Comprehensive Views of Math Learners: A Case for Modeling and Supporting Non-math Factors in Adaptive Math Software

Adaptive math software supports students’ learning by targeting specific math knowledge components. However, widespread use of adaptive math software in classrooms has not led to the expected changes in student achievement, particularly for racially minoritized students and students situated in poverty. While research has shown the power of human mentors to support student learning and reduce opportunity gaps, mentoring support could be optimized by using educational technology to identify the specific non-math factors that are disrupting students’ learning and direct mentors to appropriate resources related to those factors. In this paper, we present an analysis of one non-math factor—reading comprehension—that has been shown to influence math learning. We predict math performance using this non-math factor and show that it contributes novel explanatory value in modeling students’ learning behaviors. Through this analysis, we argue that educational technology could better address the learning needs of the whole student by modeling non-math factors. We suggest future research should take this learning analytics approach to identify the many different kinds of motivational and non-math content challenges that arise when students are learning from adaptive math software. We envision analyses such as those presented in this paper enabling greater individualization within adaptive math software that takes into account not only math knowledge and progress but also non-math factors.