Proposal Title
A discipline-specific R manual improves students' skills and confidence in their chosen field
Session Type
Poster
Room
Thames Hall Atrium
Start Date
13-7-2023 4:30 PM
End Date
13-7-2023 6:00 PM
Keywords
quantitative skills, statistical programming, R, survey, learning resource
Primary Threads
Evaluation of Learning
Abstract
Students enter the university classroom with varying levels of quantitative skills. This includes varying numerical proficiency and varying levels of proficiency with coding languages. As data science becomes more prevalent in scientific research, and use of statistical programming software is increasingly common, there have been growing calls to increase exposure to programming skills in undergraduate-level courses. ‘R’ is currently the most popular statistical programming software across ecology and evolutionary biology. The initial steep learning curve of R and the limited availability of resources for beginners result in an incompatibility between resources and students’ needs. To address this gap, we created a student-facing and department-specific R manual for use as a learning and teaching resource. Through quantitative surveys in a large-enrollment second year ecology course, we assess the effectiveness of the manual and R-based lab activities in improving student R skills and confidence. We also conducted a survey of graduate student teaching assistants and faculty who indicated that the manual meets the current learning objectives of the department. Our results highlight the variation in confidence and skills among second-year students and show that lab training and the R manual helped to close learning and skills gaps for students lacking previous experience. These results emphasize the importance of early exposure to statistical programming opportunities and activities early in undergraduate science courses to help increase skills and confidence among students. This research was approved by the University of Toronto’s Social Sciences, Humanities and Education Research Ethics Board.
Elements of Engagement
Engage participants by encouraging them to share their own experiences (trials, tribulations, and triumphs) with learning of quantitative and statistical programming skills.
A discipline-specific R manual improves students' skills and confidence in their chosen field
Thames Hall Atrium
Students enter the university classroom with varying levels of quantitative skills. This includes varying numerical proficiency and varying levels of proficiency with coding languages. As data science becomes more prevalent in scientific research, and use of statistical programming software is increasingly common, there have been growing calls to increase exposure to programming skills in undergraduate-level courses. ‘R’ is currently the most popular statistical programming software across ecology and evolutionary biology. The initial steep learning curve of R and the limited availability of resources for beginners result in an incompatibility between resources and students’ needs. To address this gap, we created a student-facing and department-specific R manual for use as a learning and teaching resource. Through quantitative surveys in a large-enrollment second year ecology course, we assess the effectiveness of the manual and R-based lab activities in improving student R skills and confidence. We also conducted a survey of graduate student teaching assistants and faculty who indicated that the manual meets the current learning objectives of the department. Our results highlight the variation in confidence and skills among second-year students and show that lab training and the R manual helped to close learning and skills gaps for students lacking previous experience. These results emphasize the importance of early exposure to statistical programming opportunities and activities early in undergraduate science courses to help increase skills and confidence among students. This research was approved by the University of Toronto’s Social Sciences, Humanities and Education Research Ethics Board.