LAUSR

Information literacy is a basic ability for college students to adapt to social needs at present, and it is also a necessary quality for self-learning and lifelong learning. It is an effective way to reveal the information literacy teaching mechanism to use the rich and diverse information literacy learning behavior characteristics to carry out the learning effect prediction analysis. This paper analyzes the characteristics of college students’ learning behaviors and explores the predictive learning effect by constructing a predictive model of learning effect based on information literacy learning behavior characteristics. The experiment used 320 college students’ information literacy learning data from Chinese university. Pearson algorithm is used to analyze the learning behavior characteristics of college students’ information literacy, revealing that there is a significant correlation between the characteristics of information thinking and learning effect. The supervised classification algorithms such as Decision Tree, KNN, Naive Bayes, Neural Net and Random Forest are used to classify and predict the learning effect of college students’ information literacy. It is determined that the Random Forest prediction model has the best performance in the classification prediction of learning effect. The value of Accuracy is 92.50%, Precision is 84.56%, Recall is 94.81%, F1-Score is 89.39%, and Kapaa coefficient is 0.859. This paper puts forward differentiated intervention suggestions and management decision-making reference in the information literacy teaching process of college students, with a view to adjusting the information literacy teaching behavior, improving the information literacy teaching quality, optimizing educational decision-making, and promoting the sustainable development of high-quality and innovative talents in the information society.Our work involving research of the thinking and direction of the sustainable development of information literacy training proved to be encouraging.