Chemical Education Research: Improving Chemistry Learning

Viewpoints: Chemists on Chemistry Chemical Education Research: Improving Chemistry Learning J. Dudley Herron and Susan C. Nurrenbern Chemical Education Research: Improving Chemistry Learning

Chemical education research is the systematic investigation

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of learning grounded in a theoretical foundation that focuses on understanding and improving learning of chemistry. This article reviews many activities, changes, and

Outline

accomplishments that have taken place in this area of

Introduction

scholarly activity despite its relatively recent emergence as a

Focus The Behaviorist Era The Dawn of Constructivist Approaches

research area. The article describes how the two predominant broad perspectives of learning, behaviorism

What Research Has Revealed What You Think You Know May Not Be the Way It Is Learning Is Not a Spectator Sport! Appropriate Outcomes Must Be Identified and Measured

and constructivism, have shaped and influenced chemical education research design, analysis, and interpretation during the 1900s. Selected research studies illustrate the range of research design strategies and results that have contributed to an increased understanding of learning in

Challenges for the Future

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Viewpoints: Chemists on Chemistry is supported by a grant from The Camille and Henry Dreyfus Foundation, Inc.

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and continuing challenges that researchers in this area face as they strive to bridge the gap between chemistry and education—disciplines with differing theoretical bases and research paradigms.

JChemEd.chem.wisc.edu • Vol. 76 No. 10 October 1999 • Journal of Chemical Education

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Viewpoints: Chemists on Chemistry

Chemical Education Research Improving Chemistry Learning J. Dudley Herron 1576 Perkins Street, Morehead, KY 40351; [email protected] Susan C. Nurrenbern Department of Chemistry, Purdue University, West Lafayette, IN 47907; [email protected]

Introduction Chemistry education research is scholarship focused on understanding and improving chemistry learning. Some of the variables examined in this research relate to chemistry content, while others focus on what the teacher or student does in a learning environment. Some questions of past and current interest are these: What is there about ideas such as temperature and milliliter that make them easier to grasp than ideas such as mole, entropy, and carboxylic acid functional group? Can we predict those ideas that will be difficult to understand? Can we modify instruction to make difficult ideas more accessible? Is it better for students to work individually or in groups? Should material be presented via lecture, reading, or direct experience? How should ideas be sequenced to promote maximum learning? What examples and nonexamples should be used to illustrate ideas? How (and how often) should ideas be illustrated? How much time is necessary to understand or grasp a concept? What function can computer technology play in student understanding? In addition to the many variables that are critical in the learning process, the research picture is complicated by disagreement about what it means to understand an idea. Do any of the items in Figure 1, for example, adequately test understanding of gases? The answer to this question may depend on the level or age of students as well as the perspective of instructors. Publication of chemical education research—systematic investigation of learning grounded in a theoretical base—is a relatively recent undertaking. In spite of its short history, chemical education research has touched on many of the variables alluded to in this brief introduction. Consequently, summarizing chemical education research for the past 50 years would be nearly impossible; selecting samples of that research to illustrate changes in focus, method, and direction is not. Whenever possible, examples are drawn from this Journal, but examples from other journals are used to illustrate the changes that have taken place in the past 50 years. Focus Perhaps no idea in chemistry has had more influence than that of atomic theory. The idea that elements are made up of unique atoms that combine in definite proportions to produce substances with different and distinct properties provides a mental framework that currently shapes chemists’ thoughts about all chemical processes. Similarly, a chemical education researcher’s working theory of learning shapes that researcher’s view and approach to chemical education. Un-

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fortunately, there is no widely accepted learning theory that is as well defined and well behaved as atomic theory. Two broad perspectives, behaviorist and constructivist, have shaped chemical education research.1 Just as the acceptance of atomic theory had a monumental impact in the early 1800s, a shift in the dominant theory of learning from behaviorism to constructivism has had a significant impact on chemical education research over the past 50 years. The changes in chemical education research that have emerged from these two perspectives seem dichotomous. Behavioristbased research attempted to narrow things down. It put learning under the microscope in order to identify salient variables that could guarantee improvement in performance. Constructivist-based research reverses that focus, using a telescope to broaden the view of learning.

