Prediction of Higher Education Students Academic Grades based on Personality Traits

Abstract

The quality of a country's education system has a significant impact on its growth. The education industry has seen significant transformations around the world. It is now recognised as industry, with its own set of challenges, the most significant of which is a drop in students' success rates and their abandonment of courses. Due to a desire to understand the underlying elements that determine academic achievement, predicting students' grades has become a major field of research in education. Early intervention of a student's failure may enable management to provide prompt counselling and coaching to improve success rates and student retention. In this research, we look at how personality features can be used to reinforce learning. Using temporal difference learning analytics and the big five factor model, we will examine the impacts of conscientiousness, neuroticism, extraversion, and other personality qualities on learners. The goal of this paper is to give a quick overview of how scholarly analytics can be utilised in educational institutions, what tools are available, and how institutions can anticipate student performance and achievement using academic analytics. We surveyed and gathered the Big Five Factor (BFF) questionnaire dataset to capture their personality attributes for this purpose. Students with a higher conscientiousness, high openness to experience, and emotional stability tend to do well in school, but those with high neuroticism are more likely to experience mood swings. As a result, extraversion and conscientiousness are important.