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Computational Intelligence and Neuroscience
Volume 2015, Article ID 123028, 10 pages
http://dx.doi.org/10.1155/2015/123028
Research Article

Design of Automatic Extraction Algorithm of Knowledge Points for MOOCs

1School of Information Management and Engineering, Shanghai University of Finance and Economics, 777 Guoding Road, Shanghai 200433, China
2School of Open Education, Shanghai Open University, 288 GuoShun Road, Shanghai 200433, China
3Shanghai Financial Information Technology Key Research Laboratory, 777 Guoding Road, Shanghai 200433, China
4School of Information Management, Shanghai Finance University, 995 Shangchuan Road, Shanghai 200433, China

Received 7 August 2014; Accepted 27 September 2014

Academic Editor: Weihui Dai

Copyright © 2015 Haijian Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

In recent years, Massive Open Online Courses (MOOCs) are very popular among college students and have a powerful impact on academic institutions. In the MOOCs environment, knowledge discovery and knowledge sharing are very important, which currently are often achieved by ontology techniques. In building ontology, automatic extraction technology is crucial. Because the general methods of text mining algorithm do not have obvious effect on online course, we designed automatic extracting course knowledge points (AECKP) algorithm for online course. It includes document classification, Chinese word segmentation, and POS tagging for each document. Vector Space Model (VSM) is used to calculate similarity and design the weight to optimize the TF-IDF algorithm output values, and the higher scores will be selected as knowledge points. Course documents of “C programming language” are selected for the experiment in this study. The results show that the proposed approach can achieve satisfactory accuracy rate and recall rate.