Pattern Mining in E-Learning

Group study is necessary in learning. Our aim is to distinguish the huge data provided into different categories based on unique patterns. These patterns will be generated according to students expertise. We extract patterns distinguishing the better from the weaker groups and get insights in the success factors. The results point to the importance of leadership and group interaction, and give promising indications if they are occurring. Patterns indicating good individual practices were also identified. Clustering is used in order to separate data into different groups. This whole idea leads to provide a better methodology in e-learning as an individual can perform online discussions as it include chat rooms, performance based on stronger and weaker groups can also be identified and by the use of sequence tool the admin can also identify the subjects which require more detail study material. For this whole concept sequence by sequence (SBS) algorithm is defined and sequential tool is designed based on different patterns.