Leveraging Data Mining Technique to Enhancing Online Education and Its Efficiency Study

Abstract

The previously conducted assessment of university education facilitated an evaluation model depending on small-scale data mining. A cloud-platform-based data mining algorithm was constructed for university education informatization to handle. Hence, we established an evaluation index system, integrated the global evaluation perspective, and subsequently obtained the three-dimensional structure of the evaluation index. The criterion was calculated by the postmining probability of the data, which was further leveraged to score each index in the evaluation system, and finally, the original index data matrix was constructed. Afterward, the membership degree vector of the index was calculated concerning which type of benefit of education informatization is evaluated. We investigated the online university education mechanism based on educational data mining. According to the computer-aided learning platform, we collected information and data related to the situation and resource browsing of students on the platform. We preprocessed these H log data. Then, the statistics of the students’ login information and resource browsing were collected. Hence, we analyzed the influencing factors through the decision tree that enabled instructors to effectively grasp the students’ learning conditions based on the mining results. The above integration of teaching content and the construction of a teaching model could substantially improve the quality of online learning. Experimental results showed that our data-mining-based evaluation model exhibited a much higher accuracy when the university evaluation was compared with the conventional model. This observation claimed that the university education evaluation by leveraging bulk data was quite useful. Besides, the cost consumption of the proposed method was superior to its competitors.