Summary of Mentored Teaching Project
Assessment in undergraduate ecology presents several challenges, particularly when evaluating students’ quantitative skills and confidence. In field-based courses, student learning can often emphasize collaborative, project-based work, making it difficult to disentangle individual skill development from group performance. Additionally, quantitative skills such as coding and data analysis are not always directly observable through traditional assessments, and students may experience low confidence even when they are developing important skills.
To address these challenges, I designed a mentored teaching project that integrates both self-reported and reflective assessment approaches into a field-based ecology lab course. The goal of this project was to develop students’ ability to use R/RStudio to analyze and interpret ecological data while increasing their confidence and perceived competence in quantitative skills. The guiding teaching question asked: what relationship exists between R instruction and students’ confidence, skills, and attitudes toward ecological data analysis?
To assess student learning, I implemented a pre- and post-course survey using Likert-scale questions and open-ended responses. This approach allowed me to measure changes in students’ confidence and attitudes, which are often overlooked in traditional assessments but are critical for persistence in quantitative disciplines. I paired these indirect assessments with course-based practices, including scaffolded R instruction and application to real, field-collected ecological data.
The results of this project showed that targeted instruction in R significantly improved students’ confidence and perceived skills, with statistically significant gains across all measures of confidence and skill. In contrast, students’ attitudes toward R were already positive at the outset and remained stable throughout the course. Qualitative responses further highlighted that students experienced reduced anxiety, increased confidence, and a stronger sense of engagement in authentic scientific work.
Overall, this project demonstrates the importance of using multiple forms of assessment, particularly when evaluating complex learning outcomes like quantitative skill development and self-efficacy. It also highlights a key challenge in assessment: while self-reported data are valuable for understanding student perceptions, they do not fully capture individual skill proficiency. This limitation points to the need for combining indirect measures (e.g., surveys) with more direct assessments (e.g., individual coding assignments) in future course design.
To address these challenges, I designed a mentored teaching project that integrates both self-reported and reflective assessment approaches into a field-based ecology lab course. The goal of this project was to develop students’ ability to use R/RStudio to analyze and interpret ecological data while increasing their confidence and perceived competence in quantitative skills. The guiding teaching question asked: what relationship exists between R instruction and students’ confidence, skills, and attitudes toward ecological data analysis?
To assess student learning, I implemented a pre- and post-course survey using Likert-scale questions and open-ended responses. This approach allowed me to measure changes in students’ confidence and attitudes, which are often overlooked in traditional assessments but are critical for persistence in quantitative disciplines. I paired these indirect assessments with course-based practices, including scaffolded R instruction and application to real, field-collected ecological data.
The results of this project showed that targeted instruction in R significantly improved students’ confidence and perceived skills, with statistically significant gains across all measures of confidence and skill. In contrast, students’ attitudes toward R were already positive at the outset and remained stable throughout the course. Qualitative responses further highlighted that students experienced reduced anxiety, increased confidence, and a stronger sense of engagement in authentic scientific work.
Overall, this project demonstrates the importance of using multiple forms of assessment, particularly when evaluating complex learning outcomes like quantitative skill development and self-efficacy. It also highlights a key challenge in assessment: while self-reported data are valuable for understanding student perceptions, they do not fully capture individual skill proficiency. This limitation points to the need for combining indirect measures (e.g., surveys) with more direct assessments (e.g., individual coding assignments) in future course design.
Artifacts
Pre-Survey
Pre-Survey Results
Post-Survey
Post-Survey Results
R/RStudio in-class assignment
R/RStudio Instructional PowerPoint:
Pre-Survey Results
Post-Survey
Post-Survey Results
R/RStudio in-class assignment
R/RStudio Instructional PowerPoint:
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Artifact Rationales
The primary artifacts for this mentored teaching project include the pre- and post-course surveys (assessment instrument), the collected survey data, and the R-based instructional materials and assignments used in the course. These artifacts were intentionally designed to address key challenges in assessing student learning in quantitative ecology, particularly the difficulty of measuring both skill development and student confidence.
The pre- and post-surveys serve as indirect assessment tools that capture students’ self-reported confidence, perceived skills, and attitudes toward R and data analysis. By using Likert-scale questions and open-ended responses, the surveys allowed me to collect both quantitative data (e.g., shifts in confidence levels) and qualitative insights (e.g., student reflections on learning and challenges).
The survey data itself is an important artifact because it demonstrates measurable learning gains. The statistically significant improvements in confidence and skill provide evidence that the instructional intervention was effective, while the stability of attitudes highlights that students already recognized the importance of R, even before instruction. Together, these results illustrate how different types of assessment data can provide a more complete picture of student learning.
In addition, the R instructional materials and assignments function as artifacts that demonstrate my ability to design scaffolded learning experiences. These materials guided students from little or no experience with R to applying it in authentic research contexts using their own field-collected data. This aligns with best practices in teaching quantitative skills, where structured support and real-world application are essential for building both competence and confidence.
Overall, these artifacts collectively demonstrate my ability to use multiple forms of assessment (indirect, reflective, and applied) to address the challenges of evaluating complex learning outcomes such as quantitative skill development and self-efficacy.
The pre- and post-surveys serve as indirect assessment tools that capture students’ self-reported confidence, perceived skills, and attitudes toward R and data analysis. By using Likert-scale questions and open-ended responses, the surveys allowed me to collect both quantitative data (e.g., shifts in confidence levels) and qualitative insights (e.g., student reflections on learning and challenges).
