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Schools are increasingly using technology to personalize instruction. Programs such as Khan Academy and Zearn produce a vast array of data on students’ behaviors and outcomes when engaged in technology-based instruction. However, these datasets rarely include equally detailed information from when students engage in non-technological learning activities. This study explores the implementation of an innovative model for adolescent mathematics instruction that includes detailed data from both technological and non-technological learning modalities. Much of the research on the implementation of technology-based personalization has focused on the effects of technology programs as isolated interventions rather than within blended models that combine instructional technology with significant changes to teacher-led instruction. Furthermore, existing studies of blended, personalized programs very rarely utilize daily programmatic data to push inside the “black box” of day-to-day interactions among students, teachers, and content. We attempt to address these gaps by using hierarchical cluster analysis, cluster trees, and clustergram heatmaps to explore and visualize data from 170,075 daily lesson assignments and assessments generated by 1,238 unique fifth through eighth grade students across five schools implementing an innovative model for blended and personalized middle school math instruction. We explore three research questions: (1) To what degree did the daily implementation of this program reflect its stated goal of personalizing instruction? (2) Did student outcomes vary based on exposure to each of the learning modalities utilized by this program? (3) Did student outcomes vary based on the academic proficiency of students entering the program? These analyses support three main findings: (a) The instructional reform succeeds in creating a highly personalized student experience, but was likely hampered in implementation by policy and logistical constraints; (b) Participation in a learning modality focused on long-term projects was associated with a lower degree of personalization but higher student outcomes than the other six learning modalities utilized by the program, particularly for some latent clusters of students; and (c) Initially higher-performing students earned higher scores on daily assessments than initially lower-performing students, despite the program’s intended goal of fostering equity in student outcomes through personalization of content to meet each student’s supposed level of readiness.

Using Technology to Personalize Middle School Math Instruction: Evidence From a Blended Learning Program in Five Public Schools