An Ontology Model for Developing a SQL Personalized Intelligent Tutoring System

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Wilairat Yathongchai Jitimon Angskun Chun Che FUNG

Abstract

     SQL contents are important for learning and teaching in all disciplines of computer courses. This paper is a proposed ontology for the SQL-Personalized Intelligent Tutoring System (SQL-PITS). The SQL-PITS is an Intelligent Tutoring System (ITS) for teaching SQL and providing adaptive content according personalized information from the learners. It is considered as the best paradigm for tutoring learners with tutorial topics that consist of content, examples, exercises, and tutoring materials that are suitable for individual learners according to their abilities, profiles, preference and background. The SQL-PITS will present units of knowledge to be learned, which are called “Topics”, as modular units separated from content and tutoring strategy. Ontology and Learning Object are used to enhance the capabilities of the SQL-PITS for effective SQL teaching. Ontology is a key concept in semantic web. It plays an important role in knowledge representation, sharing and reusing of multimedia learning materials, and content personalization. This paper presents the SQL ontology in three views: SQL Knowledge ontology, Learner Model ontology and Tutoring Strategies ontology. The structure of SQL ontology has been verified validity by domain experts which adopted the GQM (Goal, Questions, Metrics) approach for ontology evaluation. The evaluation results of SQL ontology structure in 5 ontology characteristics reveal that domain experts rate at the highest level on 4 ontology characteristics which are Preciseness, Completeness, Clarity, and Conciseness, The Consistency characteristic is in a high level.

References

Allen, C. A., & Mugisa, E. K. (2010). Improving Learning Object Reuse Through OOD: A Theory of Learning Objects. Journal of Object Technology, 9(6), 51-75.

Basili, V., Caldiera, G., & Rombach, H. D. (1994). Goal question metric approach. In J. J. Marciniak (Ed.), Encyclopedia of Software Engineering (pp. 528–532). New York: John Wiley and Sons.

Bloom, B. S. (1976). Human characteristics and school learning. New York: McGrawHill.

CM/IEEE-CS Joint Task Force for Computer Curricula 2013. (2013). Computing Curricula 192 2005: An Overview Report. Retrieved from http://www.acm.org/education/curric_vols/Cs2013-ComputerScience Curricula 2013.pdf.

Date, C. J. (2011). SQL and Relational Theory: How to Write Accurate SQL Code. USA: O'Reilly Media, Inc.

Du Boulay, B., & Luckin, R. (2001). Modelling human teaching tactics and strategies for tutoring systems. International Journal of Artificial Intelligence in Education, 12(3), 235-256.

Esendal, T., & Dean, M. (2009). A Model to Make the Learning of Internet Programming with Databases Easy. In 7th Workshop on Teaching, Learning and Assessment in Databases, Birmingham, UK, HEA.

Fleming, N. D. (2016). The VARK questionnaire 7.1. Retrieved from http:// www.vark-learn.com/

Fleming, N. D., & Mills, C. (1992). Not Another Inventory, Rather a Catalyst for reflection. To Improve the Academy, 11, 137–55.

Folland, K. A. T. (2016). viSQLizer: An interactive visualizer for learning SQL. (Master's thesis). Norwegian University of Science and Technology (NTNU), Norway.

Gennick, J. (2004). On the importance of mental model. Retrieved from http://gennick.com/mental models.html.

Goldberg, C. (2009). Do you know SQL? About Semantic Errors in Database Queries. In 7th Workshop on Teaching, Learning and Assessment in Databases, Birmingham, UK, HEA.

Judd, R. C. (1972). Use of Delphi methods in higher education. Technological Forecasting and Social Change, 4(2), 173-186.

Kearns, R., Shead, S., & Fekete, A. (1997). A teaching system for SQL: Proceedings of the 2nd Australasian conference on Computer Science education, 2-4 July 1997 (pp. 224–231). Melbourne, Australia: University of Sydney.

Likert, R. (1967). The Method of Constructing and Attitude Scale. In Reading in Fishbein. M.Ed., Attitude Theory and Measurement (pp. 90-95), New York: John Wiley & Sons.

Mohan, P., & Brooks, C. (2003). Learning Objects on the Semantic Web: Proceedings of The 3rd IEEE International Conference on Advanced Learning Technologies (ICALT 2003), 9-11 July 2003 (pp. 195-199). Athens, Greece: IEEE.

Piyayodilokchai, H., Panjaburee, P., Laosinchai, P., Ketpichainarong, W., & Ruenwongsa, P. (2013). A 5E learning cycle approach-based, multimedia-supplemented instructional unit for structured query language. Journal of Educational Technology & Society, 16(4), 146-159.

Renaud, K. V., & Van Biljon, J. A. (2004a). Teaching SQL — Which Pedagogical Horse for This Course?: Proceedings of a 21st British National Conference on Databases, BNCOD 21, 7-9 July 2004 (pp. 244-256). Edinburgh, UK.

Renaud, K., & Van Biljon, J. (2004b). Teaching SQL—Which Pedagogical Horse for This Course? In British National Conference on Databases (pp. 244-256). Springer Berlin Heidelberg, Germany.

Saidong, L., Guohua, T., Yaowen, X., & Yu, S. (2013). The Research and Implementation of a Personalized Intelligent Tutoring System: Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013) (pp. 1302-1304). France: Atlantis Press

Shishehchi, S., Banihashem, S. Y., & Zin, N. A. M. (2010). A proposed semantic recommendation system for e-learning: A rule and ontology based e-learning recommendation system. Proceedings of the 2010 International Symposium in Information Technology (ITSim), 15-17 June 2010 (pp. 1-5). Kuala Lumpur: IEEE.

Soler, J., Boada, I., Prados, F., Poch, J., & Fabregat, R. (2009). A web-based e-learning tool for the continuous assessment and skills improvement of main database course topics. In 7th Workshop on Teaching, Learning and Assessment in Databases, Birmingham, UK, HEA.

Tankelevicienea, L., & Damaseviciusb, R. (2009). Characteristics of Domain Ontologies for Web Based Learning and Their Application for Quality Evaluation. Informatics in Education, 8(1), 131-152.

W3C. (2004). SWRL: A Semantic Web Rule Language Combining OWL and RuleML. Retrieved from https://www.w3.org/Submission/SWRL/

Keywords
Ontology; Intelligent Tutoring System; Personalized learning; SQL
Section
Research Articles

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How to Cite
YATHONGCHAI, Wilairat; ANGSKUN, Jitimon; FUNG, Chun Che. An Ontology Model for Developing a SQL Personalized Intelligent Tutoring System. Naresuan University Journal: Science and Technology (NUJST), [S.l.], v. 25, n. 4, p. 88-96, sep. 2017. ISSN 2539-553X. Available at: <http://www.journal.nu.ac.th/NUJST/article/view/An%20Ontology%20Model%20for%20Developing%20a%20SQL%20Personalized%20Intelligent%20Tutoring%20System>. Date accessed: 23 apr. 2019.