Investigating How Student Attributes and Behaviors Relate to Learning Outcomes in a Free Online Python Programming Course
- Conference
- Location
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Portland, Oregon
- Publication Date
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June 23, 2024
- Start Date
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June 23, 2024
- End Date
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June 26, 2024
- Conference Session
- Tagged Division
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Computers in Education Division (COED)
- Page Count
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13
- DOI
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10.18260/1-2--47690
- Permanent URL
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https://peer.asee.org/47690
- Download Count
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103
Paper Authors
Timothy James Purdue University
Timothy James is an instructor at the University of Pittsburgh Computer Science department, as well as a Doctor of Technology student at Purdue University. Previously, Tim has spent some time in a variety of industries including Internet consulting, finance, defense contracting, aviation maintenance, telecommunications, capital markets, and sandwich artistry. Timothy hopes to continue actively engaging the community in technical training and CS education efforts.
Alejandra J. Magana
Purdue University
orcid.org/0000-0001-6117-7502
Alejandra J. Magana, Ph.D., is the W.C. Furnas Professor in Enterprise Excellence in the Department of Computer and Information Technology and Professor of Engineering Education at Purdue University.
Abstract
Students learning a programming language in a free, online environment are faced with several challenges - beyond the difficult material, the content must hold their attention and keep them coming back when there is no credit and there are minimal repercussions for failure or withdrawal. Attrition rates are high in these types of courses, and reducing attrition could have positive benefits. Determining student attributes and behaviors that could improve success could be valuable in helping many students learn a new programming language and could help to meet the high demand for computer science education.
The authors enrolled 921 students from around the world in a voluntary, noncredit, introductory Python programming course across several cohorts in 2022 and 2023. While these courses contained minor experimental variations for research purposes, the focus, topics, content, and evaluation criteria were similar. Student participation and completion were evaluated for each course. Surveys were administered to enrolled students that gathered data on experience, intent, behaviors, and demographics. Responses to these surveys indicate a racially diverse group of students with varying ages, levels of experience, educational backgrounds, and programming confidence.
This paper presents the student demographic data collected and aims to analyze these attributes to determine whether any of these factors correlate with higher rates of student success in these courses, measured by student participation rate and completion rate. Better understanding of these qualities may be used to encourage future cohorts of students and improve student achievement. This understanding may also be used to improve curriculum design so that future courses are able to effectively engage a broader audience.
James, T., & Magana, A. J. (2024, June), Investigating How Student Attributes and Behaviors Relate to Learning Outcomes in a Free Online Python Programming Course Paper presented at 2024 ASEE Annual Conference & Exposition, Portland, Oregon. 10.18260/1-2--47690
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