Investigating the role of student motivation in computer science education through one-on-one tutoring

Abstract

The majority of computer science education research to date has focused on purely cognitive student outcomes. Understanding the motivational states experienced by students may enhance our understanding of the computer science learning process, and may reveal important instructional interventions that could benefit student engagement and retention. This article investigates issues of student motivation as they arise during one-on-one human tutoring in introductory computer science. The findings suggest that the choices made during instructional discourse are associated with cognitive and motivational outcomes, and that particular strategies can be leveraged based on an understanding of the student motivational state.

Keywords:

• motivation
• tutoring
• confidence
• CS1
• introductory programming
• initiative

Acknowledgements

The authors wish to thank the following for insightful discussions and support in preparing this manuscript: Scott McQuiggan and the members of the Intellimedia Center for Intelligent Systems at NC State University, Carolyn Miller, and Tiffany Barnes. Thanks to the software development team of August Dwight and Taylor Fondren whose outstanding undergraduate research projects resulted in the software that facilitated these tutoring studies, and to the Realsearch Group at NC State University for their project development support.

 This work has been supported in part by the National Science Foundation through grants REC-0632450, IIS-0812291, the STARS Alliance grant CNS-0540523, and an NSF Graduate Research Fellowship. Any opinions, findings, conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. Support has also been provided by North Carolina State University through the Department of Computer Science and the Office of the Dean of the College of Engineering.

Notes

1. The tagging scheme was divided into cognitive and affective/motivational channels. Although the analysis presented here focuses only on the motivational tags in the affective/motivational channel, the entire tagging scheme is presented for completeness.

2. These variables are included as predictors in all logistic regression models reported in this section in order to control for the influence of incoming knowledge level and confidence on the outcomes.