Prediction and Visualization of Academic Procrastination in Online Learning

With the development of educational data mining and artificial intelligence, it has become a trend to use online learning data to evaluate students' performance. Academic procrastination is an important performance of online learning and has a negative impact on academic performance. To address the problem of academic procrastination, this paper constructed an online learning academic procrastination prediction model. Task completion times and engagement performance were converted feature vectors. K-means algorithm was used to label the behaviors as procrastinators and non-procrastinators. Five classification algorithms were used to predict academic procrastination, and the performance of different classification algorithms was evaluated. The study found that the academic procrastination prediction model has good performance in predicting procrastination. It is expected that the findings of this paper will provide some warning for procrastinators, and encourage students to keep learning enthusiasm and initiative, and improve learning performance.