A User-Item Collaborative Filtering System to Predict Online Learning Outcome

Abstract

Education has seen the rapid development of online learning. Many researchers have conducted studies on the use of recommendation systems in online learning. However, until now, several similar studies still focus on the accuracy of the prediction results. Various obstacles were encountered related to changes in the face to face learning process into online learning. This study uses the User-Item Collaborative Filtering method to predict student learning outcomes as a basis for providing recommendations to students. Data on student online learning outcomes were extracted using several methods as a basis for determining and assessing their learning outcomes. The dataset we use is dummy to match the original data. The findings of this study reveal that one of the reinforcing factors that affect student achievement in online learning is the quiz score. The students' high achievement in the quizzes was also balanced by their active involvement during the learning process. Based on the evaluation of the recommendation system, it is known that the gradient boosted tree model is the best model for predicting the final score of student online learning with an accuracy calculated using the highest correlation of 0.7 and the smallest absolute error of 13.0 and root mean square error of 17.9. Based on the results of the evaluation, this study provides recommendations in the form of material links and learning archives that are useful for students to be able to carry out independent learning.

A Prediciton Model Based on Students’s Behavior in E-Learning Environments Using Data Mining Techniques

“…In this section the most relevant works is studied. For instance, D. F. Murad et al [13] used the predicted result to provide recommendations to students by using User-Item Collaborative Filtering System which can enhance the learning out comes.…”