Understanding Students Personality to Detect Their Learning Differences

  • Ossama EmbarakEmail author
  • Zahoor Khan
  • Binod Gurung
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 29)


Students have different levels of intellectual capabilities and learning styles which affect their understanding of specific academic concepts and gaining specific skills. Educational institutions dedicate much efforts to support at-risk students. However, this support usually comes as a reaction of students’ low performance, while learners need proactive support to keep their academic performance high, this needs a deep understanding of students learning capabilities and the real support they need accordingly. This research tries to investigate students’ personalities and the impact of learners’ personalities on their academic capabilities. A sample of 180 students was involved in this pilot study to evaluate the impact of their personalities on their academic standing. The sample collected from three different computing majors which are security forensics, Networking, and Application development majors. Myers-Briggs Type Indicator (MBTI) test is conducted twice using two different platforms and in different periods to figure out any random answers might happen by the participants. We excluded anomalies found and only quit fair data are kept for processing. This paper implements t-test to check if learners’ personalities affect their academic standing which might drop them into at-risk category. The experimental results showed that students’ personalities have a direct impact on their knowledge acquisition.



This work was supported by HCT Research Grants (HRG) [Fund No: 103108]. We would also like to show our gratitude to Higher Colleges of Technology for the financial grant.


  1. 1.
    Entwistle, N., Ramsden, P.: Understanding student learning (Routledge revivals). Routledge (2015)Google Scholar
  2. 2.
    Furnham, A.: Myers-Briggs Type Indicator (MBTI). In: Encyclopedia of Personality and Individual Differences, pp. 1–4. Springer International Publishing (2017)Google Scholar
  3. 3.
    Rashid, G.J., Duys, D.K.: Counselor cognitive complexity: correlating and comparing the myers–briggs type indicator with the role category questionnaire. J. Employment Couns. 52(2), 77–86 (2015)CrossRefGoogle Scholar
  4. 4.
    Furnham, A., Crump, J.: The myers-briggs type indicator (MBTI) and promotion at work. Psychology 6(12), 1510 (2015)CrossRefGoogle Scholar
  5. 5.
    Sample, J.: A Review of the myers-briggs type indicator in public affairs education. J. Public Aff. Educ. 23(4), 979–992 (2017)CrossRefGoogle Scholar
  6. 6.
    Murphy, L., Eduljee, N.B., Croteau, K., Parkman, S.: Extraversion and introversion personality type and preferred teaching and classroom participation: a pilot study. J. Psychosoc. Res. 12(2), 437–450 (2017)Google Scholar
  7. 7.
    Boghikian-Whitby, S., Mortagy, Y.: Student preferences and performance in online and face-to-face classes using Myers-Briggs Indicator: a longitudinal quasi-experimental study. Issues Inform. Sci. Inf. Technol. 13, 89–109 (2016)CrossRefGoogle Scholar
  8. 8.
    Prince, M.: Does active learning work? a review of the research. J. Eng. Educ. 93(3), 223–231 (2004)CrossRefGoogle Scholar
  9. 9.
    Felder, R.M., Felder, G.N., Dietz, E.J.: The effects of personality type on engineering student performance and attitudes. J. Eng. Educ. 91(1), 3–17 (2002)CrossRefGoogle Scholar
  10. 10.
    Yip, M.C.: Learning strategies and their relationships to academic performance of high school students in Hong Kong. Educ. Psychol. 33(7), 817–827 (2013)CrossRefGoogle Scholar
  11. 11.
    Zuffianò, A., Alessandri, G., Gerbino, M., Kanacri, B.P.L., Di Giunta, L., Milioni, M., Caprara, G.V.: Academic achievement: the unique contribution of self-efficacy beliefs in self-regulated learning beyond intelligence, personality traits, and self-esteem. Learn. Individ. Differ. 23, 158–162 (2013)CrossRefGoogle Scholar
  12. 12.
    Truong, H.M.: Integrating learning styles and adaptive e-learning system: current developments, problems and opportunities. Comput. Hum. Behav. 55, 1185–1193 (2016)CrossRefGoogle Scholar
  13. 13.
    Faria, A.R., Almeida, A., Martins, C., Gonçalves, R., Martins, J., Branco, F.: A global perspective on an emotional learning model proposal. Telemat. Inform. 34(6), 824–837 (2017)CrossRefGoogle Scholar
  14. 14.
    Basheer, G.S., Tang, A.Y., Ahmad, M.S.: Designing teachers’ observation questionnaire based on curry’s onion model for students’ learning styles detection. TEM J. 5(4), 515 (2016)Google Scholar
  15. 15.
    Ramírez-Correa, P.E., Rondan-Cataluña, F.J., Arenas-Gaitán, J., Alfaro-Perez, J.L.: Moderating effect of learning styles on a learning management system’s success. Telemat. Inform. 34(1), 272–286 (2017)CrossRefGoogle Scholar
  16. 16.
    Fatahi, S., Moradi, H., Kashani-Vahid, L.: A of personality and learning styles models applied in virtual environments with emphasis on e-learning environments. Artif. Intell. Rev. 46(3), 413–429 (2016)CrossRefGoogle Scholar
  17. 17.
    Normadhi, N.B.A., Shuib, L., Nasir, H.N.M., Bimba, A., Idris, N., Balakrishnan, V.: Identification of personal traits in adaptive learning environment: systematic literature review. Comput. Educ. 130, 168–190 (2019)CrossRefGoogle Scholar
  18. 18.
    Embarak, O.H.: A method for solving the cold start problem in recommendation systems. In: 2011 International Conference on Paper presented at the Innovations in Information Technology (IIT) (2011)Google Scholar
  19. 19.
    Ayoubi Rami, M., Ustwani, B.: The relationship between student’s MBTI, preferences and academic performance at a Syrian university. Educ. Training 56(1), 78–90 (2014)Google Scholar
  20. 20.
    Embarak, O.: Data analysis and visualization using python. In: Data Analysis and Visualization Using Python, pp. 205–241. Apress, Berkeley (2018)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer SciencesHigher Colleges of TechnologyFujairahUAE
  2. 2.Department of EducationHigher Colleges of TechnologyFujairahUAE

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