CHARACTERIZATION OF MATHEMATICS ANXIETY IN POST-SECONDARY TECHNOLOGY STUDENT LEARNING MATHEMATICAL CONCEPTS

Demand for STEM professionals in the US is growing. Math anxiety (MA) influences STEM degree selection, retention, and success. A systematic search was conducted and showed that past MA research has heavily depended on convenience sampling, resulting in a significant data deficit for engineering and technology majors. The Abbreviated Math Anxiety Scale (AMAS) (Hopko 2003) was used to measure MA in technology (T) students. The author has developed a mixed-methods conceptual framework based on Bandura’s Self-Efficacy Theory (SET) and Pekrun’s Control-Value Theory (CVT). Results found that T students experience more acute levels of MA than other STEM students except engineering, with most exceeding the high MA threshold. Females experienced greater MA than males, but ethnic differences were not found in the population of T students, perhaps because of underrepresentation. Other variables were also explored. Technology degree type influenced learning math anxiety when MA was compared to the three constructs of math self-concept (confidence, perceived value, and perceived control). The Math Confidence Scale (MCS (Hopko et al. 2003)), perceived Math Value Scale (MVS (Hopko et al. 2003)), and perceived control over the math assessment outcome Self-Description Questionnaire (SDQ II) (Parker et al. 2014) were used for math confidence, perceived value, and perceived control, respectively. Correlational and regression analyses were performed. Regression analyses relating MA to the three constructs of math self-concept could not reliably predict MA in one unified equation. Still, when used individually, confidence, value, and control produced reliable predictions for MA. The interview phase explored why students choose their degree program. It found that the math difficulty required, and mentors (both good and bad) had the most significant influences, followed by having a passion for the subject, having hands-on or experiential learning, being a good fit for their current skill set, university characteristics; and having a connection to the industry. Students’ confidence was most influenced by their judgments of their readiness and skills. Closely behind that were pragmatic, motivation, and learning help variables driven by the immediate need for time, a favorable grade, and to be as good as their peers. Perceived value aligned with confidence, prioritizing grades and peer comparisons over long-term value. However, real-world connections became an important variable in valuing math learning. Students found success primarily through deliberative means of controlling the outcome, including finding real-world connections, setting aside focus time, actively controlling emotions and overthinking, and, just in general, being determined and persistent.