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REL Midwest Ask A REL Response

October 2019


What research is available on the relationship between personalized learning practices and student outcomes in grades K–12?


Following an established Regional Educational Laboratory (REL) Midwest protocol, we conducted a search for research reports and descriptive studies on the relationship between personalized learning practices and student outcomes in grades K–12. In particular, we focused on identifying resources focused on Midwestern states (Minnesota, Iowa, Wisconsin, Illinois, Michigan, Indiana and Ohio). Pane, Steiner, Baird, Hamilton, & Pane (2017; see page 5) define personalized learning as “instruction that is focused on meeting students’ individual learning needs while incorporating their interests and preferences.” For details on the databases and sources, keywords, and selection criteria used to create this response, please see the Methods section at the end of this memo.

Below, we share a sampling of the publicly accessible resources on this topic. References are listed in alphabetical order, not necessarily in order of relevance. The search conducted is not comprehensive; other relevant references and resources may exist. For each reference, we provide an abstract, excerpt, or summary written by the study’s author or publisher. We have not evaluated the quality of these references, but provide them for your information only.

Research References

Basham, J. D., Hall, T. E., Carter, R. A., Jr., & Stahl, W. M. (2016). An operationalized understanding of personalized learning. Journal of Special Education Technology, 31(3), 126–136.

From the ERIC abstract: “As referenced in the Every Student Succeeds Act and the National Educational Technology Plan, personalized learning is the new focus in many K-12 learning environments. Nonetheless, few people understand what personalized learning really means and even fewer can design and implement a personalized learning environment appropriate for all learners, especially learners with disabilities. This 18-month descriptive research study focused on identifying the design characteristics of personalized learning environments and the initial results of these environments. Findings indicate that personalized learning environments require more than technology, that the technology itself is simply a tool to support implementation. These personalized learning environments were highly learner self-regulated, had transparent and actionable near-real-time data, provided various structures for student voice and feedback, and integrated purposeful supports for embedding the principles of Universal Design for Learning at the cornerstone of practice. Personalized learning requires a shift in instructional practice on behalf of both the teacher and the learners. Implications for further research and practice are discussed.”

Boninger, F., Molnar, A., & Saldaña, C. M. (2019). Personalized learning and the digital privatization of curriculum and teaching. Boulder, CO: National Education Policy Center, School of Education, University of Colorado.

From the ERIC abstract: “Personalized learning programs are proliferating in schools across the United States, fueled by philanthropic dollars, tech industry lobbying, marketing by third-party vendors, and a policy environment that provides little guidance and few constraints. In this research brief, authors Faith Boninger, Alex Molnar, and Christopher M. SaldaƱa consider how we got to this point. Beginning with an examination of the history of personalized learning and the key assumptions made by its proponents, they review the research evidence and reflect on the roles and possible impacts of the digital technologies deployed by many programs. Despite many red flags, the pressure to adopt personalized learning continues to mount. The authors thus recommend that schools and policymakers pause in their efforts to promote and implement personalized learning until rigorous review, oversight, and enforcement mechanisms are established.”

Brasiel, S., Jeong, S., Ames, C., Lawanto, K., Yuan, M., & Martin, T. (2016). Effects of educational technology on mathematics achievement for K-12 students in Utah. Journal of Online Learning Research, 2(3), 205–226.

From the ERIC abstract: “Teaching mathematics has long required the use of technology due many powerful affordances. More recently, education technology has been developed to support personalized learning through the use of adaptive learning systems. Through the use of educational technology in online learning, there is great potential for improving students’ mathematics achievement. In this article, we report the results of an evaluation study, where 11 online mathematics educational technology products were distributed to close to 200,000 K-12 students and their teachers in the state of Utah to supplement classroom instruction. While only ten percent of students used the products at the recommended level over the course of the 2014-15 school year, there were six products where an educationally meaningful impact on mathematics achievement was found. While teachers responded positively, a third of teachers reported lack of access to technology as a barrier. We are already seeing improved usage during the second year of the project due to modifications to the expectations for schools based on what was learned from the first year of implementation.”

Gnagey, J., & Lavertu, S. (2016). The impact of inclusive STEM high schools on student achievement. AERA Open, 2(2).

