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Automatic generation of learning outcomes based on long short–term memory artificial neural network1.

Authors :
Suárez-Cansino, Joel
López-Morales, Virgilio
Ramos-Fernández, Julio César
Pinto, David
Beltrán, Beatriz
Singh, Vivek
Source :
Journal of Intelligent & Fuzzy Systems. 2022, Vol. 42 Issue 5, p4449-4461. 13p.
Publication Year :
2022

Abstract

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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
10641246
Volume :
42
Issue :
5
Database :
Academic Search Index
Journal :
Journal of Intelligent & Fuzzy Systems
Publication Type :
Academic Journal
Accession number :
156139427
Full Text :
https://doi.org/10.3233/JIFS-219234