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Descripción
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| This work presents a predictive analysis of the academic performance of students enrolled in project management courses in two different engineering degree programs. Data were gathered from a virtual learning environment that was designed to support the specific needs of the proposed learning experience. The analyzed data included individual attributes related to communication, time, resources, information and documentation activity, as well as behavioral assessment. Also, students? marks on two exams that took place during the first half of the course were considered as input variables of the predictive models. Results obtained using several regression and classification algorithms?support vector machines, random forests, and gradient boosted trees?confirm the usefulness of Educational Data Mining to predict students? performance. These models can be used for early identification of weak students who will be at risk in order to take early actions to prevent these students from failure. | |
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Internacional
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Si |
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Nombre congreso
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11th International Conference on Computer Supported Education |
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Tipo de participación
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960 |
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Lugar del congreso
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HERAKLION, CRETA |
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Revisores
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Si |
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ISBN o ISSN
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978-989-758-301-8 |
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DOI
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https://www.scitepress.org/Papers/2019/78435/78435 |
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Fecha inicio congreso
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02/05/2019 |
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Fecha fin congreso
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04/05/2019 |
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Desde la página
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665 |
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Hasta la página
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673 |
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Título de las actas
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Proceedings of the 11th International Conference on Computer Supported Education, 2019 |