Thursday , November 30 2023

An Ontology-Based E-Learning Framework for
Healthcare Human Resource Management*

1 I C I Bucharest
(National Institute for R & D in Informatics)

8-10 Averescu Blvd.
011455 Bucharest 1, Romania,
2 Department of Economic Informatics and Cybernetics,
Bucharest University of Economic Studies,
6 Romana Square, Bucharest, 010374, Romania

Abstract: Human resources management is an essential prerequisite to enhance performance in healthcare organizations. Improving human resources management in a university hospital, first and foremost depends on how professional and knowledgeable the management team is, as well as on the competences and skills of its members. This paper describes a highly personalized e-learning system, which is able to take into account the individual’s profile, learning style, initial knowledge and education needs. The system is based on new technologies: Semantic Web and ontologies. On these technologies are based the development of the constituent parts of the system: the student model, the model of the field of interest namely Healthcare Human Resources Management (HHRM) in Romania. The main benefit of the proposed e-learning method consists in personalized training of managers. The characteristic feature of the suggested method largely lies in the fact that specific ontologies of the field of interest are implemented with the Protégé environment which relies on a personalized methodology. The paper presents the constituent parts and architecture of the proposed ontology-based e-learning system and a framework for its implementation in a real life platform. Even though the current system is tailored to the needs of HHRM , its application could be extended to other fields of knowledge and learning contexts.

Keywords: E-learning, personalization, ontology-based model, semantic web, human resource management, LMS.

>>Full text<<

Lidia BAJENARU, Ion SMEUREANU, Alexandru BALOG, An Ontology-Based E-Learning Framework for Healthcare Human Resource Management, Studies in Informatics and Control, ISSN 1220-1766, vol. 25(1), pp. 99-108, 2016.

  1. Introduction

E-learning is currently a widely-spread technology in teaching establishments and beyond [29]. E-learning is also an alternative to continuing education in today’s world as an information society or tomorrow’s world as a knowledge society.

The great diversity of the users of e-learning systems requires new pedagogical approaches as well as an increased technological quality. In many works it is suggested that the success of e-learning resides in the quality of information technology, and use of modern technology in education must be supported [5, 18].

The Web 2.0 technology has been widely applied to e-learning systems in order to improve social communication and the transfer of knowledge to the virtual learning media [26]. Semantic Web technologies, respectively the ontologies, have been applied to e-learning systems development.

Some e-learning systems were primarily focused on the innovation process enabling students to generate new ideas and pass on knowledge [20] while other systems combined intelligent agents with an open e-learning platform with a view to providing a personalized teaching-learning process [1].

Personalization is an innovative approach into e-learning systems, representing an advanced stage in the development of learning systems. The students have different profiles according to studies, training, skills, concerns, their learning styles, objectives, preferences. All these lead to differences in individual training effectiveness through learning systems.

Health Human Resources are among the most important and expensive resources in this sector, and Human Resources Management (HRM) is regarded as a crucial element for the success of healthcare organizations [21]. Healthcare services are influenced by the diversity of patient and employee profiles, and also by the effect of technologies and economy globalization.

An increased focus on responsibility and teamwork in healthcare services provisioning are important changes with a significant impact on the quality of services provided to the patient. Advances in HRM as well as examples of training solutions with the help of Web technologies are dwelt upon in different papers [28].

The aim of HHRM is to ensure the right number of healthcare human resources with adequate knowledge, aptitudes, attitudes and qualifications, able to fulfil the right tasks in the right place and at the right time with a view to attain the public healthcare objectives [4].

This paper presents a personalized e-learning system based on the technology of semantic Web meant to answer the training needs of human resources managers in a university hospital. Among other things, managers need to have their knowledge checked and updated by adding notions required by their professional position, in keeping with their profile, learning style and expectations.

The objectives of this system are: personalized learning, adapted educational content, reuse of the educational resources and interoperability for e-learning systems, as well as human resources management systems. Founded on these bases, the system aims to solve a series of current limitations of e-learning.

Within the proposed system, learning personalization takes into account the knowledge level, learning style (in keeping with the Felder-Silverman model), the aim of learning, the student’s competences as well as his assessment and feedback. It will be achieved through innovative solutions in three fields: modelling the learning process, modelling the student, modelling the digital content.

The design of the learning process relies on the IMS (Information Management System) standards, which provide a conceptual framework for all three aspects.

Modelling is achieved with the help of ontologies implemented with the Protégé environment, by using a methodology that suits the field and the student type.

This paper is organized as follows. Section 2 briefly presents the theoretical background. Section 3 presents the overview of system. Section 4 presents logical and technical architecture description, and implementation solution. Section 5 summarizes the main conclusions.


