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An AI-based intervention for improving undergraduate STEM learning
We present results from a small-scale randomized controlled trial that evaluates the impact of just-in-time interventions on the academic outcomes of N = 65 undergraduate students in a STEM course. Intervention messaging content was based on machine learning forecasting models of data collected from...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10355461/ https://www.ncbi.nlm.nih.gov/pubmed/37467236 http://dx.doi.org/10.1371/journal.pone.0288844 |
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author | Hasan, Mohammad Rashedul Khan, Bilal |
author_facet | Hasan, Mohammad Rashedul Khan, Bilal |
author_sort | Hasan, Mohammad Rashedul |
collection | PubMed |
description | We present results from a small-scale randomized controlled trial that evaluates the impact of just-in-time interventions on the academic outcomes of N = 65 undergraduate students in a STEM course. Intervention messaging content was based on machine learning forecasting models of data collected from 537 students in the same course over the preceding 3 years. Trial results show that the intervention produced a statistically significant increase in the proportion of students that achieved a passing grade. The outcomes point to the potential and promise of just-in-time interventions for STEM learning and the need for larger fully-powered randomized controlled trials. |
format | Online Article Text |
id | pubmed-10355461 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-103554612023-07-20 An AI-based intervention for improving undergraduate STEM learning Hasan, Mohammad Rashedul Khan, Bilal PLoS One Research Article We present results from a small-scale randomized controlled trial that evaluates the impact of just-in-time interventions on the academic outcomes of N = 65 undergraduate students in a STEM course. Intervention messaging content was based on machine learning forecasting models of data collected from 537 students in the same course over the preceding 3 years. Trial results show that the intervention produced a statistically significant increase in the proportion of students that achieved a passing grade. The outcomes point to the potential and promise of just-in-time interventions for STEM learning and the need for larger fully-powered randomized controlled trials. Public Library of Science 2023-07-19 /pmc/articles/PMC10355461/ /pubmed/37467236 http://dx.doi.org/10.1371/journal.pone.0288844 Text en © 2023 Hasan, Khan https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Hasan, Mohammad Rashedul Khan, Bilal An AI-based intervention for improving undergraduate STEM learning |
title | An AI-based intervention for improving undergraduate STEM learning |
title_full | An AI-based intervention for improving undergraduate STEM learning |
title_fullStr | An AI-based intervention for improving undergraduate STEM learning |
title_full_unstemmed | An AI-based intervention for improving undergraduate STEM learning |
title_short | An AI-based intervention for improving undergraduate STEM learning |
title_sort | ai-based intervention for improving undergraduate stem learning |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10355461/ https://www.ncbi.nlm.nih.gov/pubmed/37467236 http://dx.doi.org/10.1371/journal.pone.0288844 |
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