Student Attendance Pattern Detection and Prediction

Dublin Core


Student Attendance Pattern Detection and Prediction


Ibrahim Muzaferija1, Zerina Mašetić2, Samed Jukić3, Dino Kečo4


Since the early beginnings of education systems, attendance has always played a crucial
role in student success, as well as in the overall interest of the matter. The most productive way of
increasing the student attendance rate is to understand why it decreases, try to predict when it is
going to happen, and act on causing factors in order to prevent it. Many benefits of predicted and
increased attendance rate can be achieved, including better lecture organization (i.e. lecture time and
duration, lecture class choice, etc). This paper describes the steps in the extraction of knowledge from
the university's student database and making a model that predicts whether the student will attend
the class or not. Results show that the attendance patterns are best reflected when employing a
decision tree algorithm, a C4.5 model that is interpretable and able to predict the attendance with
0.81 AUC performance measure


Data Mining, Educational Data Mining, Machine Learning





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