Open education is a new type of education, which is produced along with the development of modern information technology. It is a teaching model based on modern information technology. With the continuous reform of China’s education system, the autonomy of students in major colleges and universities is increasing. However, because students do not understand the course, it is very difficult to select courses. Therefore, it is especially important to excavate the university teaching platform database. This paper briefly introduces the concept of association rules mining algorithm, mainly studies the application of association rules mining algorithm in the open education learning system, in order to provide meaningful guidance for students’ elective courses, thus improving students’ academic performance.
Based on the learning behaviors of learners in the curriculum management system, the assessment of learning performance is carried out to provide curriculum improvement and learning suggestions. The evaluation of learning materials and online courses provides feedback for teachers and students of E-learning courses. Quality is very important. However, because many curriculum management systems do not provide specific tools that allow teachers to track and evaluate all learners' behavioral activities throughout the entire process, it is very difficult to select valuable information when faced with large amounts of system data. Educational data mining is an effective way to solve this problem. Based on the introduction of E-learning data mining process, this paper focuses on the application of data mining technologies such as statistics, visualization, classification, clustering, and association rule mining in Moodle systems.
This paper introduces the basic principles of association rule mining algorithms, and in turn studies association rule mining algorithms based on the number of variables (dimensions) involved in mining, the level of abstraction of data, and the categories of processing variables (Boolean and numeric). This paper summarizes, analyzes and compares some typical algorithms. Finally, the research direction of association rule mining algorithms is prospected.
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