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.
Highlights
Every student learns and processes information differently, mainly due to their behavioral or cultural differences [1, 2, 3]
With the current Covid-19 pandemic that has disrupted many educational institutions, there is a need to provide a personalized learning system based on students' learning styles that can support remote and isolated learners and mitigate challenges caused by disruptions in the learning process
An artificial neural network (ANN) is composed of a cell linked to a system; it is a computational model based on the brain's biological neural structure; and every link has a numeric weight, which infers the importance of that link [20,21,22]
Summary
Every student learns and processes information differently, mainly due to their behavioral or cultural differences [1, 2, 3]. Some prefer text or audio, while others prefer video, exercise, collaboration, inquiry, demonstration, etc These learning differences can be classified and develop in an online learning environment to support students' learning needs [4, 5]. Automatic methods have since been implemented to overcome the shortcomings of the traditional solution These automated methods consist of collecting experience from learners' engagement with a learning environment, which can be errorfree. A learning agent needs to be developed and integrated into the model's inference engine module to provide a system capable of personalizing learning contents to students based on their behavior in a learning environment [17]. The learning agent development can be used in the inference engine of the MSLEM for an intelligent decision based on students' learning behavior for supporting the development of a self-regulated smart learning environment
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