The Relationship Between Students’ Myers-Briggs Type Indicator and Their Behavior within Educational Systems

Akerke Alseitova, Wilk Oliveira, Zhaoxing Li, Lei Shi, Juho Hamari

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

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Abstract

Leveraging user behavior has become an increasingly valuable resource for modeling and personalizing systems based on the unique characteristics of each user. While recent studies have recently been conducted along these lines, there is still a lack of understanding of the relationship between students’ Myers-Briggs Type Indicators and their behavior within educational systems. Facing this problem, we conducted a long-term study (15 weeks) with 96 students, analyzing how their engagement metrics and communication frequency in a Moodle Learning Management System are related to their Myers-Briggs personality types (i.e., extroversion/introversion, sensing/intuition, thinking/feeling, and judging/perceiving). The primary findings indicate that i) participants identified as extroverted demonstrated heightened activity levels throughout more weeks of the course, and ii) participants characterized by judging and thinking traits engaged in a greater number of activities over the course duration. The results contribute to the field of educational technologies by providing valuable insights into the relationships between different characteristics associated with the Myers-Briggs Type Indicator and students’ behavior when using an educational system.

Original languageEnglish
Title of host publicationProceedings - 2024 IEEE International Conference on Advanced Learning Technologies, ICALT 2024
EditorsZehra Altinay, Maiga Chang, Rita Kuo, Ahmed Tlili
PublisherIEEE
Pages3-7
Number of pages5
ISBN (Electronic)979-8-3503-6205-3
DOIs
Publication statusPublished - 2024
Publication typeA4 Article in conference proceedings
EventIEEE International Conference on Advanced Learning Technologies - Hybrid, Nicosia, Cyprus
Duration: 1 Jul 20244 Jul 2024

Publication series

NameProceedings - 2024 IEEE International Conference on Advanced Learning Technologies, ICALT 2024
ISSN (Electronic)2161-377X

Conference

ConferenceIEEE International Conference on Advanced Learning Technologies
Country/TerritoryCyprus
CityHybrid, Nicosia
Period1/07/244/07/24

Keywords

  • learning management systems
  • long-term study
  • Moodle
  • Myers Briggs Test Indicator
  • Students’ behavior

Publication forum classification

  • Publication forum level 1

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Human-Computer Interaction
  • Media Technology
  • Education

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