Skip to main content

Advertisement

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

  • Published:
Education and Information Technologies Aims and scope Submit manuscript

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.

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

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • 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.

  • Allen, I. E., & Seaman, J. 2007. Online nation: Five years of growth in online learning. Sloan Consortium. PO Box 1238, Newburyport, MA 01950.

  • 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.

  • 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.

    Article  Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • Carr, S. (2000). As distance education comes of age, the challenge is keeping the students. The Chronicle of Higher Education, 46(23).

  • 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.

  • 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.

  • Darwin, C. (1998). The expression of the emotions in man and animals. USA: Oxford University Press.

    Google Scholar 

  • De Bra, P., Aroyo, L., & Cristea, A., 2004. Adaptive web-based educational hypermedia. In Web Dynamics (pp. 387–410). Springer Berlin Heidelberg.

  • Dewar, T., & Whittington, D. (2000). Online learners and their learning strategies. Journal of Educational Computing Research, 23(4), 385–403.

    Article  Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • 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.

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

  • Hartmann, P. (2006). The five-factor model: Psychometric, biological and practical perspectives. Nordic Psychology, 58(2), 150–170.

    Article  Google Scholar 

  • Henze, N., & Nejdl, W. (2004). A logical characterization of adaptive educational hypermedia. New review of Hypermedia and Multimedia, 10(1), 77–113.

    Article  Google Scholar 

  • 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 

  • James, W., 1890. The principles of psychology. Chicago: Encyclopedia Britannica. JamesPrinciples of Psychology1890.

  • 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.

  • 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.

    Article  Google Scholar 

  • 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.

  • 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.

    Article  Google Scholar 

  • 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 

  • 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 

  • 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).

  • Ortony, A., Clore, G., & Collins, A., 1988. The cognitive structure of emotions: Cambridge Uni. Press, New York.

  • Osaka, M., Yaoi, K., Minamoto, T., & Osaka, N. (2013). When do negative and positive emotions modulate working memory performance? Scientific Reports, 3.

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

  • Schultz, D. P., & Schultz, S. E., 2016. Theories of personality. Cengage Learning.

  • 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 

  • 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.

    Article  Google Scholar 

  • Weber, G. (1999). Adaptive learning systems in the world wide web. In UM99 User Modeling (pp. 371–377). Vienna: Springer.

    Chapter  Google Scholar 

  • 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 

  • 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.

  • 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.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Somayeh Fatahi.

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

Fatahi, S. An experimental study on an adaptive e-learning environment based on learner’s personality and emotion. Educ Inf Technol 24, 2225–2241 (2019). https://doi.org/10.1007/s10639-019-09868-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10639-019-09868-5

Keywords