Barriers and bottlenecks in engineering mathematics: Math completion predicts persistence to graduation

Noncognitive Predictors of Engineering Persistence for C-in-Math Students: Exploring the Generalizability of Lasso Regression

“…However, very little research has considered mid-level performers-those who receive a C in their first semester math course. At one large southeastern university, studies revealed that approximately equal numbers of students who receive a C grade in their first math course either persisted through their math requirements or left engineering by their third year (see Figure 1, [11]). As reported in [11], the large number of C-in-math students who completed the math sequence indicates that it is possible to succeed in engineering with a mid-level grade and corresponding skills.…”

“…At one large southeastern university, studies revealed that approximately equal numbers of students who receive a C grade in their first math course either persisted through their math requirements or left engineering by their third year (see Figure 1, [11]). As reported in [11], the large number of C-in-math students who completed the math sequence indicates that it is possible to succeed in engineering with a mid-level grade and corresponding skills. Simultaneously, the group of C-in-math students who left engineering present an opportunity for interventions; there is a whole group of capable students who decide not to continue but are capable.…”

Predictors of First-year Retention Among Undergraduate Engineering Students Who Earn a C in their First-semester Math Course

“…After two complete years in the engineering school, the students who earned C in their first math course had either completed the four-semester math sequence (a strong predictor of graduation), were still enrolled in the math sequence, or were not retained in the engineering school. These prior results are represented in Figure 1 from [28]. Figure 1: Initial math performance and corresponding math completion rates (a proxy for graduation rates) at the J.…”

A Data-science Approach to Flagging Non-retention in Engineering Enrollment Data

“…Students who are enrolled in the first semester of their second year can leave prior to graduation, and students who are not enrolled can re-enroll and complete a degree. Longitudinal techniques for measuring retention have also been investigated, such as pathway analysis [12,13], hierarchical discrete-time event history analysis [14], domains of progression [15], support vector machines [16], Markov chains [17,18], and critical course completion [19]. These fully longitudinal analyses require at least six years of data to begin to determine policy effectiveness and course modifications.…”