Initial Publication Date: July 12, 2018

Grand Challenge 3:

Use of Models: How can we help students understand the process by which geoscientists create and validate physical, computational, mental, systems, and feedback models and use those models to generate new knowledge about the Earth?


We have prioritized this Grand Challenge because we think that many or most citizens do not understand how modern scientific models are developed and tested, and how they are used to make predictions. Geoscientists use an ambitious and iterative process of building models, starting with mental working models and working up to computational models, testing their models against empirical data at every iteration. Only after many such cycles is the model considered robust enough to make predictions about the Earth where we have no data - including times in the past and the future. Lack of understanding of how modern scientific modeling works allows skeptics and deniers to dismiss evidence that comes from modeling, for example evidence that climate change is anthropogenic.

There is some good literature on how scientists create and validate models, including external runnable models (e.g.Nersessian, 1999), and including in geosciences (e.g. Weart, 2011; Turcotte, 2006). There is active research on how students and teachers understand the scientific practice of modeling (Clement, 2000; Gilbert & Justi, 2016; Gobert & Buckley, 2000; Grosslight et al., 1991; Justi & Gilbert, 2002; Lehrer & Schauble, 2006; Pluta, Chinn, & Duncan, 2011) and on scientists' normative, conceptual models (Schwarz & White, 2005; Schwarz & Gwekwerere, 2007; Schwarz et al., 2009). There is less understanding of how students and teachers understand external runnable models, including physical models (Miller & Kastens, 2018), and modern computational models (such as global climate models) (Bice, 2006; Colella, 2000).

There are frameworks for model-based instruction, ready for testing (e.g. Sell et al., 2006; Sibley, 2009; Wndschetl, Thomson, & Braaten, 2008; Gilbert & Ireton, 2003), but a lack of good assessments of students' ability to create and use geoscience computational models (Figure 1). There is a particular shortage of educational research at the interface between models and data: how to help students learn to use data to test models, and how to help students learn to use models to interpret data. As pointed out in an earlier theme chapter, we need further research on how to help students find the sweet spot between being overly skeptical about models and being overly trusting of models. Model-building as a collaborative process (Pennington et al, 2016) may be part of the process of creating trusted models around difficult problems.

Recommended Research Strategies

  1. Research what students at various levels understand the process by which geoscientists create and validate models (especially modern computational models) and use those models to generate new knowledge about the Earth. It has been asserted (e.g. Kastens et al., 2013) that students and the general public have little understanding of the process by which the computational models of modern science are created, validated, and used to make predictions. The breadth, depth, distribution and nature of this ignorance needs to be probed, to lay the groundwork for a comprehensive research agenda.
  2. Collaborate with cognitive/learning scientists to understand how the human mind runs mental models of the future and/or the past, and then use this understanding to research how geoscience education can improve and leverage that ability. The first step towards generating a scientific computational model of a part of the Earth system is to develop a conceptual model that can be "run" in the mind (i.e. one can envision processes that produce observable products or behaviors, and can think through how those products or behaviors would differ as circumstances or inputs change.) The ability to run mental models is thought to be unique to the human brain and is therefore a powerful cognitive tool we have to understand the world around us. Even without formal training, our brains have this inherent ability (for example, anticipating where one will and will not be able to find parking on campus), but it is unclear how this ability is applied to understanding earth systems and how we can leverage this power of the mind to inform education practices.
  3. Research how the human mind understands positive and negative feedback loops, how geoscience education can foster that ability, and how can we assess this. The Geoscience Employers' Workshop Outcomes lists the ability for students to do "systems thinking" as a valuable habit of mind. Cognitive research on ALL of systems thinking is beyond the scope of what could be accomplished in the 10 year timeframe to meet the GER Framework Goal); therefore we prioritize one critical aspect of system thinking for near-term cognitive research: feedback loops. Many, and maybe even most, environmental problems are underlain by reinforcing (aka positive) feedback loops; for example, the albedo feedback loop that strengthens the impact of climate change in the Arctic as the polar sea ice melts. Many of the potential solutions to environmental problems work by strengthening balancing (aka negative) feedback loops, or by weakening positive feedback loops. To understand environmental problems or contribute to environmental solutions in a deep and impactful way, students need to understand such processes. Practitioners find that these topics can be taught, but are challenging to teach and to assess. Feedback systems can be taught at a qualitative level or a quantitative level, and both are challenging. Understanding the cognitive underpinning of teaching and learning about feedback loops is a challenge that could benefit from collaboration with other DBER's, perhaps through the DBER-A alliance, as feedback loops are very important in life sciences (ecology, physiology) and engineering.

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