Improving Engineering Students’ Motivation for Success in Statistics and Data Science: An Innovative Visualization of Four Mathematical Models in Higher Education in United Arab Emirates

Abstract

This study aims to analyze the factors that improve engineering students’ motivation for success in statistics and data science courses at higher education institutions by using four mathematical models. The distinctiveness of this study was exemplified by the innovative graphical depiction of those models. The impact of certain factors, such as the importance of recognition and enjoyment of the course, students’ self-concept, and future aspirations, on engineering students’ motivation for achieving success in statistics and data science courses was examined. The proposed models are expected to provide beneficial academic insights to students, instructors, administrators of higher education, and societies worldwide. This paper employed a quantitative methodology, including a sample consisting of 144 female and 101 male engineering students enrolled in various statistics and data science courses at higher education institutions in the United Arab Emirates (UAE). A comprehensive survey questionnaire was developed to gather quantitative data, which were mathematically modeled via factor and regression analyses. The four mathematical models analyzed six variables derived from the survey items. According to the results, models IV, II, I, and III had the most significant influence on motivation, in decreasing order. Model IV explained 94.4% of the variation in the motivation for achieving success in statistics and data science courses, while models II and I explained 75.5% and 71.4%, respectively. The study’s limitations stem from the fact that its findings might not apply universally and are dependent on the specific educational settings or cultural contexts in which the study was conducted.