Categorizing Attributes in Identifying Learning Style using Rough Set Theory

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.

Learner Behavior in e-Learning as a Multicriteria Attribute based on Perspective of Flow Experience

“…However, it must pay attention during categorizing these variables. According to [28], different categorizing gave different results.…”

Rough Set: Utilizing Machine Learning for the Covid-19 Vaccine

“…Different categories produce varying results in terms of accuracy, the amount of data omitted, the number of forgotten attributes, and the number of generated rule criteria. It can be examined for decision-making purposes by balancing these characteristics [13]. Based on the studies that have been put forward, this paper proposes the use of the Rough Set method to classify the extent to which the public is willing and interested in vaccines in the hope of reducing the number of Covid-19 cases.…”