Annotating Knowledge Points & Recommending Questions Based on Semantic Association Rules
Wei Wei1,2,Guo Chonghui2(),Xing Xiaoyu2
1Center for Energy, Environment & Economy Research, Zhengzhou University, Zhengzhou 450001, China 2Institution of Systems Engineering, Dalian University of Technology, Dalian 116024, China
[Objective] This paper proposes a method automatically annotating the knowledge points of test questions from online education resources.[Methods] First, we introduced the concept of text semantics to establish new association rules. Then, considering the semantic matching degrees between the target questions and the rules, we proposed an automatic method for knowledge point annotation. Finally, we presented a personalized question recommendation mechanism.[Results] We examined the proposed method with test questions from middle school mathematics and high school history courses. We also compared our model’s labeling accuracy with naive Bayes, K nearest neighbor, random forest and support vector machine, and yielded better results.[Limitations] The understanding of the semantics of test questions and the labeling accuracy could be further improved.[Conclusions] The knowledge point annotation and the personalized question recommendation methods could improve smart teaching and online learning.
( Li Weidong, Yang Naisheng, Shen Qianghua , et al. Development of a Distance-learning Tutoring System Based on Knowledge Points[J]. Distance Education Journal, 2006(5):34-37.)
( Chen Zhi, Sui Guangyuan, Pi Xiuyun . Knowledge-Point is a Cognitive Unit[J]. Psychological Science, 2002,25(3):369-370.)
[4]
Oren E, Delbru R, Möller K , et al. Annotation and Navigation in Semantic Wikis [C]// Proceedings of the 1st Workshop on Semantic Wikis: From Wiki to Semantics. 2006.
[5]
傅柱 . 图书馆学研究[J]. 图书馆学研究, 2016(4):10-17.
[5]
( Fu Zhu . A Review of Semantic Annotation[J]. Research on Library Science, 2016(4):10-17.)
[6]
Song Z . Application of Cloud Desktop in Modern Chinese Multi-Category Words Part of Speech Tagging[J]. Procedia Engineering, 2017,174:1215-1220.
[7]
Meguehout H, Bouhadada T, Laskri M T . Semantic Role Labeling for Arabic Language Using Case-Based Reasoning Approach[J]. International Journal of Speech Technology, 2017,20(2):363-372.
( Deng Sanhong, Wang Hao, Qin Jiahang , et al. Research on Keywords Indexing for Chinese Bibliography Based on Word Roles Annotation[J]. Journal of Library Science in China, 2012,38(2):38-49.)
[9]
陆伟, Stephen Robertson . 基于域加权词频法的XML文档级检索实现与评价[J]. 中国图书馆学报, 2006,32(6):57-60.
[9]
( Lu Wei, Stephen Robertson . Field-Weighted XML Document Level Retrieval and Evaluation[J]. Journal of Library Science in China, 2006,32(6):57-60.)
( Xu Lei, Wang Xiaoguang . Narrative Image Annotation Ontology for Semantic Web[J]. Journal of Library Science in China, 2017,43(5):70-83.)
[11]
Zhao J, Glueck M, Breslav S , et al. Annotation Graphs: A Graph-Based Visualization for Meta-Analysis of Data Based on User-Authored Annotations[J]. IEEE Transactions on Visualization and Computer Graphics, 2016,23(1):261-270.
( Yu Chun . Comparative Study on the Application of Social Tagging in American and Chinese Academic Libraries[J]. Journal of Academic Libraries, 2014,32(1):83-89.)
( Chen Yewang, Li Wen, Peng Xin , et al. Improved Semantic Annotation Method for Documents Based on Ontology[J]. Journal of Southeast University: Natural Science Edition, 2009,39(6):1109-1113.)
( Li Hongyan, Fan Lichun, Gao Peng , et al. Social Annotation Technology for Large Speech Corpora[J]. Journal of Tsinghua University: Science and Technology, 2013,53(6):908-912.)
( Wu Lei, Liu Xianyou, Sun Bingyu . Research on Multi-label Annotation of Learning Resources Based on Incremental AHP[J]. Electronic Technology, 2015,44(4):10-15.)
( Zhu Tianyu, Huang Zhenya, Chen Enhong , et al. Cognitive Diagnosis Based Personalized Question Recommendation[J]. Chinese Journal of Computers, 2017,40(1):176-191.)
[17]
Wu D, Lu J, Zhang G . A Fuzzy Tree Matching-Based Personalized E-Learning Recommender System[J]. IEEE Transactions on Fuzzy Systems, 2015,23(6):2412-2426.
[18]
Eyharabide V, Amandi A . Ontology-Based User Profile Learning[J]. Applied Intelligence, 2012,36(4):857-869.
[19]
Salehi M, Kamalabadi I N . Hybrid Recommendation Approach for Learning Material Based on Sequential Pattern of the Accessed Material and the Learner’s Preference Tree[J]. Knowledge-Based Systems, 2013,48:57-69.
[20]
Verbert K, Manouselis N, Ochoa X , et al. Context-aware Recommender Systems for Learning: A Survey and Future Challenges[J]. IEEE Transactions on Learning Technologies, 2012,5(4):318-335.
[21]
Liu Q, Zhang H P, Yu H , et al. Chinese Lexical Analysis Using Cascaded Hidden Markov Model[J]. Journal of Computer Research and Development, 2004,41(8):1421-1429.
[22]
Wei W, Guo C H, Chen J F , et al. Textual Topic Evolution Analysis Based on Term Co-occurrence: A Case Study on the Government Work Report of the State Council(1954-2017) [C]//Proceedings of the 12th International Conference on Intelligent Systems and Knowledge Engineering(ISKE). 2017: 1-6.
[23]
Agrawal R, Imielinski T, Swami A N . Mining Association Rules Between Sets of Items in Large Databases [C]//Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data. 1993: 207-216.
( Center for Social Computing and Information Retrieval, Harbin Institute of Technology. Synonym Word Forest Expansion Version[OL].[2017-12-04]. https://www.ltp-cloud.com/download/.)
[25]
Dwivedi P, Bharadwaj K K . E-Learning Recommender System for a Group of Learners Based on the Unified Learner Profile Approach[J]. Expert Systems, 2015,32(2):264-276.
