Scaling the personalized adaptive learning program in Andhra Pradesh, India

Personalized Adaptive Learning (PAL) with edtech fosters remedial learning and mitigates learning gaps in settings where children lag behind grade-level skills (de Barros and Ganimian 2023; Muralidharan, Singh, and Ganimian 2019; Escueta et al. 2017). It can be a cost-effective way to address pandemic-related learning loss and inequities in access to education when investments in edtech have already been made (World Bank 2020). However, the effectiveness of education interventions, including learning through the use of edtech, when conducted at scale can be sensitive to the implementation features (List 2022; Banerjee et al. 2017; Kulik and Fletcher 2016). The Government of Andhra Pradesh (GoAP) in India has launched one of the first government-run PAL programs in over 500 schools, with an interest in scaling to more schools. Our research aims to evaluate the Government's PAL program and provide insights on challenges and necessary adaptations to establish sustainable and scalable PAL Labs using a “right-fit” research approach.

External Link(s)

Registration Citation

Citation

Cupito, Emily et al. 2025. "Scaling the personalized adaptive learning program in Andhra Pradesh, India." AEA RCT Registry. February 05. https://doi.org/10.1257/rct.14271-1.0

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Experimental Details

Interventions

Intervention(s)

In 2019, the School Education Department, Government of Andhra Pradesh (GoAP) has rolled out a Personalized Adaptive Learning program to students in grades 6 to 9 in 520 schools across 17 districts of Andhra Pradesh. Each of the 520 schools received 30 tablets with the PAL software with Mathematics content loaded on them and were told to setup a dedicated PAL Lab for students. Each student has an individual login through which they access the software. For each Mathematics chapter, the dynamic adaptive software identifies individual student learning gaps through a pre-assessment and provides remedial content including videos and questions. The chapters and content is mapped to the AP state curriculum. School math teachers are instructed to dedicate 2 out of 8 math periods in a week for PAL Lab. Further, the Government hired Field Management Staff (FMS) to help with operations and logistics of the PAL Labs in schools.

In collaboration with the GoAP, the researchers have been conducting 2 research studies (Randomized Controlled Trials) in Government schools. The aim of the first study is to evaluate the impact of the PAL program on student learning outcomes in mathematics. The second study aims to study how the program can be designed to be more effective at scale. These studies will contribute to the evidence base to support the scaling of targeted foundational learning programs in government systems.

Intervention Start Date

2023-09-21

Intervention End Date

2026-03-31

Primary Outcomes

Primary Outcomes (end points)

Student learning outcomes in Math

Primary Outcomes (explanation)

Math outcomes will be constructed from three sources i) Standardized learning assessments in math at endline, ii) PAL software data and iii) school assessments data provided by the state government

Secondary Outcomes

Secondary Outcomes (end points)

PAL software usage, teacher adoption rates, implementation fidelity, student interest in math and other subjects, student attendance

Secondary Outcomes (explanation)

Experimental Design

We have launched two RCTs to evaluate the PAL program by randomizing at either the student- or school-level.

Experimental Design Details

Not available

Randomization Method

Using Software such as Stata/R on office computers for three studies and using PAL software for one study

Randomization Unit

Cluster and Individual Both

Was the treatment clustered?

Yes

Experiment Characteristics

Sample size: planned number of clusters

140 schools

Sample size: planned number of observations

24,000+ students

Sample size (or number of clusters) by treatment arms

Impact Study - 60 T 60C (Schools)
Remediation Study - 2500 T 2500 C (Individual)

Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Impact Study - 0.248 standard deviations Remediation Study - 0.1543 standard deviations

Supporting Documents and Materials

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IRB

Institutional Review Boards (IRBs)

IRB Name

UChicago IRB

IRB Approval Date

2023-06-26

IRB Approval Number

IRB23-0807

Analysis Plan

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