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2017 | OriginalPaper | Chapter

Automatically Difficulty Grading Method Based on Knowledge Tree

Authors : Jin Zhang, Chengcheng Liu, Haoxiang Yang, Fan Feng, Xiaoli Gong

Published in: Knowledge Science, Engineering and Management

Publisher: Springer International Publishing

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Abstract

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%.

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Title: Automatically Difficulty Grading Method Based on Knowledge Tree
Authors: Jin Zhang
Chengcheng Liu
Haoxiang Yang
Fan Feng
Xiaoli Gong
Publisher: Springer International Publishing
Book: Knowledge Science, Engineering and Management
Print ISBN: 978-3-319-63557-6

Electronic ISBN: 978-3-319-63558-3

Copyright Year: 2017
DOI: https://doi.org/10.1007/978-3-319-63558-3_38

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