Results of the summarizing process for the arXiv paper: 1412.7813v1

The study titled "Students Behavioural Analysis in an Online Learning Environment Using Data Mining" aimed to use Educational Data Mining (EDM) techniques to conduct a quantitative analysis of 412 students' interaction with an e-learning system through instructor-led non-graded and graded courses. The research was conducted to establish guidelines for a series of online short courses for the students. The students' access behaviour in the e-learning system was analysed, and they were grouped into clusters using K-Means clustering method according to their course access log records. The results showed that more than 40% of the student group were passive online learners in both graded and non-graded learning environments. Additionally, the study found that the difference in learning environments could change the online access behaviour of a student group. Clustering divided the student population into five access groups based on their course access behaviour, with the least access group (NG-41% and G-42%) and highest access group (NG-9% and G-5%) being identified very clearly due to their distinct variation from other groups. This research provides valuable insights into how different types of e-learning environments can impact student engagement levels, which can inform future development of online courses to better support active learning strategies.

• The study aimed to use Educational Data Mining (EDM) techniques to conduct a quantitative analysis of 412 students' interaction with an e-learning system through instructor-led non-graded and graded courses.
• The research was conducted to establish guidelines for a series of online short courses for the students.
• More than 40% of the student group were passive online learners in both graded and non-graded learning environments.
• The difference in learning environments could change the online access behaviour of a student group.
• Clustering divided the student population into five access groups based on their course access behaviour, with the least access group (NG-41% and G-42%) and highest access group (NG-9% and G-5%) being identified very clearly due to their distinct variation from other groups.
• This research provides valuable insights into how different types of e-learning environments can impact student engagement levels, which can inform future development of online courses to better support active learning strategies.

Exploring Students' Behavioural Analysis in an Online Learning Environment Using Data Mining

In recent years, the use of online learning systems has become increasingly popular. As a result, it is important to understand how students interact with these systems and how their behaviour can be improved. To this end, a study titled "Students Behavioural Analysis in an Online Learning Environment Using Data Mining" was conducted to explore the access behaviour of 412 students using Educational Data Mining (EDM) techniques. The research aimed to establish guidelines for a series of online short courses for the students.

Data Collection and Methodology

The data used in this study was collected from two instructor-led non-graded and graded courses offered by an e-learning system over a period of six months. The student's access behaviour was analysed through their course access log records which were then grouped into clusters using K-Means clustering method.

Results

The results showed that more than 40% of the student group were passive online learners in both graded and non-graded learning environments. Additionally, the study found that the difference in learning environments could change the online access behaviour of a student group. Clustering divided the student population into five access groups based on their course access behaviour, with the least access group (NG-41% and G-42%) and highest access group (NG-9% and G-5%) being identified very clearly due to their distinct variation from other groups.

Conclusion

This research provides valuable insights into how different types of e-learning environments can impact student engagement levels, which can inform future development of online courses to better support active learning strategies. By understanding how different types of e-learning systems affect student engagement levels, instructors will be able to design more effective courses that are tailored towards each individual learner's needs while also providing them with greater opportunities for success in their studies.