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

Last registered on October 04, 2022

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
October 04, 2022, 8:01 PM EDT

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

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Primary Investigator

Affiliation
World Bank

Other Primary Investigator(s)

PI Affiliation
World Bank
PI Affiliation
World Bank

Additional Trial Information

Status
On going
Start date
2021-01-01
End date
2023-12-30
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. 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, and the probability of graduating on time.
External Link(s)

Registration Citation

Citation
Avitabile, Ciro, Marjorie Chinen and Diego Angel Urdinola. 2022. "The impact of Computer Assisted Learning in Higher Education: evidence from an at scale experiment in Ecuador." AEA RCT Registry. October 04. https://doi.org/10.1257/rct.9036-1.1
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
On-time graduation will be measured in year 2023 as most degrees last either 5 or 6 semesters
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.
On-time graduation will be measured through administrative data provided by Senescyt

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)
IRB

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number