Kim Kastens
LDEO of Columbia University
David Mogk
Montana State University http://serc.carleton.edu/NAGTWorkshops/DBER.html

• Emerging from various parent disciplines, with deep grounding in the discipline’s priorities, worldview, knowledge, and practices
• Investigates teaching and learning in a given discipline
• Informed by and complementary to general research on human learning and cognition
• Most commonly applied to higher education
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• Synthesize empirical research on undergraduate teaching and learning in physics, chemistry, engineering, biology, the geosciences, and astronomy.
• Examine the extent to which this research currently influences undergraduate science instruction.
• Describe the intellectual and material resources that are required to further develop DBER.
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Committee on the Status, Contributions, and Future
Directions of Discipline-Based Education Research
• SUSAN SINGER (Chair), Carleton
College
• ROBERT BEICHNER, North Carolina
State University
• STACEY LOWERY BRETZ, Miami
University
• MELANIE COOPER, Clemson
University
• SEAN DECATUR, Oberlin College
• JAMES FAIRWEATHER, Michigan
State University
• KENNETH HELLER, University of
Minnesota
• KIM KASTENS, Columbia University
• MICHAEL MARTINEZ, University of
California, Irvine
• DAVID MOGK, Montana State
University
• LAURA R. NOVICK, Vanderbilt
University
• MARCY OSGOOD, University of
New Mexico
• TIMOTHY F. SLATER, University of
Wyoming
• KARL A. SMITH, University of
Minnesota and Purdue University
• WILLIAM B. WOOD, University of
Colorado
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• Understand how people learn the concepts, practices, and ways of thinking of science and engineering.
• Understand the nature and development of expertise in a discipline.
• Help to identify and measure appropriate learning objectives and instructional approaches that advance students toward those objectives.
• Contribute to the knowledge base in a way that can guide the translation of DBER findings to classroom practice.
• Identify approaches to make science and engineering education broad and inclusive.
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Types of
Knowledge Required To Conduct DBER
• Deep disciplinary knowledge
• The nature of human thinking and learning as they relate to a discipline
• Students’ motivation to understand and apply findings of a discipline
• Research methods for investigating human thinking, motivation, and learning
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• National Science Foundation, Division of Undergraduate Education
(Grant No. 0934453)
• Committee Chair Susan Singer
• Volunteers:
– Committee
– Fifteen reviewers
– Report Review Monitor and Coordinator
• Commissioned paper authors
(M. Piburn, K. Kraft, H. Pacheco for Geo)
• NRC staff (Natalie Nielsen, Heidi Schweingruber, Margaret Hilton)
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Contributions of DBER: Conceptual
Understanding and Conceptual Change
• In all disciplines, undergraduate students have incorrect ideas and beliefs about fundamental concepts. (Conclusion 6)
• Students have particular difficulties with concepts that involve very large or very small temporal or spatial scales.
(Conclusion 6)
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Contributions of DBER: Problem Solving and the Use of Representations
• As novices in a domain, students are challenged by important aspects of the domain that can seem easy or obvious to experts. (Conclusion 7)
– complex problem solving
– domain-specific representations like graphs, models, and simulations.
• Students can be taught more expert-like problemsolving skills and strategies to improve their understanding of representations.
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Contributions of DBER:
Research on Effective Instruction
• Effective instruction includes a range of well-implemented, research-based approaches.
(Conclusion 8)
• Involving students actively in the learning process can enhance learning.
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Contributions of DBER:
Translating DBER into Practice
• Available evidence suggests that DBER and related research have not yet prompted widespread changes in teaching practice among science and engineering faculty. (Conclusion 12)
• Efforts to translate DBER and related research into practice are more likely to succeed if they:
– are consistent with research on motivating adult learners,
– include a deliberate focus on changing faculty conceptions about teaching and learning,
– recognize the cultural and organizational norms of the department and institution, and
– work to address those norms that pose barriers to change in teaching practice.
(Conclusion 13)
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DBER is an emerging field, and the strength of evidence that support findings have been variable ranging from anecdotal to robust.
• Limited Evidence: Few peer-reviewed studies with some convergence, OR Convergence with practitioner wisdom
• Moderate Evidence: Well designed, replicated study, OR
Moderate number of small-scale studies, OR A few largescale studies
• Strong Evidence: Numerous well, designed qualitative and/or quantitative studies with high convergence of findings.
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Recognizing that DBER was alive and well in Physics, Chemistry….why not geoscience?
“ We recommend that GEO and EHR both support research in geoscience education, helping geoscientists to work with colleagues in fields such as educational and cognitive psychology, in order to facilitate development of a new generation of geoscience educators.
”
NSF 97-171

