Effective Learning Behavior of Students’ Internet Based on Data Mining

Abstract

With the rapid development of information technology and the widespread application of network technology, it has had a great impact on traditional learning methods. Teaching and learning based on the network environment has become an important form and part of school teaching. As a new type of learning method, online learning has its unique advantages and potential for development. It breaks through the limitations of time and space in traditional teaching methods, and brings great convenience and freedom to learners, but there are also many problem. The purpose of this article is to analyze the effective learning behavior of students on the Internet based on data mining. This article analyzes learning behavior and builds a learning behavior mining model on this basis. Use the improved algorithm to mine and analyze the learning behavior data, including preprocessing and conversion of the data in the database, respectively mining the relationship between the learning sequence pattern and the learning behavior and effect, and finally the mining results explanation and evaluation. This paper does certain research and improvement on association rules and sequential pattern mining algorithms, and on this basis, uses the improved algorithm to collect, mine and analyze learning behavior data, and finally apply data mining technology to personalized the learning system optimizes the mechanism of online learning and the learning experience of users. Research on related methods of learning behavior mining, detailed analysis of association rules and sequence pattern mining, and optimization of Apriori algorithm and AprioriAll algorithm. The performance of the two algorithms and their optimized algorithms are compared respectively. The experimental results show that the improved algorithm has better performance improvement.