The Behaviorist Era Behaviorists reasoned that, with no way to observe the mind directly, it was best to describe learning in terms of stimuli that impinge on our senses and the observed responses to those stimuli. The prototypical experiment is Pavlov’s conditioning of dogs to salivate when a bell is rung (1). Skinner’s work with pigeons and his discussion of teaching machines (2) led directly to a variety of “programmed learning” activities in the late 1960s and early 1970s (Fig. 2) (3). Behaviorist theories make us think of knowledge as having an existence of its own. Knowledge is “out there” and the teacher’s job is to get it inside the students’ heads. Teachers transmit knowledge by providing the appropriate stimuli and conditioning students to respond appropriately. Students, in the behaviorist view, are objects manipulated by instructors or programmed materials. Students receive information from experts and respond with answers that are rewarded. Research grounded in behaviorist theory often focused on the effect of this classroom conditioning or training in relationship to a single outcome such as a correct answer, course grade, or exam score (4 ). The predominant research designs mirrored the behaviorist view and effectively imposed the physical science research model on education research. Treatment–control group designs and pre–post test designs were popular. These designs involved stating a hypothesis, usually in null form, and then collecting numerical data in such a manner that they could be analyzed using parametric statistics. Analysis of variance and t-tests were commonly applied in order to reject or accept the null hypothesis, which amounted to the question, Is method A better than method B? Factor analysis could be used to analyze data from research that incorporated several variables, but that type of analysis required larger-sized samples than were normally available.

Journal of Chemical Education • Vol. 76 No. 10 October 1999 • JChemEd.chem.wisc.edu

Chemical Education Research: Improving Chemistry Learning

The Dawn of Constructivist Approaches As accumulating evidence from research and experience revealed that students could indeed be trained to provide acceptable responses without understanding, the behavioristbased paradigm gave way to information processing (5) (Fig. 3) and constructivist (6 ) theories of learning that were in accord with John Dewey’s insights written during the first quarter of this century (7): [N]o thought, no idea, can possibly be conveyed as an idea from one person to another. When it is told, it is, to the one to whom it is told, another given fact, not an idea. The communication may stimulate the other person to realize the question for himself and to think out a like idea, or it may smother his intellectual interest and suppress his dawning effort at thought. But what he directly gets cannot be an idea.

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Figure 3. Carboxylic acid group. Research on information processing suggests that humans utilize several strategies to overcome limitations on “working memory”. Johnstone (35 ) points out that information provided by this formula may overload the working memory of beginning organic students because it is processed as individual atoms connected in a particular sequence with single or double bonds, but over time the information is “chunked” as a single bit of information: “carboxylic acid group”. (Herron, J. D. The Chemistry Classroom; American Chemical Society: Washington, DC, 1996, p 101.)

Items to Test Understanding of Gases 1. Define gas. 2. A sample of gas occupies 600 mL at 25 °C. If the pressure is held constant, what will be the volume of the sample of gas at {5 °C? 3. The following diagram represents a cross-sectional area of a steel tank filled with hydrogen gas at 20 °C and 3 atm pressure. (The dots represent the distribution of H2 molecules.)

Figure 1. Items to test understanding of gases. Items 2 and 3 are taken from Nurrenbern, S. C.; Pickering, M. J. Chem. Educ. 1987, 64, 508.

Which of the following diagrams illustrate the distribution of H2 molecules in the steel tank if the temperature is lowered to {20 °C?

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4. PV = nRT; Solve for V. 5. If the values of V, n, and R in the above equation remain constant, what will happen to the value of P when T is increased by 50%?

Figure 2. Frame from a programmed chemistry text. Programmed texts were designed to force students to make an overt response, which would be followed by feedback that provided positive reinforcement for correct answers and negative reinforcement for incorrect responses. (Banks, J. E. J. Chem. Educ. 1963, 40, 22.)


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This shift coincided with a growing awareness and understanding of psychological theories of intellectual and cognitive development such as that developed by Jean Piaget (8). Constructivism asserts that “knowledge is not passively received but is actively built up by the cognizing [learner]” (9) and shifts attention from what the teacher or program does to what goes on inside the learner (10, p 328): Students are sense makers. They interpret what has been said or read in light of what they already know, constructing knowledge…that fits their understanding of the world. The challenge for teachers, especially university teachers, becomes the question of how to design learning opportunities that result in maximal learning.

