analysis Tool for Education,” To Facilitate and Complement Peer Learning

Presented at Electrical and Computer Engineering Division (ECE) Technical Session 1

Using CCCC, the “C-C-C-C,” To Facilitate and Complement Peer Learning

Peer learning is a big umbrella describing a wide range of approaches. Boud [1] describes it as “students learning from and with each other in both formal and informal ways.” In this paper we describe the use of learning materials generated by the CCCC system in a more informal environment. These materials are provided at no cost and are available in Canvas-compatible formats as well as on a stand alone web site [BLANK].org.

Peer learning has demonstrated effectiveness in many contexts. However, Lang [2] notes that cheating is a concern in any such cooperative or peer environment. Thanks to the capabilities of the CATE system, many concerns with cheating can be mitigated.

CCCC, the “C-C-C-C,” is a software-based system that generates circuits with randomized structure and produces step-by-step analyses with equations and detailed discussion. Learning objectives are typical for a first university-level course in circuits. These include fundamental theorems of circuit behavior, mesh analysis, nodal analysis, Thevenin and Norton models, 1st and 2nd order transients, AC phasor analysis and AC power. Hundreds of worked examples are available. These materials may be used for examples, practice problems, and assessment.

Our hypothesis is that CCCC’s randomized circuit structures benefit student learning in several ways. First they provide a wide range of circuit conditions. Second, they are intended to encourage a deeper level of peer engagement, particularly when used for assessment where each student is assigned a structurally distinct circuit. And third, these are intended to mitigate concerns with cheating because each student has to derive their own equations when analyzing the circuit. The full paper will discuss additional recommendations by Lang [2] and why CCCC can help discourage cheating, including: boosting self-efficacy, the testing effect, use of lower-stakes assessments and the benefits of refreshing assignments.

Our research questions are:
To what degree can the CCCC environment approximate the benefit of students working together face-to-face?
To what degree can CCCC materials provide an environment in which students feel they can improve their self-efficacy?

Results from surveys will be described in the full paper which strongly suggest that CCCC does support both of the above research questions. An environment that can approximate the benefits of face-to-face peer learning is important as it may help instructors cope with more students without diminishing the quality of student learning.

This paper addresses new teaching and learning strategies.

[1] D. Boud, R. Cohen and J. Sampson, “Peer learning in higher education: learning from and with each other,” Routledge, 2014

[2] J. M. Lang, Cheating Lessons - Learning from Academic Dishonesty, Harvard Univ Press,
2013.

Authors
  1. Dr. Fred W DePiero California Polytechnic State University, San Luis Obispo / Allan Hancock College [biography]
  2. Dr. K. Clay McKell Orcid 16x16http://orcid.org/0000-0001-6027-0641 California Polytechnic State University, San Luis Obispo [biography]
Note

The full paper will be available to logged in and registered conference attendees once the conference starts on June 21, 2026, and to all visitors after the conference ends on June 24, 2026

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