2024 ASEE Annual Conference & Exposition

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.