Sam S Donovan

Using Rich Context and Data Exploration to Improve Engagement with Climate Data and Data Literacy: Bringing a Field Station into the College Classroom

ABSTRACT Authentic scientific data, when richly contextualized, can provide the basis for compelling learning experiences. Many undergraduate students either do not have access to primary data, or if they do, the data are so abstract that student engagement is limited. Here, we describe contextual information and data-rich, student-centered activities we developed to give life to data sets from an intensely studied place, the Rocky Mountain Biological Laboratory (RMBL). The project Web site, Digital RMBL, highlights charismatic organisms, scientists, and long-term data sets as a tool for engaging students who are unable to physically visit a field station. The Biology of Climate Change module, the focus of this paper, has been tested in college-level classrooms by 10 collaborating faculty and 243 undergraduate students at a variety of colleges and universities across the nation. Authentic long-term data sets, primary literature, data visualizations, and a flexible format suitable for laboratory sections have led to very high usability ratings by collaborating faculty. In student surveys, a surprising number of undergraduate science majors (20%–30%) report that they have never worked with authentic scientific data—even at major research universities. The percentage of students who have not worked with data is much higher at collaborating 2 y institutions (60%–80%). The majority of students report that they appreciate the opportunity to explore long-term climate science data sets despite the frustrations they experience with the “messiness” of authentic scientific data. The impact of this climate change activity is achieved through engagement with people, place, and research subjects, followed by student-centered data exploration that builds personal interest and scientific discovery skills. This paper outlines one model in which scientists can meet funding agency requirements to share data publicly while providing excellent opportunities for improving climate and data literacy at the college level.

QUBES: a community focused on supporting teaching and learning in quantitative biology

This letter provides an overview of the Quantitative Undergraduate Biology Education and Synthesis (QUBES) Project funded through the National Science Foundation. The project has five distinct, but interdependent, initiatives which work together to support faculty and students in the teaching and learning of quantitative biology (QB). QUBES has adopted an integrated strategy to improving the frequency and effectiveness of QB instruction that includes coordinating a broad consortium of professional stakeholders, supporting faculty development and the implementation of new teaching practices, providing an infrastructure for collaboration and access to high quality materials, establishing new metrics for faculty teaching scholarship and documenting the project outcomes.

Barriers to Integration of Bioinformatics into Undergraduate Life Sciences Education

Bioinformatics, a discipline that combines aspects of biology, statistics, and computer science, is increasingly important for biological research. However, bioinformatics instruction is rarely integrated into life sciences curricula at the undergraduate level. To understand why, the Network for Integrating Bioinformatics into Life Sciences Education (NIBLSE, “nibbles”) recently undertook an extensive survey of life sciences faculty in the United States. The survey responses to open-ended questions about barriers to integration were subjected to keyword analysis. The barrier most frequently reported by the ~1,260 respondents was lack of faculty training. Faculty at associate’s-granting institutions report the least training in bioinformatics and the least integration of bioinformatics into their teaching. Faculty from underrepresented minority groups (URMs) in STEM reported training barriers at a higher rate than others, although the number of URM respondents was small. Interestingly, the cohort of faculty with the most recently awarded PhD degrees reported the most training but were teaching bioinformatics at a lower rate than faculty who earned their degrees in previous decades. Other barriers reported included lack of student interest in bioinformatics; lack of student preparation in mathematics, statistics, and computer science; already overly full curricula; and limited access to resources, including hardware, software, and vetted teaching materials. The results of the survey, the largest to date on bioinformatics education, will guide efforts to further integrate bioinformatics instruction into undergraduate life sciences education.

Using simulation and structured group work to address statistical misconceptions

There is significant interest in research regarding student understanding and performance, especially in probability and statistics. Past research has focused on misconceptions in statistical inference, but, there is little research regarding statistical misconceptions for undergraduate engineering students. Additionally, engineering educators recognize that active-learning strategies can improve undergraduate STEM education, but unfortunately intervention-based research on reducing statistical misconceptions is not prevalent. This research aims to address these literature gaps by employing a simulation-based structured group work activity whose goal was to increase awareness of and help students overcome misconceptions regarding the Central Limit Theorem (CLT). The CLT was chosen based on its abstract, non-intuitive nature, prevalence in the literature, and its foundational importance to the field of probability and statistics. Informed by the work of Schwartz and Bransford, this study draws on contrasting cases in conjunction with a simulation-based group assignment given to undergraduate industrial engineering students enrolled in an intermediate-level probability and statistics course at the University of Pittsburgh. Through this active-learning intervention, the following research questions are addressed: (1) How can active-learning strategies help students overcome misconceptions in statistics? (2) How do active-learning strategies affect the retention of statistical concepts across a curriculum?