Differential Effects of Incentivized Homework on Student Achievement in Undergraduate Mathematics

Abstract

We present an analysis on the differential effects of incentivizing homework in an introductory mathematics course at the United States Military Academy. We found that including homework as part of a student’s overall course average (incentive) led to a significantly higher performance (achievement) on homework assignments. However, doing homework only led to a significantly higher exam performance for medium-ability students. One possible explanation for our results is high-ability students may be able to grasp material with minimal out-of-class practice; medium-ability students benefit significantly from solving practice problems; and low-ability students may have difficulty in understanding even if they are actively practicing problems.

Keywords:

• Homework
• academic achievement
• student perceptions

Additional information

Notes on contributors

D. Koban

D. Koban is an assistant professor of mathematics at the United States Military Academy. He received his master’s degree in operations research from North Carolina State University. His recent publications include: “A Static Bernoulli Random-Graph Model for the Analysis of Covert Networks” (MOR: Military Operations Research, 2015). His current research interests include military applications of network science.

M. Fukuzawa

M. Fukuzawa is an assistant professor of mathematics at the United States Military Academy. He received his master’s degree in applied mathematics from the Naval Postgraduate School. Recent publications include “Processing Leader Development” (Small Wars Journal, 2016); “The Spectra of DES S-Boxes” (NPS, 2014). His current research interests include the independence of the i-graph and Boolean functions used for cryptography.

R. Slocum

R. Slocum is an assistant professor of mathematics at the United States Military Academy. He received his master’s degree in operations research from North Carolina State University. His research interests include the statistical analysis of baseball and scheduling heuristics.

M. Fletcher

M. Fletcher is an instructor of mathematics at the United States Military Academy. He received his master’s degree in applied mathematics from the Naval Postgraduate School. His research interests include applications of approximate dynamic programming and network science.

J. Pleuss

J. Pleuss is an instructor of mathematics at the United States Military Academy. He received his master’s degree in operations research from Kansas State University. His published thesis is on “Using Simulated Annealing to Improve the Information Dissemination Network Structure of a Foreign Animal Disease Outbreak Response” (Kansas State University 2016). His current research interests include network science and simulation modeling.