Educational Data Mining in Moodle Data

Sushil Shrestha, Manish Pokharel


The main purpose of this research paper is to analyze the moodle data and identify the most influencing features to develop the predictive model. The research applies wrapper-based feature selection method called Boruta for the selection of best predicting features. Data were collected from eighty-one students who were enrolled in the course called Human Computer Interaction (COMP341), offered by Department of Computer Science and Engineering at Kathmandu University, Nepal. Kathmandu University uses moodle as an e-learning platform. Dataset contained eight features where Assignment.Click, Chat.Click, File.Click, Forum.Click, System.Click, Url.Click and Wiki.Click are used as the independent features and Grade is used as the dependent feature. Five classification algorithms i.e. K Nearest Neighbour, Naïve Bayes, Support Vector Machine (SVM), Random Forest and CART decision tree are applied in the moodle data. The finding shows that Support Vector Machine (SVM) has the highest accuracy in compare to other algorithms. Two features: File.Click and System.Click are found to be the most significant features. This type of research helps in the early identification of students’ performance. The growing popularity of teathe ching-learning process through an online learning system has attracted the researchers to conduct research in this area in recent time. Lots of data are generated through several online activities that can be very useful in analyzing the student’s performance which helps in the overall teaching-learning process. Academicians especially course instructors who use e-learning platform for their teaching can be benefited from such kind of research.


Educational Data Mining, Moodle, Classification, Prediction


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