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Thinking As a Geologist: Master-Novice Relations and Metacognition

David W. Mogk, Dept. of Earth Sciences, Montana State University

For geoscience majors, and to a lesser extent for all students in my classes, one of my main learning objectives is to help students to "think as a geologist". I certainly want to help students to:

Overall, I hope to help my students develop "scientific habits of the mind" (AAAS, 1989; a compiled list of these attributes is attached at the end of this essay). But to be an effective professional geoscientist (and I would also argue, to be a responsible citizen living on Earth), learners must be given the tools to go beyond simple mastery of knowledge and skill sets. To be able to make truly new contributions to understanding the complex Earth (rather than just recycling old ideas and concepts), learners must also be self-aware about how they learn in Nature and from each other in our scientific society (i.e. what are they doing, why are they doing that, with what expected outcomes?), they must be able to monitor their learning(i.e. assess whether or not they are effectively meeting their objectives), and know how to adjust their learning strategies(i.e. to be able to recognize dead ends, internal inconsistencies, impossible relationships, and to be able to respond appropriately). That is, they need to learn metacognitive skills to fully realize their potential as contributing scientists. Here are some of the abilities I hope my students will (begin to) develop in my own instructional activities, and in preparation for future development in graduate school or in employment. Like learning to play a fiddle or hit a baseball, these skills should be practiced early and often:

My personal interest related to metacognition in the geosciences is to study master-novice relations in an attempt to "unpack" the metacognitive strategies and skills employed by "master" geoscientists as they seek to understand Earth in the field, lab, experiment, and models. Much of what we do as professional geoscientists is done instantaneously, and without conscious thought on our part. I hope to explore the many ways master geoscientists look at Earth in its raw form in Nature, and in its distilled form through our various representations of Earth (maps, graphs, etc.). I hope to begin to clearly articulate the "what" geoscientists are thinking and "why" they choose a particular approach or strategy among the many options available to us. By identifying these strategies and approaches

As a final introductory thought, I have long thought that it is important to take an historical look at how our science has advanced in our standard coursework and have tried to introduce the "greats" of our science to our students by reviewing who they were, what their goals and motivations were, and some of the truly remarkable contributions that they made as examples of how we could conduct our own scientific investigations. I didn't know it at the time, but I have actually been teaching metacognition in my Mineralogy class by presenting one of the earliest contributions to metacognition: René Descartes' Discourse on the Method (1637):

The first was never to accept anything for true which I did not clearly know to be such; that is to say, carefully to avoid precipitancy and prejudice, and to comprise nothing more in my judgement than what was presented to my mind so clearly and distinctly as to exclude all ground of doubt.

The second, to divide each of the difficulties under examination into as many parts as possible, and as might be necessary for its adequate solution.

The third, to conduct my thoughts in such order that, by commencing with objects the simplest and easiest to know, I might ascend by little and little, and, as it were, step by step, to the knowledge of the more complex; assigning in thought a certain order even to those objects which in their own nature do not stand in a relation of antecedence and sequence.

And the last, in every case to make enumerations so complete, and reviews so general, that I might be assured that nothing was omitted.

References Cited

AAAS (1989), Project 2061 Science for All Americans, AAAS, Washington DC.

Bloom, B.S. (1956) Taxonomy of Educational Objectives, Handbook Domain. New York, David McKay Inc.

Harrow, A. (1972) A taxonomy of psychomotor domain: a guide for developing behavioral objectives. New York: David McKay.

Krathwohl, D.R., Bloom, B.S., and Masia, B.B. (197) Taxonomy of Educational Objectives, the Classification of Educational Goals. Handbook II: Affective Domain. New York: David McKay Co., Inc.

Simpson, E.J. (1972) The Classification of Educational Objectives in the Psychomotor Domain. Washington, DC: Gryphon House.

Scientific Habits of the Mind

—an unabridged collection of attributes collected from the literature and from the participants of previous workshops (no doubt incomplete, and constantly growing)... compiled by D. Mogk

Reasoned use of evidence
Acquisition and evaluation of the quality of evidence
Critical thinking; address questions, methods, interpretations
Inquiring, evaluating
Verifiable data, testable hypotheses
Rigorous proof
Curiosity – Wonder- Awe
Skepticism; question authority; be open minded
Openness to new ideas
Comfortable with revision to ideas
The ability to identify and avoid bias
Integrity, fairness, intellectual honesty, ethical behavior
Computational, estimation skills
Communication skills
Observational, measurement skills; detail
Ability to keep records
Ability to see patterns, relations
Check, verify, validate data
Synthesis and Analysis—know when either is appropriate
Internal consistency between data and interpretations
Data manipulation and presentation skills
Ability to make connections
Sense of wonder, excitement
Asking questions is as important as finding answers
Ability to relate data, explanations, process to a non-scientist
Multiple working hypotheses vs. "linear" thinking
Ability to communicate (accurately) what you know
Comfortable with uncertainty, ambiguity
Connections of data to other things
Positive thinking
Perseverance, follow-through
Learn from failure
Modeling, comparison

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