Learning material selection for metaverse-based mathematics pedagogy media using multi-criteria recommender system

Arif, Yunifa Miftachul and Hayati, Hani Nur (2022) Learning material selection for metaverse-based mathematics pedagogy media using multi-criteria recommender system. International Journal of Intelligent Engineering and Systems, 15 (6). pp. 541-551. ISSN 2185-3118

Preview

Text 12123.pdf - Published Version Download (557kB) | Preview

Abstract

One of the challenges of learning mathematics is making students like the subject to understand the material being taught more efficiently. Therefore, several studies propose the use of gamification elements in the mathematics learning process. Another important consideration in learning mathematics is the accuracy of the subject matter given to students. This study proposes a metaverse-based mathematics pedagogy media (MMPM) equipped with learning material selection (LMS) to adaptively select subject matter according to the level of student knowledge. We use the multi-criteria recommender system (MCRS) to support LMS in providing recommendations for determining the choice of subject matter. The method used to calculate similarity is cosine-based, while the ranking is based on average, worst-case, and aggregate. We built the MMPM system using the unity game engine in the experimental stage. At the same time, the subject matter that becomes the focus of scenario visualization is about beam, cube, prism, and pyramid. The test results show that the LMS system works well in carrying out its duties by adaptively choosing the appropriate subject matter scenarios for students based on their pre-test results. In this study, MCRS-based LMS produces the highest accuracy of 92% for two to three input items and the lowest 90% for four input items.

Item Type:	Journal Article
Keywords:	mathematics; Metaverse; learning material selection; MCRS.
Subjects:	08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080111 Virtual Reality and Related Simulation
Divisions:	Faculty of Technology > Department of Informatics Engineering
Depositing User:	Yunifa Miftachul Arif
Date Deposited:	18 Nov 2022 14:22

Downloads

Origin of downloads

Actions (login required)

View Item