The economist asking why differentiated instruction works
Alejandro Ganimian looks at strategies for meeting the needs of all students in diverse classrooms
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In his research, Alejandro Ganimian examines how to help teachers in low- and middle-income countries (LMICs) address the needs of their rapidly growing and increasingly diverse student populations. In the coming years, Alejandro plans to investigate the mechanisms of pedagogical approaches like differentiated instruction to better understand how they can improve students’ achievement. Aisha Schnellmann finds out more.
Aisha Schnellmann: What challenges are you trying to address in your research?
Alejandro Ganimian: In recent decades, LMICs have expanded access to schooling at an unprecedented pace. While it took high-income countries almost 200 years to go from 20% to 100% enrollment in primary school, low-income countries are on track to do so in just 60 years.
Such progress, however, has come at a cost. Enrollments in these countries have outpaced their capacity to build schools and hire teachers. As a result, low-income countries now have three times as many students per primary school teacher as their high-income counterparts. These larger classes make instruction much more difficult.
Equally important, many of the students who recently entered the school system are the first in their families to attend school or reach a given level of education. In low-income countries, two-thirds of primary school students are “first-generation”, compared to only 5% in high-income countries. These students often start school lagging behind the curriculum and need extra help to keep up. But they share a classroom with more advantaged peers, making it hard for teachers to cater to their needs.
“Many of the students who recently entered the school system are the first in their families to attend school or reach a given level of education.”
Unless we reform instruction to be more responsive to these students, they risk falling further behind with each grade until they can no longer keep up and drop out of school, limiting their opportunities to climb the economic ladder. Meeting the needs of these students is one of the most consequential challenges at the nexus of development economics and education policy.
AS: How has research on education interventions in LMICs developed over time?
AG: Over the past two decades, a series of randomized evaluations of education interventions in LMICs have played a key role in generating hypotheses explaining why many children in LMICs fail to develop foundational skills despite attending school regularly. Researchers have warned that overly ambitious curricula might prompt teachers to favor breadth over depth, that high-stakes exams might encourage teachers to focus on students most likely to take and pass them, and that the higher incidence of dropout among low-income students might discourage teachers from investing in these students. This work has shed light on how incentives can shape teachers’ behavior, although the link between the two has been more often inferred than directly examined.
In the last decade, interventions that seek to narrow the gap between the level at which instruction is delivered and the level at which students perform – e.g., differentiated instruction, computer-adaptive learning, remedial education, and ability tracking – have improved student achievement in South Asia and Sub-Saharan Africa. They have demonstrated that student learning improves when the constraints of business-as-usual instruction are removed. Many teachers, however, have been reluctant to integrate these approaches into regular instruction without additional time or support staff.
AS: What are the biggest mysteries in this field?
AG: The biggest mystery I see in prior work is why teachers in LMICs seem to be so sensitive to system-level incentives, such as curricula and exams, despite the strong labor protections they are typically afforded. In these countries, teachers are typically granted tenure shortly after entering the profession, largely based on their training and experience. Given that they face few consequences for not completing the curriculum or preparing students for exams, it is unclear why these pressures would influence their behavior so strongly. This apparent contradiction suggests to me that incentives alone cannot fully account for why teachers do not devote more attention to low-achieving students.
“The reason why teachers do not pay more attention to low achievers is that they are unaware of how much these students are struggling.”
My own work suggests that teachers’ capacities also affect the amount of effort they invest in remedying learning gaps. In a study in India and Bangladesh, my coauthors and I found that teachers could not accurately estimate their students’ test performance. In another study in Bangladesh, we found that teachers rarely interacted with students during class. And in Argentina and Bangladesh, we found that informing teachers of their students’ test performance increased teachers’ effort and raised student achievement. These studies suggest that the reason why teachers do not pay more attention to low achievers is that they are unaware of how much these students are struggling and have few opportunities to update their beliefs.
AS: How will your research help teachers and children?
AG: My research focuses on finding ways to lower the costs and increase the take-up of one of the most effective approaches to addressing learning variability within a class: differentiated instruction. Differentiated instruction involves assessing students, grouping them based on assessment results, and providing each group with materials suited to their performance. I want to find out whether any of these components, or combinations thereof, drive the impact of differentiation. If so, we might identify variants of differentiated instruction that are easier for teachers to adopt and more feasible to scale up rapidly.
Through this study, I also hope to shed light on the mechanisms through which differentiation improves learning. The leading explanation is that differentiation makes it possible to provide students with materials better matched to their level of preparation. While this is certainly plausible, other mechanisms may also play a role. For example, assessment results may prompt teachers to redirect their efforts towards lower-achieving students, or grouping students by achievement may encourage them to help each other more effectively. Understanding the relative contributions of these mechanisms would help improve the design not only of differentiated instruction, but also of other interventions aimed at addressing similar challenges.
“Differentiation makes it possible to provide students with materials better matched to their level of preparation.”
AS: What are your hopes for the future of education research in LMICs?
AG: I would like to see research that examines how system-level incentives—such as curricula and exams—affect teachers’ decisions about what and how to teach. Most of what we currently believe about this relationship is inferred from patterns of results in impact evaluations. And while it is true that differentiated instruction relaxes some of these constraints and improves learning, it also does many other things, making it hard to draw direct causal links. A deeper understanding of teachers’ behavior could shed light on potential approaches to shape it, and ultimately improve students’ outcomes.
I would also like to see more work on how teachers’ beliefs about what they and their students are capable of doing shape their motivation and practice. In recent years, several surveys have found that many teachers in LMICs believe there is little they can do to help their students learn if support is lacking at home. These surveys have also revealed that many teachers do not believe it is their responsibility to teach material meant to be covered in earlier grades. It seems plausible that such beliefs may prevent teachers from changing their instructional practices, but it is also possible that these beliefs are shaped by failed attempts to help students catch up.
In short, given the scale and importance of the challenge of addressing the learning variability in the classroom in LMICs, we need a better understanding of what motivates teachers if we are to provide more effective support.
Footnotes
Alejandro J. Ganimian is a Visiting Associate Professor at the Harvard Graduate School of Education and an Associate Professor at New York University’s Steinhardt School of Culture, Education, and Human Development. He holds a doctorate in Quantitative Policy Analysis in Education (with a concentration in economics) from Harvard University, where he was a fellow in the Multidisciplinary Program in Inequality and Social Policy; a master’s in Educational Research from the University of Cambridge, where he was a Gates Scholar; and a bachelor’s in International Politics from Georgetown University. He was a postdoctoral fellow at the Abdul Latif Jameel Poverty Action Lab (J-PAL). Alejandro is a 2025-2027 Jacobs Foundation Research Fellow.
Alejandro’s website.
This interview has been edited for clarity.
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