2024 ASEE Annual Conference & Exposition

In many universities across the United States, collaborative learning is a common practice in introductory engineering courses and laboratory classes. However, as students progress to more advanced technical coursework, for example, fundamental circuits in Electrical Engineering, traditional lecture-based classes with individual assignments and assessments tend to take precedence. While previous research has compared the effectiveness of collaborative and lecture-based approaches, there remains a critical gap in understanding the nuanced relationships between team composition, student perceptions of collaborative learning, and various student outcomes such as self-efficacy, test anxiety, and teamwork attitudes.

To address these gaps in the literature, this study presents a comprehensive investigation conducted within the context of an introductory circuits course in Electrical and Computer Engineering at Purdue University.

Our research aims to shed light on the following key research questions:
1. How do student perceptions of the Collaborative Learning Experience (CLE) relate to key student outcomes such as test anxiety, self-efficacy, and course performance?
2. What role do student demographics (including gender, ethnicity, major, GPA, year in school, and prior circuits experience) and pre-existing perceptions of teamwork play in shaping both the CLE and student outcomes?
3. How does the composition of teams (considering factors like gender, ethnicity, major, GPA, prior circuits experience, and year in school) influence student perceptions of the CLE and, consequently, student outcomes?

The data was collected through pre- and post-course surveys, which included a diverse range of measures and pre-existing instruments. The survey responses were gathered from students enrolled in the Electrical Engineering Fundamentals course during both the Spring 2021 (n=570 complete responses) and Fall 2021 (n=429) semesters. To address the research questions, we employed Multivariate Linear Regression (MLR) and, Hierarchical Linear Modeling (HLM) using the SAS software.

This paper provides a comprehensive overview of the study design for the quantitative analysis, introducing the HLM methodology for such data. Preliminary findings from the initial model will be presented and the subsequent steps in the data analysis process will be outlined. The MLR analysis indicates that student outcomes, including academic performance and collaborative learning experiences, exhibit associations with various factors, notably certain student demographic characteristics, levels of self-efficacy, and degrees of test anxiety. Furthermore, HLM was employed to delve deeper into the individual-level and team-level impact of the observed outcomes.

This study offers educators essential insights into improving collaborative learning in technical major-specific engineering courses, emphasizing the significance of team composition and student perceptions in pedagogical planning. Furthermore, it aids researchers in comprehending how team-level factors impact outcomes and may facilitate the development of practical strategies to enhance collaborative learning effectiveness in educational settings based on our research findings.