Data analytics and Artificial Neural Network framework to profile academic success: case study

Abstract

Academic success in higher education has attracted interest from the scientific community because of its implications for personal development and societal progress. Programmes such as Tecnologico de Monterrey’s Leaders of Tomorrow aim to nurture students’ potential and promote academic success. This study examines the attributes from participating students associated with academic success. The focus is on personality and socio-demographic factors that influence academic excellence. The research contribution of this work is data analysis supported by artificial neural networks to establish the relationship between personality tests and background information with academic performance. The findings were: (a) high school GPA predicts university success; (b) first-generation degree status is associated with higher GPA; (c) gender differences in academic performance vary by context; and (d) personality profiles are not associated with academic performance. The role of socio-demographic and personality factors in predicting the academic success of prospective students is discussed.

Keywords

• High performing students
• higher education
• educational innovation
• Leaders of Tomorrow
• artificial intelligence
• data analytics