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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Detalles Bibliográficos
Autor: Alario Hoyos, Carlos
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
Descripción
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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