Abstract
Computer-based educational approaches provide valuable supplementary support to traditional classrooms. Among these approaches, intelligent learning systems provide automated questions, answers, feedback, and the recommendation of further resources. The most difficult task in intelligent system formation is the modelling of domain knowledge, which is traditionally undertaken manually or semi-automatically by knowledge engineers and domain experts. However, this error-prone process is time-consuming and the benefits are confined to an individual discipline. In this paper, we propose an automated solution using lecture notes as our knowledge source to utilise across disciplines. We combine ontology learning and natural language processing techniques to extract concepts and relationships to produce the knowledge representation. We evaluate this approach by comparing the machine-generated vocabularies to terms rated by domain experts, and show a measurable improvement over existing techniques.
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References
Carbonell, J.R.: AI in CAI: An Artificial-Intelligence Approach to Computer-Assisted Instruction. IEEE Transactions on Man-machine Systems 11(4), 190–202 (1970)
McArthur, D., Stasz, C., Hotta, J., Peter, O., Burdorf, C.: Skill-oriented task sequencing in an intelligent tutor for basic algebra. RAND Note 17(4), 281–307 (1988)
Butz, C.J., Hua, S., Maguire, R.B.: A Web-Based Intelligent Tutoring System for Computer Programming. In: IEEE/WIC/ACM International Conference on Web Intelligence, pp. 159–165. IEEE Computer Society, USA (2004)
Stankov, S., Rosic, M., Itko, B., Grubisic, A.: TEx-Sys model for building intelligent tutoring systems. Computer and Education 51(3), 1017–1036 (2008)
Zitko, B., Stankov, S., Rosic, M., Grubisic, A.: Dynamic test generation over ontology-based knowledge representation in authoring shell. Expert Systems with Applications 36(4), 8185–8196 (2009)
Zhuge, H., Li, Y.: KGTutor: A Knowledge Grid Based Intelligent Tutoring System. In: Yu, J.X., Lin, X., Lu, H., Zhang, Y. (eds.) APWeb 2004. LNCS, vol. 3007, pp. 473–478. Springer, Heidelberg (2004)
Issa, R., Arciszewski, T.: Ontology: An Introduction, Teaching Modules (PowerPoint presentation). In: ASCE Global Center of Excellence in Computing (2011)
Toutanova, K., Klein, D., Manning, C., Singer, Y.: Feature-Rich Part-of-Speech Tagging with a Cyclic Dependency Network. In: North American Chapter of the Association for Computational Linguistics on Human Language Technology, pp. 252–259. Association for Computational Linguistics, Canada (2003)
The Stanford NLP (Natural Language Processing) Group, http://nlp.stanford.edu/software/corenlp.shtml
Manning, C.D., Raghavan, P., Schutze, H.: Introduction to Information Retrieval. Cambridge University Press, New York (2008)
Hsieh, S., Lin, H., Chi, N., Chou, K., Lin, K.: Enabling the development of base domain ontology through extraction of knowledge from engineering domain handbooks. Advanced Engineering Informatics 25, 288–296 (2011)
Gantayat, N., Iyer, S.: Automated building of domain ontologies from lecture notes in courseware. In: IEEE International Conference on Technology for Education, pp. 89–95. IIT Madras, India (2011)
Ono, M., Harada, F., Shimakawa, H.: Semantic Network to Formalize Learning Items from Lecture Notes. International Journal of Advanced Computer Science 1(1), 10–15 (2011)
HaCohen-Kerner, Y., Gross, Z., Masa, A.: Automatic Extraction and Learning of Keyphrases from Scientific Articles. In: Gelbukh, A. (ed.) CICLing 2005. LNCS, vol. 3406, pp. 657–669. Springer, Heidelberg (2005)
Rezgui, Y.: Text-based domain ontology building using tf-idf and metric clusters techniques. The Knowledge Engineering Review 22(4), 379–403 (2007)
Chen, N., Kinsuk, Wei, C., Chen, H.: Mining e-Learning Domain Concept Map from Academic Articles. In: Sixth International Conference on Advanced Learning Technologies, pp. 694–698. IEEE Computer Society, Netherlands (2006)
Apache POI- the Java API for Microsoft Documents, http://poi.apache.org/
Brown Corpus, http://en.wikipedia.org/wiki/Brown_Corpus
Cimiano, P.: Ontology Learning and Population from Text: Algorithms, Evaluation and Applications. Springer, New York (2006)
Understanding the PowerPoint MS-PPT Binary File Format, http://msdn.microsoft.com/en-us/library/gg615594.aspx#UnderstandMS_PPT_Overview
Hripcsak, G., Rothschild, A.S.: Agreement, the F-measure, and reliability in information retrieval. J. Am. Med. Inform. Assoc. 12(3), 296–298 (2005)
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Atapattu, T., Falkner, K., Falkner, N. (2012). Automated Extraction of Semantic Concepts from Semi-structured Data: Supporting Computer-Based Education through the Analysis of Lecture Notes. In: Liddle, S.W., Schewe, KD., Tjoa, A.M., Zhou, X. (eds) Database and Expert Systems Applications. DEXA 2012. Lecture Notes in Computer Science, vol 7446. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32600-4_13
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DOI: https://doi.org/10.1007/978-3-642-32600-4_13
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