Affiliations: [a] Intelligent Computing Research Group, Computation and Electronics Academic Department, Engineering and Basic Sciences Institute, Autonomous University of the Hidalgo State, Carr.Pachuca–Tulancingo Km. 4.5, Col. Carboneras, 42184, Mineral de laReforma, HGO., MX | [b] Laboratorio Nacional en Vehículos Autónomos yExoesqueletos, Universidad Politécnica de Pachuca, Carr.Pachuca–Cd. Sahagún Km. 20, 43830, Zempoala, HGO., MX
Correspondence:
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Corresponding author. Virgilio López-Morales, Intelligent Computing Research Group, Computation and Electronics Academic Department, Engineering and Basic Sciences Institute, Autonomous University of the Hidalgo State, Carr. Pachuca–Tulancingo Km. 4.5, Col. Carboneras, 42184, Mineral de la Reforma, HGO., MX. E-mail: [email protected].
Note: [1] CONACyT, Lab. Nal. en Vehículos Autónomos y Exosqueletos (LANAVEX LN299146).
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.
