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Online Learning Behavior Feature Mining Method Based on Decision Tree

OAI: DOI: 10.4018/JCIT.295244
Published by: IGI Global


This research mainly discusses the design of online learning behavior feature mining method based on decision tree. Data collection is the real-time collection of online learning behavior data from distance learning websites. OWC (Office Web Component) technology is used to draw real-time charts on the page. Online learning students are selected as the research object, and the student's system log data and questionnaire data are selected. When combining the pre-pruning method and the post-pruning method to make decisions after the tree is pruned, the same source data is used to adjust, test and evaluate the decision tree model. The evaluation process to generate a complete decision tree is completed by the c4.5tree algorithm in C4.5, which can be named with a suffix of .names The type definition file is used to record the type of each attribute item or the range of possible values. In the study, the prediction accuracy rate of predicting learning effect based on "online learning behavior" reached more than 66%.