Predictive Modeling for Student Performance Data Using Decision Tree and Support Vector Machine

Abstract

This paper is aimed to present a conceptual understanding that summarizes higher education
analytics lifecycle. This paper explores the establishment of new architecture of technologies,
experts, standards, and practices in the complex data infrastructure projects among higher
education institutes. The research on higher education analytics converges with the demands from
industry to improve the learning education systems by considering the teaching and learning
analytics capabilities enhancing the efficiency of higher education. The exploitation of massive
volume campus and learning information could be a crucial challenge for the planning of campus
resources, personalized curricula and learning experiences. In the field of higher education,
institutions look to a future of the unknown and vast speed advancement of technology. Moreover,
with more strategic data solutions used in decision making with the over increasing social needs
and political changes at national and global, competition within and among universities increase.
Higher education needs to expand local and global impact, increase financial and operational
efficiency, create the new funding models in a changing economic climate and respond to the
greater accountability demands to ensure the success of organizational at all levels and stay on top
of the ranks. Research on higher education institutes is also important because it enables maximum
benefit and perceptions on students’ performance and learning trajectories to be determined as
these two are important in adapting and personalizing curriculum and assessment. The findings of
this paper provided a view about modeling students’ performance classification by Machine
Learning models and to identify which of the predictors in the dataset contribute towards good
prediction on the students’ performance.