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Impact of Late Policies on Submission Behavior and Grades in Computer Programming

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2022 ASEE Annual Conference & Exposition


Minneapolis, MN

Publication Date

August 23, 2022

Start Date

June 26, 2022

End Date

June 29, 2022

Conference Session

CIT Division Technical Session #5

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Mandy Korpusik Loyola Marymount University

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Dr. Korpusik is an Assistant Professor of Computer Science at Loyola Marymount University. She received her B.S. in Electrical and Computer Engineering from Franklin W. Olin College of Engineering and completed her S.M. and Ph.D. in Computer Science at MIT. Her primary research interests include natural language processing and spoken language understanding for dialogue systems. Dr. Korpusik used deep learning models to build the Coco Nutritionist application for iOS that allows obesity patients to more easily track the food they eat by speaking naturally. This system was patented, as well as her work at FXPAL using deep learning for purchase intent prediction.

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Jordan Freitas Loyola Marymount University

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John David Dionisio Loyola Marymount University

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This paper investigates the effect of four different late policies on submission behavior and grades in an introductory Computer Programming Lab class taught in the Java programming language at a primarily undergraduate university. To ensure grading consistency across sections, every student was randomly assigned two labs per late policy, for a total of eight labs completed over the course of the semester. The four late policies consisted of: 1) No penalty for late submissions, 2) Early incentive (one extra credit point awarded per day early the lab was submitted, up to three points max), 3) Late penalty (25% off within 24 hours of the deadline, 50% off for 24-48 hours late, 75% off for 48-72 hours late, and zero credit after 72 hours), and 4) Combined (early incentive combined with a late penalty). For our quantitative and qualitative study, we measured, per policy (for a total of 248 submitted labs), the average lab grade and the average number of days the labs were submitted early or late, as well as the average student rankings (from 1 to 4, where 1 was their favorite and 4 their least favorite). We found that while students rated Early Incentive the highest, the policy with the highest lab grades and submitted the earliest on average was the Combined early incentive with a late penalty. The worst grades were for No penalty, which may suggest a late penalty is necessary to keep students on track.

Korpusik, M., & Freitas, J., & Dionisio, J. D. (2022, August), Impact of Late Policies on Submission Behavior and Grades in Computer Programming Paper presented at 2022 ASEE Annual Conference & Exposition, Minneapolis, MN. 10.18260/1-2--41566

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