Take-Up, Use, and Effectiveness of Remote Learning Technologies

Last registered on August 12, 2020

View Trial History

Pre-Trial

Trial Information

General Information

Title

Take-Up, Use, and Effectiveness of Remote Learning Technologies

RCT ID

AEARCTR-0006191

Initial registration date

August 11, 2020

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

August 12, 2020, 10:04 AM EDT

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

Locations

Country

Bangladesh

Region

Primary Investigator

Name

Laia Navarro-Sola

Affiliation

Stockholm University IIES

Contact Primary Investigator

Other Primary Investigator(s)

PI Name

Priya Mukherjee

PI Affiliation

U. Wisconsin-Madison

Contact Investigator

PI Name

Emily Beam

PI Affiliation

University of Vermont

Contact Investigator

Additional Trial Information

Status

In development

Start date

2020-09-01

End date

2021-04-01

Keywords

Education

Additional Keywords

JEL code(s)

Secondary IDs

Abstract

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. However, the effectiveness of these tools is largely unknown.
In partnership with the country’s government, we will evaluate two remote learning initiatives targeted at post-primary education: (a) daily televised lessons for grades 6-10, and (b) an online learning platform, that acts as repository for the televised lessons and where students can also complete quizzes.

We will evaluate three interventions aimed at decreasing the barriers to remote education and increase the effectiveness of these remote learning initiatives. First, we will reduce the cost of internet learning activities by providing discounted data packages. Second, to increase salience of the options available, we will provide information and reminders about the remote TV lessons and the online platform. Third, we will encourage students to reach out to teachers with questions or doubts about the remote learning content.

External Link(s)

Registration Citation

Citation

Beam, Emily, Priya Mukherjee and Laia Navarro-Sola. 2020. "Take-Up, Use, and Effectiveness of Remote Learning Technologies." AEA RCT Registry. August 12. https://doi.org/10.1257/rct.6191-1.0

Sponsors & Partners

Interventions

Intervention(s)

We have two information treatments and one cost reduction treatment.

The information treatments are:
- Treatment 1: SMS/voice encouragements to reach out to teachers
- Treatment 2: SMS/voice information and reminders about remote TV lessons and online platforms. We split the information salience treatment into three sub-treatments:
o 2A: SMS information/reminders about TV lessons
o 2B: SMS information/reminders about internet education platform
o 2C: SMS information/reminders about TV lessons and internet education platform

The cost reduction treatment consists of offering discounted internet data packages to offset the costs of engaging with online remote learning activities.

Intervention Start Date

2020-09-01

Intervention End Date

2020-12-01

Primary Outcomes

Primary Outcomes (end points)

• Parents and students’ time and economic investments in remote education
• Usage of TV lessons and online platform, completion of remote homework
• Student learning outcomes (about remote education content and general learning)

Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)

• Parental and child expectations on re-enrollment and grade completion
• Parental willingness to pay for remote learning options
• Student aspirations and mental health

Secondary Outcomes (explanation)

Experimental Design

Our sample consists of households that have at least one child in grades 6-10. They must have either (a) a smart phone, (b) cable TV access (necessary to access the remote TV learning channel), or (c) both. The entire sample will be cross-randomized in evenly sized cells across Treatment 1 (encouragement to reach out to teachers) and Treatment 2 (information and reminders about remote TV lessons and online platforms), creating a 2x2 design.

For households that have both smartphone access and cable TV access, they will be randomized into equal sized groups across the three sub-treatment arms within Treatment 2 – TV lessons only, internet platform only, or TV + internet education platform.

We will conduct a second randomization for data package subsidies among the set of users who (a) have smartphone access and (b) are subscribers with our telecom partner (necessary for the packages to be useful). This will be cross randomized across the other treatment arms.

We will measure ITT effects of assignment to each treatment arm on our outcomes of interest. For student learning outcomes, we will also measure LATE effects of remote platform usage, using random assignment to treatment as an instrument for usage.

Experimental Design Details

Randomization Method

Randomization done in office by a computer

Randomization Unit

Households

Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters

None

Sample size: planned number of observations

90,000 households

Sample size (or number of clusters) by treatment arms

Equal size treatment arms across the main two groups:

- No outreach, no encouragement (control): 22,500
- Encouragement, no outreach to teachers: 22,500
- Outreach to teachers, no encouragement: 22,500
- Outreach to teachers, encouragement: 22,500

Within the encouragement treatment arm (45,000 treated), we will randomly assign students to the three types of information if their household has access to a smartphone and cable television. We do not know in advance what this sample size will be, but the treatment assignment will be distributed evenly (1/3, 1/3, 1/3) for that group. If 80% of households have access to a smartphone and cable television (36,000), then the sample size for treatment arms 2a, 2b, and 2c will be 12,000 each.

Data package reduction: The exact sample size of the data reduction package treatment arm will depend on budgetary constraints, but the randomization strategy is the following:
- Take the sample of 45,000 assigned to encouragement treatment
- Restrict to those who have access to a smart phone and are subscribers of our telecom partner
- Exclude treatment 2a (information about TV only)
- Among those, randomly select X to receive the data package subsidy, and the remainder will not receive the subsidy.

Most outcomes will be collected by IVR for the full sample. Outcomes requiring in-person phone survey (such as expectations, aspirations, and mental health outcomes) will be only collected for a subsample of individuals (around 10,000).

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

Supporting Documents and Materials

IRB