Abstract:

Many studies have investigated various factors of retention and success in STEM (Science, Technologies, Engineering and Mathematics) undergraduate education, including demographics, financial aids, test scores and grades, courses and curriculums, intellectual skills and abilities, motivational factors, academic and social environments, and interventions. Many existing findings on STEM retention and success are obtained using analytical techniques that do not capture interactive, concurrent effects of multiple factors. Analytical techniques that can analyze and model interactive and concurrent effects of multiple factors are needed to produce a complete framework of STEM retention and success. This paper presents a study that fills in this gap in the current state of knowledge about STEM retention and success by employing the new PVAD (Partial-Value Association Discovery) algorithm to identify characteristics of engineering retention and success based on both individual and interactive effects of multiple factors. The PVAD algorithm also addresses a shortcoming of existing analytical techniques in their inability of modeling variable relations that exist for some but not all values of variables or are different for different value ranges of variables. This study employs the PVAD algorithm to identify characteristics of students who achieve engineering retention and success. This paper presents the PVAD algorithm and new findings of engineering retention and success characteristics. Such characteristics will lead to various models and paths for more students with various conditions and diverse characteristics (e.g., educational and social backgrounds, and skills) to achieve engineering retention and success, and thus broaden the participation, retention and success of students in engineering.

Keywords:

STEM education, engineering retention and success.