Characterization Clustering of Educational Technologists Achievement in Higher Education Using Machine Learning Analysis

Abstract

The research purpose was to develop a model for predicting cluster achievement of educational technologists. There are three research objectives: 1) to study the context of educational technologists’ achievements in higher education, 2) to construct a model for predicting learning achievement of educational technologists in higher education, and 3) to evaluate a model for predicting learning achievement of educational technologists in higher education. The research scope was to study the success cluster of educational technologists in Thailand. The research data were 98 students from the Bachelor of Arts Program in Educational Technology and Communications during the academic year 2015 to 2017. Research tools consist of two main parts: statistical tools and machine learning analysis tools. The results showed that most of the students in the program had a high-grade point average with a grade point average of 3.11. In addition, the educational technologists’ achievement cluster prediction model has an accuracy of 68.37%. The research results can be used to improve education programs to develop effective educational technologists where it is necessary to understand the context of the barriers and success factors of academic achievement.