Math anxiety has been found to negatively correlate with math achievement, affecting students’ choices to take fewer math classes and avoid math educational opportunities. Educational technology tools can ameliorate some of the negative effects of math anxiety. We examined students’ math anxiety, effort in an educational technology platform, and their relationship with students’ math achievement. Multilevel latent class analysis was used to identify student profiles of math anxiety. Regression analysis was used to examine how students of different profiles interacted with MathSpring, an adaptive intelligent tutor that provides affective supports to students during math problem-solving. The student's math achievement was measured by a standardized test. Our analysis indicated heterogeneity in math anxiety, and students could fall into one of three groups: Highly Anxious, Performance Anxious, and Calm. Highly Anxious students tended to give up more often when solving questions in MathSpring and had the lowest math achievement outcomes. For these students, using hints to solve problems in MathSpring was significantly associated with increased math outcomes. These findings have implications for the field's understanding of how students of different math anxiety profiles can demonstrate varying efforts in math educational technology platforms, and different math learning outcomes.

Students' effort and emotions are important contributors to math learning. In a recent study evaluating the efficacy of MathSpring, a scalable web-based intelligent tutoring system that provides students with personalized math problems and affective support, system usage data were collected for 804 U.S. 10-12-year-olds. To understand the patterns in students' interactions with MathSpring and how patterns vary across students, hierarchical cluster heatmap analysis was performed. Guided by the patterns from the heatmap, latent profile analysis was conducted to identify student subgroups. Both analyses indicated that there were two groups of students: ''Confident Solvers'' who solved problems on their first attempt and reported high confidence, and ''Struggling Solvers'' who gave up, did not read problems, and reported higher frustration. These analyses provide insight into students' behaviors in online learning environments that can be scaled to students nationwide. Developers can use these insights to design systems that respond to students' usage and inform teachers about students' effort and emotions.

Many districts in the U.S. are investing in education technologies to improve student learning. Yet, when technologies with established promise of evidence are deployed at scale, they frequently encounter challenges that compromise their efficacy. MathSpring is a technology-based math learning platform that offers personalized content, remedial tutoring, and affective support for students and reports for teachers. In a pilot study involving monthly usage with a researcher in the room, MathSpring showed promise of improving student learning. Following the pilot study, our team examined the efficacy of the intervention in a randomized controlled trial (RCT) with 64 fifth- and sixth-grade math teachers (34 treatment, 30 control) in 47 schools. Despite extensive training and support for teachers, usage was lower than expected. This paper presents an overview of the intervention and explores the challenges teachers faced to implement MathSpring. We discuss the factors that influence wide classroom adoption of technologies like MathSpring in a post-pandemic educational landscape.