Research on Learning—A Disciplinary “ Mixer ” , 2002
• Goals
– Develop an understanding of current state of research on learning in the geosciences
– Identify research questions of high interest to geoscience and learning scientists
– Develop a plan to apply research on learning to geoscience instruction
• Mutual Discovery
– A wealth of shared interests
• Participants
– geoscientists, science educators, cognitive scientists
• Sponsored by NSF/REC and
Johnson Foundation

• What characterizes expert thinking?
• What concepts and skills are essential?
• How do we understand the Earth system that is heterogeneous, dynamic, ambiguous, uncertain?
– Geologic time
– Spatial relations
– Complex systems
– Visualizations
• What learning environments are most effective?
– Field; laboratory, using data

• Learning from Earth
– Beauty and creativity, field, simplicity, complexity
• Learning about geoscience thinking
– Spatial, temporal, complexity, communication, visualizations
• Helping students Learn
– Modeling, computer visualizations, design-to-learn
• Contributions from
– master geoscientists,
– cognitive/learning scientists,
– master educators
Cathryn A. Manduca and David W. Mogk (eds), 2006, Geol. Soc. Amer. Special Paper 413

• Importance of catalytic events
– Face to face workshops
– Virtual workspaces, connections
• Proactive recruitment of participants
– Geosciences
– Cognitive/learning/social sciences
• Sustained networking opportunities
• Assessment and Dissemination
• On-line resources to support further work

Guest Conveners: Jim Slotta (University of California, Berkeley),
Barbara Tversky (Stanford University) http://serc.carleton.edu/NAGTWorkshops/visualization/index.html

Guest convener: Thomas R. Koballa, Jr., Department of Mathematics and Science Education, University of Georgia http://serc.carleton.edu/NAGTWorkshops/affective/index.html

Guest Convener: Jenefer Husman, Psychology in Education, Arizona State University http://serc.carleton.edu/NAGTWorkshops/metacognition/index.html

Guest Convener: Jim Slotta, Univ. of Toronto http://serc.carleton.edu/NAGTWorkshops/complexsystems/index.html

Editors: Anthony D.
Feig and Alison
Stokes
2011

Primary Dissemination—Print and Presentations
• National Association of Geoscience Teachers
– Journal of Geoscience Education
• Geological Society of America,
– Pardee Symposium, Toward a Better Understanding of the
Complicated Earth: Insights from Geologic Research,
Education, and Cognitive Science, 2002
– Geocognition: Researching Student Learning in the
Geosciences, 2008
• American Geophysical Union
– Geocognition and Geoscience Education Research: Impacts on Course Curriculum and Student Learning, 2009
– EOS articles

• Michigan State University: Julie Libarkin
– Geocognition Research Laboratory h ttps://www.msu.edu/~libarkin/LIBARKIN.html
• Weizmann Institute of Science: Nir Orion
– Center for Research and Engagement in Science and Mathematics
Education
– http://stwww.weizmann.ac.il/menu/Earth_and_Environmental_Sciences.html
• Arizona State University: Steve Reynolds, Steve Semken
– E.g. Visualizing Topography http://reynolds.asu.edu/
• North Carolina State Univ: David McConnell, Karen McNeal
– Marine, Earth and Atmospheric Science Education Research http://www.meas.ncsu.edu/faculty/mcconnell/mcconnell.html

• Indigenous People
• Riggs, E.M., Robbins, E.I., and Darner, R, 2007, Sharing the Land: Attracting Native
American Students to the Geosciences – Special Edition on Broadening
Participation in the Earth Sciences, Journal of Geoscience Education, V.55, N.
6, 478-485
• “City kids”: Students with limited experience with nature

Learning Environments: Field
• What can be uniquely taught in a field setting?
• Given the difficulties of bringing students in the field, how can we make the field learning experience efficient, effective and inclusive?
• What are the high-order learning goals for field-based learning… and then how do we assess mastery of those goals?

