Automatic Deduction of Learners’ Profiling Rules Based on Behavioral Analysis

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10448)

Abstract

E-learning has become a more flexible learning approach thanks to the extensive evolution of the Information and Communication Technologies. A perceived focus was investigated for the exploitation of the learners’ individual differences to ensure a continuous and adapted learning process. Nowadays, researchers have been oriented to use learning analytics for learner modeling in order to assist educational institutions in improving learner success and increasing learner retention. In this paper, we describe a new implicit approach using learning analytics to construct an interpretative views of the learners’ interactions, even those made outside the E-learning platform. We aim to deduce automatically a learners’ profiling rules independently of the learning style models proposed in the literature. In this way, we provide an innovative process that may help the tutors to profile learners and evaluate their performances, support the courses’ designer in their authoring tasks and adapt the learning objects to the learners’ needs.

Keywords

Behavioral indicator Learning analytics Learning profiling rules Leaner model 

References

  1. 1.
    Tadlaoui, M.A., Aammou, S., Khaldi, M., Carvalho, R.N.: Learner modeling in adaptive educational systems: a comparative study. Int. J. Mod. Educ. Comput. Sci. 8(13), 1 (2016)CrossRefGoogle Scholar
  2. 2.
    Felder, R.M., Spurlin, J.: Applications, reliability and validity of the index of learning styles. Int. J. Eng. Educ. 21(11), 103–112 (2005)Google Scholar
  3. 3.
    Catherine, B.-C., Wheeler, D.D.: The Myers-Briggs personality type and its relationship to computer programming. J. Res. Comput. Educ. 26(13), 358–370 (1994)CrossRefGoogle Scholar
  4. 4.
    Farance, F.: Draft standard for learning technology. Public and private information (PAPI) for learners (PAPI Learner) (2000)Google Scholar
  5. 5.
    IMS Learner Information Package Specification version 1.0 (2001). http://www.imsglobal.org/profiles/index.html
  6. 6.
    Messaoudi, F., Moussaoui, M., Bouchboua, A., Derouich, A.: Modeling approach to a learner based on ontology. Int. J. Soft Comput. Eng. 2, 262–265 (2013)Google Scholar
  7. 7.
    Hlioui, F., Alioui, N., Gargouri, F.: A system for composition and adaptation of educational resources based on learner profile. In: 5th International Conference on Information & Communication Technology and Accessibility (2015)Google Scholar
  8. 8.
    Tlili, A., Essalmi, F., Jemni, M., Chen, N.-S., Kinshuk: Role of personality in computer based learning. Comput. Hum. Behav. 64, 805–813 (2016)Google Scholar
  9. 9.
    Ammor, F.-Z., Bouzidi, D., Elomri, A.: Construction of deduction system of learning profile from performance indicators. Int. J. Inf. Educ. Technol. 3(12), 129 (2013)Google Scholar
  10. 10.
    Stash, N.: Incorporating cognitive learning styles in a general-purpose adaptive hypermedia system, vol. 68 (2007)Google Scholar
  11. 11.
    Bousbia, N.: Analyse des traces de navigation des apprenants dans un environnement de formation dans une perspective de détection automatique des styles d’apprentissage. l’université Pierre et Marie Curie de France et l’école nationale supérieure d’informatique d’Algérie (2011)Google Scholar
  12. 12.
    Hlioui, F., Alioui, N., Gargouri, F.: A survey on learner models in adaptive E-learning systems. In: IEEE/ACS 13th International Conference of Computer Systems and Applications (2016, in press)Google Scholar
  13. 13.
    Siemens, S.: What are learning analytics, vol. 10 (2010). Accessed MarchGoogle Scholar
  14. 14.
    Chatti, M.A., Dyckhoff, A.L., Schroeder, U., Thùs, H.: A reference model for learning analytics. Int. J. Technol. Enhanc. Learn. 4(5–6), 318–331 (2012)CrossRefGoogle Scholar
