Skip to main content
SpringerLink
Log in

An intelligent expert system for academic advising utilizing fuzzy logic and semantic web technologies for smart cities education

  • Published:
Journal of Computers in Education Aims and scope Submit manuscript

Abstract

Students attending Higher Education Institutions (HEIs) are faced with a variety of complex decisions and procedures. To provide students with more sustained and personalized advising, many HEIs turn to online academic advising systems and tools as a way to minimize costs and streamline their advising services. However, in such systems, uncertainty in the learner’s parameters is a factor, which makes the decision-making process more difficult. Fuzzy logic, a multivalued logic similar to human thinking and interpretation, is highly suitable and applicable for developing knowledge-based academic advising systems that conserve the inherent fuzziness in learner models. In this paper, an innovative hybrid software infrastructure is presented which integrates expert system, fuzzy reasoning, and ontological tools to provide reliable recommendations to students for the next appropriate learning step. The software comprises a fuzzy logic component that determines the student’s interest degree for a specific academic choice accompanied by an ontological model and a conventional rule-based expert system for the composition of personalized learning pathways. In order for the system to recommend the next step of the learning pathway, the output of the fuzzy logic component together with the knowledge that is modeled as part of the multi-facet ontology and the machine perceptible academic advising guidelines expressed as semantic rules interoperate in a dynamic and seamless manner. The paper presents the key modeling artifacts of the proposed approach and the architecture of the implemented prototype system. During the case study, the developed system yielded satisfactory results in terms of overall inter-rater reliability and usefulness.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig.11

Similar content being viewed by others

Notes

  1. https://www.invest4excellence.eu.

  2. https://www.onetonline.org/.

  3. https://efqm.org

  4. https://drools.org.

  5. https://github.com/marcingol1/fuzzy.

  6. https://github.com/protegeproject/swrlapi/wiki.

  7. https://tomcat.apache.org/

  8. https://smartdevops.eu/.

  9. https://esco.ec.europa.eu/.

  10. https://www.onetonline.org/.

  11. https://www.ilo.org/.

References

  • Abdel-Hafez, A., Tang, X., Tian, N., & Xu, Y. (2014). A reputation-enhanced recommender system. In X. Luo, J. X. Yu, & Z. Li (Eds.), Advanced data mining and applications (pp. 185–198). Springer International Publishing

    Chapter  Google Scholar 

  • Abduldaim, A. M., & Sabri, R. I. (2019). The effectiveness of LUD on digital image watermarking based on sugeno fuzzy inference system. International Journal of Latest Engineering and Management Research (IJLEMR), 4, 53–60.

    Google Scholar 

  • Adomavicius, G., & Tuzhilin, A. (2015). Context-aware recommender systems. In F. Ricci, L. Rokach, & B. Shapira (Eds.), Recommender systems handbook (2nd ed., pp. 191–226). Springer

    Chapter  Google Scholar 

  • Aguilar, J., Valdiviezo-Díaz, P., & Riofrio, G. (2017). A general framework for intelligent recommender systems. Applied Computing and Informatics, 13, 147–160. https://doi.org/10.1016/j.aci.2016.08.002

    Article  Google Scholar 

  • Aly WM, Eskaf KA, Selim AS (2017) Fuzzy mobile expert system for academic advising. In: Canadian Conference on Electrical and Computer Engineering. pp. 1187–1191

  • Anderman, E. M., Gray, D. L., & Chang, Y. (2012). Motivation and classroom learning. In I. Weiner (Ed.), Handbook of psychology (2nd ed.). American Cancer Society

    Google Scholar 

  • Mohamed Baloul, Williams, P., (2013), Fuzzy academic advising system for on probation students in colleges of applied sciences. In: International conference on computing, electrical and electronic engineering (ICCEEE). pp. 372–377

  • Bielikovà, M., Šimko, M., Barla, M., et al. (2014). ALEF: From application to platform for adaptive collaborative learning. Recommender systems for technology enhanced learning: research trends and applications (pp. 195–225). Springer

    Chapter  Google Scholar 

  • Carchiolo, V., Longheu, A., & Malgeri, M. (2010). Reliable peers and useful resources: Searching for the best personalised learning path in a trust- and recommendation-aware environment. Information Sciences, 180, 1893–1907. https://doi.org/10.1016/j.ins.2009.12.023

    Article  Google Scholar 

  • Casali A, Gerling V, Deco C, Bender C (2011) A recommender system for learning objects personalized retrieval. In: Educational Recommender Systems and Technologies: Practices and Challenges. IGI Global, pp. 182–210

