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

Autor:	Joel Suárez-Cansino, Virgilio López-Morales, Julio César Ramos-Fernández
Rok vydání:	2022
Předmět:	Statistics and Probability Artificial Intelligence General Engineering
Zdroj:	Journal of Intelligent & Fuzzy Systems. 42:4449-4461
ISSN:	1875-8967 1064-1246
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::7af1eaf493f29fb33d43a6e3cfdf5685 https://doi.org/10.3233/jifs-219234 Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
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