Research on Cognitive Domain in Geoscience Learning: Quantitative Reasoning, Problem Solving, and Use of Models

Kim A. Kastens, Lamont-Doherty Earth Observatory; Ashlee Dere, University of Nebraska at Omaha; Deana Pennington, The University of Texas at El Paso; and Vic Ricchezza, University of South Florida


Human cognition is the process of acquiring knowledge and understanding through thought, experience and the senses. Cognitive processes are habits of the mind and therefore affect learning, including the learning of geoscience concepts and skills. The GER Framework includes two chapters on areas of cognitive research that are particularly important to geoscience education: the previous chapter tackled spatial and temporal reasoning, and this chapter addresses quantitative reasoning, problem-finding and problem-solving, and the use of models.

Models (from simple mental models to complex computational models) are used by geoscientists to conceptualize and better understand the Earth system and to make predictions (Figure 1). Earth processes affect the human condition and result in hazards and complex issues that require both expert and citizenry decision-making about mitigation and adaptation. In addition, a wide range of Earth materials (e.g., mineral, rock, water) are valued resources that need sustainable management. All of these challenges require recognition of the problem (problem-finding), and the development and application of problem-solving skills. In addition, Earth system understanding and problem-solving benefit strongly from quantitative reasoning. Quantitative reasoning, problem-solving, and use of models present many daunting challenges to both students and instructors. All are valued by the professional geoscience community and by employers, and all would benefit from more education research.

In defining the Grand Challenges and recommended strategies, we favored those that are: high impact, under-researched, addressable on a ten-year time scale, and/or central to how geoscientists think about the Earth and about Earth/human interactions. Addressing each of these challenges will require innovative, creative thinking, along research pathways that are not yet clear, along with vast amounts of hard work. But we are confident that each of them is ripe for new discoveries, and we look forward to both the intellectual and practical outcomes of these efforts.

Grand Challenges

Grand Challenge 1: Quantitative Thinking: How does quantitative thinking help geoscientists and citizens better understand the Earth, and how can geoscience education move students toward these competencies?

The ability to think quantitatively is an important part of what transforms an introductory student into a geoscience major and then into a professional geoscientist. Employers value quantitative thinking. Quantitative thinking may be a sweet spot for GER research, in that there is rich trove of math education research to build upon.

Grand Challenge 2: Problem-finding and Problem-solving: How can we help students find and solve problems they care about concerning the Earth, in an information-rich society (e.g., of big data, emerging technologies, access to a wide-variety of tools, and rich multimedia)?

Historically the problems that students tackle in science classes, including geoscience classes, have been assigned by the teacher and rather constrained in scope. But many of the problems geoscience students will confront in the future are complex, messy, ill-defined, and require working across disparate knowledge, methods, and data sources.

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?

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 the past or 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.

Citation for this chapter: Kastens, Kim A.; Dere, Ashlee; Pennington, Deana; and Ricchezza, Vic (2018). "Research on Cognitive Domain in Geoscience Learning: Quantitative Reasoning, Problem Solving, and Use of Models". In St. John, K (Ed.) (2018). Community Framework for Geoscience Education Research. National Association of Geoscience Teachers.

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