Educational data science is an important branch of data science that aims to extract knowledge from various forms of massive educational data using statistical and machine learning methods. In this talk, I will talk about how data science methods can assist educational stakeholders by providing a better understanding of the big data stored in the educational administrative system. In this talk, I will present our research on identifying students at risk of dropping out, assessing the predictive validity of the admission system, identifying various factors of student success, the connection between student evaluation of teaching and grade inflation, moreover, I will also touch upon the applicability of explainable artificial intelligence tools in higher education.
Roland Molontay is an Assistant Professor at the Budapest University of Technology and Economics. He is the founder and director of the Human and Social Data Science Laboratory (HSDSLab). His research focuses on network theory and on data-driven educational research. He is the author of more than 30 scientific publications, a regular speaker at renowned international conferences, and the recipient of numerous professional awards, including the Gyula Farkas Memorial Prize.
Event recording
About the speaker
Roland Molontay is an Assistant Professor at the Budapest University of Technology and Economics, he is the founder and Director of the Human and Social Data Science Laboratory (HSDSLab). His research focuses on network theory and on data-driven educational research. He is the author of more than 30 scientific publications, a regular speaker at renowned international conferences, and the recipient of numerous professional awards, including the Gyula Farkas Memorial Prize.
Address
Registration for the event has finished. Thank you for your participation.