Automatically Difficulty Grading Method Based on Knowledge Tree

摘要

The aim of the current study is to propose a model, which can automatically grade difficulty for a question from "instruction system" question bank. The system mainly uses 4 attributes with 26 features based on principal component analysis, which are employed to be input of the Automatically Difficulty Grading Model (ADGM). A knowledge tree model and a machine learning algorithm are utilized as important parts for the classification module. The experimental dataset "instruction system" question bank is based on our built "Principles of Computer Organization" online education system, the accuracy result of difficulty classification could be 77.43% which is much higher than the accuracy of random guess 50%.