importance of investigation and problem solving in science

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Biology library

Course: biology library   >   unit 1, the scientific method.

• Controlled experiments
• The scientific method and experimental design

Introduction

• Make an observation.
• Ask a question.
• Form a hypothesis , or testable explanation.
• Make a prediction based on the hypothesis.
• Test the prediction.
• Iterate: use the results to make new hypotheses or predictions.

Scientific method example: Failure to toast

1. make an observation..

• Observation: the toaster won't toast.

2. Ask a question.

• Question: Why won't my toaster toast?

3. Propose a hypothesis.

• Hypothesis: Maybe the outlet is broken.

4. Make predictions.

• Prediction: If I plug the toaster into a different outlet, then it will toast the bread.

5. Test the predictions.

• Test of prediction: Plug the toaster into a different outlet and try again.
• If the toaster does toast, then the hypothesis is supported—likely correct.
• If the toaster doesn't toast, then the hypothesis is not supported—likely wrong.

Logical possibility

Practical possibility, building a body of evidence, 6. iterate..

• Iteration time!
• If the hypothesis was supported, we might do additional tests to confirm it, or revise it to be more specific. For instance, we might investigate why the outlet is broken.
• If the hypothesis was not supported, we would come up with a new hypothesis. For instance, the next hypothesis might be that there's a broken wire in the toaster.

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Understanding Science

How science REALLY works...

• Understanding Science 101
• There are many routes into the process of science.
• The process of science involves testing ideas with evidence, getting input from the scientific community, and interacting with the larger society.

A blueprint for scientific investigations

A scaffold for scientific investigations The process of science involves many layers of complexity, but the key points of that process are straightforward:

There are many routes into the process, including serendipity (e.g., being hit on the head by the proverbial apple), concern over a practical problem (e.g., finding a new treatment for diabetes), and a technological development (e.g., the launch of a more advanced telescope). Scientists often begin an investigation by plain old poking around: tinkering, brainstorming, trying to make some new observations , chatting with colleagues about an idea, or doing some reading.

Scientific testing is at the heart of the process. In science, all ideas are tested with evidence from the natural world , which may take many different forms —Antarctic ice cores, particle accelerator experiments , or detailed descriptions of sedimentary rock layers. You can’t move through the process of science without examining how that evidence reflects on your ideas about how the world works — even if that means giving up a favorite hypothesis .

The scientific community helps ensure science’s accuracy. Members of the scientific community (i.e., researchers, technicians, educators, and students, to name a few) play many roles in the process of science, but are especially important in generating ideas, scrutinizing ideas, and weighing the evidence for and against them. Through the action of this community, science is self-correcting. For example, in the 1990s, John Christy and Roy Spencer reported that temperature measurements taken by satellite, instead of from the Earth’s surface, seemed to indicate that the Earth was cooling, not warming. However, other researchers soon pointed out that those measurements didn’t correct for the fact that satellites slowly lose altitude as they orbit. Once these corrections were made, the satellite measurements were much more consistent with the warming trend observed at the surface. Christy and Spencer immediately acknowledged the need for that correction.

The process of science is intertwined with society. The process of science both influences society (e.g., investigations of X-rays leading to the development of CT scanners) and is influenced by society (e.g., a society’s concern about the spread of HIV leading to studies of the molecular interactions within the immune system).

Now that you have an overview of the process of science, get the details on each of the main activities above. Here are three ways to explore:

• Learn by example . Explore  Asteroids and dinosaurs , which traces the path of scientists through the flowchart as they investigate the events surrounding the extinction of the dinosaurs.
• Pick and choose . Use the flowchart interactively to learn more about different parts of the process.
• Or simply read on for a guided tour of the process of science…
• Teaching resources
• Use our  web interactive  to help students document and reflect on the process of science.
• Learn strategies for building lessons and activities around the Science Flowchart: Grades 3-5 Grades 6-8 Grades 9-12 Grades 13-16
• Find lesson plans for introducing the Science Flowchart to your students in: Grades 3-5 Grades 6-8 Grades 9-16
• Get  graphics and pdfs of the Science Flowchart  to use in your classroom. Translations are available in Spanish, French, Japanese, and Swahili.
• Introduce the flowchart to your class with a short video. Videos about a  study of spiders  and a  study of climate change  are available.

