Introduction
Data science skills, such as visualizing, analyzing, and modeling large
datasets, are increasingly needed by ecology and environmental science
undergraduate students (Farrell and Carey 2018, Auker and Barthelmess
2020, Feng et al. 2020, Cooke et al. 2021). Recent advancements in
environmental monitoring technology (e.g., Mcloughlin et al. 2019,
Nathan et al. 2022, Dauphin et al. 2023) and the rise of environmental
observatory networks (Keller et al. 2008, Weathers et al. 2013, Cleverly
et al. 2019) have resulted in a deluge of “big data” in ecology
(Hampton et al. 2013, LaDeau et al. 2017, Farley et al. 2018). As a
result, analysis of large datasets is now required across a variety of
environmental science and ecology careers, necessitating new approaches
to training researchers, instructors, and students in data science
skills (Hampton et al. 2017, National Academies of Sciences 2018, Feng
et al. 2020, Emery et al. 2021).
Currently, a lack of both student and instructor familiarity with data
science concepts, methods, and tools presents a major barrier to
incorporation of data science into undergraduate life science curricula
(Williams et al. 2019, Emery et al. 2021, Naithani et al. 2022,
Cuddington et al. 2023). This gap often exists because instructors
themselves have not received training in data science skills (Williams
et al. 2019, Emery et al. 2021), and students do not have the requisite
background skills and confidence to effectively engage in data science
training (Williams et al. 2019, Cuddington et al. 2023). Consequently,
development of educational materials approachable to both instructors
and students is needed to lower the barrier to data science education in
ecology and environmental science.
Ecological forecasting is an ideal topic for engaging instructors and
students in data science training (Willson et al. 2023). First,
ecological forecasting has the potential to guide environmental
management decisions (Johnson et al. 2018, Liu et al. 2020, Bodner et
al. 2021, Heilman et al. 2022), thereby engaging students in real-world
problem-solving. Ecological forecasts, which provide predictions of the
future state of ecosystems with uncertainty (Luo et al. 2011, Petchey et
al. 2015), are critically needed to help manage natural resources
increasingly threatened by climate and land use change (Bradford et al.
2020). Examples of societally important forecasts exist for many
ecological systems, including river temperature forecasts to guide
reservoir water release decisions and protect fish species
(Ouellet-Proulx et al. 2017), temperature-based spring onset forecasts
to inform agricultural decision-making (Carrillo et al. 2018), and
forecasts of endangered ocean species to avoid bycatch (Hazen et al.
2018).
Second, generating ecological forecasts requires students to step
through the scientific method (Moore et al. 2022, Lewis et al. 2023),
providing critical skills in developing and testing hypotheses, which
are transferable across scientific disciplines. In the iterative
forecast cycle, similar to the scientific method, researchers develop
hypotheses about how ecosystems function; instantiate hypotheses into a
predictive model; use the model to generate forecasts into the future;
evaluate forecasts with observations once the future arrives and new
data are available; and use evaluation results to iteratively update and
improve hypotheses, models, and predictions (Dietze et al. 2018).
Third, ecological forecasting problems are particularly well-suited for
active learning, in which students learn by doing rather than passively
listening or watching (Bonwell and Eison 1991). Active learning has been
shown to enhance student outcomes (Freeman et al. 2014), especially for
underrepresented groups (Theobald et al. 2020). Key components of active
learning that can be easily embedded within ecological forecasting
curricula include: authentic assessments that engage students in
real-world, relevant problems similar to what they will encounter in
their future careers (Villarroel et al. 2018); scaffolding to help
students progressively build more complex skills and problem-solve
(Belland 2014); and formative assessments that provide students with
specific, actionable guidance on their progress, with opportunities to
apply that guidance moving forward (Wiliam 2011).
To effectively use ecological forecasting as a platform for teaching
data science in undergraduate classrooms, instructors must have both
pedagogical knowledge of active learning and disciplinary
knowledge of data science and ecological forecasting (Auerbach and
Andrews 2018, Andrews et al. 2019). However, research has demonstrated
substantial gaps in instructor knowledge in both active learning
(Auerbach and Andrews 2018, Andrews et al. 2019) and data science
(Williams et al. 2019, Emery et al. 2021). Given that ecological
forecasting is an emerging field (Lewis et al. 2022), and educational
resources in ecological forecasting remain rare (Willson et al. 2023),
it is unlikely that many instructors have training in this area.
To address gaps in instructor knowledge in the life sciences, multiple
models of instructor professional development have been trialed,
including short, intensive trainings for teaching assistants (Hughes and
Ellefson 2013, Schussler et al. 2015), department-wide training programs
for faculty (Owens et al. 2018), and multi-year, multi-institutional
programs for postdoctoral researchers (Ebert-May et al. 2011, D’Avanzo
et al. 2012, Derting et al. 2016). Outcomes of these professional
development activities frequently rely solely on instructor feedback
(Ebert-May et al. 2011). However, instructor and student perceptions of
the effectiveness of teaching practices in the classroom can differ from
each other (Heim and Holt 2018). Consequently, the effectiveness of
instructor professional development should be evaluated using multiple
methods (e.g., reflection and feedback, observing teaching practices,
student assessments; Ebert-May et al. 2011, Heim and Holt 2018) and
incorporate input from both students and faculty. Moreover, as lack of
time is often cited as a barrier to instructor professional development
(Williams et al. 2019), instructional materials associated with active
learning activities should include short, accessible definitions and
examples of key concepts to provide “just-in-time” (sensu Novak
et al. 1999) pedagogical, data science, and ecological forecasting
training for instructors as well as students.
To lower the barrier of entry to data science for both undergraduate
students and instructors in ecology and environmental science, we
developed and assessed a modular curriculum within the Macrosystems
EDDIE (Environmental Data-Driven Inquiry and Exploration) program (Carey
et al. 2020, Hounshell et al. 2021, Moore et al. 2022, Woelmer et al.
2023a) that uses active learning techniques to teach data science skills
in the context of ecological forecasting. While previous educational
materials on ecological forecasting have been developed for advanced
students, primarily at the graduate level (e.g., Dietze 2017a, Ernest et
al. 2023), our curriculum is one of the first that is specifically
targeted to undergraduates (Willson et al. 2023). In addition, all
materials are designed to be approachable to both instructors and
students, as coding experience is not a necessary prerequisite and each
module is accompanied by substantial introductory and supporting
materials for instructors. Moreover, students work with data from the
U.S.’s National Ecological Observatory Network (NEON) to address
relevant societal challenges such as predicting freshwater quality
impairment.
Here, we present an overview of the Macrosystems EDDIE ecological
forecasting curriculum and examples of how it has been implemented in
various course contexts. We also analyze student and instructor
assessment data to address the following questions: (1) How does
completion of Macrosystems EDDIE ecological forecasting modules affect
student confidence and understanding of data science and ecological
forecasting skills? (2) What are instructor perceptions of module ease
of use and efficacy in teaching data science and ecological forecasting
concepts? We were specifically focused on student confidence and
instructor perceptions, as previous work has shown that two major
barriers to integrating data science active learning activities into
existing curricula are a lack of instructor training (Williams et al.
2019, Emery et al. 2021) and student confidence (Williams et al. 2019,
Cuddington et al. 2023).