Automatic generation of learning outcomes based on long short–term memory artificial neural network

Building a good instructional design requires a sound organization management to program and articulate several tasks based for instance on the time availability, process follow-up, social and educational context. Furthermore, learning outcomes are the basis involving every educational activity. Thu...

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Veröffentlicht in:Journal of intelligent & fuzzy systems 2022-01, Vol.42 (5), p.4449
Hauptverfasser: Suárez-Cansino, Joel, López-Morales, Virgilio, Ramos-Fernández, Julio César
Format: Artikel
Sprache:eng
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Zusammenfassung:Building a good instructional design requires a sound organization management to program and articulate several tasks based for instance on the time availability, process follow-up, social and educational context. Furthermore, learning outcomes are the basis involving every educational activity. Thus, based on a predefined ontology, including the instructional educative model and its characteristics, we propose the use of a Long Short–Term Memory Artificial Neural Network (LSTM) to organize the structure and automatize the obtention of learning outcomes for a focused instructional design. We present encouraging results in this direction through the use of a LSTM using as the training data, a small learning outcomes set predefined by the user, focused on the characteristics of an educative model previously defined.
ISSN:1064-1246
1875-8967
DOI:10.3233/JIFS-219234