From the ERIC abstract: “This study is one of the first to estimate the impact of ‘inclusive’ science, technology, engineering, and mathematics (STEM) high schools using student-level data. We use multiple statistical strategies to estimate the effect on student achievement from 2 years of attendance at six such high schools in Ohio. The results indicate that two schools had positive effects on science achievement that appear to come at the expense of achievement in social studies. The other schools had negligible or, often, negative effects across both STEM and, particularly, non-STEM subjects. These results are consistent with studies indicating that inclusive STEM schools typically focus on problem-based, personalized learning rather than science and mathematics content. The analysis also reveals the importance of accounting for students’ prior test scores in science, in addition to math and reading, when estimating models that use only 1 year of prior test score data—something that existing studies fail to do.”

Gross, B., & DeArmond, M. (2018). Personalized learning at a crossroads: Early lessons from the Next Generation Systems Initiative and the Regional Funds for Breakthrough Schools Initiative. Seattle, WA: Center on Reinventing Public Education, University of Washington.

From the ERIC abstract: “The Center for Reinventing Public Education (CRPE) conducted a multiyear, multimethod effort to learn how school districts, charter schools, and regional partners can support the successful implementation, expansion, and sustainability of personalized learning (PL) in schools. The vision for PL is to tailor instruction to individual students’ strengths, needs, and personal interests—often integrating technology—to boost student outcomes. CRPE researchers used a combination of field studies, surveys, and secondary data analysis to explore how schools, districts, and partner organizations help to seed and grow PL, and what the results were. Key questions for the project included: What do principals, teachers, and system leaders need to know and be able to do to successfully support, implement, and scale up PL; What policies and practices—at the classroom, school, district, partnership, and state levels—offered important supports (and barriers) for successfully implementing and scaling up PL; and What were the early results for teachers and students?”

Gross, B., Tuchman, S., & Patrick, S. (2018). A national landscape scan of personalized learning in K-12 education in the United States. Vienna, VA: iNACOL.

From the ERIC abstract: “K-12 education is at the beginning of what many hope will be a systemic transformation toward personalized learning. Across the nation, schools are experimenting with personalized learning to better meet each student’s unique needs and ensure broader access to a world-class education. Many of these experiments have been captured in individual case studies and other vivid narratives, but the field lacks a broad-based understanding of how personalized learning is emerging in classrooms across the United States. As a result, it is difficult to know the extent to which personalized learning is actually taking hold across the country. The lack of systematic data on personalized learning also makes it hard for advocates and others to identify the kinds of challenges policymakers and practitioners alike may need to address in the years ahead. To better understand how personalized learning is playing out across the nation, a national survey of teachers and students was conducted. This report summarizes what these teacher and student surveys revealed about how personalized learning is—and is not—taking hold nationwide.”

Halverson, R., Barnicle, A., Hackett, S., Rawat, T., Rutledge, J., Kallio, J., et al. (2015). Personalization in practice: Observations from the field (WCER Working Paper No. 2015-8). Madison, WI: Wisconsin Center for Education Research.

From the ERIC abstract: “Personalized learning places the interests and abilities of learners at the center of their education experience. Educators develop environments in which students and teachers together build plans for learners to achieve both interest-based and standards-based goals. Researchers from the University of Wisconsin-Madison School of Education worked with leaders at the Institute for Personalized Learning (IPL) to identify five K-12 schools for a 6-month research project documenting what personalized learning looks like in these schools. This research report presents the initial findings. The central questions and findings that guide this report are: How do IPL schools encourage students to be active participants in their learning? Educators in the IPL schools attempt to create a culture of agency by designing opportunities for students (and educators) to collaboratively control the time, pace, space, place, content and goals of their learning. How do IPL schools shift the role of educators to support personalization? IPL schools enable educators to engage in regular, data-driven consultation with students to construct learning pathways and set learning goals. How are learning technologies used in IPL schools? IPL schools develop socio-technical ecologies, that is, environments where technologies are selected by educators to address the interests and needs of all learners. The ecologies have three dimensions: (1) all schools provide information technologies that allow students to coordinate and document learning processes and outcomes; (2) all schools provide computer-adaptive assessment and curriculum programs that individuate skill and content development learning in math and reading; (3) some schools create digital media spaces to foster creativity in activities such as gaming, coding, performance, production and making. Across the IPL schools we found a shared vision of teaching and learning that framed the practices to provide students with agency over their learning, the interactions of teachers and students, and the selection and use of technologies. Commitment to a core pedagogy of student ability to design, track and assess a learning program pervades our observations at the IPL schools.”

