Learning Tracks for Equity: Re-imagining Instruction in Education Systems With Significant Misalignment

• In Event: Curriculum for Equity and Inclusion: Integrating Socio-Emotional, Digital, and Social Perspectives in Public Education

Proposal

Motivation
In many lower and middle-income countries (LMICs), there is a persistent misalignment between students' actual learning levels and the demands placed upon them by national curricula and the corresponding learning materials (Atuhurra & Kaffenberger, 2022; Glewwe, Kremer, & Moulin, 2009). This system-level misalignment leads to ineffective learning for many students, exacerbating educational inequities and perpetuating learning poverty (Pritchett & Beatty, 2015).
Lev Vygotsky’s Zone of Proximal Development (ZPD) emphasises the importance of aligning instruction just beyond a student's current abilities (Vygotsky, 1978), yet many educational systems fail to achieve this balance. The gap between the curriculum and students’ abilities reveals the need for realigning educational content to match learning levels, which could significantly improve learning outcomes, especially for students most at risk of falling behind.
Empirical evidence supports the promise of targeted instruction, as demonstrated by computer-adaptive learning models and differentiated classroom approaches. However, these technology-driven solutions often remain inaccessible at scale in LMICs. Non-technology-based interventions, such as tutoring and tailored instruction, have shown similar potential in addressing diverse learning needs. Many initiatives in LMICs demonstrate the feasibility and impact of aligning instruction at scale in improving foundational learning. Despite these successes, educational systems often set curriculum expectations far above the learning levels of most students, many times teaching students at the top of the class, thereby misaligning instruction for a majority of students. As research increasingly recognises these various forms of misalignment, there is growing support for more differentiated approaches that consider differences across school-grade combinations and are feasible to implement at scale.
A track-based approach to aligning instruction at scale begins by assigning a grade-specific median learning level for all primary grades in the system — thereby reducing the misalignment between the curriculum and the learning levels of students. In cases where significant heterogeneity exists between different schools, the track-based approach can help assign a group of schools to one of many tracks with common learning levels and trajectories for grade-on-grade progression. The track-based approach is cost-effective and also scalable, as a large group of schools can be clubbed based on shared characteristics.
In our work, we discuss several data-driven approaches to curate learning tracks and assess the value-add of these approaches, while accounting for heterogeneity at multiple levels and offer a framework for education stakeholders—particularly in contexts where such a framework can help with learning inequity and bridge learning gaps for more inclusivity.



Research Questions
How do different approaches to assigning schools to different learning tracks influence instructional alignment across different schools and grades in an education system?


What is the value-add of introducing additional tracks in contexts with different levels of learning heterogeneity?
Methods and Data
We draw on pupil-level data with differing levels of learning heterogeneity, collected via primary data collection methods, from contexts such as Kwara, Jigawa from Nigeria and Islamabad from Pakistan, covering more than 100,000 children. The data includes assessments of foundational literacy, specifically oral reading fluency, and numeracy across a broad range of skills. These contexts were deliberately selected to capture variation in learning heterogeneity, ranging from Jigawa’s largely homogeneous low-performing classrooms to Kwara and Islamabad, where between- and within-school variation is substantial.
We analyze four approaches to assign schools to learning tracks: (1) maximizing the share of pupils whose learning level matches the assigned track level; (2) minimizing the average gap between pupil learning levels and the assigned track level; (3) maximizing the number of school-grades whose median learning level matches the assigned track level; and (4) minimizing the average gap between school-grade median and the assigned track level. For each dataset, we compare trade-offs across these assignment methods and model the incremental value of introducing one or more additional tracks, quantifying improvements in alignment between instruction and pupil learning levels. We operationalize (2) and (4) by assigning a number to each learning level (A=1, B=2, C=3 etc.), and then finding the difference in absolute terms between a student’s level and the hypothetical classroom instruction level, and then taking the average. The higher the number is, the worse it is from an instruction point of view.
Preliminary Findings
Across contexts, we find that the specific approach used to assign schools to tracks has limited impact: 85–100 percent of schools are consistently placed in the same track regardless of optimization method. This suggests that track assignment decisions are relatively robust to methodological variation. However, the number of tracks available produces substantial differences in alignment outcomes. In Jigawa, where nearly all pupils perform at similarly low levels, a single track captures most of the value, with limited gains from adding additional tracks. By contrast, in Kwara and Islamabad, contexts with significant heterogeneity in both between-schools and within-classrooms, introducing multiple tracks dramatically improves alignment. In Kwara, pupil placement at the correct level rises from 45 percent under a single track to 82 percent with three tracks; Islamabad shows a comparable improvement (58 to 83 percent). These findings highlight that track-based instructional alignment approaches, when tailored to context, can substantially reduce learning divides and enhance instructional equity.


Contribution
The contribution of our work lies in offering new ways such as track-based instruction as not merely a technical adjustment, but as a pathway towards tackling inequity in education. In contexts marked by deep learning divides, classrooms often reflect broader societal inequalities—where instruction pitched to the top of the class inadvertently marginalizes middle-and-bottom of the class learners. By demonstrating that tailored track assignment can meaningfully reduce these divides, our findings reimagine how education systems can foster inclusion in times of fragmentation. In Jigawa, the evidence shows that resources can be focused without unnecessary complexity, while in heterogeneous settings like Kwara and Islamabad, multiple tracks enable all pupils to participate with dignity at their level. Such alignment is an act of justice in education: ensuring that no child is left behind, and that classrooms become spaces of shared progress rather than exclusion. In divided societies, this inclusive pedagogy is itself a peacebuilding intervention.

Authors

• Pranav Bhargava, NewGlobe Education

• Timothy Sullivan

• Priscilla Lu, NewGlobe Education

• Keuna Cho, NewGlobe Education

• Demi Chen