Prerequisite Coursework as a Predictor of Performance in Subsequent Science Courses
Session Format
Conference Session (20 minutes)
Target Audience
Post Secondary Education
Location
Research Burst 6 (PARB 127)
Abstract for the conference program
The authors investigated the relationship of prerequisite science course results as predictors of student success in subsequent science courses by analyzing pre-health undergraduate students in a STEM program. The data analysis revealed a significant relationship between the student outcomes in the first and second course in the three course sequence. A significant relationship was also found between the first and third courses as well as the second and third course. A simple linear regression model with the first two courses as predictors was developed and found to be significant. This model was found to be statistically significant with a p-value of 0.000. The results support the first course in the sequence as a predictor of student success in the second and third subsequent courses in the sequence. These results can be utilized for future implementation of remediation to increase student persistence, student retention, and student outcomes in future studies.
Proposal Track
R2: Completed Projects
Start Date
3-23-2019 11:00 AM
End Date
3-23-2019 11:20 AM
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Recommended Citation
Wireman, Mark and Russell, Samantha, "Prerequisite Coursework as a Predictor of Performance in Subsequent Science Courses" (2019). Interdisciplinary STEM Teaching & Learning Conference (2012-2019). 41.
https://digitalcommons.georgiasouthern.edu/stem/2019/2019/41
Prerequisite Coursework as a Predictor of Performance in Subsequent Science Courses
Research Burst 6 (PARB 127)
The authors investigated the relationship of prerequisite science course results as predictors of student success in subsequent science courses by analyzing pre-health undergraduate students in a STEM program. The data analysis revealed a significant relationship between the student outcomes in the first and second course in the three course sequence. A significant relationship was also found between the first and third courses as well as the second and third course. A simple linear regression model with the first two courses as predictors was developed and found to be significant. This model was found to be statistically significant with a p-value of 0.000. The results support the first course in the sequence as a predictor of student success in the second and third subsequent courses in the sequence. These results can be utilized for future implementation of remediation to increase student persistence, student retention, and student outcomes in future studies.