Research questions and design responded to this change, and efforts were redirected along the lines of the constructivist perspective. For example, if students construct knowledge on the basis of what they already know rather than from the perspective of being “empty vessels” to be filled with knowledge, then instruction could be more effective when teachers know what students think about the world around them. This change in perspective has brought about a redefinition and measurement of learning and has necessitated moving away from the short-term, treatment–control research design in order to obtain relevant information about learning. Data collection techniques such as individual interviews, case studies, and field notes allow for the recognition that cognitive learning has an important social aspect. Individual interviews following strict protocols such as the one in the box below have been used to infer students’ understanding of key concepts. Designs that reflect accepted research in the social sciences or the growing discipline of cognitive science—for ex-

ample, qualitative research, longitudinal studies, action research, and ethnographic techniques—replace short-term, treatment–control group designs. Nonparametric statistical analyses that can be applied reliably to small data sets helped to avoid some of the restrictive assumptions of parametric statistical methods. Meta-analysis allows for statistical comparison of results among a large number of research articles that have common variables—for example, cooperative learning or problem solving. The analysis technique of triangulating data allows researchers to gather data in several ways— for example, quantitative and qualitative ways—or collect information from several sources, to identify broad trends and patterns. What Research Has Revealed Education research has revealed important insights into the learning process.

What You Think You Know May Not Be the Way It Is Individual interview techniques patterned after those used by Piaget to study intellectual development have revealed that a substantial percentage of students at all age levels hold deep-seated ideas about the natural world that differ from empirical evidence and from explanations accepted in science (11). This is true for both what happens (e.g., heavy objects always sink in water, bubbles in boiling water are hydrogen or oxygen, rapidly boiling water is at a higher temperature than gently boiling water, mass changes when matter melts or boils) (12) and why things happen (e.g., caloric theory for understanding heat) (13). Recent studies focus on specific misconceptions and go beyond listing deficiencies to describe sources of confusion and suggest measures that can be taken

Interview Protocol: Temperature and Heat 1. Use the suggestions in “Individual Interviews: General Considerations” to set the student at ease. 2. Begin the interview by involving the student in the demonstration as follows: “I have a solution of 1 M HCl and a solution of 1 M NaOH that have been sitting at room temperature. Do you know what will happen if we mix these solutions?” 3. Allow response. Does the S know that they will react? Does the S know that the reaction is exothermic? “Well, let’s see. You take this thermometer, the HCl solution, and this graduate. Measure 25 mL of solution in the graduate and measure the temperature of the solution. While you check the HCl solution, I’ll measure 25 mL of the NaOH solution and measure its temperature.” 4. Measure the solutions and record temperatures. T’s should be the same. “Why do you think the temperature of the two solutions is the same?” 5. Does S understand that matter gains or loses heat until its temperature equals that of surroundings? Does S conclude that since both solutions have been sitting in the same room for some time, their temperatures should be same? “O.K. Now let’s pour the solutions together in this Styrofoam cup and check the temperature again.” 6. Mix solutions and record T. “Why did the temperature increase?”

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7. Does S conclude that heat is generated as reaction occurs? “If more of the chemicals react, will more heat be produced?” 8. Allow response. “Let’s double the amount of each solution and do the experiment again. You measure out 50 mL of the HCl solution while I measure out 50 mL of the NaOH solution.” 9. Measure solutions. “Before we mix them, what do you think the temperature will be when we mix these solutions?” 10. Allow prediction. Mix. Measure T. “How can you explain the fact that the temperature change was the same as in the first experiment?” 11. Ask probing questions if it seems appropriate. Does S still believe that the heat produced in the second experiment is twice the heat produced in the first? Can S explain the difference between heat and temperature? “Can you think of any way we could do this reaction between HCl and NaOH so that the temperature change would double when we double the amount of HCl and NaOH that react?” 12. Does S see that the T will double when the heat doubles only if the amount of matter heated is the same in both cases? Thank the S for helping and terminate the interview.



to overcome the confusion (14). More than 100 references relative to research on chemistry misconceptions were listed in a review of an ERIC database that was begun in 1989 (15), and chemistry misconceptions continues to be an active area of research.

Learning Is Not a Spectator Sport! Several constructivist models for teaching have been developed (16 ) but the most widely used and researched strategy is cooperative learning (17). Cooperative learning refers to learning activities organized around small groups of students working together to achieve common learning goals. Research on cooperative learning usually contrasts achievement of students working in small, cooperating groups with that of students who work independently. A meta-analysis by Johnson and Johnson of 323 research studies revealed that, when used properly, cooperative learning strategies enhance learning: “The average cooperator performed at about two-thirds of a standard deviation above average competitors (effect size = 0.67) and three-quarters of a standard deviation above the average person working within an individualistic situation (effect size = 0.75)” (18). A meta-analysis of research studies that focus on problem solving in cooperative vs competitive settings showed similar results (19) and has important implications for development of problem-solving skills in chemistry, an issue that continues to challenge chemical education (Fig. 4). Dinan and Frydrychowski (20) combined cooperative strategies with individual efforts in an organic chemistry class and reported results consistent with the meta-analyses. More than 80% of the students responded positively to a strategy that involved taking minitests following a 10-minute group session where students helped each other with learning objectives or problems for that day. Each 10-minute minitest was taken first by individuals, then collectively in groups; this was followed by class discussion of the minitest. Although this was not a true experiment, the students in the class that involved cooperative activities performed better on the final exam than did students in previous courses taught by the lecture method. In addition, the amount of material covered with the cooperative strategy was 14% greater than the material covered in previous courses (16 vs 14 chapters)! Towns and Grant (21) reported similar results in a graduate-level thermodynamics course that combined cooperative strategies with individual effort. One of three classes

