Smart Education Using Machine Learning for Outcome Prediction in Engineering Course

Abstract

Emerging technologies in the past decades have enabled many possibilities and higher education is no exception. Digital transformation in higher education has started many discussion from how to run a university to how to conduct a course. When looking at teach aspect specifically, it is mind blowing on the potential benefit the education system could have acquired if all data were put to the right application or system. With the support of various study on students traits and behaviors and how they affect their success, this study proposed an approach to harvest logged data from an online learning system of Suranaree University of Technology, then derived the learners' behaviors and used them as the dataset. The study developed total of five machine learning models to predict learners' score using the behavior data. The dataset used for the model training was related to the course progress. Thus, it was possible to predict the learners score as soon as the first week of the course. The results of this study shows promising accuracy, which can be used as a guideline approach to develop a decision support system to give immediate feedback to learners and resulting in transforming the way the learners learn.