How are maths-anxious students identified and what are the key predictors of maths anxiety? Insights gained from PISA results for Korean adolescents

ABSTRACT

Maths anxiety has been of great concern for many educators and educational policymakers because of its adverse effects on students’ maths performance and career path. Various empirical studies have been conducted to explore the factors predicting maths anxiety, and they have typically been based on a limited set of pre-specified variables, such as maths performance and student self-concept. However, to fully grasp the nature of maths anxiety, an exploratory study based on more elaborate prediction models using a wider variety of variables can also benefit educators. To explore the important predictors of maths anxiety and examine the possibility of achieving an acceptable level of prediction accuracy, this study employed the random forest algorithm, logistic regression, and the hierarchical general linear model to build prediction models for maths anxiety based on 194 variables collected from PISA student questionnaires. Among the factors predicting maths anxiety, enjoying maths, self-concept, and attributions to failure were revealed as being the most significant predictors. Confidence in oneself, persistent behavioural characteristics, and pressures from parents or teachers were also selected as important predictors. Educational implications are drawn from the findings of this study, and the advantages and drawbacks of each prediction model are discussed.

KEYWORDS:

• Hierarchical general linear model
• logistic regression
• maths anxiety
• PISA
• random forest

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This paper was supported by Konkuk University in 2016.OSF link for the R script: https://osf.io/rymgd/

Notes on contributors

Hyun Sook Yi

Hyun Sook Yi is a professor in the Department of Education at Konkuk University, South Korea, where she teaches educational assessment, evaluation policy, and educational statistics. Her earlier work focused on applying educational measurement and statistics theories to large-scale assessment and investigating issues related to test development and estimation of student performance. Her current research interests include the application of measurement and statistical models, such as cognitive diagnostic modelling and structural equation modelling in K-12 education settings and the prediction of cognitively and emotionally at-risk students.

Wooyoul Na

Wooyoul Na is a Ph.D. student in the Department of Education at Konkuk University, South Korea. He holds a Master’s degree in Counselling Psychology and is currently in the educational measurement programme. He is interested in applying educational measurement and statistics theories in the field of counselling psychology and has recently published articles on longitudinal analyses of high school student dropouts, school violence, and the characteristics of adolescent runaways.