Investigating Predictors of Academic Success in a Foundational Business Mathematics Course

Abstract

<p>Research reveals that the majority of students are unprepared in making the transition from high school to college mathematics. Many students choose majors outside of STEM and business fields to pursue degrees with less rigorous mathematics requirements. According to recent findings, it is likely that over 25% of all freshmen will fail their first mathematics course. Few studies examine student success in business mathematics courses, and business is currently the most popular major in the United States. Thus, the purpose of this research was to examine what factors predict success in a foundational business mathematics course (BUS 111) at a large Northeastern university. </p> <p>Eight independent variables were examined: gender, high school GPA, mathematics SAT score, score on the university’s placement exam, student attitudes using the Attitudes Towards Mathematics Inventory (ATMI), anxiety using the Mathematics Anxiety Scale (MAS), number of hours per week spent on mathematics, and number of classes missed. The dependent variable was BUS 111 final average. All students (<em>n</em> = 247) enrolled in BUS 111 were invited to participate during the Fall 2015. Upon completion of pre- and post-surveys, multiple regression was used to determine which variables were significant predictors. Statistical findings revealed that placement score, high school GPA, a combination of ATMI/MAS scores, and number of classes missed were the best predictors of BUS 111 average overall (R<sup>2</sup> = 44.2%, <em>p</em> = 0.000). Different models are presented for comparison and examination. Significant correlations found between perceived instructor effectiveness and student attitudes, anxiety, and course grades are also presented. Overall, combinations of non-affective and affective measures serve as the best predictors of success in business mathematics.</p>

Predicting student outcomes using digital logs of learning behaviors: Review, current standards, and suggestions for future work

“…Common kinds of performance data found in our review were assessment scores (e.g., Choi, Lam, Li, & Wong, 2018;Cooper & Pearson, 2012;Saqr, Fors, & Tedre, 2017) and number of courses/hours completed and grades or scores earned (e.g., Barber & Sharkey, 2012;Culver, 2014;Bird, 2012). Some models included scores on placement exams as predictors (D'Aloisio, 2016;Culver, 2014;Gultice, Witham, & Kallmeyer, 2015;McFate & Olmsted, 1999).…”