Rough Set and Machine Learning Approach for Identifying Flow Experience in E-learning
Dadang Syarif Sihabudin Sahid (a*), Riswan Efendi (b), Emansa Hasri Putra (c)

a) Department of Information Technology, Politeknik Caltex Riau
Jl. Umbansari No. 1, Rumbai, Pekanbaru, Riau-Indonesia
*dadang[at]pcr.ac.id
b) Department of Mathematics, Universitas Islam Negeri Sultan Syarif Kasim
Pekanbaru, Riau-Indonesia
c) Department of Electrical Engineering, Politeknik Caltex Riau
Jl. Umbansari No. 1, Rumbai, Pekanbaru, Riau-Indonesia

Abstract

Flow experience as a psychological theory has been implemented in various fields. According the theory, flow is represented as anxiety, boredom, and flow. Considering psychological condition in learning activities can improve student performance. Thus, involving flow in learning particularly in e-learning becomes important. The challenge is how to identify flow state during students interaction with e-learning. In previous study, flow was measured by conducting questionnaire series after a learning process. This is inefficient. Additionally, this is often unnatural, since it cannot capture students learning behaviour. Therefore, this study presented flow experience identification when students interact with e-learning using rough set and machine learning approaches. Rough set is an efficient tool to solve uncertainty, imprecision, and vagueness. While, the fuzzy rule and decision tree, parts of machine learning methods were implemented as a comparison. As the results, the accuracy level of the rough set is 92.92%, fuzzy rule is 91.86% and decision tree is 92.39%. As a conclusion, this study showed that flow experience could be identified with high accuracy. In the context of e-learning, it can be used by e-learning to provide an adaptation. Appropriate adaptation is expected can keep the psychological condition of the students in the flow state.

Keywords: flow experience; rough set; machine learning; e-learning

Topic: Computer Science and Engineering

Link: https://ifory.id/abstract-plain/rYhN67QyTH3R

Web Format | Corresponding Author (Dadang Syarif Sihabudin Sahid)