Peer Learning in Rural India

Last registered on March 18, 2025

Pre-Trial

Trial Information

General Information

Title
Peer Learning in Rural India
RCT ID
AEARCTR-0015527
Initial registration date
March 10, 2025

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published
March 18, 2025, 10:13 AM EDT

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Locations

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Primary Investigator

Affiliation
Harvard University

Other Primary Investigator(s)

PI Affiliation
Columbia University
PI Affiliation
University of British Columbia
PI Affiliation
Harvard Kennedy School
PI Affiliation
University of California San Diego

Additional Trial Information

Status
On going
Start date
2024-08-01
End date
2026-05-31
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
The majority of students in rural India still lack foundational literacy and numeracy skills. In a resource-constrained environment, overworked teachers and a heavy reliance on traditional instruction have failed to provide the necessary individual attention to improve academic outcomes for students. This study evaluates the effect of a peer tutoring intervention in rural Bihar on students’ academic outcomes and non-cognitive skills. The intervention focuses on a sample of 14,077 students from grades 3 to 5 across 176 schools in the district of Bhagalpur. In treated schools, high academic ability students are assigned leadership roles where they lead daily remedial classes for mathematics in small groups, in one period every school day. The study investigates how personal attention for learners and positions of responsibility for leaders affect students’ proficiency and interest in mathematics, while also fostering the development of relevant social and non-cognitive skills. It examines whether the intervention reduces the math anxiety that is prevalent under traditional instruction. Separate analysis is carried out for leaders and learners to understand if endowing students with positions of responsibilities from an early age lead to changes in specific soft skills relevant for future labour market success. Finally, the study explores whether peer tutoring programs reshape classroom social networks and create a more conducive classroom environment. By analyzing the impacts on both academic and social growth, this research presents scalable and cost-effective solutions to improve academic outcomes in ways that are sustainable over time.
External Link(s)

Registration Citation

Citation
Bhargava, Palaash et al. 2025. "Peer Learning in Rural India." AEA RCT Registry. March 18. https://doi.org/10.1257/rct.15527-1.0
Experimental Details

Interventions

Intervention(s)
This study investigates a peer tutoring intervention in government schools in Bihar, India. The program identifies high-performing students (through a baseline survey) as peer leaders, who are tasked with tutoring small groups of peer learners in foundational mathematics. In the treatment group, peer leaders teach learners in small groups, while the control group follows traditional instruction without structured peer tutoring. Peer learning sessions take place every school day for 40 minutes over a period of at least 2-3 months. The intervention is implemented at the school level and monitored by our NGO partner.
Intervention Start Date
2024-09-01
Intervention End Date
2025-03-31

Primary Outcomes

Primary Outcomes (end points)
We will analyze the following main outcomes of interest:
(1) Academic achievement (Math): e.g., subtraction/division, word problems, etc.
(2) Non-cognitive outcomes: e.g., confidence, grit, big 5 traits, etc.
(3) Social network outcomes: e.g., friends, study partners, most “popular” individuals, etc.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
(1) Teaching practices
(2) Continuation of peer learning after monitoring and implementation ends by partner
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Selection of schools into study:
Within each block, We restrict to schools that are within 50-95 percentile of the total number of students enrolled in grades 3-5. We then randomly select schools in each block such that their counts are in proportion to the fraction of total number of schools in the block. The study covers 176 schools, with an average enrollment of over 120 students in grades 3 to 5. If budget permits, there is a possibility of running the experiment for another set of schools in the following academic year.

Randomization protocol for schools:
We take principal components of the total number of teachers in a school, total number of students enrolled in grades 3-5 in a school and distance of the school to the Bhagalpur city center. We then assign to each school the sample dummy variables for above/below median of the first and second principal components separately, leading to 4 categories. We then randomly assign half of the schools in each category within each block into treatment and control groups
Treatment schools implement the peer tutoring program under the supervision of our NGO partner, which ensures adherence to the original design through regular monitoring. In control schools, traditional teaching methods are maintained.

Group formation:
The experiment involves implementing a peer tutoring program in government schools in Bhagalpur, Bihar, targeting grades 3 to 5. Students are ranked within their classrooms based on baseline numeracy test scores, with the top 15% identified as peer leaders and the remainder as peer learners. Each peer learner is randomly assigned to a group of 3-4 learners within their grade, led by a peer leader. One class period per school day, lasting 40 minutes, is dedicated to peer learning sessions, and the program runs for at least 2-3 months.

The key comparisons are between (i) the whole student group between treatment and control schools and (ii) peer learners (and leaders) in treatment schools and hypothetical learners (and leaders) in control schools.

Data
We conduct the following surveys with students:
- Baseline survey
- Endline survey
The baseline and endline surveys are conducted in person.

In addition to the above, our NGO partner aims to collect (subject to feasibility) the following information:
- Administrative data for schools
- Enrolment and attendance data for students in schools


Empirical Strategy:
We will estimate the average treatment effects of the program on the outcomes of interest by conditioning on baseline covariates and including randomization strata fixed effects. The unit of randomization is the school, while the unit of analysis is the student.
Our primary estimation equation is as follows:

y_{ics} = \alpha_0 + \alpha_1 T_s + X_{ics} \beta + Z_s + \gamma_0 y_{0ics} + \delta_p + \epsilon_{ics}

where y_{ics} is the outcome of interest for student i in classroom c in school s, and T_s is a binary treatment indicator that equals 1 if school s is in the treatment arm and 0 otherwise. y_{0ics} is the baseline measure of the outcome, X_{ics} is a vector of student-level covariates which could include gender, parental background, and socio-economic status, Z_s are school-level covariates, and \delta_p represents strata fixed effects. \alpha_1 is the coefficient of interest, measuring the effect of the program when implemented as intended relative to the control group. All standard errors will be clustered at the school level.

We will also conduct the analysis separately for peer leaders and learners.
Experimental Design Details
Not available
Randomization Method
Randomization done in office by a computer.
Randomization Unit
The intervention is randomized at the school level.
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
176 schools.
Sample size: planned number of observations
Our baseline survey constituted a total sample size of over 14,000 students and we expect to reach at least 70% of these students at the endline. Attrition in the sample mainly occurs due to dropouts extremely common in rural India, which is not in the research teams control.
Sample size (or number of clusters) by treatment arms
81 treatment schools and 95 control schools.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
The MDE for test scores is 0.2 sd
IRB

Institutional Review Boards (IRBs)

IRB Name
Monk Prayogshala
IRB Approval Date
2024-09-27
IRB Approval Number
#152-024
IRB Name
Harvard University Institutional Review Board
IRB Approval Date
2024-12-19
IRB Approval Number
IRB24-1240