An Artificial Neural Network (ANN)-Based Learning Agent for Classifying Learning Styles in Self-Regulated Smart Learning Environment

Authors

  • Yusufu Gambo University of the West of Scotland, UK School of Computing, Engineering and Physical Sciences
  • Muhammad Zeeshan Shakir University of the West of Scotland, UK School of Computing, Engineering and Physical Sciences https://orcid.org/0000-0003-4777-4719

DOI:

https://doi.org/10.3991/ijet.v16i18.24251

Keywords:

Self-regulated learning, smart learning environment, personalized learning, learning styles, artificial neural network

Abstract


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.

Author Biographies

Yusufu Gambo, University of the West of Scotland, UK School of Computing, Engineering and Physical Sciences

PhD Student

Muhammad Zeeshan Shakir, University of the West of Scotland, UK School of Computing, Engineering and Physical Sciences

Reader (Associate Professor) in Computer Networks Artificial Intelligence, Visual Communications and Networks Research Centre

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Published

2021-09-20

How to Cite

Gambo, Y., & Shakir, M. Z. (2021). An Artificial Neural Network (ANN)-Based Learning Agent for Classifying Learning Styles in Self-Regulated Smart Learning Environment. International Journal of Emerging Technologies in Learning (iJET), 16(18), pp. 185–199. https://doi.org/10.3991/ijet.v16i18.24251

Issue

Vol. 16 No. 18 (2021)

Section

Papers

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