Enhancing Ontology-Based Educational Content Search Service with Competency

Byoungchol Chang, Yoonsoo Lee, Sua Ko, Jaehyuk Cha1 Division of Information and Communications, Hanyang University, Korea

{bcchang, eclipse, angelsua, chajh}@hanyang.ac.kr

1 Corresponding Author.

Abstract

In this paper we present a methodology for designing competency based web search framework using ontology to acquire the learning contents that meets the individuals’ competency. We used ontology with OWL-DL to built competency ontology, content ontology and domain ontology to implement competency based contents search system. The basic ontology-based search system, however, lacks the method of ranking the search results. To present more appropriate learning contents for the learner, we applied competency clustering to rank the search results. 1. Introduction Competency-based learning which enables to identify and stay focused on what learners need to improve in terms of their performance can help in improving personal competency development in more effective way[1,2]. In order to achieve the development of individuals’ and organizations’ competency by adopting competency-based learning in an appropriate way, it is important to define, diagnose the competency and provide the appropriate learning content according to the result. In an environment where the online contents are shared actively as SCORM pursues, selection of appropriate contents for the development of individuals’ competency may not be so easy. To solve the problem, we present the content search system using ontology for searching appropriate contents in this paper. By describing the individuals’ competency and information on contents with ontology, we developed a search system to acquire the learning contents that meets the individuals’ competency through ontology-based search and deduction. The basic ontology-based search system, however, lacks the method of ranking the search results which make it

difficult to identify which result is more relevant if there are more than one search results. To solve this problem, we applied competency clustering techniques to rank the search results which eventually will help finding more appropriate learning contents for the learner. 2. System Architecture The system is consisted of three parts: competency diagnosis, ontology creation and contents search area. In the competency diagnosis part, the necessary competency for individual is identified. We assume that this competency identification is carried out by the supervisor of the department where the individual belongs to. The evaluation result drawn out of the method above is stored in the form of XML file according to RDCEO[3] for the compatibility inside and outside of organization. On the ontology creation level, competency instance based on the predefined competency ontology scheme of the individuals’ competency is added by parsing individual competency ontology written in accordance with IMS RDCEO Protocol. Contents Ontology creation is also carried out on the Ontology creation level. We adopted the research of [4] that discussed about the LOM Binding of KEM(Korea Educational Metadata)[5] which is the Korean extension of LOM(Learning Object Metadata) for the Contents Ontology. In the meanwhile, we added the predefined domain Ontology to the system in order to enhance the efficiency of contents search using competency ontology and contents ontology. It aims to improve the low efficiency problem in case the competency ontology and contents ontology is connected to the Keyword based search system. In order to improve the efficiency of search, the organization can build and add the necessary domain ontology using the method presented in this paper. In case of the contents search,

Eighth IEEE International Conference on Advanced Learning Technologies

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we adopted the method from the research of [4]. We performed contents search using integrated ontology of contents ontology, domain ontology and competency ontology. In order to present more suitable contents to the individuals, we implemented the clustering technique to rank the searched contents. For the prototype system, we have built the competency ontology using competency which is necessary in learning MOS(Microsoft Office Specialist) of Microsoft[6].

Figure 1: System Architecture

3. Implementation and Test In this research, we developed a system that can provide the most adequate and necessary contents search results for individual user using deduction, based upon the analysis and deduction of lacking individual competency resulted from the individual competency evaluation with user ontology and contents ontology. We have adopted the research of [4] for the contents search system and KAON2[7] for the ontology deduction engine implemented in this paper. The query language used for the ontology deduction is SPARQL. In order to judge which content is more adequate than other for the user, it is unavoidable to decide ranking of search results in one way or another. In this paper, we present the method to decide ranking on the searched contents using the clustering technique of data mining. This method works as follows: i. Every time when the content search for an

individual occurs, the competency of each learner is recorded on the database in accordance with the competency map.

ii. When the number of records exceeds certain level, we conducted grouping for the user competency log using clustering technique.

iii. If a request for the content search of a new learner occurs, first identify which clustered competency group the user belongs to.

iv. Among the search result list for the new learner deduced based on the ontology-based search engine, the search engine compares the list with contents list used by the competency group where the new user belongs to, and then present the matching content at the top.

As a result, we can acquire more adequate contents because the contents previously chosen by the similar competency group possesses the higher possibility to meet the requirement. 4. Conclusion Competency-based learning is an appropriate approach for both the personal competency development and human resources development at the organizations’ level. In the advent of more active contents development and contents share over the internet, the search of e-learning contents which is suitable for the improvement of personal competency will not be an easy task. Therefore, the competency based contents search system using ontology in this paper can be an effective tool for providing contents that is met by the competency requirement. 5. References [1] Amal Zouaq et al. Using a Competency Model to aggregate Learning Knowledge Objects. In Proc. of the 7th IEEE International Conference on Advanced Learning Technologies, july 2007, pages 836-840. [2] Kopper R., Tattersall C. New directions for lifelong learning using network technologies. British Journal of Educational Technology, vol 35(6), page 689~700. [3] IMS Global. http://www.imsglobal.org [4] B. Chang et al. Educational Information Search Sercice Using Ontology. ICALT 2007. pages 414-415. [5] Jin-Gon Shon, Yong-Sang Cho, Kwang-Sik Chung. Research on Metadata(KEM v3.0) for Higher Educational Information and Digital Rights Management. Research Report KR-2005-27, KERIS, 2005. [6] Microsoft Office Specialist. http://www.microsoft.com/ learning/mcp/officespecialist/default.mspx. [7] U. Hustadt, B. Motik, U. Sattler. Reasoning in Description Logics with a Concrete Domain in the Framework of Resolution.: Proc. of the 16th European Conference on Artificial Intelligence (ECAI 2004), Valencia, Spain, August, 2004, pp.353-357.

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