Editorial Cite This: J. Chem. Educ. 2019, 96, 611−612
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Can Today’s Chemistry Curriculum Actually Produce Tomorrow’s Adaptable Chemist? Thomas A. Holme*
J. Chem. Educ. 2019.96:611-612. Downloaded from pubs.acs.org by 46.161.63.16 on 04/09/19. For personal use only.
Department of Chemistry, Iowa State University, Ames, Iowa 50011, United States ABSTRACT: Chemistry as a profession has seemed to avoid the kind of largescale disruption that has visited other professions in the 21st century. While the probability that jobs for chemists will be filled by robots or machine-learning computers may be modest, the arrival of these tools will undoubtedly affect the workplace that students who graduate from chemistry programs encounter when they leave school. To what extent does the current curriculum help these students leverage the opportunities presented by these emerging technological trends, and to what extent will their education leave them wanting? The time for some critical self-analysis of our curricular biases and choices is perhaps overdue.
KEYWORDS: General Public, Curriculum, Interdisciplinary/Multidisciplinary, Professional Development
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While these traits are generally important, including for chemists working in teams, those who constructed this model typically apply it to fields such as health care. Thus, the final component, creativity, perhaps better described as creative intelligence, represents the trait most likely to not only insulate future chemists from disruptive innovation but also place chemists in positions to help solve pressing issues of earth and societal systems that have emerged over the past few decades.3 A specific definition for creative intelligence has been argued by scholars for decades, but ultimately at least two traits are required: originality and adaptiveness.4 Thus, while machine learning or robotics may not replace the chemist of the future, the chemist who uses these new tools may be well-positioned to replace the chemist who cannot or will not. Thinking about how traits of creative intelligence connect to models of how people think and learn allows the development of a perspective on creativity and cognition.5 This model, depicted in Figure 1, provides a basis from which to consider the nature of the chemistry curriculum in enhancing the creative intelligence of students who complete it. The top tier of Figure 1 represents the most important features that are provided by critical thinking approaches for tackling important challenges. Problem Finding summarizes the concept that problems must be identified, defined, and accepted as important to solve. Distinguishing between, for example, “problems that can be solved” and “situations that must be worked within” requires creative and critical thinking skills. The term Ideation also encompasses several cognitive skills, including fluency (or productivity), originality, and flexibility. This summary of ideation is clearly core to defining a
nderstanding the nature of technological disruption across industries is almost a cottage industry at this time. The hope of determining broad trends such as voting patterns across regions of the United States or economic prospects of whole generations of Americans has motivated many of the most visible efforts in this endeavor.1 At the same time, however, more granular studies directed at specific fields, or skill sets, have also emerged. While any prediction of future trends has significant uncertainty, looking at what futurepredicting studies suggest as possibilities for professionals in almost any field is worthwhile. Chemistry is no exception, so what might a future imbued with robotic automation and machine learning suggest for students who walk into general chemistry classrooms this decade, or the next, and complete a bachelor’s degree from a U.S. college or university? Among the more compelling models for considering this question, that proposed by Frey and Osborne2 provides a breakdown of the types of skills that are most readily reproduced by near-future automation trends and those most likely to continue to rely on humans. The key human capabilities that present bottlenecks for disruptive innovation are (i) perception and manipulation, (ii) social intelligence, and (iii) creativity. To the extent that jobs require these attributes, such jobs are more likely to require humans to accomplish them. Perception and manipulation in this definition refer predominantly to traits of physical dexterity; so, for example, the need for finger dexterity or the ability to work in cramped spaces or awkward positions remains challenging for robotic automation. Tasks that require these traits are less prone to disruption from automation. Social intelligence includes perceptiveness about the reactions of others, abilities to negotiate and reconcile differences, an ability to persuade others, and an ability to assist or care for others. © 2019 American Chemical Society and Division of Chemical Education, Inc.
Published: April 9, 2019 611
DOI: 10.1021/acs.jchemed.9b00112 J. Chem. Educ. 2019, 96, 611−612
Journal of Chemical Education
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Editorial
AUTHOR INFORMATION
Corresponding Author
*E-mail:
[email protected]. ORCID
Thomas A. Holme: 0000-0003-0590-5848 Notes
Views expressed in this editorial are those of the author and not necessarily the views of the ACS. Thomas A. Holme is a Morrill Professor in the Department of Chemistry, Iowa State University. His research has two distinct strands: chemical education research and human−computer interaction. He served as Director of the Examinations Institute of the American Chemical Society from 2002 to 2015 and conducts education research to improve the quality of information that can be obtained from exams and other forms of assessment. His current work in human−computer interaction is focused on the development and testing of augmented reality interfaces for use in chemistry education. He is the editor-in-chief designee of the Journal and will become editor-in-chief on January 1, 2020.
