Abstract
A learning style is an issue related to learners. In one way or the other, learning style could assist learners in their learning activities if students ignore their learning styles, it may influence their effort in understanding teaching materials. To overcome these problems, a model for reliable automatic learning style detection is needed. Currently, there are two approaches in detecting learning styles: data driven and literature based. Learners, especially those with changing learning styles, have difficulties in adopting these two approach since they are not adaptive, dynamic and responsive (ADR). To solve the above problems, a model using agent learning approach is proposes. Agent learning involves performing activities in four phases, i.e. initialization, learning, matching and, recommendations to decide the learning styles the students use. The proposed system will provide instructional materials that match the learning style that has been detected. The automatics detection process is performed by combining the data-driven and literature-based approaches. We propose an evaluation model agent learning system to ensure the model is working properly.
Highlights
Learning style can be defined as ways used properly by the learners to improve their concentration in learning through the learning behaviour, such as reading, viewing, listening and imitating [1]
The VARK learning style model has the following characteristics: (a) visual information that is usually depicted in charts, graphs, flow charts, symbols, arrows and hierarchies of teaching materials; (b) audio information that is represented by tutorials, verbal material, cassettes, group discussions, talking and discussing issues in online media; (c) read/write learning style, in which each input and output is text-based, such as e-books, ejournals, e-paper and the e-library; and (d) kinesthetic, which has characteristics based on experience or practice through the learner interaction with teaching materials to gain an understanding
This paper proposes a detection model of learning styles with Adaptive, Dynamic, and Responsive (ADR) approach
Summary
Learning style can be defined as ways used properly by the learners to improve their concentration in learning through the learning behaviour, such as reading, viewing, listening and imitating [1]. The automatic detection model of learning styles is divided into two methods: data driven and literature based. The learning styles detection model using data-driven method applies the method of artificial intelligence (AI) in the detection process, such as NbTree AI [3], the Bayesian Model [4] and the Decision Tree-Hidden Markov [5].the literature based learning styles detection model uses the access result of the learners toward the available teaching materials. The learning styles which are usually detected in the data driven or literature based research use Felder Silverman Learning Style Model (FSLSM) [6]. The model proposed to detect the optimal learning style is based on the teaching materials available by combining the data-driven and literature-based approach.
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