Figure 4. Students engaged in cooperative learning exercise. Interactions among students in cooperative learning lead to clarification of ideas, increased conceptual understanding, and improved problem solving skills.

per week was devoted to group discussion and presentation of problem solutions on topics and problems identified by the professor at the beginning of the week. Students brought their individual solutions to the problems to class on the designated discussion and presentation day. At the beginning of that class, students were randomly assigned to groups of 6 or 7. Each group was assigned one of the problems and given 10 minutes to discuss the solution to that problem, followed by 10 minutes for presentation of the solution to the entire class. The authors report that on the basis of their questions and answers to examination problems, students reflected a better conceptual understanding than students who had taken the course previously. Qualitative research strategies led to the conclusion that the cooperative learning experience moved students away from rote memorization toward meaningful learning and developed students’ interpersonal and communication skills. Additional research to identify the reasons for the success of cooperative learning is linked to learning theories. First of all, cooperative groups must cooperate if they are to make progress in learning (18). Knight and Bohlmeyer cite several factors that may account for the favorable effect of cooperative strategies on learning (22). The quality and nature of cognitive processing that occurs as students try to explain an idea or try to understand the explanations of others helps students use elaborative and metacognitive strategies and higher-level reasoning more frequently than independent, individual learning. Teachers give students more responsibility for learning and distribute attention more equitably when serving as mentors, facilitators, and resource persons rather than dispensers of information (23). Other factors that could account for increased learning in cooperative settings, but are as yet not completely substantiated by research, are academic task structure, reward structures (e.g., recognition from peers), and time spent on sharing ideas related to the learning task.

Appropriate Outcomes Must Be Identified and Measured While the changes in research focus and methodology have revealed the positive effects of active learning techniques such as cooperative learning, inquiry learning, and the Karplus learning cycle (16 ), research has failed to provide similar understanding of other teaching strategies. For example, identification of the learning functions of the laboratory and of technology has been elusive. Early research on methods of laboratory teaching were prompted by large increases in enrollments between 1910 and 1930. Consequently, demonstrations were frequently substituted for individual laboratory work, and research studies at that time commonly compared the effectiveness of demonstrations versus individual work. One of the better studies of this type was reported by Horton in 1928 (24 ). Horton studied the outcomes of laboratory teaching designed to develop laboratory techniques and the ability to use those techniques in problem solving. Pupils devised their own experiments in response to an assignment given the day preceding the regular laboratory day. Typical assignments were to determine whether metals other than zinc liberate hydrogen from hydrochloric acid or to determine which of several substances such as silk, wool, wood, and soft coal give off hydrogen sulfide by destructive distillation. Students were given regular laboratory instruction via demonstration and, in addition, were given opportunities to use the ideas that came from these experiences in the


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Figure 5. TOPS overhead projection apparatus (J. Chem. Educ. 1962, 39, 12). Although chemists have employed various technologies to enhance teaching, few have been subjected to systematic evaluation of their effectiveness.

solution of problems. Experimental groups did much better than controls on tests requiring students to solve a lab problem and on tests requiring them to set up apparatus required for an experiment without doing the experiment. Horton’s findings in favor of laboratory instruction are anomalous. Generally no difference is found in the achievement of students who participate in laboratory instruction and of those who do not (25). Horton seems to have had the good sense to test things that laboratory work is most likely to impact. In the 31st NSSE yearbook summary of research, Curtis points out that the value of laboratory instruction depends on what you are trying to teach (26 ). Inquiry-oriented laboratory activities teach inquiry better than lecture/demonstration or verification lab exercises, but only if teachers are skilled in inquiry teaching methods and students are given the time and guidance required to become comfortable with the new methods and expectations. Bates reached the same conclusion 45 years later in his review of research on the role of laboratory in secondary school science (27 ). Continued interest in the role and methods of science teaching and its impact on nurturing cognitive development may lead to new strategies in which properly designed laboratory activities will have a central role.