  • Chen Y, Pan C, Yang G, Bai J (2014) Intelligent decision system for accessing academic performance of candidates for early admission to university. In: 10th International Conference on Natural Computation (ICNC). pp. 687–692

  • Chen, C.-M., & Duh, L.-J. (2008). Personalized web-based tutoring system based on fuzzy item response theory. Expert Systems with Applications, 34, 2298–2315. https://doi.org/10.1016/j.eswa.2007.03.010

    Article  Google Scholar 

  • Chen, C.-M., Lee, H.-M., & Chen, Y.-H. (2005). Personalized e-learning system using item response Theory. Computers & Education, 44, 237–255. https://doi.org/10.1016/j.compedu.2004.01.006

    Article  Google Scholar 

  • Dias, A. D. S., & Wives, L. K. (2019). Recommender system for learning objects based in the fusion of social signals, interests, and preferences of learner users in ubiquitous e-learning systems. Personal and Ubiquitous Computing, 23, 249–268. https://doi.org/10.1007/s00779-018-01197-7

    Article  Google Scholar 

  • Díaz-Díaz, J. M., & Galpin, I. (2020). Evaluating models for a higher education course recommender system using state exam results. Springer

    Book  Google Scholar 

  • Drachsler, H., Verbert, K., Santos, O. C., & Manouselis, N. (2015). Panorama of recommender systems to support learning. In F. Ricci, L. Rokach, & B. Shapira (Eds.), Recommender systems handbook (pp. 421–451). Springer

    Chapter  Google Scholar 

  • du Boulay, B., Avramides, K., Luckin, R., Martínez-Mirón, E., Méndez, G. R., & Carr, A. (2010). Towards systems that care: A conceptual framework based on motivation, metacognition and affect. International Journal of Artificial Intelligence in Education, 20, 197–229. https://doi.org/10.3233/JAI-2010-0007

    Article  Google Scholar 

  • Duarte, R., de Oliveira Pires, A. L., & Nobre, Â. L. (2018). Mature learners’ participation in higher education and flexible learning pathways: Lessons learned from an exploratory experimental research. In M. M. Nascimento, G. R. Alves, & E. V. A. Morais (Eds.), Contributions to higher engineering education (pp. 33–53). Springer

    Chapter  Google Scholar 

  • Durao, F., Dolog, P., (2009). Social and behavioral aspects of a tag-based recommender system. In: ISDA 2009—9th International Conference on Intelligent Systems Design and Applications. pp. 294–299

  • Eccles, J. S., (1983). Expectancies, values, and academic behavior. Achievement and achievement motives: Psychological and sociological approaches. pp. 75–146

  • Essa, A. (2016). A possible future for next generation adaptive learning systems. Smart Learning Environments, 3, 16. https://doi.org/10.1186/s40561-016-0038-y

    Article  Google Scholar 

  • Fallahnejad, M., & Moshiri, B. (2014). The performance of B-spline and gaussian functions in the structure of a Neuro-Fuzzy network. Technical and Vocational University, 4, 1622–1636.

    Google Scholar 

  • Farzan, R., & Brusilovsky, P. (2006). Social navigation support in a course recommendation system. In V. P. Wade, H. Ashman, & B. Smyth (Eds.), Lecture notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 91–100). Springer

    Google Scholar 

  • Garrido, A., & Morales, L. (2014). E-Learning and intelligent planning: Improving content personalization. Revista Iberoamericana De Tecnologias Del Aprendizaje, 9, 1–7. https://doi.org/10.1109/RITA.2014.2301886

    Article  Google Scholar 

  • Henderson, L. K., & Goodridge, W. (2015). AdviseMe: An intelligent web-based application for academic advising. (IJACSA) International Journal of Advanced Computer Science and Applications. https://doi.org/10.14569/IJACSA.2015.060831

    Article  Google Scholar 

  • Horrocks, I., Patel-Schneider, P. F., Boley, H., et al (2010) SWRL: A semantic web rule language combining OWL and RuleML. In: W3C Member Submission. Retrieved January 30, 2017, from https://www.w3.org/Submission/SWRL/

  • Iatrellis, O., Kameas, A., & Fitsilis, P. (2017). Academic advising systems: A systematic literature review of empirical evidence. Education Sciences, 7, 90. https://doi.org/10.3390/educsci7040090

    Article  Google Scholar 

  • Iatrellis, O., Kameas, A., & Fitsilis, P. (2018). EDUC8: Self-evolving and personalized learning pathways utilizing semantics. IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS), 2018, 1–8.

