Investigating the relationship between state mathematics anxiety and performance using physiological real-time measures

Autor: Foulkes, Megan Louise, Weiers, Hanna, Schons, Christian
Rok vydání: 2023
Předmět:
DOI: 10.17605/osf.io/bt9j4
Popis: Mathematics anxiety (MA) is a multidimensional construct which has been found to have a substantial impact on mathematics performance. Recent research has made a distinction between a trait (how anxious one generally feels) and state component of MA (how anxious one feels in a specific situation), which might have implications for measurement. Using self-report measures might be more suitable for measuring trait MA, as trait MA is considered stable over time. Conversely, the experiences associated with state MA may be better captured using physiological measures. Physiological measures allow for real-time assessment of anxiety without interrupting the situation. In the present study, we use a combination of self-report measures and physiological real time measures to examine the relationship between state MA and performance in mathematical tasks of varying difficulties (carry/non-carry addition verification problems). Specifically, we will investigate the relationship between state MA (self-reported and measured through electrodermal activity) and processing efficiency (measured through reaction time and eye-movements). The findings might provide an initial step for evaluating the validity of different physiological measures for assessing experiences associated with state MA in real-time.
Databáze: OpenAIRE