Learning Environments: Laboratory http://serc.carleton.edu/introgeo/studentresearch/index.html
• What can be uniquely taught in a laboratory setting?
• What is the relationship between laboratory work and conceptual understanding?
• What are the high-order learning goals for laboratory-based learning… and then how do we assess mastery of those goals?

Learning Environments: Computer
On-line education
DBER methods and findings

Individualize instruction around presence or absence of misconceptions
Strong DBER finding:
• Misconceptions (aka alternative conceptions) abound, across all disciplines.
• There are research-based techniques for dislodging misconceptions and constructing more normative conceptions, but they tend to be timeconsuming
Test for DBERdocumented misconception no yes
Proceed to next topic
Conceptual change module https://www.learner.org/teacherslab/pup/

Micro-experiments in support of curriculum design decisions
Strong DBER finding: representations matter, and are challenging
Less common in textbooks --------- More common in
Laura Novick & Kefyn Catley, http://www.vanderbilt.edu/peabody/novick/evol_diagrams.html

• Which representation is better understood by students?
• Which wording is better understood by students?
• In which order should I teach these elements of the lesson, A before B, or vice versa?

Capture information about student performance of tasks that are inherently digital:
Example 1: Analyze and interpret “big” data

• What paths do students follow as they explore the data set, and does that differ from experts?
• What interface characteristics support students in trying to find their way through unfamiliar data?
• To make inferences from data requires combining
(a) information in the data, (b) representational competence, (c) knowledge of the represented system. How can this integration be supported?

Capture information about student performance of tasks that are inherently digital:
E xample 2: Develop and use computational models
Educational Global Climate Modeling http://edgcm.columbia.edu
Goddard Institute for Space Studies
NASA/Columbia University

EdGCM
Slide from http://serc.carleton.edu/eet/envisioningclimatechange/index.html

• What strategies are they using to design their
“experiments”?
• How are they evolving their strategies in response to their early findings?
• How are they using the model output as evidence for their claims?
• Are they deepening their understanding of how scientists use models to develop new knowledge?

An emphasis on question-asking and problem-solving.
Use of visualizations, natural examples and graphics; annotations showing key features and process

Emphasis:
•Scale
•Knowing what to look for (disembedding)
•Interpreting process and history
•Metacognition

• Learning in the laboratory, in the field
• Upper division teaching and learning
• MOOC’s and other on-line learning
• What “works” for diverse student audiences
• Geoscience habits of mind/ ways of knowing
• Research experiences for undergraduates
• How to assess mastery of:
– Spatial, temporal, systems thinking
• Research technologies
– Eye tracking, GPS, click-stream analysis….
• GER in K-12 and informal education
• Education around controversial issues (fracking,
GMO’s, climate change)

A Phylogenetic Tree of DBER in the Geosciences
Complex Systems
Live Virtual
Modeling
Master-Novice
Visualizations
Field Environment
Geoscience
Expertise
Spatial
Affective Domain
Learning
Resources
Temporal Pre-Mis-
Conceptions
Audience
Curricula
Cognitive
Studies Learning Outcomes
What Works?
Programs
Assessment
WE HAVE A LOT OF
WORK TO DO!

Looking forward to seeing you at the
Earth Educators’ Rendezvous!
http://serc.carleton.edu/NAGTWorkshops/DBER.html

• Synthesis of Research and Learning in the Geosciences
– Funded by NSF/REC
– Journal Club
– Synthesis review articles written by teams of geoscientist and learning/cognitive scientist
– Earth and Mind Blog http://serc.carleton.edu/earthandmind/index.html
• Spatial Intelligence and Learning Center (SILC)
– Multi-institutional collaboration
– Interdisciplinary
• Geoscience Affective Research Network (Garnet)
– Derived from Affective Domain workshop