Efforts by schools to promote college and career readiness among all students are sometimes hampered by challenges stemming from wide disparities in students’ prior academic preparation and interests. One approach to addressing these disparities is the adoption of Personalized Learning (PL) environments that tailor instruction to individual student needs. An important component of most PL models is a competency-based system that involves assessing students’ initial performance, placing them in appropriate instructional environments, and using data on performance to determine whether students advance to the next grade or level. PL has the potential to promote acceleration of high-achieving students and effective remediation to meet the needs of struggling students, but effective implementation requires educators to have access to high-quality data on student progress and to understand how to use those data appropriately for instructional decision making.

This paper examines data use among educators who are implementing PL models in 24 schools that were funded through the Bill & Melinda Gates Foundation’s Next Generation Learning Challenges initiative. It addresses the following questions:

1. What features of PL did schools implement and what assessments and data-use initiatives did they adopt to support PL?
2. To what extent, and for what specific purposes, did teachers and school leaders use student data on achievement as well as interpersonal and intrapersonal skills? How did they engage students in these activities?
3. What challenges and facilitators did educators encounter in their data-use efforts?
4. What features of PL implementation are associated with school-level achievement growth?

The paper draws on data from document reviews, administrator interviews, teacher surveys and logs, and site visits to nine schools (including teacher interviews and student focus groups). We examine achievement growth using data from the spring and fall administrations of the NWEA Measures of Academic Progress (MAP) in PL schools as well as data from a matched “virtual comparison group” (VCG).

Preliminary findings suggest that PL schools adopted a variety of curricula and assessments to promote personalization, and that they are providing data-use supports that include professional development and online learning management systems. Students corroborated this information, with large majorities indicating that they kept track of their own learning progress and were required to demonstrate mastery before moving on in the curriculum. Challenges include a lack of useful assessment data to determine competency, difficulty finding high-quality measures of interpersonal and intrapersonal skills, difficulty integrating multiple data sources to support the development of individualized student learning plans, inadequate time for PD on data use, tension between the desire to have students work at an appropriate developmental level and pressures to tailor instruction toward meeting grade-level proficiency on state tests, and parent resistance to competency-based advancement.

In light of the growing prevalence of PL in schools, as well as the increasing availability of data systems and assessments that are intended to foster the use of data for decision making, the results from this study will be of interest to practitioners and policymakers who are working to identify strategies for meeting all students’ needs.