Skip to main content
SpringerLink
Log in

A multilayer inference engine for individualized tutoring model: adapting learning material and its granularity

  • S.I. : Information, Intelligence, Systems and Applications
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Computer-supported approaches have been widely used for enriching the learning process. The technological advances have led tutoring systems to embody intelligence in their functionalities. However, so far, they fail to adequately incorporate intelligence and adaptivity in their diagnostic and reasoning mechanisms. In view of the above, this paper presents a novel expert system for the instruction of the programming language Java. A multilayer inference engine was developed and used in this system to provide individualized instruction to students according to their needs and preferences. The multilayer inference engine incorporates a set of algorithmic methods in different layers promoting personalization in the tutoring strategies. In particular, an artificial neural network and multi-criteria decision analysis are used in one layer for adapting the learning units based on students’ learning style, and a fuzzy logic model is applied in the other layer for defining the granularity of learning units according to students’ profile characteristics, such as learning style, knowledge level and misconceptions. The students’ learning style is based on the Honey and Mumford model. The evaluation of the system was conducted using an established framework and Student’s t test, and the results showed a high level of acceptance of the presented model.

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

Similar content being viewed by others

References

  1. Nkambou R, Mizoguchi R, Bourdeau J (2010) Advances in intelligent tutoring systems, vol 308. Springer, Berlin

    MATH  Google Scholar 

  2. Lynch T, Ghergulescu I (2016) An evaluation framework for adaptive and intelligent tutoring systems. In: E-learn: world conference on e-learning in corporate, government, healthcare, and higher education, pp 1385–1390

  3. Kurilovas E (2019) Advanced machine learning approaches to personalise learning: learning analytics and decision making. Behav Inf Technol 38(4):410–421

    Article  Google Scholar 

  4. Bodily R, Verbert K (2017) Review of research on student-facing learning analytics dashboards and educational recommender systems. IEEE Trans Learn Technol 10(4):405–418

    Article  Google Scholar 

  5. Vrablecová P, Šimko M (2016) Supporting semantic annotation of educational content by automatic extraction of hierarchical domain relationships. IEEE Trans Learn Technol 9(3):285–298

    Article  Google Scholar 

  6. Hassan MA, Habiba U, Khalid H, Shoaib M, Arshad S (2019) An adaptive feedback system to improve student performance based on collaborative behavior. IEEE Access 7:107171–107178

    Article  Google Scholar 

  7. Tozadore D, Pinto AH, Valentini J, Camargo M, Zavarizz R, Rodrigues V et al (2019) Project r-castle: robotic-cognitive adaptive system for teaching and learning. IEEE Trans Cogn Dev Syst 11(4):581–589

    Article  Google Scholar 

  8. Lavoué E, Monterrat B, Desmarais M, George S (2018) Adaptive gamification for learning environments. IEEE Trans Learn Technol 12(1):16–28

    Article  Google Scholar 

  9. Brusilovsky P, Somyürek S, Guerra J, Hosseini R, Zadorozhny V, Durlach PJ (2015) Open social student modeling for personalized learning. IEEE Trans Emerg Top Comput 4(3):450–461

    Article  Google Scholar 

  10. Aquino G, Rubio JDJ, Pacheco J, Gutierrez GJ, Ochoa G, Balcazar R et al (2020) Novel nonlinear hypothesis for the delta parallel robot modeling. IEEE Access 8:46324–46334

    Article  Google Scholar 

  11. Rubio DJJ (2009) SOFMLS: online self-organizing fuzzy modified least-squares network. IEEE Trans Fuzzy Syst 17(6):1296–1309

    Article  Google Scholar 

  12. Chiang HS, Chen MY, Huang YJ (2019) Wavelet-based EEG processing for epilepsy detection using fuzzy entropy and associative petri net. IEEE Access 7:103255–103262

    Article  Google Scholar 

  13. Elias I, Rubio JDJ, Martinez DI, Vargas TM, Garcia V, Mujica-Vargas D et al (2020) Genetic algorithm with radial basis mapping network for the electricity consumption modeling. Appl Sci 10(12):4239

    Article  Google Scholar 

  14. Meda-Campaña JA (2018) On the estimation and control of nonlinear systems with parametric uncertainties and noisy outputs. IEEE Access 6:31968–31973

    Article  Google Scholar 

  15. Hernández G, Zamora E, Sossa H, Téllez G, Furlán F (2020) Hybrid neural networks for big data classification. Neurocomputing 390:327–340

    Article  Google Scholar 

  16. Rabiha SG, Kurniawan A, Moniaga J, Wahyudi DI, Wilson E (2018) Face detection and recognition based e-learning for students authentication: study literature review. In: 2018 international conference on information management and technology (ICIMTech), pp 472–476

  17. Li G, Wang Y (2018) Research on Leamer's emotion recognition for intelligent education system. In: 2018 IEEE 3rd advanced information technology, electronic and automation control conference (IAEAC), pp 754–758

  18. Holmes M, Latham A, Crockett K, O’Shea JD (2017) Near real-time comprehension classification with artificial neural networks: decoding e-learner nonverbal behavior. IEEE Trans Learn Technol 11(1):5–12