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Questions 1. What conclusion(s) about the behavior of nitrogen gas can you draw from the graph?

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2. The line in the graph does not pass through all points. Why is the line drawn in this manner? What assumption would you make about the measurement represented by the point that is farthest from the line?

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Research into the effective use of technologies faces the same challenge as research into laboratory instruction: identifying what you are trying to teach or enhance, and then choosing appropriate tasks to test performance that is most likely affected by that technology. Throughout the past 50 years chemists have investigated a variety of technologies to improve chemistry education. Sanderson developed molecular models and explained how to use them to clarify various chemistry concepts (28). Alyea built equipment and adapted demonstrations for presentation to large groups using tools such as overhead projectors (29) (Fig. 5). Young, Day, and others developed programmed learning materials and investigated their pedagogical value (30). All these technologies have affected the way chemistry is taught, but how do they compare with the use of computers and communication networks spawned by the Internet? Rapid increases in processing speeds and memory have led to the development of animated sequences aimed at addressing a long-standing problem in chemistry education: the difficulty students have in connecting macroscopic chemical events with hypothesized changes taking place at the atomic level and the symbolic representations (formulas, equations, etc.) used to describe those events (Fig. 6).

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Figure 6. Measurements made on a sample of nitrogen gas. Chemists make use of various representational systems to which students must give meaning. [Herron, J. D. The Chemistry Classroom, American Chemical Society: Washington, DC, 1996, pp 174–175.]



Figure 7. Computer animation of electrochemical cells. Greenbowe’s program for exploring electrochemical cells (31) is representative of research designed to study the effect of animated sequences on learning.

Greenbowe’s program for exploring electrochemical cells (Fig. 7) is representative of research designed to study the effect of animated sequences on learning (31). Williamson and Abraham have shown that even short exposure to computer animations of molecular events can improve the understanding of the particulate nature of matter (32). Both content and management of technology can be guided by learning theories. Managing the components for the technology center at the University of Illinois that supports more than 5000 students in chemistry each year has been described by Smith (33). The management focuses on

minimizing students’ efforts to learn to use the system so that their energy and time can be focused on learning chemistry. Logistical problems are minimized so that instructors can focus their attention on teaching. This management involves networking computers, so that students can access the system from various workstations scattered across the campus, and a grade book, by which students can evaluate their standing in the class at any time. Research has yet to document whether these management strategies bring about the desired changes in student and teacher behavior.

Challenges for the Future Chemistry education research involves a complex interplay between the more global perspective of the social sciences (i.e., the process of learning) and the analytical perspective of the physical sciences (i.e., the content). Because it is difficult, if not impossible, to set up experiments that control all variables that could conceivably affect human achievement and performance in chemistry, the pursuit of the “right answer” is doomed to failure. As chemical education researchers enter the 21st century they will be challenged to incorporate the best aspects of quantitative and qualitative methods into carefully planned research projects. They must be familiar with the field of cognitive science, so that the research questions asked are grounded in useful theory and relevant to known learning difficulties. They must collect data from a variety of sources using qualitative or quantitative methods as appropriate and analyze the data in a manner appropriate for answering the questions posed. They must communicate educationally significant findings in straightforward language that is understood by chemists and resist the temptation to publish equivocal results just to lengthen a vita. The scope of what research can reveal about learning and the conditions of learning has broadened during the past 50 years. At the same time, these changes have been disconcerting for chemists whose training and education were based on the behaviorist perspective. They often perceive research using more qualitative techniques as imprecise and lacking in rigor, as indeed can be the case. Chemical education research continues to face the challenge of providing research evidence that is suffi-

ciently convincing and compelling that colleagues will apply it to their own teaching. To establish validity with chemists, qualitative research must be planned just as carefully as the quantitative approaches that were often identified with the behaviorist perspective. Communication about research results must include enough information to enable colleagues to understand the intellectual basis for the research and judge the reliability of the execution of the research project. The shift from a behaviorist to a constructivist perspective over the past 50 years has shifted attention from observable behavior to inferences about what transpires in students’ heads, but research efforts still face the challenge of getting inside students’ heads, something the behaviorists considered to be impossible and Committees for the Protection of Human Subjects are reluctant to allow. But perhaps not forever, given recent advances in fMRI (34 ). Chemical education research methods will continue to emerge and respond to increasing knowledge from a variety of disciplines in a manner similar to that taking place in the scientific study of the solid state and organometallic chemistry. Even now, the frontiers of science education research are moving beyond the investigation of individual cognition and the individual as the unit of analysis and are exploring the influence of social and cultural aspects in learning. The future of chemical education research appears daunting, yet the potential for bringing about effective changes in teaching and learning as society moves from the industrial age to the information age is intriguing and exciting.