    Google Scholar 

  • Iatrellis, O., Kameas, A., & Fitsilis, P. (2019a). A novel integrated approach to the execution of personalized and self-evolving learning pathways. Education and Information Technologies. https://doi.org/10.1007/s10639-018-9802-7

    Article  Google Scholar 

  • Iatrellis, O., Kameas, A., & Fitsilis, P. (2019b). EDUC8 ontology: Semantic modeling of multi-facet learning pathways. Education and Information Technologies. https://doi.org/10.1007/s10639-019-09877-4

    Article  Google Scholar 

  • Iatrellis, O., Panagiotakopoulos, T., Gerogiannis, V. C., et al. (2020). Cloud computing and semantic web technologies for ubiquitous management of smart cities-related competences. Education and Information Technologies. https://doi.org/10.1007/s10639-020-10351-9

    Article  Google Scholar 

  • Iatrellis, O., Savvas, I. K., Kameas, A., & Fitsilis, P. (2020). Integrated learning pathways in higher education: A framework enhanced with machine learning and semantics. Education and Information Technologies. https://doi.org/10.1007/s10639-020-10105-7

    Article  Google Scholar 

  • Imran, H., Belghis-Zadeh, M., Chang, T.-W., Kinshuk, & Graf, S. (2016). PLORS: A personalized learning object recommender system. Vietnam Journal of Computer Science, 3, 3–13. https://doi.org/10.1007/s40595-015-0049-6

    Article  Google Scholar 

  • Irfan, M., Alam, C. N., & Tresna, D. (2019). Implementation of fuzzy mamdani logic method for student drop out status analytics. Journal of Physics: Conference Series. https://doi.org/10.1088/1742-6596/1363/1/012056

    Article  Google Scholar 

  • Kaklauskas, A., Zavadskas, E. K., Seniut, M., Stankevic, V., Raistenskis, J., Simkevičius, C., Stankevic, T., Matuliauskaite, A., Bartkiene, L., Zemeckyte, L., Paliskiene, R., Cerkauskiene, R., & Gribniak, V. (2013). Recommender system to analyze student’s academic performance. Expert Systems with Applications, 40, 6150–6165. https://doi.org/10.1016/j.eswa.2013.05.034

    Article  Google Scholar 

  • Kaufmann, H. R., Bengoa, D., Sandbrink, C., Kokkinaki, A., Kameas, A., Valentini, A., & Omiros, I. (2020). DevOps competences for smart city administrators. CORP, 2020, 213–223.

    Google Scholar 

  • Kerkiri, T., Manitsaris, A., Mavridou, A., (2008). Reputation metadata for recommending personalized e-learning resources. In: Second International Workshop on Semantic Media Adaptation and Personalization. pp. 110–115

  • Luyi, Li., Yanlin, Z., Ogata, H., Yano, Y., (2004). A framework of ubiquitous learning environment. In: The Fourth International Conference on Computer and Information Technology. pp. 345–350

  • Martín, E., & Carro, R. M. (2009). Supporting the development of mobile adaptive learning environments: A case study. IEEE Transactions on Learning Technologies, 2, 23–36. https://doi.org/10.1109/TLT.2008.24

    Article  Google Scholar 

  • McCarthy, W. E. (2003). The REA modeling approach to teaching accounting information systems. Issues in Accounting Education, 18, 427–441. https://doi.org/10.2308/iace.2003.18.4.427

    Article  Google Scholar 

  • Medsker, L. R. (1995). Hybrid intelligent systems. Springer

    Book  Google Scholar 

  • Molina-Solana, M., Birch, D., & Guo, Y. K. (2017). Improving data exploration in graphs with fuzzy logic and large-scale visualisation. Applied Soft Computing Journal, 53, 227–235. https://doi.org/10.1016/j.asoc.2016.12.044

    Article  Google Scholar 

  • Nauta, M. M. (2010). The development, evolution, and status of Holland’s theory of vocational personalities: Reflections and future directions for counseling psychology. Journal of Counseling Psychology, 57, 11–22. https://doi.org/10.1037/a0018213

    Article  Google Scholar 

  • Park, D. H., Kim, H. K., Choi, I. Y., & Kim, J. K. (2012). A literature review and classification of recommender systems research. Expert Systems with Applications, 39, 10059–10072. https://doi.org/10.1016/j.eswa.2012.02.038

    Article  Google Scholar 

  • Pintrich, P. (2003). Motivation and classroom learning. In I. B. Weiner (Ed.), Handbook of psychology. Wiley

