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The Programming Performance Prophecies: Predicting Student Achievement in a First-Year Introductory Programming Course

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Conference

2011 ASEE Annual Conference & Exposition

Location

Vancouver, BC

Publication Date

June 26, 2011

Start Date

June 26, 2011

End Date

June 29, 2011

ISSN

2153-5965

Conference Session

SE Tools and Techniques

Tagged Division

Software Engineering Constituent Committee

Page Count

18

Page Numbers

22.1490.1 - 22.1490.18

Permanent URL

https://peer.asee.org/18930

Download Count

32

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Paper Authors

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Jeff Ringenberg University of Michigan

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Jeff Ringenberg is a Lecturer at the University of Michigan's College of Engineering. His research interests include mobile learning software development, tactile programming, methods for bringing technology into the classroom, and studying the effects of social networking and collaboration on learning. He holds B.S.E., M.S.E., and Ph.D. degrees in Computer Engineering from the University of Michigan.

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Marcial Lapp University of Michigan

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Marcial Lapp is a graduate student in the Industrial and Operations Engineering Department at the University of Michigan. His research interests lie in modeling and solving large-scale optimization problems focused on the transportation and logistics industries. He holds a Masters and Bachelors degree in Computer Science from the University of Michigan.

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Apoorva Bansal University of Michigan

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B.S.E., Mechanical Engineering, 2011.
B.S.E., Computer Engineering, 2011.

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Parth Shah University of Michigan

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Abstract

The Programming Performance Prophecies: Predicting Student Achievement in a First-Year Introductory Programming CourseAbstractEnsuring student success in first-year introductory programming courses presents a uniquechallenge when considering the diversity of student educational backgrounds. Some studentsenter college having programmed for several years, while others have had little to no exposure tobasic programming concepts. In addition, students frequently possess a wide range of skills inareas of study that are related to programming. When students with widely differing skill setsare enrolled in the same introductory programming course, a competency imbalance isimmediately established that negatively impacts the course and leads to high attrition ratesamong students with less experience.In order to create a more equitable experience and to ensure that students are placed in a coursewhose difficulty is commensurate with their abilities, we present a methodology to predictstudent performance in first-year introductory programming courses. Our technique requires thatall students take an online survey at the beginning of the term consisting of a variety ofalgorithmic and logic-based multiple choice questions that are not tied to a specific programminglanguage. From a sample of over 600 students, we show a significant correlation between theperformance on our survey and overall course performance, thereby giving us the ability toproperly accommodate students based upon our expectation of their performance in the courseand to minimize any potential imbalances that may occur.

Ringenberg, J., & Lapp, M., & Bansal, A., & Shah, P. (2011, June), The Programming Performance Prophecies: Predicting Student Achievement in a First-Year Introductory Programming Course Paper presented at 2011 ASEE Annual Conference & Exposition, Vancouver, BC. https://peer.asee.org/18930

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