AERA Announces New Cohort of Deeper Learning Fellows
AERA Announces New Cohort of Deeper Learning Fellows

December 2019

AERA has announced a new cohort of the Fellowship Program on the Study of Deeper Learning. The four fellows were selected from a highly competitive pool of early career scholars who proposed studies using the Deeper Learning data set.

The fellows are each developing a research project that focuses on students, teachers, and schools that implement the Deeper Learning model and are included in this rich data set. The projects address topics related to students’ transition from high school to postsecondary education, the effectiveness of the Deeper Learning program across schools and students, the use of technology to improve the quality of students writing, and the psychometric properties of the Deeper Learning data.

  • See the table below for details on the new fellows and their projects.

The AERA Fellowship Program on the Study of Deeper Learning is made possible by a $1.5 million grant from the William and Flora Hewlett Foundation. Now completing its first year, this grant will support two additional cohorts beyond the four fellows just funded. The program provides research funding, professional development, and research training for early career scholars who wish to use the Deeper Learning data in their work. An aim of the program is to create a community of scholars studying the Deeper Learning model and a body of research related to the data.

“We are excited that these scholars will develop new research and create new knowledge using the Deeper Learning data,” said George L. Wimberly, fellowship principal investigator and AERA director of professional development. “We are grateful that the Hewlett Foundation is funding this important initiative to build research capacity and produce research that is important for scholars, policymakers, and practitioners.”

The inaugural cohort of the program started in 2016. Since then, that group has published substantive research in peer-reviewed journals. Over the next few years, AERA will award grants to early career scholars to participate in the ongoing program.

The Deeper Learning data are collected by the American Institutes for Research (AIR). The data include a wealth of information from schools, students, teachers, and principals at a sample of Deeper Learning network and non-network high schools.

Fellows work closely with a cadre of researchers and scholars to analyze the Deeper Learning data to help inform their research. The new cohort recently participated in a training workshop led by AIR scientists that focused on technical issues and potential research questions that can be addressed using these data. Ongoing professional networking and mentoring activities will connect fellows with senior scholars and researchers, helping the fellows build their research agendas, create new knowledge, advance manuscripts toward publication, and gain professional socialization to the academic research field.

The program provides early career scholars with up to $25,000 in research support and affords opportunities to participate in research training activities, small conferences, and the AERA Annual Meeting.

AERA is currently accepting proposals for research projects using the Deeper Learning data to address research questions on topics such as students’ socio-emotional learning and development, student achievement, and postsecondary trajectories and outcomes. Studies that emphasize methods and the psychometric properties of the data set will be also considered.

The next proposal deadline for the Fellowship Program on the Study of Deeper Learning is February 10. For further information about the Fellowship Program contact George L. Wimberly, director of professional development ( or 202-238-3200).

New Cohort of the AERA Fellowship Program on the Study of Deeper Learning
Recipient Project Title
Wendy Chan
University of Pennsylvania
The Generalizability of Deeper Learning Using Small Area Estimation
Lanette Jimerson
University of Houston
Deeper Learning and Technology Integration: Implications for Writing Assignments and Quality Student Work
Ting Shen
Missouri University of Science and Technology
Deeper Learning Effectiveness and Variation: Evidence From Quasi-Experimental Analysis
Abigail Todhunter-Reid
Independent Researcher
Mediators and Moderators of the Association of Deeper Learning Network School Attendance With College Enrollment: A Path Analysis


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