@inproceedings{e472aa85fd67434c8394ef9324732a93,
title = "The Relationship Between Students{\textquoteright} Myers-Briggs Type Indicator and Their Behavior within Educational Systems",
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{\textquoteright} 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{\textquoteright} behavior when using an educational system.",
keywords = "learning management systems, long-term study, Moodle, Myers Briggs Test Indicator, Students{\textquoteright} behavior",
author = "Akerke Alseitova and Wilk Oliveira and Zhaoxing Li and Lei Shi and Juho Hamari",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; IEEE International Conference on Advanced Learning Technologies ; Conference date: 01-07-2024 Through 04-07-2024",
year = "2024",
doi = "10.1109/ICALT61570.2024.00008",
language = "English",
series = "Proceedings - 2024 IEEE International Conference on Advanced Learning Technologies, ICALT 2024",
publisher = "IEEE",
pages = "3--7",
editor = "Zehra Altinay and Maiga Chang and Rita Kuo and Ahmed Tlili",
booktitle = "Proceedings - 2024 IEEE International Conference on Advanced Learning Technologies, ICALT 2024",
address = "United States",
}