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Executive Summary Continued Progress Executive Summary The Bill & Melinda Gates Foundation has engaged RAND to carry out an ongoing study of foundation-funded schools that are employing promising approaches to personalized learning. This research is part of the foundation's public commitment to spread effective practices across districts and charter networks, develop innovative roles for teachers, and support implementation of college-ready standards. This is the second report in a series focused on the achievement data, school design characteristics, and teacher and student perceptions of schools that are implementing personalized learning. The achievement findings in this report focus on 62 public charter and district schools that are pursuing a variety of personalized learning practices. In a smaller set of 32 schools, the report examines details of personalized learning implementation and the relationship of implementation to outcomes.

An environment that works the way nonprogrammers expect is more inviting and helps users become more confident and productive.Over the last six years, we have been working to create programming languages and environments that are more natural, or closer to the way people think about their tasks. Our goal is to make it possible for people to express their ideas in the same way they think about them. To achieve this, we have performed various studies about how people think about programming tasks, both when trying to create a new program and when trying to find and fix bugs in existing programs. We then use this knowledge to develop new tools for programming and debugging. Our user studies have shown the resulting systems provide significant benefits to users.

This article examines the effectiveness of a technology-based algebra curriculum in a wide variety of middle schools and high schools in seven states. Participating schools were matched into similar pairs and randomly assigned to either continue with the current algebra curriculum for 2 years or to adopt Cognitive Tutor Algebra I (CTAI), which uses a personalized, mastery-learning, blendedlearning approach. Schools assigned to implement CTAI did so under conditions similar to schools that independently adopt it. Analysis of posttest outcomes on an algebra proficiency exam finds no effects in the first year of implementation, but finds evidence in support of positive effects in the second year. The estimated effect is statistically significant for high schools but not for middle schools; in both cases, the magnitude is sufficient to improve the median student's performance by approximately eight percentile points.

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In the current educational context, school models that leverage technology to personalize instruction have proliferated, as has student enrollment in, and funding of, such school models. However, even the best laid plans are subject to challenges in design and practice, particularly in the dynamic context of a school. In this collective case study, we identify challenges, disruptions, and contradictions as they occur across schools engaged in implementing technology-mediated personalized learning. Using cultural historical activity theory—a theoretical framework concerned with the individual and contextual factors influencing school change—to frame the analysis, we also examine some of the structural and contextual sources of these disruptions and contradictions. Our findings enable us to offer recommendations for policymakers and for practitioners engaged in implementing personalized learning models, as well as directions for future research on this topic.

Evaluators report effects of education initiatives as standardized effect sizes, a scale that has merits but obscures interpretation of the effects’ practical importance. Consequently, educators and policymakers seek more readily interpretable translations of evaluation results. One popular metric is the number of years of learning necessary to induce the effect. We compare years of learning to three other translation options: benchmarking against other effect sizes, converting to percentile growth, and estimating the probability of scoring above a proficiency threshold. After enumerating the desirable properties of translations, we examine each option’s strengths and weaknesses. We conclude that years of learning performs worst, and percentile gains performs best, making it our recommended choice for more interpretable translations of standardized effects.