  1. ALEXANDRU, A., E. TIRZIU, E. TUDORA, O. BICA, Enhanced Education by Using Intelligent Agents in Multi-Agent Adaptive e-learning Systems, Studies in Informatics and Control, vol. 24, no.1, 2015, pp. 13-22.
  2. ALLEMANG, D., J. HENDLER, Semantic Web for the Working Ontologist. Modeling in RDF, RDFS, and OWL, Journal of Knowledge Management, vol. 6, no. 1, 2002, pp. 40-51.
  3. BAJENARU, L., I. A. MARINESCU, I. SMEUREANU, A Framework for Designing an Ontology-Based E-Learning System in Healthcare Human Resource Management, Proceeding of the 14-th International Conference on Informatics in Economy (IE2015), 2015, pp. 176-181.
  4. BAJENARU, L., I. SMEUREANU, An Ontology Based Approach for Modeling E-Learning in Healthcare Human Resource Management, Economic Computation and Economic Cybernetics Studies and Research (ECECSR), vol. 49, no. 1, 2015, pp. 23-40.
  5. BALOG, A., Acceptance of e-Learning Systems: a Serial Multiple Mediation Analysis, Studies in Informatics and Control, vol. 24, no.1, 2015, pp. 101-110.
  6. BERNERS-LEE, T., Weaving the Web: The Original Design and Ultimate Destiny of the World Wide Web by Its Inventor, HarperCollins Publishers, 1999.
  7. CAKULAA, S., M. SEDLENIECEA, Development of a Personalized e-learning Model Using Methods of Ontology, Procedia Computer Science, vol. 26, 2013, pp. 113-120.
  8. DUNN, N., J. S. BEAUDRY, A. KLAVAS, Survey of Research on Learning Styles, Educational Leadership, vol. 46, no. 6, 1989, pp. 50-58.
  9. ESSALMI, F., L. J. B. AYED, M. JEMNI, KINSHUK, S. GRAF., A Fully Personalization Strategy of E-learning Scenarios, Computers in Human Behavior, vol. 26, 2010, pp. 581-591.
  10. FELDER, R. M., L. K. SILVERMAN, Learning and Teaching Styles in Engineering Education, Engineering Education, vol. 78(7), 1988, pp. 674-681.
  11. FELDER, R. M., R. BRENT, Understanding Student Differences, Journal of Engineering Education, vol. 94, no. 1, 2005, pp. 57-72.
  12. FERNÁNDEZ-LÓPEZ, M., A. GÓMEZ-PÉREZ, N. JURISTO, Methondology: From Ontological Art Towards Ontological Engineering, AAAI Symposium on Ontological Engineering, Stanford, 1997, pp. 33-40.
  13. GIGURUWA, N., D. H. ANH, D. PISHVA, A Multimedia Integrated Framework for Learning Management Systems, eLearning – Theories, Design, Software and Applications, Ritsumeikan Asia Pacific Univ., Japan, InTech, 2012, pp. 153-172.
  14. HERRMANN, N., The Creative Brain, Lake Lure, N.C.: Brain Books, 1990.
  17. KOLB, D. A., Experiential Learning: Experience as the Source of Learning and Development, Prentice-Hall, 1984.
  18. KURILOVAS, E., A. JUSKEVICIENE, Creation of Web 2.0 Tools Ontology to Improve Learning, Computers in Human Behavior, vol 51, 2015, pp. 1380-1386.
  19. LO, J. J., Y. C. CHAN, S. W. YEH, Designing an Adaptive Web-based Learning System based on Students’ Cognitive Styles Identified Online, Comp. & Ed., vol. 58, 2012, pp. 209-222.
  20. NICULESCU, A., G. THORSTEINSSON, Enabling Idea Generation through Computer-Assisted Collaborative Learning, Studies in Informatics and Control, vol. 20, no.4, 2011, pp. 403-410.
  21. NILES, N. J., Basic concepts of Health Care Human Resource Management, Jones & Bartlett Publishers, 2013.
  22. NOY, N., D. L. MCGUINESS, Ontology Development 101: A Guide to Creating Your First Ontology, Stanford Knowledge Systems Laboratory Technical Report KSL-01-05, 2001.
  23. ONGENAE, F., M. CLAEYS, T. DUPONT, W. KERCKHOVE, P. VERHOEVE, T. DHAENE, F. TURCK, A Probabilistic Ontology-based Platform for Self-learning Context-aware Healthcare Applications, Expert Systems with Applications 40, 2013, pp. 7629-7646.
  24. PRAMITASARI, L., A. N. HIDAYANTO, S. AMINAH, A. A. KRISNADHI, L. M. A. RAMADHANIE, Development of Student Model Ontology for Personalization in an e-Learning System based on Semantic Web, Proceedings of the International Conference on Advanced Computer Science and Information Systems, 2009, pp. 434-439.
  25. .Protégé,
  26. ROLLETT, H., M. LUX, M. STROHMAIER, G. DOSINGER, The Web 2.0 Way of Learning with Technologies, International Journal of Learning Technology, vol. 3, no. 1, 2007, pp. 87-107.
  27. RUIZ, J. G., M. J. MINTZER, R. M. LEIPZIG, The Impact of e-Learning in Medical Education, Academic Medicine, vol. 81, no. 3, 2006, pp. 207-212.
  28. SINGH, G., K. S. NEGI, Human Resource Management Practices in Large Hospitals of Dehradun, Uttarakhand, Global Journal of Management and Business Studies, vol. 3, no. 5, 2013, pp. 555-560.
  29. SMEUREANU, I., N. ISAILA, New Information Technologies for an Innovative Education, World Journal on Educational Technology, vol. 3, no.3, 2011, pp. 177-189.
  30. Studiu de Evaluare a Nevoii de Formare a Managerilor Sistemelor Calităţii în Spitalele din România, proiect: LLP-LdV-ToI-2012/RO/019, 2012, evaluare nevoi de formare.pdf.
  31. TRAUSAN-MATU, S., D. MARASCHI, S. CERRI, Ontology-Centered Personalized Presentation of Knowledge Extracted from the Web, Computer Science, nr. 2363, 2002, pp. 259-269.
  32. VALASKI, J., A. MALUCELLI, S. REINEHR, Recommending Learning Materials According to Ontology-based Learning Styles, ICITA, Australia, 2011.
  33. WELLER, M., Virtual Learning Environments: Using, Choosing and Developing Your VLE, Routledge, London, 2007.


* This paper is based on two previous presentations in: “Journal of Economic Computation and Economic Cybernetics Studies and Research” and “The 14th International Conference on Informatics in Economy” (see references 3 and 4). This work presents new data from our research about the evaluation of the system ontology, and implementation of the developed solution.