Portrait of College Students’ Online Learning Behavior Based on Artificial Intelligence Technology

With the development of Internet technology, online learning is becoming more and more popular. However, college students have different online learning behaviors and attitudes toward artificial intelligence (AI) learning tools. In this paper, a portrait model is proposed for college students, which focuses on their online learning behavior and attitudes toward AI learning tools. Moreover, the proposed portrait model is built based on AI technology, i.e., random forest algorithm and long short-term memory (LSTM) algorithm are applied. In this model, there are three main parts: data pre-processing, building a multi-dimensional label system, and portrait model of college students. Firstly, the information collected through the questionnaire is quantified and its quality is improved by deleting invalid data. Then, a multi-dimensional label system is built for college students’ portraits, including basic attributes, online behavior attributes, behavioral attributes of using learning software, and psychological attributes of AI learning tools. Since each label consists of multiple indexes, the variance-based filtering method is used to streamline the indexes of online behavior attributes and behavioral attributes of using learning software, the random forest algorithm is applied to reduce the dimension of psychological attributes of AI learning tools. Next, the portrait of college students’ online learning behavior is realized by the K-means clustering algorithm, and the LSTM algorithm is performed to get the mapping mechanism between data and portrait categories. Through the mapping mechanism, the portrait of any new college student who is not included in the original dataset can be obtained quickly. Finally, the validity of the proposed model is verified by analyzing the questionnaire results of college students. Additionally, the portrait results provide a data basis for the development and popularization of AI learning tools.