A novel integrated approach to the execution of personalized and self-evolving learning pathways
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One of the main challenges to be confronted by Higher Educational Institutions (HEI), so as to increase quality, is the provision of personalized education services in a wide range of educational settings, often beyond the course sequences historically offered to students. However, this personalization requires the continuous reconfiguration and adaptation of the selected academic plans, since each student is a unique case and both the educational options and current circumstances inside an educational institution change rapidly. In this paper, we present an innovative software environment offered to the academic staff and personnel that provides an integrated information technology solution concerning the dynamic and personalized composition of students’ learning pathways during execution phase. The software environment comprises a process execution engine based on a semantic infrastructure (ontology) for configuring the learning pathways. During the execution of learning pathways, the system reasons over the rules and dynamically recommends the next steps of the learning. The implemented graphical user interface and the respective business logic for the recommendation and execution of the learning pathway processes is presented, alongside with a graphical rule generator interface for the definition of the rule-set for the learning pathways in a user-friendly way.
KeywordsSemantic meta-modeling Educational process Learning pathways Academic advising Personalization
- ACM. (2001). ACM curriculum guidelines for Computer Science. The Joint Task Force on Computing Curricula Society, IEEE Computer Machinery, Association for Computing, 1(3). https://doi.org/10.1145/384274.384275.
- ACM CSS. (2012). Retrieved from https://www.acm.org/publications/class-2012
- BBC - Ontologies - Curriculum Ontology. (n.d.). Retrieved from https://www.bbc.co.uk/ontologies/curriculum
- Cerverón-lleó, V., Cabotà-soro, J., Grimaldo-moreno, F., & Ferrís-castell, R. (2014). Bpm for quality assurance systems in higher education. Journal of Teaching and Education, 03(02), 175–183.Google Scholar
- HEI-UP Business Process Management in Higher Education Institutions. (n.d.). Retrieved from http://www.bpm-hei.eu/
- Jami, S. I., & Shaikh, Z. A. (2007). A workflow based academic management system using multi agent approach. Proceeding of the 11th Wseas International Conference on Computers: Computer Science and Technology, Vol 4, 201–206.Google Scholar
- Janssen, J., Hermans, H., Berlanga, A., & Koper, R. (2010). Learning path information model - version 1.3. Retrieved November, (march), 1–23.Google Scholar
- K.V.Zhukova and A.Yu.Pleshkova. (2016). Business process modeling: Case of undergraduate program. In Proceedings of the International Conference on Communication, Management and Information Technology (Iccmit 2016) (pp. 179–186).Google Scholar
- Kakasevski, G., Mihajlov, M., Arsenovski, S., & Chungurski, S. (2008). Sealms: Semantically Enhanced Adaptive Learning Management System, 4(5), 613–618.Google Scholar
- Machinery, A. for C., & IEEE Computer Society. (2016). Computer Engineering Curricula 2016 CE2016 Curriculum Guidelines for Undergraduate Degree Programs in Computer Engineering. Retrieved from https://www.acm.org/binaries/content/assets/education/ce2016-final-report.pdf
- Noy, N. F., & Mcguinness, D. L. (2000). Ontology development 101 : A guide to creating your first ontology, 1–25.Google Scholar
- OKI. (2004). Open Service Interface Definitions. Retrieved from http://heanet.dl.sourceforge.net/ project/okiproject/Doc %28previous OSID versions%29/ Full Documentation Set/OSID_Documentation_rc6.1.pdf.
- Ouf, S., Abd Ellatif, M., Salama, S. E., & Helmy, Y. (2016). A proposed paradigm for smart learning environment based on semantic web. Computers in Human Behavior. https://doi.org/10.1016/j.chb.2016.08.030.
- Panagiotopoulos, I., Kalou, A., Pierrakeas, C., & Kameas, A. (2012). An ontology-based model for student representation in intelligent tutoring systems for distance learning. IFIP Advances in Information and Communication Technology, 381(AICT(PART 1)), 296–305. https://doi.org/10.1007/978-3-642-33409-2_31.CrossRefGoogle Scholar
- Pavlenko, V., Prokhorov, A., Kuzminska, O., & Mazorchuk, M. (2017). Competence approach to modeling and control of students’ learning pathways in the cloud service. CEUR Workshop Proceedings, 1844, 257–264.Google Scholar
- Porter, M. E. (1985). Competitive advantage: Creating and sustaining superior performance. New York: FreePress.Google Scholar
- Rius, A., & Conesa, J. (2013). Specifying patterns of educational settings by means of ontologies. Journal of Universal Computer Science, 19(3), 353–382.Google Scholar
- Seng, D., & Churilov, L. (2003). Business process-oriented information support for a higher education Enterprise. 7th Pacific Asia Conference on Information Systems, (July), 1055–1074. Retrieved from http://espace.library.uq.edu.au/view/UQ:309426
- The EFQM Excellence Model. (2013). Retrieved January 30, 2017, from http://www.efqm.org/the-efqm-excellence-model
- U-Multirank | Universities compared. Your way. (2011). Retrieved from http://www.umultirank.org
- Wen, F., & McGreal, F. (2007). E-advisor: A multi-agent system for academic advising. Workshop on Agent-Based Systems for Human and Entertainment (ABSHLE) at AAMAS, 2007(1988), 34–37 Retrieved from http://io.acad.athabascau.ca/~oscarl/pub/ABSHL2007.pdf.Google Scholar
- Yarandi, M., Tawil, A.-R., & Jahankhani, H. (2011). Adaptive E-learning system using ontology. 2011 22nd International Workshop on Database and Expert Systems Applications, (c), 511–516. https://doi.org/10.1109/DEXA.2011.9