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Take-Up, Use, and Effectiveness of Remote Learning Technologies
Last registered on April 30, 2021


Trial Information
General Information
Take-Up, Use, and Effectiveness of Remote Learning Technologies
Initial registration date
August 11, 2020
Last updated
April 30, 2021 3:58 PM EDT

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Primary Investigator
Stockholm University IIES
Other Primary Investigator(s)
PI Affiliation
U. Wisconsin-Madison
PI Affiliation
University of Vermont
Additional Trial Information
On going
Start date
End date
Secondary IDs
Governments and educational organizations worldwide are trying to quickly adapt to the unprecedented circumstances created by the pandemic, by developing or scaling up distance education modalities to continue delivering educational content to students and maintain students’ connection to formal education. This study evaluates four interventions designed to reduce the barriers to remote education. The first two treatment arms target information constraints: The first provides information about remote learning options, and the second provides information about an adaptive learning internet resource. The last two treatment arms target price constraints: The third reduces the cost of internet learning activities by providing a discounted data package, and the fourth pairs students with a teacher that provides weekly support.

The main outcomes of interest include parents' and students’ time and economic investments in remote education, take up and usage of remote learning resources, and learning outcomes.
External Link(s)
Registration Citation
Beam, Emily, Priya Mukherjee and Laia Navarro-Sola. 2021. "Take-Up, Use, and Effectiveness of Remote Learning Technologies." AEA RCT Registry. April 30. https://doi.org/10.1257/rct.6191-2.0.
Experimental Details
We have two information treatments and two treatments that reduce the price of educational inputs.

The information treatments are:
- Treatment 1 – Remote learning information: Information and reminders about government remote learning initiatives. We split the information salience treatment into two sub-treatments:
o 1A – “TV”: SMS information/reminders about TV lessons
o 1B – “TV+Internet”: SMS information/reminders about TV lessons and internet lessons
- Treatment 2 – Adaptive learning: Information about an adaptive learning internet resource

The cost reduction treatments are:
- Treatment 3 – Data subsidy: Reduce costs of using internet remote learning technologies by providing a free 1-month data package for 10 GB.
- Treatment 4 – Teacher support: Pair students with a teacher who provides weekly check-ins and support.
Intervention Start Date
Intervention End Date
Primary Outcomes
Primary Outcomes (end points)
Parental behaviors (Time and economic investments in children’s education), take-up and usage of learning resources, student time investment, student learning (performance on math and Bangla questions), student engagement and re-enrollment
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
Parental beliefs and expectations on re-enrollment and grade completion, student educational aspirations and expectations, student outside options
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
Our baseline sample consists of 7,576 households that have (a) at least one child in grades 6-10 (grades 7-11 in January 2021) and (b) have at least one smartphone in the house. The entire sample was cross-randomized in evenly sized cells across Treatment 1 (information and reminders about remote TV lessons and online platforms) and Treatment 2 (adaptive learning). 6,746 households agreed to be recontacted and form the sample for our midline and endline surveys.

Treatment arm 3 (data subsidy) is cross randomized equally across Treatments 2 and/or 1b (TV + internet). Treatment arm 4 (teacher support) was initially cross-randomized to 1/4 of students (teacher support) across Treatments 1a and 1b (TV only and TV + internet only), though we increased the share assigned to treatment in response to substantial treatment attrition.

Our main empirical specifications will estimate intention-to-treat effects, reflecting the causal impact of assignment to each treatment arm on our outcomes of interest.
Experimental Design Details
Not available
Randomization Method
Randomization done in office by a computer
Randomization Unit
Was the treatment clustered?
Experiment Characteristics
Sample size: planned number of clusters
Sample size: planned number of observations
6,746 households
Sample size (or number of clusters) by treatment arms
Below, we provide an exhaustive list of treatment arms that arise from cross-randomizing each of the main treatment arms.

• 0. Control : 1,894
• 1a. TV : 525
• 1b. TV + internet : 253
• 2a. Adaptive learning : 947
• 2b. Adaptive learning + TV : 471
• 2c. Adaptive learning + TV + internet : 473
• 3a. Data subsidy + TV + internet : 288
• 3b. Data subsidy + Adaptive learning : 947
• 3c. Data subsidy + Adaptive learning + TV : 476
• 3d. Data subsidy + Adaptive learning + TV + internet : 474
• 4a. Teacher support + TV : 422
• 4b. Teacher support + TV + internet : 223
• 4c. Teacher support + Data subsidy + TV + internet : 183
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB Name
Innovations for Poverty Action Institutional Review Board #0004745
IRB Approval Date
IRB Approval Number
Analysis Plan

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