USING BAYESIAN NETWORKS TO UNDERSTAND RELATIONSHIPS AMONG MATH ANXIETY, GENDERS, PERSONALITY TYPES, AND STUDY HABITS AT A UNIVERSITY IN JORDAN

Mathematics is the foundation of all sciences, but most students have problems learning math. Although students’ success in life related to their success in learning, many would not take a math course unless it is their university’s core requirements. Multiple reasons exist for students’ po...

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Glavni autor: Smail, Linda (Autor)
Format: EJournal Article
Izdano: Department of Doctoral Program on Mathematics Education, Sriwijaya University, 2016-12-25.
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245 0 0 |a USING BAYESIAN NETWORKS TO UNDERSTAND RELATIONSHIPS AMONG MATH ANXIETY, GENDERS, PERSONALITY TYPES, AND STUDY HABITS AT A UNIVERSITY IN JORDAN 
260 |b Department of Doctoral Program on Mathematics Education, Sriwijaya University,   |c 2016-12-25. 
500 |a https://ejournal.unsri.ac.id/index.php/jme/article/view/3405 
520 |a Mathematics is the foundation of all sciences, but most students have problems learning math. Although students’ success in life related to their success in learning, many would not take a math course unless it is their university’s core requirements. Multiple reasons exist for students’ poor performance in mathematics, but one prevalent variable worth consideration is the personality type. This work seeks to uncover relationships, if any, between students’ math anxiety and the students’ learning type in learning math and preparing for exams and tests. We use Bayesian networks to link those different variables and detect possible relationships among these variables. The data was obtained from population of 468 students during spring in 2009 at a U.S. institution in Jordan. Not many researches have been performed in the area of math study and personality types so we undertook this study to add to this area of knowledge. The study reveals that math anxiety related to gender, personality type, and study habits among this group of university students. Keywords: Mathematics Anxiety, MBTI, Personality Types, Bayesian Networks DOI: http://dx.doi.org/10.22342/jme.8.1.3405.17-34 
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