Figure 1. Traits associated with creative intelligence. Adapted with permission from ref 5. Copyright 1995 Plenum Publishing.
problem-solving approach within the chemical sciences as creative. Finally, Evaluation can be considered the ability to judge, with an acceptable level of accuracy, the outcome of creative problem-solving efforts. This aspect of creative thinking ties strongly to metacognition.6 Finally, the lower tier shows how all three of these traits for creative problem solving require both knowledge and motivation. Taking this idea a step further requires that teaching efforts acknowledge differences between procedural and declarative knowledge as well as between intrinsic and extrinsic motivation. While the connections between these characteristics for creative intelligence are complex, even looking at the individual traits themselves and considering the coursework curriculum of undergraduate chemistry can be enlightening. As an example, consider a few possible questions we could ask about the undergraduate curriculum. (1) Where in our classes do we allow students to identify problems, or the role chemistry plays in either generating problems or solutions to problems? (2) Does the curriculum promote the concept of original and flexible solutions to societal concerns that involve chemistry, or do our students mostly gain a sense that chemistry knowledge is useful mostly in chemistry classrooms? (3) How often do we ask students to judge whether an answer to a particular question is the only, or most desirable, solution? If the answers to these questions, and others like them, are generally “no” or “seldom/never”, the opportunity cost in terms of undeveloped creative intelligence may be steep for our students and the future of our profession. If machine-learning algorithms can learn enough of the chemistry that appears on our exams to do well in a course, what knowledge are we helping our students gain that will sustain them in meaningful careers in the future? The answers we devise to questions such as these can provide a springboard to considering a host of alternative approaches to the teaching and learning of chemistry. Core concepts related to machine learning, for example, have been described in the Journal, both many years ago7 and quite recently.8 Knowledge and motivation will remain important in any changes that might be developed, yet connecting these to the creative traits of finding problems, ideation of those problems, and evaluation of potential solutions will require connecting the content of chemistry to wider societal issues and concerns. As the pace of disruptive change quickens in the world around us, we owe it to our students to view our teaching decisions with an eye toward the future their knowledge will encounter and help create.
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REFERENCES
(1) Vermeulen, B.; Kesselhut, J.; Pyka, A.; Saviotti, P. P. The Impact of Automation on Employment: Just the Usual Structural Change? Sustainability 2018, 10, 1661. (2) Frey, C. B.; Osborne, M. The Future of Employment. Technol. Forecasting & Soc. Change 2017, 114, 254−280. (3) Steffen, W.; Richardson, K.; Rockström, J.; Cornell, S. E.; Fetzer, I.; Bennett, E. M.; Biggs, R.; Carpenter, S. R.; de Vries, W.; de Wit, C. A.; Folke, C.; Gerten, D.; Heinke, J.; Mace, G. M.; Persson, L. M.; Ramanathan, V.; Reyers, B.; Sörlin, S. Planetary Boundaries: Guiding Human Development on a Changing Planet. Science 2015, 347 (6223), 736−746. (4) Rothernberg, A.; Hausman, C. R. The Creativity Question; Duke University Press: Durham, NC, 1976. (5) Runco, M. A.; Chand, I. Cognition and Creativity. Educ. Psych. Rev. 1995, 7, 243−267. (6) Puryear, J. S. Metacognition as a Moderator of Creative Ideation and Creative Production. Creativity Res. Journal 2015, 27 (4), 334− 341. (7) Spining, M. T.; Darsey, J. A.; Sumpter, B. G.; Nold, D. W. Opening Up the Black Box of Artificial Neural Networks. J. Chem. Educ. 1994, 71 (5), 406−411. (8) Joss, L.; Müller, E. A. Machine Learning for Fluid Property Correlations: Classroom Examples with MATLAB. J. Chem. Educ. 2019, DOI: 10.1021/acs.jchemed.8b00692.
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DOI: 10.1021/acs.jchemed.9b00112 J. Chem. Educ. 2019, 96, 611−612