Midwest Comprehensive Center. (2018). Transforming systems for high levels of learning for all students: Personalized learning in Wisconsin. Washington, DC: American Institutes for Research.

From the ERIC abstract: “Since 2010, the Institute for Personalized Learning (I4PL), a division of Cooperative Educational Service Agency (CESA) 1, has been collaborating with the Wisconsin Department of Public Instruction to inform the state’s work with the Innovation Lab Network (ILN) managed by the Council of Chief State School Officers. ILN is a network of states that pilots and scales student-centered approaches, including personalized and competency-based learning. I4PL has played a critical role in helping implement personalized learning approaches in Wisconsin schools and districts. One goal of Wisconsin’s personalized learning effort has been to tell the story of how personalized learning has unfolded in Wisconsin. To achieve this goal, the Midwest Comprehensive Center conducted six interviews with education leaders from six different districts that have been collaborating with I4PL to implement personalized learning initiatives in their schools. Each interview focused on one of the six stages of transformation used by I4PL and was aligned with each district’s stage of transformation. This document provides a snapshot of each district’s journey and shares one approach to achieving each stage in the transformation process. This work illustrates how districts are making a systemic shift to personalized learning.”

National Forum on Education Statistics. (2019). Forum guide to personalized learning data (NFES2019160). Washington, DC: National Center for Education Statistics, U.S. Department of Education. Retrieved from

From the abstract: “The Forum Guide to Personalized Learning Data is designed to assist education agencies as they consider whether and how to use personalized learning. It provides an overview of personalized learning and describes best practices used by education agencies to collect data for personalized learning; to use those data to meet goals; and to support relationships, resources, and systems needed for the effective use of data in personalized learning. Personalized learning is still a developing prospect in many locations. therefore, the concepts and examples provided are intended to help facilitate idea sharing and discussion.”

Pane, J. F. (2018). Strategies for implementing personalized learning while evidence and resources are underdeveloped. Santa Monica, CA: RAND Corporation. Retrieved from

From the ERIC abstract: “Innovators are exploring new designs for the primary and secondary education system under the umbrella of personalized learning, but consensus is lacking on a precise definition of personalized learning or on which component practices are essential. Practitioners and policymakers seeking to implement personalized learning are creating custom designs for their specific contexts. Those who want to use rigorous research evidence to guide their designs will find many gaps and will be left with important unanswered questions about which practices or combinations of practices are effective. Despite the lack of evidence, there is considerable enthusiasm about personalized learning among practitioners and policymakers, and implementation is spreading. Thus, the purpose of this Perspective is to offer strategic guidance for designers of personalized learning programs to consider while the evidence base is catching up. This guidance draws on theory, basic principles from learning science, and the limited research that does exist on personalized learning and its component parts.”

Pane, J. F., Steiner, E. D., Baird, M. D., Hamilton, L. S., & Pane, J. D. (2017). Informing progress: Insights on personalized learning implementation and effects. Santa Monica, CA: RAND Corporation. Retrieved from

From the ERIC abstract: “The basic concept of personalized learning (PL)—instruction that is focused on meeting students’ individual learning needs while incorporating their interests and preferences—has been a longstanding practice in U.S. K–12 education. Options for personalization have increased as personal computing devices have become increasingly affordable and available in schools and developers created software to support individual student learning. In recent years, it has become more common for schools to embrace schoolwide models of PL. We collected data from schools in the Next Generation Learning Challenges (NGLC)’s Breakthrough School Models program. Our study seeks to describe the practices and strategies these schools used to implement PL, understand some of the challenges and facilitators, and consider these alongside achievement findings to discern patterns that may be informative. Teachers and students reported higher levels of many aspects of personalization than their counterparts in a national sample. These included time for one-on-one tailored support for learning; using up-to-date information on student progress to personalize instruction and group students; students tracking their own progress; competency-based practices; and flexible use of staff, space, and time. However, some more-difficult-to-implement aspects did not appear to differ from practices in schools nationally, such as student discussions with teachers on progress and goals; keeping up-to-date documentation of student strengths, weaknesses, and goals; and student choice of topics and materials. We estimate study students gained about 3 percentile points in mathematics relative to a comparison group of similar students. In reading, there was a similar trend, though it was not statistically significant. Low-performing and high-performing students appeared to benefit.”

