Skip to main content

Advertisement

SpringerLink
Log in

On modeling the quality of concept mapping toward more intelligent online learning feedback: a fuzzy logic-based approach

  • Long Paper
  • Published:
Universal Access in the Information Society Aims and scope Submit manuscript

Abstract

A new model, namely fuzzy inference system (FIS) concept mapping (FISCMAP), is proposed here that explores the fuzzy logic constructs within a computer-based concept mapping (CM) environment. FISCMAP involves modeling techniques as a vehicle to improve the intelligence of an online learning feedback environment that could promote personalization and adaptation to the student’s online educational needs, thus, fighting info-exclusion. From this perspective, the CMapTools is used as the online environment that captures the students’ actions and choices during the CM construction. Eight CMapTools measurements are considered to form inputs to a five-level FIS, equipped with 115 expert’s fuzzy rules. The CMapTools data were drawn from a blended (b)-learning course offered by a Greek Higher Education Institution, involving 20 Master’s students. Experimental results have shown that the proposed FISCMAP scheme, when used for the evaluation of users’ Quality of Concept Map (QoCM) via constructive CM variables (metrics), can provide intelligent descriptors regarding the students’ online CM. Furthermore, the FISCMAP’s dynamic analysis of QoCM and identification of the students’ transitional step behavior, during the development of the CM, provide further insight in the CM building strategies they adopt, forming constructive feedback. The latter reinforces the students’ ability to reflect on and analyze material in order to form reasoned judgments, clearly contributing to their critical thinking and deeper learning.

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

Similar content being viewed by others

References

  1. Novak, J.D.: Learning, Creating, and Using Knowledge: Concept Maps as Facilitative Tools in Schools and Corporations. Taylor and Francis, New York (2010)

    Google Scholar 

  2. Novak, J.D., Gowin, D.B.: Learning How to Learn. Cambridge University Press, Cambridge (1984)

    Google Scholar 

  3. Novak, J.D., Cañas, A.J.: The theory underlying concept maps and how to construct and use them. Technical Report IHMC Cmap Tools. IHMC, Pensacola, Florida (2008)

  4. Álvarez-Montero, F.J., Sáenz-Pérez, F., Vaquero-Sánchez, A.: Using datalog to provide just-in-time feedback during the construction of concept maps. Expert Syst. Appl. 42(3), 1362–1375 (2015)

    Google Scholar 

  5. Kwon, S.Y., Cifuentes, L.: Using computers to individually-generate vs. collaboratively-generate concept maps. J. Educ. Technol. Soc. 10(4), 269–280 (2007)

    Google Scholar 

  6. Omar, M.A.: Improving reading comprehension by using computer-based concept maps: a case study of ESP students at Umm-Alqura University. Br. J. Educ. 3(4), 1–20 (2015)

    Google Scholar 

  7. Michinov, N., Michinov, E.: Face-to-face contact at the midpoint of an online collaboration: its impact on the patterns of participation, interaction, affect, and behavior over time. Comput. Educ. 50(4), 1540–1557 (2008)

    Google Scholar 

  8. Tan, J., Biswas, G., Schwartz, D.: Feedback for metacognitive support in learning by teaching environments. In: Proceedings of the 28th Annual Meeting of the Cognitive Science Society, Vancouver, Canada, pp. 828–833 (2006)

  9. Gerard, L.F., Ryoo, K., McElhaney, K.W., Liu, O.L., Rafferty, A.N., Linn, M.C.: Automated guidance for student inquiry. J. Educ. Psychol. 108(1), 60–81 (2016)

    Google Scholar 

  10. Levy, Y.A., Weld, S.D.: Intelligent internet systems. Artif. Intell. 118, 1–14 (2000)

    Google Scholar 

  11. Beck, E.J., Stern, K.M.: Bringing back the AI to AI & ED. In: Lajoie, S.P., Vivet, M. (eds.) Artificial Intelligence in Education, pp. 233–240. IOS Press, Amsterdam (1999)

    Google Scholar 

  12. Sison, R., Simura, M.: Student modeling and machine learning. Int. J. Artif. Intell. Educ. 9, 128–158 (1998)

    Google Scholar 

  13. Gertner, S.A., Conati, C., Vahlehn, K.: Procedural help in ANDES: generating hints using a Bayesian network student model. In: Frasson, C., Gauthier, C., McGalla, G.I. (eds.) Proceedings of the 2nd International Conference of Intelligent Tutoring Systems. Springer, Berlin (1992)

  14. Zadeh, A.L.: Fuzzy sets. Inf. Control 8, 338–353 (1965)

    MATH  Google Scholar 

  15. Dias, S.B., Dolianiti, F.S., Hadjileontiadou, S.J., Diniz, J.A., Hadjileontiadis, L.J.: FISCMAP: A fuzzy logic-based quality of concept mapping modelling approach fostering reflective feedback. In: The 7th International Conference on Software Development and Technologies for Enhancing Accessibility and Fighting Info-exclusion (DSAI2016). Conference Proceedings Series (ISBN: 978-1-4503-4748-8), pp. 293–300. ACM, Vila Real (2016)

