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dc.rights.licenseReconocimiento-NoComercial 4.0 Internacional. (CC BY-NC)-
dc.contributor.authorda Silva, Nataliaes
dc.description.abstractIn recent years, particularly after the COVID-19 pandemic, the use of different learning management systems (LMS) for various objectives has become a key tool for education. A huge volume of student and teacher data are generated by LMS on a daily basis. Transforming these data into relevant information for decision-making and educational public policy is a major challenge due to the complexity of the data structure and the difficulty of summarizing the learning process with registered information. In this work, we combine several computational, statistical, and visualization tools to tackle this challenge with data from primary schools in Uruguay. Plan Ceibal (*) is a universal public policy implemented in Uruguay since 2010, it is part of the global initiative One Laptop per Child (OLPC, 2005). This program consists of providing every student and teacher in kindergarten, primary and middle school with a laptop or tablet and internet access in the school. Plan Ceibal has covered all public schools in the country and it has improved equality of access to technology, as well as ensured internet access in all public schools. This talk is focused on statistical tools for the evaluation and monitoring the use of LMS by students and teachers. We provide indicators of student engagement based on its activity registered by LMS and include these indicators in a web-application designed as a tool that allows monitoring the use of educational platforms in a systematic, standardized, and simple way. This might help to answer questions at different levels of analysis, for different actors in the education system and are key to strengthening both distance and face-to-face education. Daily usage data of CREA (the main LMS of Plan Ceibal) information for teachers and students are available from 2019 to 2021. The data structure, size among others presents a lot of challenges in this project. Most of the challenges are solved using efficient computational tools, for each stage of data analysis. R package data.table is used for data wrangling, Postgres as a SQL engine, and R packages shiny, plotly and ggplot2 are used for interactive visualization
dc.publisherInstitute of Mathematical Statisticses
dc.rightsAcceso abierto*
dc.subjectMonitoring learning technologies in primary schoolses
dc.subjectData science in education problemses
dc.titleEducational data science: Monitoring learning technologies in primary schoolses
dc.typeDocumento de conferenciaes
dc.subject.aniiCiencias Sociales
dc.subject.aniiCiencias de la Educación
dc.anii.institucionresponsableFacultad de Ciencias Económicas y de Administraciónes
dc.anii.institucionresponsableInstituto de Estadisticaes
dc.anii.institucionresponsableUniversidad de la Repúblicaes
dc.anii.subjectcompleto//Ciencias Sociales/Ciencias de la Educaciónes
dc.ceibal.researchlineMonitoreo y evaluaciónes
dc.ceibal.researchtemaMetodologías innovadoras de monitoreo y evaluación de proyectos y políticas digitaleses
Aparece en las colecciones: Fundación Ceibal

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Monitor_ICSDS_22.pdfPresentación realizada en IMS International Conference on Statistics and Data Science 2022, Florencia1.47 MBAdobe PDFDescargar

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