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

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

Tutkimustuotos: KonferenssiartikkeliTieteellinenvertaisarvioitu

6 Lataukset (Pure)

Abstrakti

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.

AlkuperäiskieliEnglanti
OtsikkoProceedings - 2024 IEEE International Conference on Advanced Learning Technologies, ICALT 2024
ToimittajatZehra Altinay, Maiga Chang, Rita Kuo, Ahmed Tlili
KustantajaIEEE
Sivut3-7
Sivumäärä5
ISBN (elektroninen)979-8-3503-6205-3
DOI - pysyväislinkit
TilaJulkaistu - 2024
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaIEEE International Conference on Advanced Learning Technologies - Hybrid, Nicosia, Kypros
Kesto: 1 heinäk. 20244 heinäk. 2024

Julkaisusarja

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

Conference

ConferenceIEEE International Conference on Advanced Learning Technologies
Maa/AlueKypros
KaupunkiHybrid, Nicosia
Ajanjakso1/07/244/07/24

Julkaisufoorumi-taso

  • Jufo-taso 1

!!ASJC Scopus subject areas

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

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