Conference paper 2021

Knowledge modelling for ill-defined domains using learning analytics: Lineworkers case

Advances in Intelligent Systems and Computing
Conference · Vol. 1266 AISC · pp. 409-418
Abstract

Representation of knowledge used by E-learning systems to modulate learning processes plays a key role in its effectiveness. In ill-defined domains where training is carried out using an apprenticeship model such as the area of Technical and Vocational Education and Training, building a Knowledge Model is not straightforward. In such areas the knowledge model heavily depends on the journeyman tacit expertise which is spread across text documents such as manuals, books, reports, competency descriptions, among others. Hence, in this work it is proposed to employ Learning Analytics for building a Knowledge Model from text documents used in lineworkers vocational education. The model is organized by declarative, procedural, and competency layers. Each of these contains a semantic networked built from extracted concepts, and the semantic relations between concepts is obtained using the Normalized Web Distance. Initial results shows that building knowledge models for ill-defined domains is promising, although more experimentation is required. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

Keywords

Author Keywords

Learning Analytics Natural language processing Ill-defined domain Knowledge Model Normalized Web Distance

Index Keywords

Learning systems Apprentices Vocational education Semantics Technical and vocational education and trainings Learning process Knowledge model Knowledge representation Ill-defined domains Knowledge modelling Semantic relations Text document
Author Affiliations
CONACYT-INEEL, Cuernavaca, MOR, Mexico, Instituto Nacional de Electricidad y Energías Limpias, Cuernavaca, MOR, Mexico
Universidad de Colima, Colima, COL, Mexico
Instituto Nacional de Electricidad y Energías Limpias, Cuernavaca, MOR, Mexico
Laboratorio de Decisiones Intelligentes, Tecnológico de Monterrey, Monterrey, NLE, Mexico
Funding & Acknowledgements
Consejo Nacional de Ciencia y Tecnología, CONACYT
GSB thanks the Cátedra CONACYT program for supporting this research.
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