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Note 1. The behaviorist perspective encompasses approaches such as classical conditioning, operant or instrumental conditioning, and other psychological theories that focus on external stimuli and responses to those stimuli while paying little attention to the mental processes that direct those responses. The constructivist perspective includes information processing and extensions of Piaget’s research that provide models for the mental processes that take place during learning.

Literature Cited 1. Vygotsky, L. S. Educational Psychology; St. Lucie Press: Boca Raton, FL, 1926/1997. 2. Skinner, B. F. Harvard Educ. Rev. 1954, 24, 86–97. 3. Banks, J. E. Chemical Equilibrium and Solutions: A Programmed Introduction; McGraw-Hill: New York, 1967. Gordon, J. E. How to Succeed in Organic Chemistry; Wiley: New York, 1979. Runquist, O. A. A Programmed Guide to Beilstein’s Handbuch; Burgess: Minneapolis, 1966. 4. Lawrence, A. E. J. Chem. Educ. 1955, 32, 25. Burkhalter, T. S. J. Chem. Educ. 1956, 33, 406. Lander, A. J. Chem. Educ. 1965, 42, 231. Stoppel, D. J. Chem. Educ. 1966, 43, 556. 5. Handbook of Learning and Cognitive Processes, Volume 5: Human Information Processing; Estes, W. K. Ed.; Lawrence Erlbaum Associates: Hillsdale, NJ, 1978. White, R. T. Learning Science; Blackwell: New York, 1988. 6. von Glassersfeld, E. In The Impact of the Piagetian Theory on Education, Philosophy, and Psychology; Murray, F. B., Ed.; University Park Press: Baltimore, MD, 1979; pp 109–122. 7. Dewey, J. Democracy and Education: An Introduction to the Philosophy of Education; Macmillan: New York, 1916/1926; p 188.

8. Inhelder, B.; Piaget, J. The Growth of Logical Thinking from Childhood to Adolescence; Basic Books: New York, 1958. 9. Wheatley, G. H. Sci. Educ. 1991, 75, 9–21. 10. Dana, T. M.; Davis, N. T. In The Practice of Constructivism in Science Education; AAAS Press: Washington, DC, 1993; pp 325–333. 11. Osborne, R.; Gilbert, J. Eur. J. Sci. Educ. 1980, 2, 311–321. Sutton, C. R. Eur. J. Sci. Educ. 1980, 10, 107–120. Posner, G. J.; Gertzog, W. A. Sci. Educ. 1982, 66, 195–209. Novak, J. D.; Gowin, D. B. Learning How to Learn; Cambridge University Press: Cambridge, 1984. Bowen, C. W. J. Chem. Educ. 1994, 71, 184–190. 12. Pfundt and Duit maintain a bibliography of more than 2,000 articles related to student misconceptions in science: Pfundt, H.; Duit, R. Students’ Alternative Frameworks and Science Education, 3rd ed.; IPN Reports-in-Brief. Institut für die Pädagogik der Naturwissenschaften an der Universität Kiel: Olshausenstrabe 62, D-2300 Kiel 1, Germany, 1991; ERIC No. ED342643. This bibliography, which was reviewed by Robinson in this Journal (Robinson, W. R. J. Chem. Educ. 1998, 75, 1074), is available from ERIC. 13. Albert, E. Sci. Educ. 1978, 62, 389–399. Erickson, G. Sci. Educ. 1979, 63, 221–230. Erickson, G. Sci. Educ. 1980, 64, 323–336. Shayer, M.; Wylam, H. J. Res. Sci. Teach. 1981, 18, 419–434. Hewson, M. Eur. J. Sci. Educ. 1984, 6, 245–262. 14. Huddle, P. A.; Pillay, A. E. J. Res. Sci. Teach. 1996, 33, 65–77. Smith, K. J.; Metz, P. J. Chem. Educ. 1996, 73, 233. Ogude, N. A.; Bradley, J. D. J. Chem. Educ. 1996, 73, 1145–1149. Noh, T.; Scharmann, L. C. J. Res. Sci. Teach. 1997, 34, 199–217. Sanger, M. J.; Greenbowe, T. J. J. Chem. Educ. 1997, 74, 819. 15. Robinson, W. R. J. Chem. Educ. 1998, 75, 1074. 16. Cosgrove, M.; Osborne, R. In Learning in Science: The Implications of Children’s Science; Osborne, R.; Fryberg, P., Eds.; Heinemann: Auckland, 1985; pp 101–111. Lawson, A.; Abraham,

