Exploring Learners' Behaviors in Interactive Educational Environments by Data Mining Algorithms

Data mining as a science is a search process for finding new, valuable, and unknown information among a wealth of data. This approach is among the top 10 developing knowledge that will face the next decade with a technological revolution and has been expanding rapidly in recent years. The purpose of this research is to explore learners' behavior in interactive educational environments using data mining algorithms. Exploration and extraction of meaningful relationships among different factors with the help of data mining algorithms can be used to provide solutions to improve learners' performance. The research instrument was a questionnaire. The validity of the questionnaire was obtained through content analysis and its reliability was measured through re-testing. The data collection discussed in this study relates to the activities and interactions of 200 active students in a university social network. After gathering information about learners' interactions, the Apriori algorithm of data mining is used to analyze the behavior of learners through the use of rules. The main goal is to facilitate the education along with obtaining the proficiency of the courses. Improved interactive content is obtained from feedback information based on the data mining techniques of the data management system. Therefore, the results of this research can help educational system managers to plan their education and optimize educational processes in the interactive environment.