2024 ASEE Annual Conference & Exposition

Experts and researchers have indicated that the integration of programming languages (e.g., MATLAB, Python, Mathematica, etc.) into linear algebra classes would be advantageous. In response, we restructured APMA 3080 - Linear Algebra by adding four numerical computational components, utilizing MATLAB as the principal tool in this course. This modification is elaborately depicted in a published ASEE paper. In this study, our interest lies in thoroughly analyzing students' perceptions of the efficacy of integrating numerical computational components through MATLAB to bolster their success, shifting the focus from solely expert perspectives. More specifically, the key research questions are:

1. How, if at all, do students' perceptions of MATLAB as a program evolve over the course of the semester? Do these perceptions vary based on factors such as academic major, gender, or race/ethnicity?

2. How, if at all, do students' perceptions of MATLAB as a tool for learning linear algebra evolve over the course of the semester? Do these perceptions vary based on academic major, gender, or race/ethnicity?

3. How do students perceive different numerical computational components in this course, along with the accompanying support resources? Is there any relationship between students' perceptions across different components?

With IRB approval, this study recruits undergraduate students enrolled in APMA 3080 - Linear Algebra to participate. They are asked to complete two surveys: a pre-survey at the beginning of the semester and a post-survey at the end. These surveys examine students' feelings towards MATLAB as both a programming language and a tool for learning Linear Algebra, and their perceptions of how numerical computational components in MATLAB enhances their understanding of linear algebra concepts. The pre-survey also aims to capture demographic information, including gender, race, major, and prior experience with programming and linear algebra knowledge. Students are further asked to grant permission for the use of their course artifacts, such as worksheets, exams, and projects, in the analysis.

The data collected from participants will be analyzed using statistical tools such as boxplots, linear regressions, hypothesis tests, and classifications. The results will be reported both in aggregate (for quantitative and quantized qualitative data) and individually (for qualitative data). Care will be taken to ensure individual students cannot be identified when data is reported individually or broken down by student demographics. This will be achieved by employing measures such as using pseudonyms and withholding identifiable information. The management of the collected data adheres to the university's policy.