A comparative study of major clustering techniques for MAR learning usability prioritization processes

• Main Authors: Lim, Kok Cheng, Selamat, Ali, Mohamed Zabil, Mohd. Hazli, Selamat, Md. Hafiz, Alias, Rose Alinda, Mohamed, Farhan, Krejcar, Ondrej
• Format: Conference or Workshop Item
• Published: 2020
• Subjects: T Technology (General)
• Online Access: http://eprints.utm.my/id/eprint/92148/; http://dx.doi.org/10.3233/FAIA200577

This paper presents and discusses a comparative study of three major clustering categories namely Hierarchical-based, Iterative mode-based and Partition-based in analyzing and prioritizing Mobile Augmented reality (MAR) Learning (MAR-learning) usability data. This paper first discusses the related works in usability and clustering before moving on to the identification of gaps that can be addressed through experimentation. This paper will then propose a research methodology to measure four common clustering techniques on MAR-learning usability data. The paper will then discourse comparative results showing how Mini-batch K-means to be an ideal technique within the experimental setup. The paper will then present important research highlights, discussion, conclusion and future works.