Predicting elementary academic performance using motivation and self-beliefs with digital math learning

• Katherine Petersdorf, Psychology, Wesleyan University

• Teomara Rutherford, School of Education, University of Delaware

There has been increasing understanding that interest among elementary school students may shape students’ self-beliefs, motivation, and achievement in mathematics. Additionally, with increasing usage of technology in classrooms, digitization and gamification of mathematics lessons can provide a new avenue for students to become more interested and therefore motivated in the subject. The aim of this study was to investigate if motivation can predict varying modes of achievement among fifth grade students, using data from a mathematics education program, ST Math, where students answered a questionnaire with their self-beliefs around self-efficacy and valuing mathematics and completed an objective on the program about multiplication. Results indicated that the students’ self-reports of self-efficacy and value predicted the percentage of levels passed for the objective, however only self-efficacy statistically predicted the time it took to complete the objective, and neither the self-reports of self-efficacy or value were statistically significant in predicting the students’ post-quiz score. These findings illustrate that self-efficacy and value play a part in predicting some measures of achievement, however further research is needed to identify how shifts in motivation throughout the school year and other related factors affect this prediction of achievement.