    Article  Google Scholar 

  19. Almotiri J, Elleithy K, Elleithy A (2017) Comparison of autoencoder and principal component analysis followed by neural network for e-learning using handwritten recognition. In: 2017 IEEE long island systems, applications and technology conference (LISAT), pp 1–5

  20. Shen X, Yi B, Zhang Z, Shu J, Liu H (2016) Automatic recommendation technology for learning resources with convolutional neural network. In: 2016 international symposium on educational technology (ISET), pp 30–34

  21. Khodke PA, Tingane MG, Bhagat AP, Chaudhari SP, Ali MS (2016) Neuro fuzzy intelligent e-learning systems. In: 2016 online international conference on green engineering and technologies (IC-GET), pp 1–7

  22. Saito T, Watanobe Y (2018) Learning path recommender system based on recurrent neural network. In: 2018 9th international conference on awareness science and technology (iCAST), pp 324–329

  23. El Hammoumi O, Benmarrakchi F, Ouherrou N, El Kafi J, El Hore A (2018) Emotion recognition in e-learning systems. In: 2018 6th international conference on multimedia computing and systems (ICMCS), pp 1–6

  24. Chen J, Huang K, Wang F, Wang H (2009) E-learning behavior analysis based on fuzzy clustering. In: 2009 third international conference on genetic and evolutionary computing, pp 863–866

  25. Sevarac Z (2006) Neuro fuzzy reasoner for student modeling. In: Sixth IEEE international conference on advanced learning technologies (ICALT'06), pp 740–744

  26. Karaci A (2019) Intelligent tutoring system model based on fuzzy logic and constraint-based student model. Neural Comput Appl 31(8):3619–3628

    Article  Google Scholar 

  27. Alves P, Amaral L, Pires J (2008) Case-based reasoning approach to adaptive web-based educational systems. In: 2008 eighth IEEE international conference on advanced learning technologies, pp 260–261

  28. Crockett K, Latham A, Whitton N (2017) On predicting learning styles in conversational intelligent tutoring systems using fuzzy decision trees. Int J Hum Comput Stud 97:98–115

    Article  Google Scholar 

  29. Samarakou M, Prentakis P, Mitsoudis D, Karolidis D, Athinaios S (2017) Application of fuzzy logic for the assessment of engineering students. In: 2017 IEEE global engineering education conference (EDUCON), pp 646–650

  30. Aajli A, Afdel K (2016) Generation of an adaptive e-learning domain model based on a fuzzy logic approach. In: 2016 IEEE/ACS 13th international conference of computer systems and applications (AICCSA), pp 1–8

  31. Gogo KO, Nderu L, Mwangi RW (2018) Fuzzy logic based context aware recommender for smart e-learning content delivery. In: 2018 5th international conference on soft computing and machine intelligence (ISCMI), pp 114–118

  32. Perumal SP, Arputharaj K, Sannasi G (2016) Fuzzy family tree similarity based effective e-learning recommender system. In: 2016 eighth international conference on advanced computing (ICoAC), pp 146–150

  33. Matazi I, Bennane A, Messoussi R, Touahni R, Oumaira I, Korchiyne R (2018) Multi-agent system based on fuzzy logic for e-learning collaborative system. In: 2018 international symposium on advanced electrical and communication technologies (ISAECT), pp 1–7

  34. Joseph N, Pradeesh N, Chatterjee S, Bijlani K (2017) A novel approach for group formation in collaborative learning using learner preferences. In: 2017 international conference on advances in computing, communications and informatics (ICACCI), pp 1564–1568

  35. Weis R, Dean EL, Osborne KJ (2016) Accommodation decision making for postsecondary students with learning disabilities: individually tailored or one size fits all? J Learn Disabil 49(5):484–498

    Article  Google Scholar 

  36. Chrysafiadi K, Troussas C, Virvou M (2018) A framework for creating automated online adaptive tests using multiple-criteria decision analysis. In: 2018 IEEE international conference on systems, man, and cybernetics (SMC), pp 226–231

  37. Başaran S (2016) Multi-criteria decision analysis approaches for selecting and evaluating digital learning objects. Procedia Comput Sci 102:251–258

    Article  Google Scholar 

  38. Honey P, Mumford A (1982) Manual of learning styles. Schmeck, RR

    Google Scholar 

  39. Triantaphyllou E (2000) Multi-criteria decision making methods: a comparative study, vol 4. Kluwer Academic Publication, Dordrecht, pp 1–288

    Book  MATH  Google Scholar 

  40. Mustafa HM, Tourkia FB, Ramadan RM (2017) An overview on evaluation of e-learning/training response time considering artificial neural networks modeling. J Educ e-Learn Res 4(2):46–62

    Article  Google Scholar 

  41. Kirschner PA (2017) Stop propagating the learning styles myth. Comput Educ 106:166–171

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Informatics, University of Piraeus, Piraeus, Greece

    Christos Troussas, Akrivi Krouska & Maria Virvou

Authors
  1. Christos Troussas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Akrivi Krouska
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Maria Virvou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Akrivi Krouska.

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

Troussas, C., Krouska, A. & Virvou, M. A multilayer inference engine for individualized tutoring model: adapting learning material and its granularity. Neural Comput & Applic 35, 61–75 (2023). https://doi.org/10.1007/s00521-021-05740-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-021-05740-1

Keywords

Search

Navigation