Open AccessAn Artificial Neural Network (ANN)-Based Learning Agent for Classifying Learning Styles in Self-Regulated Smart Learning Environment
Author(s) -
Yusufu Gambo,
Muhammad Zeeshan Shakir
Publication year - 2021
Publication title -
international journal of emerging technologies in learning/international journal: emerging technologies in learning
Language(s) - English
Resource type - Journals
eISSN - 1868-8799
pISSN - 1863-0383
DOI - 10.3991/ijet.v16i18.24251
Subject(s) - artificial intelligence , machine learning , computer science , artificial neural network , learning environment , multi task learning , learning styles , consistency (knowledge bases) , process (computing) , active learning (machine learning) , task (project management) , mathematics education , engineering , psychology , systems engineering , operating system
The increasing development in smart and mobile technologies are transforming learning environments into a smart learning environment. Students process information and learn in different ways, and this can affect the teaching and learning process. To provide a system capable of adapting learning contents based on student's learning behavior in a learning environment, the automated classification of the learners' learning patterns offers a concrete means for teachers to personalize students' learning. Previously, this research proposed a model of a self-regulated smart learning environment called the metacognitive smart learning environment model (MSLEM). The model identified five metacognitive skills-goal settings (GS), help-seeking (HS), task strategies (TS), time-management (TM), and self-evaluation (SE) that are critical for online learning success. Based on these skills, this paper develops a learning agent to classify students' learning styles using artificial neural networks (ANN), which mapped to Felder-Silverman Learning Style Model (FSLSM) as the expected outputs. The receiver operating characteristic (ROC) curve was used to determine the consistency of classification data, and positive results were obtained with an average accuracy of 93%. The data from the students were grouped into six training and testing, each with a different splitting ratio and different training accuracy values for the various percentages of Felder-Silverman Learning Style dimensions.