Abstract
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.
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References
Entwistle, N., Ramsden, P.: Understanding student learning (Routledge revivals). Routledge (2015)
Furnham, A.: Myers-Briggs Type Indicator (MBTI). In: Encyclopedia of Personality and Individual Differences, pp. 1–4. Springer International Publishing (2017)
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)
Furnham, A., Crump, J.: The myers-briggs type indicator (MBTI) and promotion at work. Psychology 6(12), 1510 (2015)
Sample, J.: A Review of the myers-briggs type indicator in public affairs education. J. Public Aff. Educ. 23(4), 979–992 (2017)
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)
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)
Prince, M.: Does active learning work? a review of the research. J. Eng. Educ. 93(3), 223–231 (2004)
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)
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)
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)
Truong, H.M.: Integrating learning styles and adaptive e-learning system: current developments, problems and opportunities. Comput. Hum. Behav. 55, 1185–1193 (2016)
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)
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)
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)
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)
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)
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)
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)
Embarak, O.: Data analysis and visualization using python. In: Data Analysis and Visualization Using Python, pp. 205–241. Apress, Berkeley (2018)
Acknowledgments
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.
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Embarak, O., Khan, Z., Gurung, B. (2019). Understanding Students Personality to Detect Their Learning Differences. In: Barolli, L., Xhafa, F., Khan, Z., Odhabi, H. (eds) Advances in Internet, Data and Web Technologies. EIDWT 2019. Lecture Notes on Data Engineering and Communications Technologies, vol 29. Springer, Cham. https://doi.org/10.1007/978-3-030-12839-5_35
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DOI: https://doi.org/10.1007/978-3-030-12839-5_35
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