Analysis of high‐frequency and long‐term data in undergraduate ecology classes improves quantitative literacy

Leveraging public data to offer online inquiry opportunities

Inquiry activities have become increasingly common in Ecology and Evolution courses, but the rapid shift to remote instruction for many faculty members in response to the COVID-19 pandemic has created new challenges for maintaining these student-centered activities in a distance learning format. Moving forward, many instructors will be asked to create flexible course structures that allow for a mix of different teaching modalities and will be looking for resources to support student inquiry in both online and in-person settings. Here, we propose the use of data-driven inquiry activities as a flexible option for offering students experiences to build career-relevant skills and learn fundamental ecological concepts. We share lessons learned from our experiences teaching a two-semester course-based research experience in global change ecology that leverages publicly available datasets to engage students in broadly relevant scientific inquiry.

Power, pitfalls, and potential for integrating computational literacy into undergraduate ecology courses

Environmental research requires understanding nonlinear ecological dynamics that interact across multiple spatial and temporal scales. The analysis of long-term and high-frequency sensor data combined with simulation modeling enables interpretation of complex ecological phenomena, and the computational skills needed to conduct these analyses are increasingly being integrated into graduate student training programs in ecology. Despite its importance, however, computational literacy-that is, the ability to harness the power of computer technologies to accomplish tasks-is rarely taught in undergraduate ecology classrooms, representing a major gap in training students to tackle complex environmental challenges. Through our experience developing undergraduate curricula in long-term and high-frequency data analysis and simulation modeling for two environmental science pedagogical initiatives, Project EDDIE (Environmental Data-Driven Inquiry and Exploration) and Macrosystems EDDIE, we have found that students often feel intimidated by computational tasks, which is compounded by the lack of familiarity with software (e.g., R) and the steep learning curves associated with script-based analytical tools. The use of prepackaged, flexible modules that introduce programming as a mechanism to explore environmental datasets and teach inquiry-based ecology, such as those developed for Project EDDIE and Macrosystems EDDIE, can significantly increase students' experience and comfort levels with advanced computational tools. These types of modules in turn provide great potential for empowering students with the computational literacy needed to ask ecological questions and test hypotheses on their own. As continental-scale sensor observatory networks rapidly expand the availability of long-term and high-frequency data, students with the skills to manipulate, visualize, and interpret such data will be well-prepared for diverse careers in data science, and will help advance the future of open, reproducible science in ecology.

Life Science Majors' Math-Biology Task Values Relate to Student Characteristics and Predict the Likelihood of Taking Quantitative Biology Courses

Expectancy-value theory of achievement motivation predicts that students' task values, which include their interest in and enjoyment of a task, their perceptions of the usefulness of a task (utility value), and their perceptions of the costs of engaging in the task (e.g., extra effort, anxiety), influence their achievement and academic-related choices. Further, these task values are theorized to be informed by students' sociocultural background. Although biology students are often considered to be math-averse, there is little empirical evidence of students' values of mathematics in the context of biology (math-biology task values). To fill this gap in knowledge, we sought to determine 1) life science majors' math-biology task values, 2) how math-biology task values differ according to students' sociocultural background, and 3) whether math-biology task values predict students' likelihood of taking quantitative biology courses. We surveyed life science majors about their likelihood of choosing to take quantitative biology courses and their interest in using mathematics to understand biology, the utility value of mathematics for their life science career, and the cost of doing mathematics in biology courses. Students on average reported some cost associated with doing mathematics in biology; however, they also reported high utility value and were more interested in using mathematics to understand biology than previously believed. Women and first-generation students reported more negative math-biology task values than men and continuing-generation students. Finally, students' math-biology task values predicted their likelihood of taking biomodeling and biostatistics courses. Instructional strategies promoting positive math-biology task values could be particularly beneficial for women and first-generation students, increasing the likelihood that students would choose to take advanced quantitative biology courses.