An Ontology Model for Developing a SQL Personalized Intelligent Tutoring System


Wilairat Yathongchai Jitimon Angskun Chun Che FUNG


     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.


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


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: <>. Date accessed: 09 feb. 2023.