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Abstract
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Before
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.
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After
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.
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Trial End Date
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Before
December 30, 2023
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After
December 31, 2024
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Last Published
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Before
October 04, 2022 08:01 PM
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After
August 08, 2024 07:48 AM
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Primary Outcomes (End Points)
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Before
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
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After
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
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Primary Outcomes (Explanation)
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Before
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
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After
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.
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