Abstract
Several concepts related to knowledge have emerged recently: knowledge management, knowledge society, knowledge engineering, knowledge bases, etc. We are here specifically interested in “scientific knowledge” in the context of student learning assessment. Therefore, we develop a framework within which knowledge is decomposed into grains called knowlets so that it can be quantified. Knowledge becomes then a measurable quantity in very much the same way information is known to be a measurable quantity (in the sense of Shannon’s information theory). We then define an appropriate metric that we use in the specific domain of learning assessment. The proposed framework may be utilized for knowledge acquisition in the context of ontology learning and population.
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References
Krathwohl, D.R.: A Revision of Bloom’s Taxonomy: An Overview. Theory into Practice 41(4) (Autumn 2002)
Knight, A.J.: Differential Effects of Perceived and Objective Knowledge Measures on Perceptions of Biotechnology. AgBioForum 8(4), 221–227 (2005)
Godfrey-Smith, P.: Knowledge, trade-offs, and tracking truth. Philosophy and Phenomenological Research LXXIX (1), 231–239 (2009)
Todd, R.J.: From information to knowledge: charting and measuring changes in students’ knowledge of a curriculum topic. Information Research 11(4), paper 264 (2006), http://InformationR.net/ir/11-4/paper264.html
Liebowitz, J., Suen, C.Y.: Developing knowledge management metrics for measuring intellectual capital. Journal of Intellectual Capital 1(1), 54–67 (2000)
d’Amato, C., et al.: Semantic Similarity Measure for Expressive Description Logics. In: Proc. CILC, Rome, Italy (June 2005)
Buitelaar, P., Cimiano, P., Magnini, B.: Ontology Learning from Text: An Overview, pp. 1–9. IOS Press (2003)
Cheniti-Belcadhi, L., Braham, R., Henze, N., Nejdl, W.: A Generic Framework for Assessment in Adaptive Educational Hypermedia. In: Proc. IADIS WWW / Internet, Madrid, Spain, pp. 397–404 (2004)
Cheniti-Belcadhi, L., Braham, R.: Assessment Personalization on the Semantic Web. Journal of Computational Methods in Sciences and Engineering, Special issue: Intelligent Systems and Knowledge Management 8, 1–20 (2008)
Gardner, L., Sheridan, D., White, D.: A web-based learning and assessment system to support flexible education. Journal of Computer Assisted Learning 18(2), 125–136 (2002)
Gruber, T.R.: Toward Principles for the Design of Ontologies used for Knowledge Sharing Technical Report KSL 93-04, Knowledge Systems Lab (1993)
He, L.: A Novel web-based educational assessment system with Bloom’s taxonomy. Current Developments in Technology-Assisted Education, 1861–1865 (2006)
University of Victoria: Hot Potatoes (2010), http://hotpot.uvic.ca/
IMS Global Learning Consortium: IMS Question and Test Interoperability Overview, Version 2.1 (2006), http://www.imsglobal.org/question/qtiv2p1pd2/imsqti_oviewv2p1pd2.html
Lin, D.: An Information Theoretic Definition of Similarity. In: Proc. of the 15th Int. Conference on Machine Learning (1998)
Mihalcea, R., et al.: Corpus-based and Knowledge-based Measures of Text Semantic Similarity. In: Proc. of 21st National Conference on AI, pp. 775–780. AAAI (2006)
Mons, B.: Calling on a million minds for community annotation. In: WikiProteins (2008), http://genomebiology.com/2008/9/5/R89
Newell, A.: The Knowledge Level. AI Magazine, 1–20 (Summer 1981)
Oliver, D., Dobele, T., Greber, M., Roberts, T.: This Course Has A Bloom Rating Of 3.9. In: Proc. Sixth Australasian Computing Education Conference (ACE 2004), Conferences in Research and Practice in Information Technology, vol. 30 (2004), http://crpit.com/confpapers/CRPITV30Oliver.pdf
Palloff, M., Pratt, K.: How do we know they Know? Student Assessment Online. In: Proc. 22nd Annual Conf. on Distance Teaching and Learning, Wisconsin, pp. 1–5 (2006)
Resnik, P.: Semantic Similarity in a Taxonomy: An Information-Based Measure and its Application to Problems of Ambiguity in Natural Language. Journal of Artificial Intelligence Research 11, 95–130 (1999)
Shannon, C.: A Mathematical Theory of Communication. The Bell System Technical Journal 27, 379–423/623–656 (1948)
Slimani, T., Ben Yaghlane, B., Mellouli, K.: A New Semantic Similarity Measure based on Edge Counting. World Academy of Science, Engineering and Technology 23, 34–38 (2006)
Smyth, P., Goodman, R.M.: An Information theoretic Approach to Rule Induction from Databases. IEEE Trans. on Knowledge and Data Engineering 4(4), 301–316 (1992)
Warin, M., Oxhammar, H., Volk, M.: Enriching an Ontology with WordNet based on Similarity Measures. In: Proc. of the MEANING Workshop (2005)
Zheng, A.Y.: Application of Bloom’s Taxonomy Debunks the MCAT Myth. Science 319, 414–415 (2008)
Zouaq, A., Nkambou, R.: Building Domain Ontologies from Text for Educational Purposes. IEEE Trans. on Learning Technologies 1(1), 49–62 (2008)
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Braham, R. (2013). A Quantitative Knowledge Measure and Its Applications. In: Fred, A., Dietz, J.L.G., Liu, K., Filipe, J. (eds) Knowledge Discovery, Knowledge Engineering and Knowledge Management. IC3K 2010. Communications in Computer and Information Science, vol 272. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29764-9_13
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DOI: https://doi.org/10.1007/978-3-642-29764-9_13
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