  15. 15.
    Ait-Adda, S., Bousbia, N.: Evaluation de la désorientation de l’apprenant dans un systéme d’apprentissage. Eiah 2015, Agadir, vol. 9 (2015)Google Scholar
  16. 16.
    El Haddioui, I., Khaldi, M.: Learner behavior analysis on an online learning platform. Int. J. Emerg. Technol. Learn. IJET 7(12), 22–25 (2012)Google Scholar
  17. 17.
    Papanikolaou, K.A.: Constructing interpretative views of learners’ interaction behavior in an open learner model. IEEE Trans. Learn. Technol. 8(12), 201–214 (2015)CrossRefGoogle Scholar
  18. 18.
    Marty, J.-C., Carron, T.: Observation of collaborative activities in a game-based learning platform. IEEE Trans. Learn. Technol. 4(11), 98–110 (2011)CrossRefGoogle Scholar
  19. 19.
    May, M., George, S., Prévôt, P.: TrAVis to enhance online tutoring and learning activities: real-time visualization of students tracking data. Interact. Technol. Smart Educ. 8(11), 52–69 (2011)CrossRefGoogle Scholar
  20. 20.
    Dyckhoff, A.L., Zielke, D., Bùltmann, M., Chatti, M.A., Schroeder, U.: Design and implementation of a learning analytics toolkit for teachers. Educ. Technol. Soc. 15(13), 58–76 (2012)Google Scholar
  21. 21.
    Campos, A.M.: Analyzing the effectiveness of learning objects and designs. In: 11th IEEE International Conference on Advanced Learning Technologies (ICALT) (2011)Google Scholar
  22. 22.
    Halawa, M.S., Hamed, E.M.R., Shehab, M.E.: Personalized E-learning recommendation model based on psychological type and learning style models. In: IEEE Seventh International Conference on Intelligent Computing and Information Systems (2015)Google Scholar
  23. 23.
    Bousbia, N., Gheffar, A., Balla, A.: Adaptation based on navigation type and learning style. In: International Conference on Web-Based Learning (2013)Google Scholar
  24. 24.
    Omheni, N., Mazhoud, Kalboussi, A., Kacem, A.H.: Prediction of human personality traits from annotation activities. In: WEBIST (2014)Google Scholar
  25. 25.
    Djouad, T., Mille, A., Reffay, C., Benmohammed, S.: Collaborative Activity Indicators Engineering: Using modeled traces in the context of Technology Enhanced Learning Systems. Rapport de recherche RR-LIRIS-2010-014 (2010)Google Scholar
  26. 26.
    May, M., George, S.: Using learning tracking data to support students’ self-monitoring. In: CSEDU (2011)Google Scholar
  27. 27.
    Graf, S., Liu, T.-C., Kinshuk: Analysis of learners’ navigational behaviour and their learning styles in an online course. J. Comput. Assist. Learn. 26(2), 116–131 (2010)Google Scholar
  28. 28.
    Louifi, A., Bousbia, N., Azouaou, F., Merzoug, F.: ESITrace: a user side trace and annotation collection tool. In: International Conference on Web-Based Learning (2012)Google Scholar
  29. 29.
    Michel, J.-F.: Les 7 profils d’apprentissage, Editions Eyrolles (2013)Google Scholar
  30. 30.
    Petchboonmee, P., Phonak, D., Tiantong, M.: A comparative data mining technique for David Kolb’s experiential learning style classification. Int. J. Inf. Educ. Technol. 5(9), 672 (2015)Google Scholar
  31. 31.
  32. 32.
    Halawa, M.S., Hamed, E.M.R., Shehab, M.E.: Predicting student personality based on a data-driven model from student behavior on LMS and social networks. In: Fifth International Conference on Digital Information Processing and Communications (2015)Google Scholar
  33. 33.
    Garcia, E., Romero, C., Ventura, S., Calders, T.: Drawbacks and solutions of applying association rule mining in learning management systems. In: Proceedings of the International Workshop on Applying Data Mining in E-learning, Crete, Greece (2007)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Multimedia InfoRmation System and Advanced Computing LaboratoryUniversity of SfaxSfaxTunisia

Personalised recommendations