The impact of Computer Assisted Learning in Higher Education: evidence from an at scale experiment in Ecuador

Last registered on August 08, 2024
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Pre-Trial

Trial Information

General Information

Title
The impact of Computer Assisted Learning in Higher Education: evidence from an at scale experiment in Ecuador
RCT ID
AEARCTR-0009036
Initial registration date
February 28, 2022

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
February 28, 2022, 5:06 PM EST

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

Last updated
August 08, 2024, 7:50 AM EDT

Last updated is the most recent time when changes to the trial's registration were published.

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Primary Investigator

Name
Ciro Avitabile
Affiliation
World Bank
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Other Primary Investigator(s)

PI Name
Diego Angel Urdinola
PI Affiliation
World Bank
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PI Name
Marjorie Chinen
PI Affiliation
World Bank
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Additional Trial Information

Status
On going
Start date
2021-01-01
End date
2024-12-31
Keywords
Education
Additional Keywords
computer-aided learning, math instruction, personalized learning, remedial education, technical higher education
JEL code(s)
C93, I21, I23
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Students often enroll in higher education lacking basic competencies and this is one of the most common reasons for student dropout. This study evaluates the impact of personalized learning delivered through technology in the context of technical higher education in a developing country. We conduct an at scale cluster randomized experiment that involves 71 technical institutions in Ecuador to study whether the use of Assessment and Learning in Knowledge Spaces (ALEKS) during the first semester of higher education can improve short and medium terms outcomes by improving student readiness for higher education. In the short term, we evaluate the impact of the platform on student math knowledge during the first year of university; the probability of enrolling in the third semester and failing a course. In the medium term, we assess whether treated students displayed better labor market outcomes, including the probability of being employed in the formal sector, wages, and the type of occupation.
External Link(s)

Registration Citation

Citation
Avitabile, Ciro, Marjorie Chinen and Diego Angel Urdinola. 2024. "The impact of Computer Assisted Learning in Higher Education: evidence from an at scale experiment in Ecuador." AEA RCT Registry. August 08. https://doi.org/10.1257/rct.9036-1.3
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2021-01-01
Intervention End Date
2021-05-31

Primary Outcomes

Primary Outcomes (end points)
Student math knowledge measured 1 month after the end of the intervention (June 2021)
Student enrollment in the third semester and course failure will be measured in November 2021
Probability of being employed in the formal sector upon study completion, measured between June 2023 and June 2024
Type of occupation in the formal sector, measured between June 2023 and June 2024
Average wage over the same time period
Primary Outcomes (explanation)


Student math knowledge will be measured through an application of the test that students have to take in order to enroll in higher education “Examen de Acceso a la Educación Superior” (EAES). The test will be made available online.
Enrollment in the third semester of higher education will be measured through administrative outcomes provided by Senecyt. The course failure will measure whether students had to repeat any subject since they enrolled into higher education and will be based on administrative records.
Students in the sample took higher education courses that last between 4 and 6 semesters. Therefore students in the sample would graduate on time if they complete their studies between Fall 2022 and Spring 2023. At the time of this update (August 2024), administrative data from Senecyt provide data on graduation only until December 2022. Therefore on-time graduation could only be measured for half of the sample.
Social security data that will be used to measure labor market outcomes. The current version of the data covers all formal jobs between 2018 and June 2024. Therefore relevant labor market outcomes will be measured in the time window between June 2023 - when all students in the sample are supposed to have completed their studies, and June 2024, the last available month.
Available data are at monthly level. For each individual employed in a formal job, wages, sector and type of occupations are available.

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Randomized cluster trial with 39 institutions assigned to the treatment group and 32 institutions to the control group. In order to improve precision a stratified random assignment will be used, where terciles for institute size (enrollment) will be used as strata.
Experimental Design Details
Not available
Randomization Method
randomization done in office by a computer using a Stata code
Randomization Unit
institution (school) level
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
71 institutions
Sample size: planned number of observations
11,431 students
Sample size (or number of clusters) by treatment arms
39 treatment
32 control
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number
Analysis Plan