Patrick, S., Worthen, M., Frost, D., & Gentz, S. (2016). Promising state policies for personalized learning. Vienna, VA: International Association for K-12 Online Learning.

From the ERIC abstract: “Students, teachers, and school leaders are seeking flexibility and supports to enable powerful, personalized learning experiences both inside and outside of the traditional classroom. In personalized learning, instruction is tailored to each student’s strengths, needs, and interests—including enabling student voice and choice in what, how, when, and where they learn—to provide flexibility and supports to ensure mastery of the highest standards possible. This is in contrast to the one-size-fits-all approach of the traditional K-12 education model, in which learning is not differentiated and students are expected to progress through the same curriculum at the same pace. This report is designed for policymakers who want to advance policies that support personalized learning in their states. This report provides examples of promising state policies to scale and enable personalized learning. The intent is to inform and empower the field with examples from states creating supportive policy environments.”

Rutledge, S. A., & Cannata, M. (2015). Identifying and understanding effective high schools: Personalization for academic and social learning & student ownership and responsibility. Nashville, TN: National Center on Scaling Up Effective Schools.

From the ERIC abstract: “What are the policies, programs and practices that make some high schools in the same state and district context more effective than others? Motivated to understand the differences between schools with similar size and demographics yet different attendance, graduation and levels of student academic growth, the National Center for Scaling Up Effective Schools (NCSU)—a federally-funded project aimed at identifying, developing and implementing processes to scale up effective practices in urban high schools—embarked on year-long initiative to identify the major differences between two high and two low performing high schools in two districts—Broward County, Florida and Fort Worth, Texas. The study and findings are discussed in this report. In both districts it was found that the more effective high schools successfully mobilized both the academic and social emotional systems at their schools in the service of students. Administrators, guidance counselors and teachers at the effective schools worked together to bridge the academic and social emotional elements of schooling, seeing them as interwoven. They implemented teaching strategies, cultural habits, and organizational routines that promoted interconnections between the classroom and the social emotional lives of students. As such, students in the higher performing schools were much more likely than those in the lower performing schools to say that adults in the school supported them in developing both cognitive and non-cognitive skills necessary for their academic success and social wellbeing. Further, the high performing schools in the districts mobilized these academic and social emotional practices in different ways, particular to their local context and needs.”

Somers, M.-A., & Garcia, I. (2016). Helping students make the transition into high school: The effect of ninth grade academies on students’ academic and behavioral outcomes. New York, NY: MDRC.

From the ERIC abstract: “Ninth Grade Academies (NGAs)—also called Freshman Academies—have attracted national attention as a particularly intensive and promising approach for supporting a successful transition for high school freshmen. An NGA is a self-contained learning community for ninth-graders that operates as a school within a school. NGAs have four core structural components: (1) a designated separate space within the high school, (2) a ninth-grade administrator who oversees the academy, (3) a faculty assigned to teach only ninth-grade students, and (4) teachers organized into interdisciplinary teams that have both students and a planning period in common. The theory of action behind NGAs is that when these components are employed together, they interact to create a more personalized learning environment where ninth-grade students feel less anonymous and more individually supported. This, in turn, should help students succeed in school and stay on track to high school graduation. NGAs have shown promising results when employed as part of a whole-school reform model, but in these cases schools have received external support from a developer to create and sustain them. A growing number of schools and districts have been experimenting with NGAs on their own, but the little research that exists on their effectiveness is limited to anecdotal accounts. This study, which is based on a quasi-experimental research design, examines the effect of NGAs on students’ progress toward graduation, their academic achievement, and their behavior in several school districts in Florida. The sample for this study includes 27 high schools that created NGAs between 2001-2002 and 2006-2007, along with 16 comparison high schools that serve ninth-grade students with similar characteristics as students in the NGA schools. As context for understanding the impact findings, this study also looks at the extent to which the key features of the NGA model were implemented in the NGA schools in the study and how this differs from the structures and supports in the comparison schools. The key finding is that the NGAs in this study do not appear to have improved students’ academic or behavioral outcomes (credit earning, state test scores, course marks, attendance, suspensions, or expulsions). The findings also suggest that it can be difficult for schools to fully implement the components of the NGA model without expert assistance: Three years after their creation, only half the NGAs in the study had all four structural components of the model in place. Nationally, school districts continue to create NGAs, and recent efforts to implement them have incorporated various enhancements that are intended to strengthen and improve their implementation, but little is known about their effectiveness. Because students’ experience in ninth grade is an important predictor of their future success, these efforts to create and improve NGAs should be examined in future studies.”

