Determining Significant Classification Factors for Senior Learning: A Case Study of Thai Seniors and Social Media Skill Learning

Abstract

The senior learns in order to have a better quality of life. The challenge of seniors in learning is their learning ability that deteriorates because of age. Suitable management for different types of seniors, so called personalized learning is required. Therefore, this study focuses on determining significant classification factors for classification of seniors which is an important component of personalized learning. In this study, the assumption of personal background and health issue can be used for classifying types of seniors. The decision tree is used for determining significant classification factors and constructing the model. The study is conducted with 75 seniors for social network skill learning. The classification results show that the significant classification factors affecting the classification model of senior learning are age, daily internet time spending, number of applications, memory problem, and education background. The model constructed by decision tree provides 93.33% classification accuracy. Also, the obtained factors are verified by testing with two machine learning methods including artificial neural network (ANN) and K-nearest neighbors (K-NN). The comparison results show that 5 factors provide high classification accuracy for both classifiers, which are 93.33% and 92.00% for ANN, and K-NN, respectively.