University courses often employ "one-size-fits-all" approaches, disregarding the heterogeneity in students' cognitive and motivational characteristics. This study evaluates an instructional method for online teaching in higher education called digital differentiation grid.In a randomized controlled trial with N = 438 university students (57% female, mean age M = 20.96 years), we investigated the effects of digital differentiation grids on students' motivation (self-concept, self-efficacy, intrinsic and utility task values), on their performance, and, because our sample consisted of teacher students, on their professional development with regard to inclusive education. Employing structural equation modeling, we found that the intervention positively affected the self-concepts of effort avoidant students. The intervention also positively impacted students' attitudes and self-efficacy towards inclusive education, but had no effect on course performance, course-related self-efficacy and task values. Moreover, learning analytics data revealed in-depth information on students' learning behavior. Results are discussed with regard to possible intervention strategies to be implemented in future versions of the learning environment.