Before accepting a position as professor and chair of the Department of Physical Sciences at Morehead State University in Morehead, Kentucky, J. Dudley Herron was a faculty member at Purdue University. At Purdue, he held a joint appointment in chemistry and education except for two-years (1989–1991) when he was head of the Department of Curriculum and Instruction. Dudley has had a significant influence on the growth and national recognition of the cross-discipline research Ph.D. program in Science Education and has directed graduate research projects in that area. His international experiences include being the training advisor for the Regional Education Centre for Science and Mathematics in Penang, Malaysia, and studying curriculum research in Israel and Scotland. Throughout his career Dudley has been active in science education at many levels. He chaired the Task Force on Chemical Education Research of the ACS Division of Chemical Education that prepared the 1994 report describing and clarifying the goals and nature of chemical education research as a scholarly activity (J. Chem. Educ. 1994, 71, 850). He has published more than 60 articles related to chemical education, possibly the most recognized being “Piaget for Chemists”, pubJ. Dudley Herron lished in this Journal (J. Chem. Educ. 1976, 53, 146). He has authored several Visiting Faculty, Department of Chemistry books, including The Chemistry Classroom: Formulas for Successful Teaching (ACS University of North Carolina—Wilmington 1996), Understanding Chemistry: A Preparatory Course (Random House, 2nd ed., Ph.D., 1965, Florida State University 1986), and Heath Chemistry (D. C. Heath, 1993). He was a member of the authoring M.Ed., 1960, University of North Carolina team that developed Intermediate Science Curriculum Study (ISCS), the individualB.A., 1958, University of Kentucky ized, laboratory-centered program for middle school science. He edited the Journal ’s feature column High School Forum for five years. He has been an active member of the ACS Division of Chemical Education, serving on the Executive Committee and the Examinations Institute Board of Trustees. He was on the editorial board for the Journal of Research in Science Teaching (JRST) and reviews manuscripts for JRST, this Journal, and other science education journals. Dudley’s awards include Visiting Scientist of the Year (Western Connecticut Section, ACS, 1983), Catalyst Award (Chemical Manufacturers Association, 1983), Outstanding Science Educator (Association for the Education of Teachers of Science, 1985), and Lilly Endowment Faculty Open Fellowship (1982).

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18. 19. 20. 21. 22. 23.

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M.; Renner, J. A Theory of Instruction: Using the Learning Cycle to Teach Science Concepts and Thinking Skills; NARST Monograph No. 1; National Association for Research in Science Teaching, 1989. Glasson, G.; Lalik, R. J. Res. Sci. Teach. 1993, 30, 187–207. Aronson, E. The Jigsaw Classroom; Sage: Beverly Hills, CA, 1978. Johnson, D. W. Educ. Res. 1981, 10, 5–10. Johnson, D.; Johnson, R.; Maruyana, G. Rev. Educ. Res. 1983, 53, 5–54. Sharan, S.; Kussell, P.; Hertz-Lazarowitz, R.; Bejarano, Y.; Raviv, S.; Sharan, Y.; Brosh, T.; Peleg, R. Cooperative Learning in the Classroom: Research in Desegregated Schools; Lawrence Erlbaum Associates: Hillsdale, NJ, 1984. Johnson, D. W.; Johnson, R. T. In Research on Motivation in Education, Volume 2: The Classroom Milieu; Academic: Orlando, 1985; pp 249–286. Slavin, R.; Sharan, S.; Kagan, S.; Lazarowitz, R. H.; Webb, C.; Schmuck, R. Learning to Cooperate, Cooperating to Learn; Plenum: New York, 1985. Slavin, R. Cooperative Learning: Theory, Research, and Practice; Prentice Hall: Englewood Cliffs, NJ, 1990. Patterson, M.; Dansereau, D. F.; Newbern, D. J. Educ. Psychol. 1992, 84, 453–461. Cooperative Learning: Theory and Research; Sharan, S. Ed; Praeger: New York, 1992. Johnson, D.; Johnson, R. In Cooperative Learning: Theory and Research; Sharon, S., Ed.; Praeger: New York, 1992; pp 23–37. Qin, A.; Johnson, D. W.; Johnson, R. Rev. Educ. Res. 1995, 65, 129–143. Dinan, F. J.; Frydrychowski, V. A. J. Chem. Educ. 1995, 72, 429. Towns, M. H.; Grant, E. R. J. Res. Sci. Teach. 1997, 34, 819. Knight, G. P.; Bohlmeyer, E. In Cooperative Learning: Theory and Research; Sharon, S., Ed.; Praeger: New York, 1992; pp 1–22. Hertz-Lazarowitz, R.; Shachar, H. In Cooperative Learning: Theory and Research; Sharon, S., Ed.; Praeger: New York, 1992; pp 77– 94. Horton, R. E. Measurable Outcomes of Individual Laboratory Work

25.

