Are high school students accurate in predicting their AP exam scores?: Examining inaccuracy and overconfidence of students’ predictions

ABSTRACT

We examined whether students were accurate in predicting their test performance in both low-stakes and high-stakes testing contexts. The sample comprised U.S. high school students enrolled in an advanced placement (AP) statistics course during the 2017–2018 academic year (N = 209; M_age = 16.6 years). We found that even two months before taking the AP exam, a high stakes summative assessment, students were moderately accurate in predicting their actual scores (κ_weighted = .62). When the same variables were entered into models predicting inaccuracy and overconfidence bias, results did not provide evidence that age, gender, parental education, number of mathematics classes previously taken, or course engagement accounted for variation in accuracy. Overconfidence bias differed between students enrolled at different schools. Results indicated that students’ predictions of performance were positively associated with performance in both low- and high-stakes testing contexts. The findings shed light on ways to leverage students’ self-assessment for learning.

KEYWORDS:

• self-assessment
• statistics education
• advanced placement
• course engagement

Acknowledgments

We would like to acknowledge teachers and students involved in this project, along with other members of the Learning Analytics and Measurement in Behavioral Sciences Lab for their reviews of drafts of this manuscript as well as their contributions to the broader discussion of the topic.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the National Science Foundation CAREER award (Grant #DRL-1350787) to Dr. Ying Cheng

Notes on contributors

Teresa M. Ober

Teresa M. Ober, Ph.D., is an Assistant Research Professor at the University of Notre Dame working in the Learning Analytics and Measurement in Behavioral Sciences (LAMBS) Lab.

Maxwell R. Hong

Maxwell R. Hong, Ph.D., received a Ph.D. in Psychology with a concentration in Quantitative Psychology from the University of Notre Dame.

Matthew F. Carter

Matthew F. Carter is the Manager of the Learning Analytics and Measurement in Behavioral Sciences (LAMBS) Lab.

Alex S. Brodersen

Alex S. Brodersen is a Ph.D. student in Psychology with a concentration in Quantitative Psychology from the University of Notre Dame.

Daniella Rebouças-Ju

Daniella Rebouças-Ju, Ph.D., received a Ph.D. in Psychology with a concentration in Quantitative Psychology from the University of Notre Dame.

Cheng Liu

Cheng Liu, Ph.D., is the Lead Data Scientist for the Center for Social Science Research (CSSR) in the Lucy Family Institute for Data & Society and concurrent Research Assistant Professor in Psychology at the University of Notre Dame.

Ying Cheng

Ying Cheng, Ph.D., is a Professor in the Department of Psychology, Fellow of the Institute for Educational Initiatives, Associate Director of the Lucy Family Institute for Data and Society, and Director of the Learning Analytics and Measurement in Behavioral Sciences (LAMBS) Lab at the University of Notre Dame.