Categorizing Attributes in Identifying Learning Style using Rough Set Theory

Dadang Syarif Sihabudin Sahid,Muhammad Wahyudi,Riswan Efendi,Emansa Hasri

doi:10.14569/ijacsa.2020.0110136

Dadang Syarif Sihabudin Sahid, Muhammad Wahyudi + Show 2 more

Open Access

https://doi.org/10.14569/ijacsa.2020.0110136

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Abstract

In a learning process, learning style becomes one crucial factor that should be considered. However, it is still challenging to determine the learning style of the student, especially in an online learning activity. Data-driven methods such as artificial intelligence and machine learning are the latest and popular approaches for predicting the learning style. However, these methods involve complex data and attributes. It makes it quite heavy in the computational process. On the other hand, the literate based driven approach has a limitation in inconsistency between results with the learning behavior. Combination, both approaches, gives a better accuracy level. However, it still leaves some issues such as ambiguity and a wide of range of attributes value. These issues can be reduced by finding the right approach and categorization of attributes. Rough set proposed the simple way that can compromise with the ambiguity, vague, and uncertainty. Rough set generated the rules that can be used for prediction or classification decision attributes. Yet, due to the method based on categorical data, it must be careful in determining the category of attributes. Hence, this research investigated several categorizing attributes in the identification learning style. The results showed that the approach gives a better prediction of the learning style. Different categories give different results in terms of accuracy level, number of eliminated data, number of eliminated attributes, and number of generated rules criteria. For decision making, it can be considered by balancing of these criteria.

Highlights

The shifting paradigm in the learning process from teachercentered learning to students centered learning has changed the way of learning
After conducting several steps of the research, there are some results that can be achieved in identifying learning styles using rough set theory
The application has the capability for converting data from quantitative to categorical, eliminated data based on the rough set, generate the rule, and conduct evaluation through accuracy level

Summary

Introduction

The shifting paradigm in the learning process from teachercentered learning to students centered learning has changed the way of learning. Each student has a unique and different way of learning. They have their own way and learning style. Technology attracts the student in a different way of learning. This situation has encouraged the development of learning models designed to follow students' personal needs in the form of learning personalization. In case of e-learning, the design of e-learning models that were initially technology-oriented and general in nature became more oriented to the needs, characteristics, situations, and conditions of students such as learning styles, prior knowledge, learning goals, cognitive abilities, learning interests, and motivation as parameters in learning [3]. The identification of student learning styles is significant in the learning process

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