  16. Kosko, B.: Fuzzy Thinking. Harper/Collins, London (1994)

    MATH  Google Scholar 

  17. Tsoukalas, H.L., Uhrig, R.E.: Fuzzy and Neural Approaches in Engineering. Wiley, New York (1996)

    Google Scholar 

  18. Novak, J.D., Cañas, A.: The origins of the concept mapping tool and the continuing evolution of the tool. Inf. Vis. 5, 175–184 (2006)

    Google Scholar 

  19. Goswami, M., O’Connor, K.M., Bhattarai, K.P., Shamseldin, A.Y.: Assessing the performance of eight real-time updating models and procedures for the Brosna River. Hydrol. Earth Syst. Sci. 9, 394–411 (2005)

    Google Scholar 

  20. Cañas, A.J., Bunch, L., Novak, J.D., Reiska, P.: Cmapanalysis: an extensible concept map analysis tool. J. Educ. Teach. Train. 4(1), 36–46 (2013)

    Google Scholar 

  21. Jimogiannis, A., Siorenta, A.: Modeling as a tool for development of critical and creative thinking (in Greek). In: Koulaidis, B. (ed.) Modern Teaching Approaches to Critical Development-Creative Thinking on Secondary Education, pp. 241–268. Organization of Educational Training, Athens (2007)

    Google Scholar 

  22. Kaur, A., Kaur, A.: Comparison of Mamdani-type and Sugeno-type fuzzy inference systems for air conditioning system. Int. J. Soft Comput. Eng. (IJSCE) 2, 323–325 (2012)

    Google Scholar 

  23. Mamdani, E.H., Assilian, S.: An experiment in linguistic synthesis with a fuzzy logic controller. Int. J. Man Mach. Stud. 7(1), 1–13 (1975)

    MATH  Google Scholar 

  24. Kahneman, D.: Thinking, Fast and Slow. Farrar, Straus and Giroux, New York (2011)

    Google Scholar 

  25. Quinton, S., Smallbone, T.: Feeding forward: using feedback to promote student reflection and learning—a teaching model. Innov. Educ. Teach. Int. 47(1), 125–135 (2010)

    Google Scholar 

  26. Carletta, J.: Assessing agreement on classification tasks: the kappa statistic. Comput. Linguist. 22(2), 249–254 (1996)

    Google Scholar 

  27. Dellschaft, K., Staab, S.: On How to Perform a Gold Standard Based Evaluation of Ontology Learning. In: The 5th International Semantic Web Conference, pp. 228–241, Athens, GA, USA (2006)

  28. Villalon, J., Calvo, R.A.: Concept maps as cognitive visualizations of writing assignments. J. Educ. Technol. Soc. 14(3), 16–27 (2011)

    Google Scholar 

  29. Tisdell, C.C.: Pedagogical alternatives for triple integrals: moving towards more inclusive and personalized learning. Int. J. Math. Educ. Sci. Technol. 49(5), 792–801 (2018)

    Google Scholar 

  30. Roberts, M.W., Haden, C., Thompson, M.K., Parker, P.J.: Assessment of systems learning in an undergraduate civil engineering course using concept maps. In: 121st ASEE Annual Conference Proceedings, Indianapolis, IN, June 15–18, Paper ID 9330 (2014)

Download references

Acknowledgements

The authors would like to thank the 20 Master’s students of the Aristotle University of Thessaloniki (Greece) that participated in the experimental setting of this study and provided useful comments and opinions. Dr. Dias acknowledges the financial support by the Foundation for Science and Technology (FCT, Portugal), Postdoctoral Grant (SFRH/BPD/496004/20) and the Interdisciplinary Centre for the Study of Human Performance (CIPER, Portugal).

Author information

Authors and Affiliations

  1. Faculdade de Motricidade Humana, Universidade de Lisboa, 1499-002, Lisbon, Portugal

    Sofia B. Dias & José A. Diniz

  2. Department of Early Childhood Education, Aristotle University of Thessaloniki, 54124, Thessaloníki, Greece

    Foteini S. Dolianiti

  3. Hellenic Open University, 10562, Athens, Greece

    Sofia J. Hadjileontiadou

  4. Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, 54124, Thessaloníki, Greece

    Leontios J. Hadjileontiadis

  5. Department of Electrical and Computer Engineering, Khalifa University of Science and Technology, P.O. Box 127788, Abu Dhabi, UAE

    Leontios J. Hadjileontiadis

Authors
  1. Sofia B. Dias
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Foteini S. Dolianiti
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sofia J. Hadjileontiadou
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. José A. Diniz
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Leontios J. Hadjileontiadis
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sofia B. Dias.

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

Dias, S.B., Dolianiti, F.S., Hadjileontiadou, S.J. et al. On modeling the quality of concept mapping toward more intelligent online learning feedback: a fuzzy logic-based approach. Univ Access Inf Soc 19, 485–498 (2020). https://doi.org/10.1007/s10209-019-00656-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10209-019-00656-z

Keywords

Search

Navigation