The real process of science

Exploration and discovery

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Teaching Creativity and Inventive Problem Solving in Science

• Robert L. DeHaan

Division of Educational Studies, Emory University, Atlanta, GA 30322

Search for more papers by this author

Engaging learners in the excitement of science, helping them discover the value of evidence-based reasoning and higher-order cognitive skills, and teaching them to become creative problem solvers have long been goals of science education reformers. But the means to achieve these goals, especially methods to promote creative thinking in scientific problem solving, have not become widely known or used. In this essay, I review the evidence that creativity is not a single hard-to-measure property. The creative process can be explained by reference to increasingly well-understood cognitive skills such as cognitive flexibility and inhibitory control that are widely distributed in the population. I explore the relationship between creativity and the higher-order cognitive skills, review assessment methods, and describe several instructional strategies for enhancing creative problem solving in the college classroom. Evidence suggests that instruction to support the development of creativity requires inquiry-based teaching that includes explicit strategies to promote cognitive flexibility. Students need to be repeatedly reminded and shown how to be creative, to integrate material across subject areas, to question their own assumptions, and to imagine other viewpoints and possibilities. Further research is required to determine whether college students' learning will be enhanced by these measures.

INTRODUCTION

Dr. Dunne paces in front of his section of first-year college students, today not as their Bio 110 teacher but in the role of facilitator in their monthly “invention session.” For this meeting, the topic is stem cell therapy in heart disease. Members of each team of four students have primed themselves on the topic by reading selected articles from accessible sources such as Science, Nature, and Scientific American, and searching the World Wide Web, triangulating for up-to-date, accurate, background information. Each team knows that their first goal is to define a set of problems or limitations to overcome within the topic and to begin to think of possible solutions. Dr. Dunne starts the conversation by reminding the group of the few ground rules: one speaker at a time, listen carefully and have respect for others' ideas, question your own and others' assumptions, focus on alternative paths or solutions, maintain an atmosphere of collaboration and mutual support. He then sparks the discussion by asking one of the teams to describe a problem in need of solution.

Science in the United States is widely credited as a major source of discovery and economic development. According to the 2005 TAP Report produced by a prominent group of corporate leaders, “To maintain our country's competitiveness in the twenty-first century, we must cultivate the skilled scientists and engineers needed to create tomorrow's innovations.” ( www.tap2015.org/about/TAP_report2.pdf ). A panel of scientists, engineers, educators, and policy makers convened by the National Research Council (NRC) concurred with this view, reporting that the vitality of the nation “is derived in large part from the productivity of well-trained people and the steady stream of scientific and technical innovations they produce” ( NRC, 2007 ).

For many decades, science education reformers have promoted the idea that learners should be engaged in the excitement of science; they should be helped to discover the value of evidence-based reasoning and higher-order cognitive skills, and be taught to become innovative problem solvers (for reviews, see DeHaan, 2005 ; Hake, 2005 ; Nelson, 2008 ; Perkins and Wieman, 2008 ). But the means to achieve these goals, especially methods to promote creative thinking in scientific problem solving, are not widely known or used. An invention session such as that led by the fictional Dr. Dunne, described above, may seem fanciful as a means of teaching students to think about science as something more than a body of facts and terms to memorize. In recent years, however, models for promoting creative problem solving were developed for classroom use, as detailed by Treffinger and Isaksen (2005) , and such techniques are often used in the real world of high technology. To promote imaginative thinking, the advertising executive Alex F. Osborn invented brainstorming ( Osborn, 1948 , 1979 ), a technique that has since been successful in stimulating inventiveness among engineers and scientists. Could such strategies be transferred to a class for college students? Could they serve as a supplement to a high-quality, scientific teaching curriculum that helps students learn the facts and conceptual frameworks of science and make progress along the novice–expert continuum? Could brainstorming or other instructional strategies that are specifically designed to promote creativity teach students to be more adaptive in their growing expertise, more innovative in their problem-solving abilities? To begin to answer those questions, we first need to understand what is meant by “creativity.”

What Is Creativity? Big-C versus Mini-C Creativity

How to define creativity is an age-old question. Justice Potter Stewart's famous dictum regarding obscenity “I know it when I see it” has also long been an accepted test of creativity. But this is not an adequate criterion for developing an instructional approach. A scientist colleague of mine recently noted that “Many of us [in the scientific community] rarely give the creative process a second thought, imagining one either ‘has it’ or doesn't.” We often think of inventiveness or creativity in scientific fields as the kind of gift associated with a Michelangelo or Einstein. This is what Kaufman and Beghetto (2008) call big-C creativity, borrowing the term that earlier workers applied to the talents of experts in various fields who were identified as particularly creative by their expert colleagues ( MacKinnon, 1978 ). In this sense, creativity is seen as the ability of individuals to generate new ideas that contribute substantially to an intellectual domain. Howard Gardner defined such a creative person as one who “regularly solves problems, fashions products, or defines new questions in a domain in a way that is initially considered novel but that ultimately comes to be accepted in a particular cultural setting” ( Gardner, 1993 , p. 35).

