Using differentiated feedback to improve performance in introductory statistics

ABSTRACT

In this study, we applied the regulatory focus theory to design a feedback intervention to promote learning in an introductory statistics course at a Midwestern public university in the USA. In total, 67 students were randomly assigned to receive feedback that either fitted or not fitted their regulatory focus. Through the analysis of covariance, we found that students in the fit condition had better performance than the non-fit condition, and there was a significant interaction between the individual’s regulatory focus and the type of feedback received. Further analysis showed that this interaction was significant only for the middle-performing students, but not for the lower or higher achievers. Findings from this study have implications for feedback design in higher education classrooms.

KEYWORDS:

• Differentiated feedback
• regulatory focus
• regulatory fit
• introductory statistics
• learning performance

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. We also tested the other cases, where we switched the type of regulatory focus for the two students, or identified both of them as having a promotion or prevention focus. We found the same conclusion in all these cases.

Additional information

Notes on contributors

Qijie Cai

Dr Qijie Cai is an assistant professor and graduate coordinator of Educational Technology at Minnesota State University, Mankato. She received her PhD from Curry School of Education at the University of Virginia. She has work experiences as an instructional designer in both K12 and higher education contexts and has published manuscripts in peer-reviewed journals and conference proceedings. Her research interest includes instructional and curriculum design, technology integration and online learner support.

Bodong Chen

Dr Bodong Chen is an assistant professor at the University of Minnesota in the Department of Curriculum and Instruction. His research is at the intersection of computer-supported collaborative learning, learning analytics, online learning and network analysis. His research projects involve devising pedagogical practices, computer software and data analytics for collaborative learning; investigating complex learning processes in authentic settings; and applying nascent computational methods to both empirical research and software engineering.

Han Wu

Dr Han Wu is a professor of statistics in Minnesota State University, Mankato. He received his PhD in Statistics from Iowa State University in 2006. His research interests are in mathematical statistics and general statistical methods. Besides teaching and committee responsibilities, he has been supervising graduate students. He has co-authored an elementary statistics textbook and has over 20 manuscripts published in peer-reviewed journals and conference proceedings. He has also been reviewing for professional journals and publishers. He is a member of American Statistical Association, Institute of Mathematical Statistics and International Chinese Statistical Association.

Glenn Trussell

Glenn Trussell is a graduate from the Department of Psychology at Minnesota State University, Mankato.