EDUC8 pathways: executing self-evolving and personalized intra-organizational educational processes

One of the main challenges to be confronted by modern tertiary sector, so as to improve quality is the personalization of learning, which has to be combined with a minimization of the respective costs. However, personalization requires continuous reconfiguration of the academic plans since the academic status of each student, educational options and circumstances inside a Higher Educational Institution constantly change. In this paper, we present EDUC8 (EDUCATE) software environment that provides an integrated information technology solution concerning the dynamic recommendation and execution of personalized education processes. The implemented EDUC8 prototype aggregates a process execution engine, a rule engine and a semantic infrastructure for reconfiguring the learning pathways for each student. The semantic infrastructure consists of an ontology enclosing the required knowledge and a semantic rule-set. During the execution of learning pathways, the system reasons over the rules and reconfigures the next steps of the learning process. At the same time, new knowledge and facts originated from both the rule base and the learning pathway meta-models that are established during their execution are created, which constitute the evolving knowledge base of EDUC8 platform. The completeness and performance of the implemented infrastructure was tested for the modeling and selection of a set of appropriate academic recommendations regarding the Network Engineering specialization field of the Computer Science program.

[1]  Margaret M. Nauta The development, evolution, and status of Holland's theory of vocational personalities: Reflections and future directions for counseling psychology. , 2010, Journal of counseling psychology.

[2]  William E. McCarthy,et al.  An ontological analysis of the economic primitives of the extended-REA enterprise information architecture , 2002, Int. J. Account. Inf. Syst..

[3]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[4]  Robert C. Reardon,et al.  Revitalizing Educational Counseling: How Career Theory Can Inform a Forgotten Practice. , 2011 .

[5]  Jan Recker,et al.  Business Process Management Education in Academia: Status, Challenges, and Recommendations , 2010, Commun. Assoc. Inf. Syst..

[6]  Plamen Angelov,et al.  Outside the box: an alternative data analytics framework , 2014, J. Autom. Mob. Robotics Intell. Syst..

[7]  Miguel-Ángel Sicilia,et al.  Representing instructional design methods using ontologies and rules , 2012, Knowl. Based Syst..

[8]  Samir Chatterjee,et al.  A Design Science Research Methodology for Information Systems Research , 2008 .

[9]  Plamen P. Angelov,et al.  A new type of simplified fuzzy rule-based system , 2012, Int. J. Gen. Syst..

[10]  Vicente Cerverón-Lleó,et al.  BPM FOR QUALITY ASSURANCE SYSTEMS IN HIGHER EDUCATION , 2014 .

[11]  Olena Kuzminska,et al.  Competence Approach to Modeling and Control of Students' Learning Pathways in the Cloud Service , 2017, ICTERI.

[12]  Dimitar Filev,et al.  On-Line Evolution of Takagi-Sugeno Fuzzy Models , 2004, IFAC Proceedings Volumes.

[13]  Mahmoud Abd Ellatif,et al.  A proposed paradigm for smart learning environment based on semantic web , 2017, Comput. Hum. Behav..

[14]  Ebrahim Mamdani,et al.  Applications of fuzzy algorithms for control of a simple dynamic plant , 1974 .

[15]  Zoran Budimac,et al.  Protus 2.0: Ontology-based semantic recommendation in programming tutoring system , 2012, Expert Syst. Appl..

[16]  Achilles Kameas,et al.  Academic Advising Systems: A Systematic Literature Review of Empirical Evidence , 2017 .

[17]  Somjit Arch-int,et al.  FUSE: A Fuzzy-Semantic Framework for Personalizing Learning Recommendations , 2018, Int. J. Inf. Technol. Decis. Mak..

[18]  Eugenijus Kurilovas,et al.  Web 3.0 - Based personalisation of learning objects in virtual learning environments , 2014, Comput. Hum. Behav..

[19]  N. F. Noy,et al.  Ontology Development 101: A Guide to Creating Your First Ontology , 2001 .

[20]  Frantisek Hunka,et al.  Detail REA production planning model using value chain , 2011, WCIT.

[21]  Keeley A. Crockett,et al.  A fuzzy model for predicting learning styles using behavioral cues in an conversational intelligent tutoring system , 2013, 2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).