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Analyzing Pivoting Among Weekly Many Small Programs in a CS1 Course

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2020 ASEE Virtual Annual Conference Content Access


Virtual On line

Publication Date

June 22, 2020

Start Date

June 22, 2020

End Date

June 26, 2021

Conference Session

Computers in Education Division Technical Session 6: Computer Science Freshman Courses

Tagged Division

Computers in Education

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Paper Authors


Joe Michael Allen University of California, Riverside

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Joe Michael Allen is a Ph.D. student in Computer Science at the University of California, Riverside. His current research focuses on finding ways to improve CS education, specifically focusing on introductory programming courses known as CS1. Joe Michael is actively researching the impact of using a many small programs (MSP) teaching approach in CS1 courses. His other interests include educational games for building skills for college-level computer science and mathematics.

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Frank Vahid University of California, Riverside

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Frank Vahid is a Professor of Computer Science and Engineering at the Univ. of California, Riverside. His research interests include embedded systems design, and engineering education. He is a co-founder of

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In Fall 2018, our university fully switched from using a weekly one large program (OLP) approach to using a many small programs (MSP) approach in our CS1 course, utilizing a program auto-grader with immediate points feedback and partial credit possible. The switch led to positive results such as an increase in student grade performance and a reduction of student stress. We also saw students making good use of MSPs in their learning, such as spending sufficient time programming each week, and starting earlier on programming assignments. A unique benefit of MSPs is the ability for students to pivot, meaning to switch among programs if they get stuck. This paper investigates such pivoting, and seeks to answer common questions related to pivoting. We analyze how many students pivot and the number of pivots done each week. Given a full-credit threshold (50 of 70 points on 7 programs worth 10 points each with partial credit possible), we examine how students complete the subset of required points. We compare pivot data between a class with a full-credit threshold and a class without. We examine whether students who pivot eventually return to the program from which they pivoted, or if they leave the program unsolved. Finally, we analyze student workflow to observe various pivot patterns. By analyzing student pivot behavior, we hope the community can better understand the pros and cons of pivoting, to help decide whether to adopt an MSP approach and possibly a full-credit threshold.

Allen, J. M., & Vahid, F. (2020, June), Analyzing Pivoting Among Weekly Many Small Programs in a CS1 Course Paper presented at 2020 ASEE Virtual Annual Conference Content Access, Virtual On line . 10.18260/1-2--34149

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