    Google Scholar 

  • Prasad, M., Liu, Y. T., Li, D. L., et al. (2017). A new mechanism for data visualization with TSK-type preprocessed collaborative fuzzy rule based system. Journal of Artificial Intelligence and Soft Computing Research, 7, 33–46. https://doi.org/10.1515/jaiscr-2017-0003

    Article  Google Scholar 

  • Ricci, F., Shapira, B., & Rokach, L. (2015). Recommender systems handbook (2nd ed.). Springer

    Book  Google Scholar 

  • Ryan, R. M., & Deci, E. L. (2000). Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary Educational Psychology, 25, 54–67. https://doi.org/10.1006/ceps.1999.1020

    Article  Google Scholar 

  • Salehi, M., & Kmalabadi, I. N. (2012). A hybrid attribute–based recommender system for e–learning material recommendation. IERI Procedia, 2, 565–570. https://doi.org/10.1016/j.ieri.2012.06.135

    Article  Google Scholar 

  • Santos, O. C., Boticario, J. G., & Pérez-Marín, D. (2014). Extending web-based educational systems with personalised support through user centred designed recommendations along the e-learning life cycle. Science of Computer Programming, 88, 92–109. https://doi.org/10.1016/j.scico.2013.12.004

    Article  Google Scholar 

  • Schoefegger, K., Seitlinger, P., & Ley, T. (2010). Towards a user model for personalized recommendations in work-integrated learning: A report on an experimental study with a collaborative tagging system. Procedia Computer Science, 1, 2829–2838.

    Article  Google Scholar 

  • Shatnawi, R., Althebyan, Q., Ghalib, B., Al-Maolegi, M., (2014). Building a smart academic advising system using association rule mining

  • Takano, K., Li, K. F., (2009). An adaptive personalized recommender based on web-browsing behavior learning. In: Proceedings—International Conference on Advanced Information Networking and Applications, AINA. pp. 654–660

  • Thanh-Nhan, H-L., Nguyen, H-H., Thai-Nghe, N., (2016). Methods for building course recommendation systems. In: 2016 Eighth International Conference on Knowledge and Systems Engineering {KSE}. pp. 163–168

  • Troussas, C., Krouska, A., & Virvou, M. (2020). Using a Mult module model for learning analytics to predict learners’ cognitive states and provide tailored learning pathways and assessment. In M. Virvou, E. Alepis, G. A. Tsihrintzis, & L. C. Jain (Eds.), Machine learning paradigms: Advances in learning analytics (pp. 9–22). Springer International Publishing.

    Chapter  Google Scholar 

  • Upendran, D., Chatterjee, S., Sindhumol, S., & Bijlani, K. (2016). Application of predictive analytics in intelligent course recommendation. Procedia Computer Science, 93, 917–923. https://doi.org/10.1016/j.procs.2016.07.267

    Article  Google Scholar 

  • Wigfield, A., & Cambria, J. (2010). Students’ achievement values, goal orientations, and interest: Definitions, development, and relations to achievement outcomes. Developmental Review, 30, 1–35. https://doi.org/10.1016/j.dr.2009.12.001

    Article  Google Scholar 

  • Xu, J., Xing, T., & van der Schaar, M. (2016). Personalized course sequence recommendations. IEEE Transactions on Signal Processing, 64, 5340–5352. https://doi.org/10.1109/TSP.2016.2595495

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the INVEST4EXCELLENCE project under the H2020-IBA-SwafS-Support-2-2020 program (Project No. 101035815, www.invest-alliance.eu) for providing support and thank the other project partners.

Author information

Authors and Affiliations

  1. Department of Digital Systems, University of Thessaly, Larissa, Greece

    Omiros Iatrellis & Nicholas Samaras

  2. Department of Business Administration, University of Thessaly, Larissa, Greece

    Evangelos Stamatiadis & Panos Fitsilis

  3. School of Science and Technology, Hellenic Open University, Patras, Greece

    Theodor Panagiotakopoulos

  4. Business School, University of Nicosia, Nicosia, Cyprus

    Theodor Panagiotakopoulos

Authors
  1. Omiros Iatrellis
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Evangelos Stamatiadis
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nicholas Samaras
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Theodor Panagiotakopoulos
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Panos Fitsilis
    View author publications

    You can also search for this author in PubMed Google Scholar

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.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Iatrellis, O., Stamatiadis, E., Samaras, N. et al. An intelligent expert system for academic advising utilizing fuzzy logic and semantic web technologies for smart cities education. J. Comput. Educ. 10, 293–323 (2023). https://doi.org/10.1007/s40692-022-00232-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40692-022-00232-0

Keywords

Search

Navigation