A Machine Learning Approch For Tracking And Predicting Student Performance

Volume 20 No 7 (2022)

K.Tulasiram , M.Shirisha, P.Nikitha, S.Akhila

DOI: 10.14704/nq.2022.20.7.NQ33290

Abstract

Accurately predicting students’ future performance based on their ongoing academic records is crucial for effectively carrying out necessary pedagogical interventions to ensure students’ on time and satisfactory graduation. Although there is a rich literature on predicting student performance when solving problems or studying for courses using data-driven approaches, predicting student performance in completing degrees (e.g. college programs) is much less studied and faces new challenges: (1)

Keywords

Random Forest, CART Analysis, Length of Stay, KNN

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