[go: up one dir, main page]

Skip to main content

Advertisement

Log in

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

  • Original Paper
  • Published:
Evolving Systems Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • ACM (2001) ACM curriculum guidelines for Computer Science. The Joint Task Force on Computing Curricula Society. In: IEEE computer machinery, association for computing, vol 1, no 3

  • Angelov P et al (2004) On-line evolution of Takagi-Sugeno fuzzy models. In: 2nd IFAC workshop on advanced fuzzy/neural control. http://onlinelibrary.wiley.com/doi/10.1002/cbdv.200490137/abstract. Accessed 21 May 2018

    Article  Google Scholar 

  • Angelov P (2014) Outside the box: an alternative data analytics framework. J Autom Mob Robot Intell Syst 8(4):3–10

    Google Scholar 

  • Angelov P, Yager R (2012) A new type of simplified fuzzy rule-based system. Int J Gen Syst 41(2):163–185

    Article  MathSciNet  Google Scholar 

  • Anon (2003) Higher education & research. Industry software. SAP. https://www.sap.com/industries/higher-education-research.html. Accessed 30 May 2018

  • Anon (2011) U-multirank. Universities compared. your way. http://www.umultirank.org. Accessed 14 Aug 2018

  • Anon (2013a) HEI-UP business process management in higher education institutions. http://www.bpm-hei.eu/. Accessed 19 May 2018

  • Anon (2013b) The EFQM excellence model. http://www.efqm.org/the-efqm-excellence-model. Accessed 30 Jan 2017

  • Anon (2013c) BBC—Ontologies—Curriculum Ontology. https://www.bbc.co.uk/ontologies/curriculum. Accessed 23 Feb 2017

  • Anon (2017) Higher education industry solution—digital campus experience. https://www.oracle.com/industries/higher-education/digital-campus.html. Accessed 20 May 2018

  • Bandara W et al (2010) Business process management education in academia: status, challenges, and recommendations. Commun Assoc Inf Syst 27:743–776

    Google Scholar 

  • Begam MF, Ganapathy G (2012) SEALMS: semantically enhanced adaptive learning management system. In: Proceedings of international conference on soft computing, Artificial intelligence (SAI-12)

  • Cerverón-lleó V et al (2014) Bpm for quality assurance systems in higher education. J Teach Educ 03(02):175–183

    Google Scholar 

  • Crockett K et al (2013) A fuzzy model for predicting learning styles using behavioral cues in an conversational intelligent tutoring system. In: Fuzzy systems (FUZZ), 2013 IEEE international conference, pp 1–8

  • Cuong NDH, Arch-Int N, Arch-Int S (2018) FUSE: a fuzzy-semantic framework for personalizing learning recommendations. Int J Inf Technol Decis Mak 17(04):1173–1201

    Article  Google Scholar 

  • Geerts GL, McCarthy WE (2003) An ontological analysis of the economic primitives of the extended-rea enterprise information architecture. Int J Acc Inf Syst 3(1):1–16

    Article  Google Scholar 

  • Horrocks I et al (2010) SWRL: a semantic web rule language combining OWL and rule ML. W3C member submission. https://www.w3.org/Submission/SWRL/. Accessed 30 Jan 2017

  • Hunka F et al (2011) Detail REA production planning model using value chain. Procedia Comput Sci 3:408–413

    Article  Google Scholar 

  • Iatrellis O, Kameas A, Fitsilis P (2017) Academic advising systems: a systematic literature review of empirical evidence. Educ Sci 7(4):90

    Article  Google Scholar 

  • Jami SI, Shaikh ZA (2007) A workflow based academic management system using multi agent approach. In: Proceeding of the 11th Wseas international conference on computers: computer science and technology, vol 4, pp 201–206

  • Janssen J et al (2010) Learning path information model—version 1.3. pp 1–23. https://www.academia.edu/22122142/Learning_Path_Specification. Accessed 21 May 2017

  • Kašík J, Hunka F (2011) Business process modelling using rea ontology. Econ Manag 1047–1053. http://w3.bgu.ac.il/lib/customproxy.php?url=http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=61822106&site=eds-live&authtype=ip,uid&custid=s4309548&groupid=main&profile=eds. Accessed 13 May 2018

  • Kurilovas E, Kubilinskiene S, Dagiene V (2014) Web 3.0—based personalisation of learning objects in virtual learning environments. Comput Hum Behav 30:654–662

    Article  Google Scholar 

  • Machinery & IEEE Computer Society (2016) Computer engineering curricula 2016 CE2016 curriculum guidelines for undergraduate degree programs in computer engineering. https://www.acm.org/binaries/content/assets/education/ce2016-final-report.pdf. Accessed 11 May 2018

  • Mamdani EH (1974) Application of fuzzy algorithms for control of simple dynamic plant. Proc Inst Elect Eng 121(12):1585

    Article  Google Scholar 

  • Nauta MM (2010) The development, evolution, and status of holland’s theory of vocational personalities: reflections and future directions for counseling psychology. J Couns Psychol 57(1):11–22

    Article  MathSciNet  Google Scholar 

  • Noy NF, Mcguinness DL (2000) Ontology development 101: a guide to creating your first ontology. Tech. Rep. Stanford University, Stanford, CA. http://protege.stanford.edu/publications/ontology_development/ontology101.pdf. Accessed 30 Jan 2017

  • Ouf S et al (2016) A proposed paradigm for smart learning environment based on semantic web. Comput Hum Behav. https://doi.org/10.1016/j.chb.2016.08.030

    Article  Google Scholar 

  • Panagiotopoulos I et al (2012) An ontology-based model for student representation in intelligent tutoring systems for distance learning. In: IFIP advances in information and communication technology, 381 AICT (Part 1), pp 296–305

    Google Scholar 

  • Pavlenko V et al (2017) Competence approach to modeling and control of students’ learning pathways in the cloud service. CEUR Workshop Proc 1844:257–264

    Google Scholar 

  • Peffers K et al (2007) A design science research methodology for information systems research. J Manag Inf Syst 24(3):45–77

    Article  Google Scholar 

  • Reardon RC, Bertoch SC (2011) Revitalizing educational counseling: how career theory can inform a forgotten practice. Prof Couns 1(2):109–121

    Google Scholar 

  • Sugeno M, Takagi T (1985) Fuzzy identification of systems and its applications to modeling and control. IEEE Trans Syst Man Cybern 15(1):116–132

    MATH  Google Scholar 

  • Vesin B et al (2012) Protus 2.0: ontology-based semantic recommendation in programming tutoring system. Expert Syst Appl 39(15):12229–12246

    Article  Google Scholar 

  • Vidal-Castro C, Sicilia MÁ, Prieto M (2012) Representing instructional design methods using ontologies and rules. Knowl Based Syst 33:180–194. https://doi.org/10.1016/j.knosys.2012.04.005

    Article  Google Scholar 

  • Zhukova KV, Pleshkova AYu (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

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Omiros Iatrellis.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

A preliminary version of this paper appeared as “EDUC8: Self-evolving and Personalized Learning Pathways Utilizing Semantics” in the proceedings of the IEEE EAIS2018 international conference, Rhodes, Greece, May 25–27, 2018.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Iatrellis, O., Kameas, A. & Fitsilis, P. EDUC8 pathways: executing self-evolving and personalized intra-organizational educational processes. Evolving Systems 11, 227–240 (2020). https://doi.org/10.1007/s12530-019-09287-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12530-019-09287-4

Keywords