Our Work
A number of AIR projects demonstrate our flexibility and creativity in designing tailored studies that answer critical questions related to program development, efficacy, and improvement.
In addition, we are conducting deep investigations of the types of measures that might be most useful for measuring incremental change in rapid-cycle studies. See examples highlighted below.
Examples of Our Projects
Impact Evaluation of Parent Messaging Strategies on Student Attendance
AIR and our partners from the University of Chicago, the North Carolina State University, and 2M are evaluating the use of text messaging to inform parents about the importance of regular student attendance and to increase parent engagement to reduce student absenteeism in elementary schools. This cutting-edge study, funded by the U.S. Department of Education, is ...
Efficacy Study of Online Credit Recovery in Algebra I
AIR conducted a short-duration randomized controlled trial in the Chicago Public Schools to test the impact of using an online course for credit recovery. The study was designed to address a pressing problem in U.S. education, regarding the effectiveness of online courses for helping students who fail courses get back on track for graduation. Online courses are in wide use for this purpose because they are convenient and flexible, but evidence is sorely lacking.
Evaluation of Playground Physics
AIR worked closely with the New York Hall of Science, as a research and development partner, to support and inform the development of Playground Physics, a technology-based application and aligned curriculum materials. The program is designed to support middle school students’ learning of the key physics concepts of motion, force, and energy. The program is meant to be implemented during the physics units of middle school science courses, typically about two to four weeks in duration, thus readily lending itself to rapid-cycle studies.
Pathways to Success
Pathways to Success is a 12-session social psychological intervention that helps students identify images of who they may become in both the near and distant future, connect those future images to current strategies in school, and learn to reframe difficulty in school as meaning “this is important” rather than “this is impossible.” In a prior efficacy trial conducted in Detroit Public Schools, Pathways to Success had a positive impact on student attendance, grades, and effort in school ...
How malleable is a teacher’s instructional practice?
AIR is examining this question by conducting a systematic review and meta-analysis of randomized experiments of interventions directed at instructional practice. The goal of this project is to understand how much teachers’ instructional practice changes in randomized intervention studies. A second goal is to understand how changes in teacher practice relate to characteristics of the interventions, characteristics of instructional practice observation rubrics, and characteristics of the teacher and student samples. This work will help us better understand the ways to target and study teacher practice in rapid-cycle studies of teaching interventions, such as coaching.
How much does school climate change within an academic year and across multiple academic years?
AIR is using nine years of longitudinal data from a large urban school district to study this question and to provide guidance to researchers who are designing intervention studies that target school climate outcomes. Our focus is on four domains of school climate: safety, support, challenge, and the social and emotional learning environment of the school. This study will help us better understand the benchmarks for reasonable and remarkable changes in domains of school climate and the conditions under which those practices are most likely to change. The study will also help to inform the design of experiments targeting school climate outcomes.
How can aspects of students’ learning experiences be measured for rapid-cycle studies without burdensome data collection, such as frequent surveys
Unobtrusive measures of learning and “noncognitive” factors are of great interest to developers and researchers in need of routine information about how programs are operating. Are the programs engaging? Are they helping students learn? Particularly when students are working in online learning environments, data “exhaust” can be mined to generate useful measures of engagement, motivation, persistence, and learning. The fields of educational data mining and learning analytics are growing, and data scientists and content experts are collaborating to apply these methods for use in rapid-cycle studies.
For more information, check out an AIR working document on measuring student “noncognitive” factors in rapid-cycle studies: