Advertisement

An experimental study on an adaptive e-learning environment based on learner’s personality and emotion

  • Somayeh FatahiEmail author
Article
  • 36 Downloads

Abstract

E-learning enables learners to learn everywhere and at any time but this kind of learning lacks the necessary attractiveness. Therefore, adaptation is becoming increasingly important and the recent research interest in the adaptive e-learning system. Since emotions and personality are important parts of human characteristics, and they play a significant role in parts of adaptive e-learning systems, it is essential to consider them in designing these systems. This paper presents an empirical study on the impact of using an adaptive e-learning environment based on learner’s personality and emotion. This adaptive e-learning environment uses the Myers-Briggs Type Indicator (MBTI) model for personality and the Ortony, Clore & Collins (OCC) model for emotion modeling. The adaptive e-learning environment is compared with a simple e-learning environment. The results show that students deal with the adaptive e-learning environment (experimental group) gained high scores than others (control group). The rate of progress in quiz score of the experimental group is almost 4.6 times more than the control group. Also, the rate of hint use is decreased more among the experimental group rather than the control group because the level of their knowledge is increased through learning in an adaptive environment. Furthermore, the findings display that the control group tries more to answer the questions in post-quiz while the experimental group has a low effort. Finally, the students expressed the adaptive e-learning environment is more attractive and close to their personality traits. Moreover, it can understand their emotional state better, has a suitable reaction to them, and improves their learning rate.

