Tor Einar Møller, Laura De Luca Peña, Kristian A. Haaga, Henriette Linge, Bjarte Hannisdal
Student learning of dynamical interactions in complex Earth systems is a major challenge in geoscience education (Assaraf & Orion, 2005, J. Res. Sci. Teach. 42; Scherer et al., 2017, J. Geosci. Educ. 65). Students exposed to traditional teaching have been found to maintain their default perception of causal relationships as linear chains of events and to struggle with dynamical systems thinking (Raia, 2008, J. Geosci. Educ. 56).
The development of systems thinking goes hand-in-hand with computational thinking and practices (Weintrop et al. 2016, J. Sci. Educ. Technol. 25). In 2017, in a course offered to 3rd semester students in geology with no prior computational experience, we introduced a computational activity involving Daisyworld – a virtual planet with a simple interacting climate and biosphere (Watson & Lovelock, 1983, Tellus B 35). This simple analogue invites students to carefully define the components and processes in a system, and then couple them together to discover and explore nonlinear behavior, feedbacks, and thresholds, which are key properties of natural systems.
Students worked in groups using R in a three-stage activity, where each stage had overlapping learning outcomes intended to build a coherent learning progression. Each student group presented their findings and performed a written, critical self-reflection and evaluation. We found that the lack of a common language for systems and computational thinking reduced student engagement in the learning process. Faced with well-structured “textbook” problems, students were still uncomfortable doing repeated rounds of trial and error. Moreover, students did not perceive any real-world implications of the imaginary Daisyworld scenario, suggesting that greater authenticity would enhance their learning motivation.
Learning from this experience, we will test new learning activities for a revamped course in 2019. We replace Daisyworld with the global carbon cycle, a real-world system of vital importance. Leaning on the ‘problem-solving in practice’ framework for ill-structured problems (Holder et al., 2017, J. Geosci. Educ. 65), we hypothesise that computational practices improve student learning of complex dynamical systems in geoscience. To test our hypothesis, we will assign students to an experimental group that uses computational practices, and a control group that reads the same instructional material, and use pre- and post-instruction quizzes to assess their progress from novice towards expert-like thinking.
We solicit input from ISSoTL18 participants on our proposed experiment, specifically on (1) setting up scaffolding to foster and sustain student motivation, and (2) integrated assessment and evaluation of computational practices.
