Predictors of Persistence and Success in an Engineering Program

1995 ◽  
Vol 15 (1) ◽  
pp. 15-21 ◽  
Author(s):  
James Levin ◽  
John H. Wyckoff
Keyword(s):  
Academic Advising ◽  
Logistic Models ◽  
Predictor Variables ◽  
Mathematics Courses ◽  
Undergraduate Engineering ◽  
Engineering Program ◽  
Noncognitive Variables ◽  
And Mathematics ◽  
Over Time

This study identifies student variables that predict persistence and success in an undergraduate engineering program. Three logistic models were developed that predicted the probability of persisting successfully. Significant predictors included both cognitive and noncognitive variables; students who did well in science and mathematics courses and who were genuinely interested in engineering were more likely to persist and succeed. Predictor variables were not constant over time but changed as students progressed through the first two years of study, with performance in prerequisite science and mathematics courses emerging as the best predictors. The authors briefly discuss academic advising implications.

Data Analytics and STEM Student Success: The Impact of Predictive Analytics-Informed Academic Advising Among Undeclared First-Year Engineering Students

2018 ◽  
Vol 22 (3) ◽  
pp. 497-521 ◽  
Author(s):  
Yu (April) Chen ◽  
Sylvester Upah
Keyword(s):  
Higher Education ◽  
Student Success ◽  
Academic Advising ◽  
Data Analytics ◽  
Predictive Analytics ◽  
First Year ◽  
Program Of Study ◽  
Science Technology ◽  
Mathematics Student ◽  
And Mathematics

Science, Technology, Engineering, and Mathematics student success is an important topic in higher education research. Recently, the use of data analytics in higher education administration has gain popularity. However, very few studies have examined how data analytics may influence Science, Technology, Engineering, and Mathematics student success. This study took the first step to investigate the influence of using predictive analytics on academic advising in engineering majors. Specifically, we examined the effects of predictive analytics-informed academic advising among undeclared first-year engineering student with regard to changing a major and selecting a program of study. We utilized the propensity score matching technique to compare students who received predictive analytics-informed advising with those who did not. Results indicated that students who received predictive analytics-informed advising were more likely to change a major than their counterparts. No significant effects was detected regarding selecting a program of study. Implications of the findings for policy, practice, and future research were discussed.

Logic and Mathematics Courses

1958 ◽  
Vol 65 (7) ◽  
pp. 501
Author(s):  
Louis O. Kattsoff
Keyword(s):  
Mathematics Courses ◽  
And Mathematics

Student Performance in Mathematics: Should we be Concerned?

2018 ◽  
Vol 9 (1) ◽  
pp. 59-72
Author(s):  
Mary C. Enderson ◽  
Manveer Mann
Keyword(s):  
High School ◽  
Mathematics Achievement ◽  
Student Performance ◽  
Mathematical Tasks ◽  
College Level ◽  
Act Scores ◽  
Mathematics Courses ◽  
Course Grades ◽  
And Mathematics ◽  
Interdisciplinary Study

This article describes how for many college students the transition to college-level mathematics courses presents new challenges beyond those that were part of the high school experience. In this interdisciplinary study forty-four non-mathematics and non-science majors, enrolled in a retail-buying course, were studied to examine student confidence in performing applied mathematical tasks, mathematics achievement in college, and the relationship between predictors of college success (mathematics studied in high school, SAT/ACT scores, and mathematics courses taken in college). Measurements used for the study included a subset of items from the Mathematics Self-Efficacy Scale (MSES) on a 5-point Likert-type scale, course grades, number of years studying mathematics in high school and number of mathematics courses in college. Findings indicate that mathematics courses taken in college increased confidence in working mathematical tasks and were significant predictors of achievement in the retail course. In addition, SAT/ACT scores also were critical to the overall mathematics achievement.

Promoting Student Engagement with School using the Check & Connect Model

1999 ◽  
Vol 9 (S1) ◽  
pp. 169-184 ◽  
Author(s):  
Sandra L. Christenson ◽  
Mary F. Sinclair ◽  
Martha L. Thurlow ◽  
David Evelo
Keyword(s):  
High School ◽  
High Risk ◽  
School Engagement ◽  
Coping Skills ◽  
School Dropout ◽  
Predictor Variables ◽  
Dropping Out ◽  
Middle School Youth ◽  
School Youth ◽  
Over Time

Enhancing coping skills of students at high risk of school dropout is critical, particularly when school demands increase. The data-based Check & Connect school engagement model, originally designed to reduce the risk for dropping out among secondary/middle school youth with learning and emotional/behavioural disabilities, is described. Also described in this article are the students' patterns of school engagement over time, for whom national US statistics indicate only 42 per cent to 61 per cent typically complete high school. Results indicate that the levels of school engagement vary and change over time, among these high risk adolescents, supporting the importance of focusing on alterable indicators of risk, rather than status predictor variables. Furthermore, students' risk for disengagement from school was significantly lower for the youth who received sustained intervention throughout grade nine, compared to similar youth who received intervention support throughout grade eight.

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