Improving remote learning in the context of a developing country: a tutoring intervention.

Last registered on August 10, 2021


Trial Information

General Information

Improving remote learning in the context of a developing country: a tutoring intervention.
Initial registration date
March 09, 2021
Last updated
August 10, 2021, 10:19 AM EDT



Primary Investigator


Other Primary Investigator(s)

Additional Trial Information

Start date
End date
Secondary IDs

The learning crisis caused by the covid-19 pandemic is unprecedented. As governments seek to address the learning loss, so far, we know tutoring has been a widely proposed solution because it has proven to have a big positive effect on learning. We propose an RCT which can contribute to the literature of tutoring by looking at the interactions between interventions such as tutoring and tracking, and how socio emotional learning can affect the student’s self-perception. Additionally, we can contribute to the very limited existing causal literature on peer tutoring and its effects on learning. Given that the number of tutors that we can recruit may vary, we add a treatment arm that uses Khan Academy and very limited interaction with tutors to ask questions on office hours. We will be working with primary school students grades 3 and 6 and focusing on math tutoring. The tutors will be recruited among the universities and normal schools of the participant states.
External Link(s)

Registration Citation

Aguilar Llanes, Salome et al. 2021. "Improving remote learning in the context of a developing country: a tutoring intervention.." AEA RCT Registry. August 10.
Sponsors & Partners

There are documents in this trial unavailable to the public. Use the button below to request access to this information.

Request Information
Experimental Details


We will recruit tutors (volunteers of universities and normal schools seeking to meet their social service requirements) assign them into WhatsApp groups of 5 kids that will meet 2 times a week. Tutors will be instructed to serve as a support to the teacher and if the teacher doesn’t ask for help in a specific topic then tutors can make use of a protocol that we will provide which will contain exercises and activities that they can do with the students. All tutors will receive initial training on how to teach math and socio-emotional development and we will also create a platform that tutors can use to communicate among each other and share tips and questions. We will try out different versions of these tutoring sessions, some will have an important socio emotional component. In some treatment arms students will be assigned to tutoring groups randomly and in others they will be assigned based on their performance on the baseline evaluation. In one treatment arm we will have a version of peer tutoring and in a last treatment arm the ratio student tutor will be considerably bigger and we will assign students to a Khan Academy class and offer office hours to solve questions.
Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
Outcomes: 1) Learning measured by the Math and Spanish score on standardized evaluation PLANEA (same evaluation used in previous years to evaluate the educational system). We will apply this evaluation before and after the intervention. 2) Self-perception measured by the answers of a survey. 3)Student tutor interactions measured by using a machine learning approach to extract information of the recordings of the tutoring sessions
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
We will try to study the differentiated effects of the same treatment if its delivered via text, or phone call or video call. And also if the tutor teaching it is a university student or a normal school student. Additionally, we will look, ex post at the differential success of the intervention depending on the goodness of match between students and tutors for example by gender or region.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Grades will be randomly assigned to treatments and tutors too. The they will be randomly matched within each group. Randomization will be stratified based on baseline scores and connectivity of students.
Experimental Design Details
Not available
Randomization Method
We will do a stratified randomization using a computer.
Randomization Unit
The unit of randomization will be school grade.
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
We have 300 school and two grades in each school so 600 units.
Sample size: planned number of observations
Within each grade we have an average of 40 students so we will be working with around 24,000 students.
Sample size (or number of clusters) by treatment arms
85 grades (meanind 3rd or 6th grade of schools) in each treatment arm.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
running simmulations with historical data we have an MDE of 0.17 standard deviation in math scores.

Institutional Review Boards (IRBs)

IRB Name
Committee on the Use of Humans as Experimental Subjects (COUHES) at MIT
IRB Approval Date
IRB Approval Number
Analysis Plan

There are documents in this trial unavailable to the public. Use the button below to request access to this information.

Request Information