Taege, J., Krauter, K., & Lees, J. (2015). Personalized learning in Wisconsin: FLIGHT Academy. Connect: Making learning personal. Philadelphia, PA: Center on Innovations in Learning, Temple University.

From the ERIC abstract: “This field report is the third in a series produced by the Center on Innovations in Learning’s League of Innovators. The series describes, discusses, and analyzes policies and practices that enable personalization in education. Issues of the series will present either issue briefs or, like this one, field reports on lessons learned by practitioners recounting the successes and obstacles to success encountered in implementing personalized learning. This issue is a field report on the FLIGHT (Facilitating Learning through Integration, Guidance, High expectations, and Technology) Academy in Waukesha, Wisconsin. The FLIGHT Academy is a personalized learning program functioning within a traditional school model. The diversity of students in the program is representative of the school district. The program provides services for students with learning and emotional disabilities, students who are English language learners, and students who are gifted and talented. There is no grade or ability requirement to enroll in FLIGHT; students and their parents only have to fill out an application showing interest. This field report describes the steps that went into the development of the FLIGHT Academy, and the modifications that have been made to the program in response to various challenges that surfaced after the implementation of the program.”

Additional Organizations to Consult

American Institutes for Research –

From the website: “While there are many definitions and frameworks for personalized learning, they coalesce around a few central themes: student-centered learning, student agency, demonstrated mastery of competencies, flexible learning, and supporting the whole child. AIR’s approach to personalized learning draws upon our rigorous research base and strong field experience in facilitating educational system change efforts across the nation and globe. We support states, districts, and schools as they navigate the unique challenges and opportunities in implementing personalized learning programs, as well as identify areas where the field would benefit from further research.”

Rand Corporation –

From the website: “Technology has the potential to enhance teaching and learning in a variety of ways, such as by providing innovative instructional materials and assessments, as well as tools to help enable educators to provide more personalized learning for their students. Over the past few decades, thousands of education technology products have been developed, yet relatively few have been demonstrated to meaningfully improve student outcomes. RAND’s research program on education technology and personalized learning seeks to identify the most promising tools and approaches, deploy them in schools to measure their effects and gain rich implementation details, and then to use those findings to guide policies, implementation strategies, and future product development—all toward maximizing technology’s benefits for students.”


Keywords and Search Strings

The following keywords and search strings were used to search the reference databases and other sources:

  • Individualized instruction

  • Personalized learning

  • Personalized learning Illinois

  • Personalized learning Ohio

  • Personalized learning Wisconsin

Databases and Search Engines

We searched ERIC for relevant resources. ERIC is a free online library of more than 1.6 million citations of education research sponsored by the Institute of Education Sciences (IES). Additionally, we searched IES and Google Scholar.

Reference Search and Selection Criteria

When we were searching and reviewing resources, we considered the following criteria:

  • Date of the publication: References and resources published over the last 15 years, from 2004 to present, were included in the search and review.

  • Search priorities of reference sources: Search priority is given to study reports, briefs, and other documents that are published or reviewed by IES and other federal or federally funded organizations.

  • Methodology: We used the following methodological priorities/considerations in the review and selection of the references: (a) study types—randomized control trials, quasi-experiments, surveys, descriptive data analyses, literature reviews, policy briefs, and so forth, generally in this order, (b) target population, samples (e.g., representativeness of the target population, sample size, volunteered or randomly selected), study duration, and so forth, and (c) limitations, generalizability of the findings and conclusions, and so forth.
This memorandum is one in a series of quick-turnaround responses to specific questions posed by educational stakeholders in the Midwest Region (Illinois, Indiana, Iowa, Michigan, Minnesota, Ohio, Wisconsin), which is served by the Regional Educational Laboratory (REL Midwest) at American Institutes for Research. This memorandum was prepared by REL Midwest under a contract with the U.S. Department of Education’s Institute of Education Sciences (IES), Contract ED-IES-17-C-0007, administered by American Institutes for Research. Its content does not necessarily reflect the views or policies of IES or the U.S. Department of Education nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government.