26.

27.

28. 29. 30.

31. 32. 33. 34. 35.

in High School Chemistry. Teachers College Contributions to Education, No. 303; Columbia University: New York, 1928. Blosser, P. A Critical Review of the Role of the Laboratory in Science Teaching; ERIC Clearinghouse for Science, Mathematics, and Environmental Education: Columbus, OH, 1980. (Eric Document Reproduction Service No. ED 206 445). Hofstein, A.; Lunetta, V. Rev. Educ. Res. 1980, 52, 201–217. Curtis, F. D. In The Thirty-First Yearbook of the National Society for the Study of Education, Part I: A Program for Teaching Science; Craig, G. S., et al., Eds.;. Public School Publishing Company: Bloomington, IL, 1932; Chapter VII, pp 91–108. Bates, G. C. In What Research Says to the Science Teacher; Rowe, M. B., Ed.; National Science Teachers Association: Washington, DC, 1978; pp 55–82. Sanderson, R. T. Teaching Chemistry with Models; Van Nostrand: Princeton, NJ, 1962. TOPS demonstrations were a regular feature of the Journal from 1962 through 1970. J. Chem. Educ. 1962–1970, 39–47. Young, J. A. J. Chem. Educ. 1961, 38, 463. Day, J. H. J. Chem. Educ. 1962, 39, 50. Young, J. A. J. Chem. Educ. 1963, 40, 11. Young introduces articles by Young; Day; Ford; Bakker, Benfey, and Stratton; Banks; Powell; Cohen and Fetterolf; and Reusch and Cornell that describe various implementations of programmed learning in chemistry. Greenbowe, T. J. J. Chem. Educ. 1994, 71, 555. Sanger, M. J.; Greenbowe, T. J. J. Chem. Educ. 1997, 74, 819. Williamson, V.; Abraham, M. J. Res. Sci. Teach. 1995, 32, 521. Smith, S.; Stovall, I. J. Chem. Educ. 1996, 73, 911. Dehaene, S.; Spelke, E.; Pinel, P.; Stanescu, R.; Tsivkin, S. Science 1999, 284, 970–974. Johnstone, A. Chem. Soc. Rev. 1980, 9, 365–380. (An abbreviated version appeared in J. Chem. Educ. 1983, 60, 968–971.)

Susan C. Nurrenbern is a professional staff member of the Purdue University chemistry department. Her responsibilities include management and facilitation of instruction in the general chemistry course sequence for approximately 2000 science and engineering majors. Her background and interests have been applied toward implementing active learning and teaching strategies in labs, recitations, and lectures during the department’s fall orientation program for incoming graduate students and to continued efforts with faculty and graduate instructors throughout the semester (J. Chem. Educ. 1999, 76, 114). Sue served 13 years as a faculty member of the University of Wisconsin–Stout Chemistry Department, before returning to Purdue in 1993. At UW–Stout she developed and implemented student-centered instructional strategies for nonscience majors. Her scholarly work focuses on the use of paired conceptual and algorithmic questions on classroom exams (J. Chem. Educ. 1987, 64, 508). She has also explored the use of diagrams to represent the particulate nature of matter (PNM), thus helping students to bridge the gaps between the macroscopic, microscopic, and symbolic components of chemistry and to evaluate their understanding of the nature Susan C. Nurrenbern of matter. She was chemistry department chair at UW–Stout, held leadership roles Professional Staff, Instructional Specialist in the university faculty governance structure, and was a Wisconsin Teaching FelPurdue University, Chemistry Department low. Ph.D., 1979, Purdue University Sue is active in the ACS Division of Chemical Education, having served on M.S., 1970, Indiana State University several general chemistry exam writing committees (including the conceptual exam) B.S., 1967, Indiana State University and the Research Committee. She reviews manuscripts and materials for the Journal of Research in Science Teaching, the Journal of Chemical Education, and commercial publishers. She has an extensive list of presentations, has conducted several workshops, and has been an instructor in the Institute for Chemical Education workshops at the University of Wisconsin–Madison and Purdue University workshops for teachers of Advanced Placement chemistry.


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