1. bookVolumen 3 (2019): Edición 1 (October 2019)
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eISSN
2391-8160
Primera edición
15 Aug 2014
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access type Acceso abierto

Neural Networks in the Educational Sector: Challenges and Opportunities

Publicado en línea: 04 May 2020
Volumen & Edición: Volumen 3 (2019) - Edición 1 (October 2019)
Páginas: 332 - 337
Conferencia Detalles
License
Formato
Conferencia
eISSN
2391-8160
Primera edición
15 Aug 2014
Calendario de la edición
1 tiempo por año
Idiomas
Inglés
Abstract

Given their increasing diffusion, deep learning networks have long been considered an important subject on which teaching efforts should be concentrated, to support a fast and effective training. In addition to that role, the availability of rich data coming from several sources underlines the potential of neural networks used as an analysis tool to identify critical aspects, plan upgrades and adjustments, and ultimately improve learning experience. Analysis and forecasting methods have been widely used in this context, allowing policy makers, managers and educators to make informed decisions. The capabilities of recurring neural networks—in particular Long Short-Term Memory networks—in the analysis of natural language have led to their use in measuring the similarity of educational materials. Massive Online Open Courses provide a rich variety of data about the learning behaviors of online learners. The analysis of learning paths provides insights related to the optimization of learning processes, as well as the prediction of outcomes and performance. Another active area of research concerns the recommendation of suitable personalized, adaptive, learning paths, based on varying sources, including even the tracing of eye-path movements. In this way, the transition from passive learning to active learning can be achieved. Challenges and opportunities in the application of neural networks in the educational sector are presented.

Keywords

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