But there is another level of inventiveness termed by various authors as “little-c” ( Craft, 2000 ) or “mini-c” ( Kaufman and Beghetto, 2008 ) creativity that is widespread among all populations. This would be consistent with the workplace definition of creativity offered by Amabile and her coworkers: “coming up with fresh ideas for changing products, services and processes so as to better achieve the organization's goals” ( Amabile et al. , 2005 ). Mini-c creativity is based on what Craft calls “possibility thinking” ( Craft, 2000 , pp. 3–4), as experienced when a worker suddenly has the insight to visualize a new, improved way to accomplish a task; it is represented by the “aha” moment when a student first sees two previously disparate concepts or facts in a new relationship, an example of what Arthur Koestler identified as bisociation: “perceiving a situation or event in two habitually incompatible associative contexts” ( Koestler, 1964 , p. 95).

In this essay, I maintain that mini-c creativity is not a mysterious, innate endowment of rare individuals. Instead, I argue that creative thinking is a multicomponent process, mediated through social interactions, that can be explained by reference to increasingly well-understood mental abilities such as cognitive flexibility and cognitive control that are widely distributed in the population. Moreover, I explore some of the recent research evidence (though with no effort at a comprehensive literature review) showing that these mental abilities are teachable; like other higher-order cognitive skills (HOCS), they can be enhanced by explicit instruction.

Creativity Is a Multicomponent Process

Efforts to define creativity in psychological terms go back to J. P. Guilford ( Guilford, 1950 ) and E. P. Torrance ( Torrance, 1974 ), both of whom recognized that underlying the construct were other cognitive variables such as ideational fluency, originality of ideas, and sensitivity to missing elements. Many authors since then have extended the argument that a creative act is not a singular event but a process, an interplay among several interactive cognitive and affective elements. In this view, the creative act has two phases, a generative and an exploratory or evaluative phase ( Finke et al. , 1996 ). During the generative process, the creative mind pictures a set of novel mental models as potential solutions to a problem. In the exploratory phase, we evaluate the multiple options and select the best one. Early scholars of creativity, such as J. P. Guilford, characterized the two phases as divergent thinking and convergent thinking ( Guilford, 1950 ). Guilford defined divergent thinking as the ability to produce a broad range of associations to a given stimulus or to arrive at many solutions to a problem (for overviews of the field from different perspectives, see Amabile, 1996 ; Banaji et al. , 2006 ; Sawyer, 2006 ). In neurocognitive terms, divergent thinking is referred to as associative richness ( Gabora, 2002 ; Simonton, 2004 ), which is often measured experimentally by comparing the number of words that an individual generates from memory in response to stimulus words on a word association test. In contrast, convergent thinking refers to the capacity to quickly focus on the one best solution to a problem.

The idea that there are two stages to the creative process is consistent with results from cognition research indicating that there are two distinct modes of thought, associative and analytical ( Neisser, 1963 ; Sloman, 1996 ). In the associative mode, thinking is defocused, suggestive, and intuitive, revealing remote or subtle connections between items that may be correlated, or may not, and are usually not causally related ( Burton, 2008 ). In the analytical mode, thought is focused and evaluative, more conducive to analyzing relationships of cause and effect (for a review of other cognitive aspects of creativity, see Runco, 2004 ). Science educators associate the analytical mode with the upper levels (analysis, synthesis, and evaluation) of Bloom's taxonomy (e.g., Crowe et al. , 2008 ), or with “critical thinking,” the process that underlies the “purposeful, self-regulatory judgment that drives problem-solving and decision-making” ( Quitadamo et al. , 2008 , p. 328). These modes of thinking are under cognitive control through the executive functions of the brain. The core executive functions, which are thought to underlie all planning, problem solving, and reasoning, are defined ( Blair and Razza, 2007 ) as working memory control (mentally holding and retrieving information), cognitive flexibility (considering multiple ideas and seeing different perspectives), and inhibitory control (resisting several thoughts or actions to focus on one). Readers wishing to delve further into the neuroscience of the creative process can refer to the cerebrocerebellar theory of creativity ( Vandervert et al. , 2007 ) in which these mental activities are described neurophysiologically as arising through interactions among different parts of the brain.

The main point from all of these works is that creativity is not some single hard-to-measure property or act. There is ample evidence that the creative process requires both divergent and convergent thinking and that it can be explained by reference to increasingly well-understood underlying mental abilities ( Haring-Smith, 2006 ; Kim, 2006 ; Sawyer, 2006 ; Kaufman and Sternberg, 2007 ) and cognitive processes ( Simonton, 2004 ; Diamond et al. , 2007 ; Vandervert et al. , 2007 ).

