Novel Considerations in the ML/AI Modeling of Large-Scale Learning Loss
This study is a path forward for the large-scale, data-driven quantitative analysis of noisy open-source data resources. The goal is to support qualitative findings of smaller studies with extensive open-source data-driven analytics in a new way. The study presented in this research focuses on learn...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10829573/ |
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| author | Mirna Elizondo June Yu Daniel Payan LI Feng Jelena Tesic |
| author_facet | Mirna Elizondo June Yu Daniel Payan LI Feng Jelena Tesic |
| author_sort | Mirna Elizondo |
| collection | DOAJ |
| description | This study is a path forward for the large-scale, data-driven quantitative analysis of noisy open-source data resources. The goal is to support qualitative findings of smaller studies with extensive open-source data-driven analytics in a new way. The study presented in this research focuses on learning interventions. It uses nine publicly accessible datasets to understand and mitigate factors contributing to learning loss and the practical learning recovery measures in Texas public school districts after the recent school closures. The data came from the Census Bureau 2010, USAFACTS, Texas Department of State Health Services (DSHS), the National Center for Education Statistics (CCD), the US Bureau of Labor Statistics (LAUS), and three sources from the Texas Education Agency (STAAR, TEA, ADA, ESSER). We demonstrate a novel data-driven approach to discover insights from an extensive collection of heterogeneous public data sources. For the pandemic school closure period, the mode of instruction and prior score emerged as the primary resilience factors in the learning recovery intervention method. Grade level and census community income level are the most influential factors in predicting learning loss for both Math and Reading. We demonstrate that data-driven unbiased data analysis at a larger scale can offer policymakers an actionable understanding of how to identify learning-loss tendencies and prevent them in public schools. |
| format | Article |
| id | doaj-art-6393b0dd808d493f87e86b552416592f |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-6393b0dd808d493f87e86b552416592f2025-01-15T00:03:15ZengIEEEIEEE Access2169-35362025-01-01137780779210.1109/ACCESS.2025.352641210829573Novel Considerations in the ML/AI Modeling of Large-Scale Learning LossMirna Elizondo0https://orcid.org/0000-0002-9627-0755June Yu1https://orcid.org/0000-0003-3554-0552Daniel Payan2LI Feng3https://orcid.org/0000-0002-0536-1791Jelena Tesic4https://orcid.org/0000-0002-9972-9760Department of Computer Science, Texas State University, San Marcos, TX, USAState of Texas Legislative Budget Board, Austin, TX, USALove’s Travel Stops and Country Stores, Yukon, OK, USADepartment of Finance and Economics, Texas State University, San Marcos, TX, USADepartment of Computer Science, Texas State University, San Marcos, TX, USAThis study is a path forward for the large-scale, data-driven quantitative analysis of noisy open-source data resources. The goal is to support qualitative findings of smaller studies with extensive open-source data-driven analytics in a new way. The study presented in this research focuses on learning interventions. It uses nine publicly accessible datasets to understand and mitigate factors contributing to learning loss and the practical learning recovery measures in Texas public school districts after the recent school closures. The data came from the Census Bureau 2010, USAFACTS, Texas Department of State Health Services (DSHS), the National Center for Education Statistics (CCD), the US Bureau of Labor Statistics (LAUS), and three sources from the Texas Education Agency (STAAR, TEA, ADA, ESSER). We demonstrate a novel data-driven approach to discover insights from an extensive collection of heterogeneous public data sources. For the pandemic school closure period, the mode of instruction and prior score emerged as the primary resilience factors in the learning recovery intervention method. Grade level and census community income level are the most influential factors in predicting learning loss for both Math and Reading. We demonstrate that data-driven unbiased data analysis at a larger scale can offer policymakers an actionable understanding of how to identify learning-loss tendencies and prevent them in public schools.https://ieeexplore.ieee.org/document/10829573/Noisy tabular datadata in the wildgradient boostingfeature selectiondimensionality reduction |
| spellingShingle | Mirna Elizondo June Yu Daniel Payan LI Feng Jelena Tesic Novel Considerations in the ML/AI Modeling of Large-Scale Learning Loss IEEE Access Noisy tabular data data in the wild gradient boosting feature selection dimensionality reduction |
| title | Novel Considerations in the ML/AI Modeling of Large-Scale Learning Loss |
| title_full | Novel Considerations in the ML/AI Modeling of Large-Scale Learning Loss |
| title_fullStr | Novel Considerations in the ML/AI Modeling of Large-Scale Learning Loss |
| title_full_unstemmed | Novel Considerations in the ML/AI Modeling of Large-Scale Learning Loss |
| title_short | Novel Considerations in the ML/AI Modeling of Large-Scale Learning Loss |
| title_sort | novel considerations in the ml ai modeling of large scale learning loss |
| topic | Noisy tabular data data in the wild gradient boosting feature selection dimensionality reduction |
| url | https://ieeexplore.ieee.org/document/10829573/ |
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