Keywords

Adaptive e-learning Personality Emotion MBTI OCC 

Notes

References

  1. Alhathli, M.A.E., Masthoff, J.F.M. and Beacham, N.A., 2018. Impact of a Learner’s Personality on the Selection of the Next Learning Activity. In Proceedings of Intelligent Mentoring Systems Workshop Associated with the 19th International Conference on Artificial Intelligence in Education, AIED 2018.Google Scholar
  2. Allen, I. E., & Seaman, J. 2007. Online nation: Five years of growth in online learning. Sloan Consortium. PO Box 1238, Newburyport, MA 01950.Google Scholar
  3. Bajraktarevic N, Hall W, Fullick P., 2003. ILASH: Incorporating learning strategies in hypermedia. Paper presented at the fourteenth conference on hypertext and hypermedia, august 26–30, Nottingham, UK.Google Scholar
  4. Blanchette, I., & Richards, A. (2010). The influence of affect on higher level cognition: A review of research on interpretation, judgement, decision making and reasoning. Cognition & Emotion, 24(4), 561–595.CrossRefGoogle Scholar
  5. Bourkoukou, O., El Bachari, E., & El Adnani, M. (2016). A personalized E-learning based on recommender system. International Journal Learning Teacher, 2(2), 99–103.Google Scholar
  6. Buckley, P., & Doyle, E. (2017). Individualising gamification: An investigation of the impact of learning styles and personality traits on the efficacy of gamification using a prediction market. Computers & Education, 106, 43–55.CrossRefGoogle Scholar
  7. Carr, S. (2000). As distance education comes of age, the challenge is keeping the students. The Chronicle of Higher Education, 46(23).Google Scholar
  8. Chalfoun, P., Chaffar, S., & Frasson, C., 2006. Predicting the emotional reaction of the learner with a machine learning technique. In Workshop on Motivaional and Affective Issues in ITS, ITS'06, International Conference on Intelligent Tutoring Systems.Google Scholar
  9. Conati, C., & Zhou, X., 2002. Modeling students' emotions from cognitive appraisal in educational games. In Intelligent Tutoring Systems: 6th International Conference, ITS 2002, Biarritz, France and San Sebastian, Spain, June 2–7, 2002. Proceedings (p. 944). Springer Berlin/Heidelberg.Google Scholar
  10. Darwin, C. (1998). The expression of the emotions in man and animals. USA: Oxford University Press.Google Scholar
  11. De Bra, P., Aroyo, L., & Cristea, A., 2004. Adaptive web-based educational hypermedia. In Web Dynamics (pp. 387–410). Springer Berlin Heidelberg.Google Scholar
  12. Dewar, T., & Whittington, D. (2000). Online learners and their learning strategies. Journal of Educational Computing Research, 23(4), 385–403.CrossRefGoogle Scholar
  13. El Bachari, E., Abdelwahed, E., & El Adnani, M. (2010). Design of an adaptive e-learning model based on learner’s personality. Ubiquitous Computing and Communication Journal, 5(3), 1–8.Google Scholar
  14. Fatahi, S., & Moradi, H. (2016). A fuzzy cognitive map model to calculate a user's desirability based on personality in e-learning environments. Computers in Human Behavior, 63, 272–281.CrossRefGoogle Scholar
  15. Fatahi, S., Kazemifard, M., & Ghasem-Aghaee, N. (2009). Design and implementation of an e-learning model by considering learner's personality and emotions. Advances in Electrical Engineering and Computational Science, 423–434.Google Scholar
  16. Fatahi, S., Moradi, H., & Kashani-Vahid, L. (2016). A survey of personality and learning styles models applied in virtual environments with emphasis on e-learning environments. Artificial Intelligence Review, 46(3), 413–429.CrossRefGoogle Scholar
  17. Garcia-Cabot, A., de-Marcos, L., & Garcia-Lopez, E. (2015). An empirical study on m-learning adaptation: Learning performance and learning contexts. Computers & Education, 82, 450–459.CrossRefGoogle Scholar
  18. Grigoriadou, M., Papanikolaou, K., Kornilakis, H., & Magoulas, G. (2001). INSPIRE: an intelligent system for personalized instruction in a remote environment. In P. D. Bra, P. Brusilovsky & A. Kobsa (Eds.), Proceedings of 3rd Workshop on Adaptive Hypertext and Hypermedia (pp. 13–24). Sonthofen: Technical University Eindhoven.Google Scholar
  19. Hartmann, P. (2006). The five-factor model: Psychometric, biological and practical perspectives. Nordic Psychology, 58(2), 150–170.MathSciNetCrossRefGoogle Scholar
  20. Henze, N., & Nejdl, W. (2004). A logical characterization of adaptive educational hypermedia. New review of Hypermedia and Multimedia, 10(1), 77–113.CrossRefGoogle Scholar
  21. Inan, F. A., Yukselturk, E., & Grant, M. M. (2009). Profiling potential dropout students by individual characteristics in an online certificate program. International Journal of Instructional Media, 36(2), 163–177.Google Scholar
  22. James, W., 1890. The principles of psychology. Chicago: Encyclopedia Britannica. JamesPrinciples of Psychology1890.Google Scholar
  23. Kim, C., & Pekrun, R. (2014). Emotions and motivation in learning and performance. In J. Spector, M. Merrill, J. Elen, & M. Bishop (Eds.), Handbook of research on educational communications and technology (pp. 65–75). New York, NY: Springer.Google Scholar
  24. Kim, J., Lee, A., & Ryu, H. (2013). Personality and its effects on learning performance: Design guidelines for an adaptive e-learning system based on a user model. International Journal of Industrial Ergonomics, 43(5), 450–461.CrossRefGoogle Scholar
  25. Kotsiantis, S. B., Pierrakeas, C. J., & Pintelas, P. E., 2003. Preventing student dropout in distance learning using machine learning techniques. In International conference on knowledge-based and intelligent information and engineering systems (pp. 267–274). Springer, Berlin, Heidelberg.Google Scholar
  26. Lykourentzou, I., Giannoukos, I., Nikolopoulos, V., Mpardis, G., & Loumos, V. (2009). Dropout prediction in e-learning courses through the combination of machine learning techniques. Computers & Education, 53(3), 950–965.CrossRefGoogle Scholar
  27. Marsella, S., Gratch, J., & Petta, P. (2010). Computational models of emotion. A Blueprint for Affective Computing-A Sourcebook and Manual, 11(1), 21–46.Google Scholar
  28. Mustafa, Y. E. A., & Sharif, S. M. (2011). An approach to adaptive e-learning hypermedia system based on learning styles (AEHS-LS): Implementation and evaluation. International Journal of Library and Information Science, 3(1), 15–28.Google Scholar
  29. Niesler, A., & Wydmuch, G., 2009. User profiling in intelligent tutoring systems based on Myers-Briggs personality types. In Proceedings of the international multiconference of engineers and computer scientists (Vol. 1).Google Scholar
  30. Ortony, A., Clore, G., & Collins, A., 1988. The cognitive structure of emotions: Cambridge Uni. Press, New York.Google Scholar
  31. Osaka, M., Yaoi, K., Minamoto, T., & Osaka, N. (2013). When do negative and positive emotions modulate working memory performance? Scientific Reports, 3.Google Scholar
  32. Paulus, M. P., & Angela, J. Y. (2012). Emotion and decision-making: Affect-driven belief systems in anxiety and depression. Trends in Cognitive Sciences, 16(9), 476–483.CrossRefGoogle Scholar
  33. Rani, M., Nayak, R., & Vyas, O. P. (2015). An ontology-based adaptive personalized e-learning system, assisted by software agents on cloud storage. Knowledge-Based Systems, 90, 33–48.CrossRefGoogle Scholar
  34. Rodríguez, L. F., Ramos, F., & Wang, Y., 2011. Cognitive computational models of emotions. In Cognitive Informatics & Cognitive Computing (ICCI* CC), 2011 10th IEEE International Conference on (pp. 75–84). IEEE.Google Scholar
  35. Schultz, D. P., & Schultz, S. E., 2016. Theories of personality. Cengage Learning.Google Scholar
  36. Trantafillou, E., Pomportsis, A., & Georgiadou, E. (2002). AES-CS: Adaptive educational system based on cognitive styles. In P. Brusilovsky, N. Henze, & E. Millan (Eds.), Proceedings of the workshop on adaptive systems for web-based education. Held in conjunction with AH (p. 2002). Spain: Malaga.Google Scholar
  37. Wang, Y. H., & Liao, H. C. (2011). Data mining for adaptive learning in a TESL-based e-learning system. Expert Systems with Applications, 38(6), 6480–6485.CrossRefGoogle Scholar
  38. Weber, G. (1999). Adaptive learning systems in the world wide web. In UM99 User Modeling (pp. 371–377). Vienna: Springer.CrossRefGoogle Scholar
  39. Willging, P. A., & Johnson, S. D. (2009). Factors that influence students' decision to dropout of online courses. Journal of Asynchronous Learning Networks, 13(3), 115–127.Google Scholar
  40. Wolf C., 2003. iWeaver: Towards learning style-based e-learning. In Greening T, Lister R (eds) Conferences in Research and Practice in Information Technology. Proc. Fifth Australasian Computing Education Conference (ACE2003), Adelaide, Australia., pp. 273–279.Google Scholar
  41. Yukselturk, E., Ozekes, S., & Türel, Y. K. (2014). Predicting dropout student: An application of data mining methods in an online education program. European Journal of Open, Distance and E-Learning, 17(1), 118–133.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Iranian Research Institute for Information Science and Technology (IRANDOC)TehranIran

Personalised recommendations