Learner classification based on learning behavior and performance

A learner classification is an important process in providing online lessons to suit each individual learner. In this paper, the concept of learner classification is considered on learning behavior and performance. There are two main processes for generating the classification model as follows: 1) Applying K-means clustering to analyze learning behaviors of each learner based on learner´s profile from e-learning system; and 2) Applying a decision tree classifier to generate the learner classification model based on the learning behaviors and student´s performance. The experimental results show that the learner classification model is achieved in 83.8% of precision, 85.4% of recall and 85.5% of F-measure.