Evaluation of Clustering Methods for Student Learning Style Based Neuro Linguistic Programming

Abstract

Students' performance is a key point to get a better first impression during a job interview with an employer. However, there are several factors, which affect students' performances during their study. One of them is their learning style, which is under Neurolinguistic Programming (NLP) approach. Learning style is divided into a few behavioral categories, Visual, Auditory and Kinesthetics (VAK). This paper addresses the evaluation of clustering methods for the identification of learning style based on system preferences. It starts with the distribution of questionnaires to acquire the information on the VAK for each student. About 167 respondents in the Faculty of Computer and Mathematical Science are collected. It is then pre-processed to prepare the data for clustering method evaluations. Three clustering methods; Simple K-Mean, Hierarchical and Density-Based Spatial Clustering of Applications with Noise are evaluated. The findings show that Simple K-Mean offers the most accurate prediction. Upon completion, by using the dataset, Simple K-Means technique estimated four clusters that yield the highest accuracy of 74.85 % compared to Hierarchical Clustering, which estimated four clusters and Density-Based Spatial Clustering of Applications with Noise which estimated three clusters with 52.69% and 61.68 % respectively. The clustering method demonstrates the capability of categorizing the learning style of students based on three categories; visual, auditory and kinesthetic. This outcome would be beneficial to lecturers or teachers in university and school with an automatically clustering the students' learning style and would assist them in teaching and learning, respectively.

Mapping Research Themes and Future Directions in Learning Style Detection Research: A Bibliometric and Content Analysis

“…In cluster 3, denoted by the color blue, the research theme centers around machine learning and artificial intelligence in the context of adaptive learning. This cluster primarily focuses on adaptive learning approaches utilizing diverse machine learning algorithms, such as decision tree (Dutsinma and Temdee, 2020), k-means (Yusoff, Najib Bin Fathi and ., 2018), knn (Shekapure and Patil, 2019), and deep learning (Zhang et al, 2021c). These algorithms play a crucial role in analyzing student data and delivering personalized learning experiences tailored to the unique needs of individual learners.…”