What can innovation in engineering education do for you as a student and what can you do as a student for Innovation in engineering education?
Descripción del Articulo
Innovation in education in general and innovation in engineering education in particular must be supported by properly collected and analyzed data to guide decisionmaking processes. Today it is possible to collect data from many more stakeholders (not just students), and also to collect much more da...
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| Formato: | objeto de conferencia |
| Fecha de Publicación: | 2020 |
| Institución: | Universidad de Lima |
| Repositorio: | ULIMA-Institucional |
| Lenguaje: | inglés |
| OAI Identifier: | oai:repositorio.ulima.edu.pe:20.500.12724/11151 |
| Enlace del recurso: | https://hdl.handle.net/20.500.12724/11151 |
| Nivel de acceso: | acceso abierto |
| Materia: | Formacion profesional Ingeniería Innovaciones educativas Data mining Vocational training Engineering Educational innovations Ciencias sociales / Educación http://purl.org/pe-repo/ocde/ford#2.02.04 |
| Sumario: | Innovation in education in general and innovation in engineering education in particular must be supported by properly collected and analyzed data to guide decisionmaking processes. Today it is possible to collect data from many more stakeholders (not just students), and also to collect much more data from each stakeholder. Nevertheless, low-level data collected by monitoring the interactions of the multiple stakeholders with learning platforms and other computing systems must be transformed into meaningful high-level indicators and visualizations that guide decision-making processes. The aim of this paper is to discuss some notable trends in data-driven innovation in engineering education, including 1) improvement of educational content; 2) improvement of learners’ social interactions; 3) improvement of learners’ self-regulated learning skills; and 4) prediction of learners’ behavior. However, there are also significant risks associated with data collection and processing, such as privacy, transparency, biases, misinterpretations, etc., which must also be taken into account, and require creating specialized units and training the personnel in data management. |
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Nota importante:
La información contenida en este registro es de entera responsabilidad de la institución que gestiona el repositorio institucional donde esta contenido este documento o set de datos. El CONCYTEC no se hace responsable por los contenidos (publicaciones y/o datos) accesibles a través del Repositorio Nacional Digital de Ciencia, Tecnología e Innovación de Acceso Abierto (ALICIA).
La información contenida en este registro es de entera responsabilidad de la institución que gestiona el repositorio institucional donde esta contenido este documento o set de datos. El CONCYTEC no se hace responsable por los contenidos (publicaciones y/o datos) accesibles a través del Repositorio Nacional Digital de Ciencia, Tecnología e Innovación de Acceso Abierto (ALICIA).