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Predicting Course Performance from Homework Habits

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Conference

2013 ASEE Annual Conference & Exposition

Location

Atlanta, Georgia

Publication Date

June 23, 2013

Start Date

June 23, 2013

End Date

June 26, 2013

ISSN

2153-5965

Conference Session

Assessment of Student Learning 1

Tagged Division

Educational Research and Methods

Page Count

12

Page Numbers

23.974.1 - 23.974.12

DOI

10.18260/1-2--22359

Permanent URL

https://peer.asee.org/22359

Download Count

116

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Paper Authors

biography

Kevin Rawson University of California, Riverside

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Mr. Rawson received his B.S.E. in Mechanical Engineering and B.S. in Mathematics from Walla Walla University in 2001. He received his M.S. in Mechanical Engineering from UC Riverside in 2005, where he currently is working towards his Ph.D.

Research interests include sketch understanding, machine learning, pen-based computing, and educational informatics.

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biography

Tom Stahovich University of California, Riverside

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Dr. Stahovich received his B.S in Mechanical Engineering from UC Berkeley in 1988. He received his S.M. and Ph.D. from MIT in 1990 and 1995 respectively. He conducted his doctoral research at the MIT Artificial Intelligence Lab. After serving as an Assistant and Associate Professor of Mechanical Engineering at Carnegie Mellon University in Pittsburgh, PA, Dr. Stahovich joined the Mechanical Engineering Department at UC Riverside in 2003 where he is currently a Professor and Chair. His research interests include pen-based computing, educational technology, design automation, and design rationale management.

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Abstract

Predicting Course Performance from Homework HabitsHomework exercises are a cornerstone of modern instruction, especially in engineering. In thiswork, we seek to understand how student homework habits correlate with course performance.We conducted a study in which students in an undergraduate statics course used LivescribeTMsmartpens to complete all of their coursework, including homework, quizzes, and exams. Thesesmartpens serve the same purpose as traditional ink pens but additionally digitize thehandwriting, producing a digital, time-stamped record of the students' work. From this record,we can compute a variety of quantitative features characterizing the student’s homework habits.For example, for each assignment, we can compute the total amount of ink written, the totalamount of time spent completing the problems, the total amount of break time taken, the time ofthe day the student is working, and so on.We use regression models to examine how these features correlate with overall courseperformance, i.e., the final course grade. When constructing these models, we control for studentability using the students’ scores on the Force Concept Inventory (FCI), which is completedduring the first week of class. The FCI itself has some predictive ability; The FCI scorecorrelated with final course grade with R2 = 0.27. However, combining this score with the totalink written on the third homework assignment, which is the first assignment to includeequilibrium problems, produced a much stronger correlation with R2 = 0.35. A regression modelincluding the FCI score and four features computed from this assignment resulted in R2 = 0.43.Thus, by the end of the third week of a ten week course, it is possible to explain a significantamount of the variance in final course grade by considering homework habits.These results provide important insights about successful and unsuccessful homework habits.This work also provides a foundation for building early warning systems that examinehomework activity to identify students at risk of performing poorly in a course.

Rawson, K., & Stahovich, T. (2013, June), Predicting Course Performance from Homework Habits Paper presented at 2013 ASEE Annual Conference & Exposition, Atlanta, Georgia. 10.18260/1-2--22359

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