Educational Data Mining Applied to a Massive Course

Abstract

In the current context of distance learning, learning management systems (LMSs) make it possible to store large volumes of data on web browsing and completed assignments. To understand student behavior patterns in this type of environment, educators and managers must rethink conventional approaches to the analysis of these data and use appropriate computational solutions, such as educational data mining (EDM). Previous studies have tested the application of EDM on small datasets. The main contribution of the present study is the application of EDM algorithms and the analysis of the results in a massive course delivered by a Brazilian University to 181,677 undergraduate students enrolled in different fields. The use of key algorithms in educational contexts, such as decision trees and clustering, can reveal relevant knowledge, including the attribute type that most significantly contributes to passing a course and the behavior patterns of groups of students who fail.

A Systematic Analysis on Learning Outcomes in Researches Using Data Mining Method in Distance Education

“…• Uzaktan eğitimde bırakma davranışını engellemek için öğrenci gruplarının davranışının veri madenciliği analizinin yapılarak sonucun bilinmesi gelecekteki sınıf yöneticisine yardımcı olacaktır. Hatta öğrenci grubu bir sonraki sınıfa geçtiğinde önceki sınıfın davranışının sürüp sürmediğini değerlendirebilmek için haftalık olarak analiz edilmelidir (Bezerra & Silva, 2020).…”

“…Bilgi, nihai sonuç için etkili bir karar sürecidir (Bezerra & Silva, 2020). Bu verilerin ya da bilgilerin artmasıyla birlikte kavramları işleyebilen depolayabilen teknikleri kullanmak, büyük oranda önem kazanmaktadır.…”

Mining of association rules between students’ behavior and academic achievements

“…The educational management system that only has the function of simple search and query cannot discover the potential value behind large-scale data. Traditional teaching methods and simple data query and management services can no longer meet the practical needs of teaching management [3] . Through the data mining and analysis of students' different behaviors, we can find the information rules hidden behind the data, help managers to fully understand students, and put forward personalized, scientific and efficient students management measures based on the specific behavior patterns of students, so as to realize the construction and management of intelligent campus [4] .…”