The survey data itself is an important artifact because it demonstrates measurable learning gains. The statistically significant improvements in confidence and skill provide evidence that the instructional intervention was effective, while the stability of attitudes highlights that students already recognized the importance of R, even before instruction. Together, these results illustrate how different types of assessment data can provide a more complete picture of student learning.
In addition, the R instructional materials and assignments function as artifacts that demonstrate my ability to design scaffolded learning experiences. These materials guided students from little or no experience with R to applying it in authentic research contexts using their own field-collected data. This aligns with best practices in teaching quantitative skills, where structured support and real-world application are essential for building both competence and confidence.
Overall, these artifacts collectively demonstrate my ability to use multiple forms of assessment (indirect, reflective, and applied) to address the challenges of evaluating complex learning outcomes such as quantitative skill development and self-efficacy.
Interpretation / Reflection
This mentored teaching project reinforced for me that confidence and skill development are deeply interconnected but develop differently in the classroom. While students entered the course with generally positive attitudes toward R and quantitative skills, they lacked confidence in their ability to actually use these tools. The results showed that confidence can improve rapidly when students are given structured, hands-on opportunities to apply skills in meaningful contexts.
One of the most important insights I gained is the value of experiential learning with real data. Students did not just learn how to run code, they began to see themselves as scientists working with real ecological data. Their reflections highlighted a shift from feeling intimidated by data analysis to feeling engaged and even excited by it. This emphasizes the importance of designing assignments that are not only technically instructive but also authentic and relevant.
At the same time, this project highlighted limitations in my current assessment approach. Because much of the course work was completed in groups, I was unable to directly measure individual student proficiency in R. As a result, I relied heavily on self-reported survey data to assess learning. While these data are valuable for understanding student perceptions and confidence, they do not fully capture actual skill development. This has made me more aware of the need to balance indirect assessments (like surveys) with direct assessments (such as individual coding assignments or exams).
Moving forward, I would adapt this project by incorporating more individualized assessments of coding ability, such as short independent assignments or checkpoints that require students to demonstrate specific skills. I would also be interested in exploring longer-term assessment, such as whether students continue to use R in future courses or research experiences, to better understand skill retention.
More broadly, this project will influence how I design my courses by encouraging me to:
Overall, this experience strengthened my understanding that effective assessment in college teaching requires more than measuring outcomes, it requires understanding how students experience learning, and using that information to refine and improve my teaching practice.
One of the most important insights I gained is the value of experiential learning with real data. Students did not just learn how to run code, they began to see themselves as scientists working with real ecological data. Their reflections highlighted a shift from feeling intimidated by data analysis to feeling engaged and even excited by it. This emphasizes the importance of designing assignments that are not only technically instructive but also authentic and relevant.
At the same time, this project highlighted limitations in my current assessment approach. Because much of the course work was completed in groups, I was unable to directly measure individual student proficiency in R. As a result, I relied heavily on self-reported survey data to assess learning. While these data are valuable for understanding student perceptions and confidence, they do not fully capture actual skill development. This has made me more aware of the need to balance indirect assessments (like surveys) with direct assessments (such as individual coding assignments or exams).
Moving forward, I would adapt this project by incorporating more individualized assessments of coding ability, such as short independent assignments or checkpoints that require students to demonstrate specific skills. I would also be interested in exploring longer-term assessment, such as whether students continue to use R in future courses or research experiences, to better understand skill retention.
More broadly, this project will influence how I design my courses by encouraging me to:
- Use scaffolded instruction to gradually build student confidence and competence
- Include multiple forms of assessment to capture different dimensions of learning
- Integrate reflection opportunities to help students recognize their own growth
- Be more intentional about aligning learning goals, assessments, and classroom practices
Overall, this experience strengthened my understanding that effective assessment in college teaching requires more than measuring outcomes, it requires understanding how students experience learning, and using that information to refine and improve my teaching practice.
Project Mentor's Evaluation
"Great job on this report. I’m really impressed with all the work you put into it and how well everything went. I’ll definitely continue using R in my class from now on."
"Moriah completed a teaching certification at Michigan State, and for her project she updated the statistics assignment in the Ecology lab so that students used R. She delivered a lecture, led the statistics assignment, and provided ongoing support to help students use R throughout all of the statistical analyses in the course.
As part of the project, Moriah also ran pre- and post-course surveys to look at students’ confidence, attitudes, and perceptions around using R. The attached report explains the results in more detail, but overall it was a big success. Students completed all of their statistical analyses in R and showed clear improvements in both confidence and perceived skills in ecological data analysis.
I’m extremely happy with Moriah’s work and thought you’d be interested to hear how well it went."
"Moriah completed a teaching certification at Michigan State, and for her project she updated the statistics assignment in the Ecology lab so that students used R. She delivered a lecture, led the statistics assignment, and provided ongoing support to help students use R throughout all of the statistical analyses in the course.
As part of the project, Moriah also ran pre- and post-course surveys to look at students’ confidence, attitudes, and perceptions around using R. The attached report explains the results in more detail, but overall it was a big success. Students completed all of their statistical analyses in R and showed clear improvements in both confidence and perceived skills in ecological data analysis.
I’m extremely happy with Moriah’s work and thought you’d be interested to hear how well it went."
Full write-up of mentored teaching project:
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