This is a complete evidence-based paper, which presents the implementation of a new course to mitigate the effects of student math preparation on student performance. At ??? University, a first-year engineering course has been developed based on the Wright State Model for Engineering Mathematics Education. At ???U, the student population has been found to be similar to the population at WSU based on incoming ACT math scores and preparation; however, the curriculum does not have room to add an additional required course as was done at WSU. Since the research to date shows that the majority of the impact of the course is on students that are not calculus ready, the course developed at ???U focuses on those students. Consequently, this course is optional and marketed toward students who are not in calculus, enrolled in either a pre-calculus math course or trigonometry.
The course was first offered in Fall of 2016. Much of the course is based on the course materials and text developed as part of the Wright State Model; however, due to constraints, the lab portion was limited to in-class demonstrations and group activities. In addition, a new module for the course was created to address student unfamiliarity with logarithmic and exponential functions.
This paper presents the new course along with data to assess the effect it has had on students’ performance. Each year qualitative data was collected regarding student impressions of the course and feedback. Additionally, quantitative data has been collected in the form of student course grades and GPA. This performance is measured based on retention in the engineering program and final grades in courses that have a strong correlation to graduation rates (calculus, physics, and select 2nd year engineering courses). Data collected on students that took the new course is compared with data on students with the same preparation and courses, who did not take the course. In addition to quantitative performance data, qualitative data is also be presented with some lessons learned.
Nicholas Baine, Ph.D., is an Assistant Professor in the School of Engineering at Grand Valley State University. His expertise is in the design of electrical control systems and sensor data fusion. As an instructor, he specializes in teaching freshman courses as well as control systems and design of digital and embedded systems. While at Wright State University, he was part of the group which developed a new model to teach mathematics to engineering students. As a faculty member at Grand Valley State University, he is working to develop and improve the freshman engineering curriculum.
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