Creativity Is Widely Distributed and Occurs in a Social Context

Although it is understandable to speak of an aha moment as a creative act by the person who experiences it, authorities in the field have long recognized (e.g., Simonton, 1975 ) that creative thinking is not so much an individual trait but rather a social phenomenon involving interactions among people within their specific group or cultural settings. “Creativity isn't just a property of individuals, it is also a property of social groups” ( Sawyer, 2006 , p. 305). Indeed, Osborn introduced his brainstorming method because he was convinced that group creativity is always superior to individual creativity. He drew evidence for this conclusion from activities that demand collaborative output, for example, the improvisations of a jazz ensemble. Although each musician is individually creative during a performance, the novelty and inventiveness of each performer's playing is clearly influenced, and often enhanced, by “social and interactional processes” among the musicians ( Sawyer, 2006 , p. 120). Recently, Brophy (2006) offered evidence that for problem solving, the situation may be more nuanced. He confirmed that groups of interacting individuals were better at solving complex, multipart problems than single individuals. However, when dealing with certain kinds of single-issue problems, individual problem solvers produced a greater number of solutions than interacting groups, and those solutions were judged to be more original and useful.

Consistent with the findings of Brophy (2006) , many scholars acknowledge that creative discoveries in the real world such as solving the problems of cutting-edge science—which are usually complex and multipart—are influenced or even stimulated by social interaction among experts. The common image of the lone scientist in the laboratory experiencing a flash of creative inspiration is probably a myth from earlier days. As a case in point, the science historian Mara Beller analyzed the social processes that underlay some of the major discoveries of early twentieth-century quantum physics. Close examination of successive drafts of publications by members of the Copenhagen group revealed a remarkable degree of influence and collaboration among 10 or more colleagues, although many of these papers were published under the name of a single author ( Beller, 1999 ). Sociologists Bruno Latour and Steve Woolgar's study ( Latour and Woolgar, 1986 ) of a neuroendocrinology laboratory at the Salk Institute for Biological Studies make the related point that social interactions among the participating scientists determined to a remarkable degree what discoveries were made and how they were interpreted. In the laboratory, researchers studied the chemical structure of substances released by the brain. By analysis of the Salk scientists' verbalizations of concepts, theories, formulas, and results of their investigations, Latour and Woolgar showed that the structures and interpretations that were agreed upon, that is, the discoveries announced by the laboratory, were mediated by social interactions and power relationships among members of the laboratory group. By studying the discovery process in other fields of the natural sciences, sociologists and anthropologists have provided more cases that further illustrate how social and cultural dimensions affect scientific insights (for a thoughtful review, see Knorr Cetina, 1995 ).

In sum, when an individual experiences an aha moment that feels like a singular creative act, it may rather have resulted from a multicomponent process, under the influence of group interactions and social context. The process that led up to what may be sensed as a sudden insight will probably have included at least three diverse, but testable elements: 1) divergent thinking, including ideational fluency or cognitive flexibility, which is the cognitive executive function that underlies the ability to visualize and accept many ideas related to a problem; 2) convergent thinking or the application of inhibitory control to focus and mentally evaluate ideas; and 3) analogical thinking, the ability to understand a novel idea in terms of one that is already familiar.

LITERATURE REVIEW

What do we know about how to teach creativity.

The possibility of teaching for creative problem solving gained credence in the 1960s with the studies of Jerome Bruner, who argued that children should be encouraged to “treat a task as a problem for which one invents an answer, rather than finding one out there in a book or on the blackboard” ( Bruner, 1965 , pp. 1013–1014). Since that time, educators and psychologists have devised programs of instruction designed to promote creativity and inventiveness in virtually every student population: pre–K, elementary, high school, and college, as well as in disadvantaged students, athletes, and students in a variety of specific disciplines (for review, see Scott et al. , 2004 ). Smith (1998) identified 172 instructional approaches that have been applied at one time or another to develop divergent thinking skills.

Some of the most convincing evidence that elements of creativity can be enhanced by instruction comes from work with young children. Bodrova and Leong (2001) developed the Tools of the Mind (Tools) curriculum to improve all of the three core mental executive functions involved in creative problem solving: cognitive flexibility, working memory, and inhibitory control. In a year-long randomized study of 5-yr-olds from low-income families in 21 preschool classrooms, half of the teachers applied the districts' balanced literacy curriculum (literacy), whereas the experimenters trained the other half to teach the same academic content by using the Tools curriculum ( Diamond et al. , 2007 ). At the end of the year, when the children were tested with a battery of neurocognitive tests including a test for cognitive flexibility ( Durston et al. , 2003 ; Davidson et al. , 2006 ), those exposed to the Tools curriculum outperformed the literacy children by as much as 25% ( Diamond et al. , 2007 ). Although the Tools curriculum and literacy program were similar in academic content and in many other ways, they differed primarily in that Tools teachers spent 80% of their time explicitly reminding the children to think of alternative ways to solve a problem and building their executive function skills.

Teaching older students to be innovative also demands instruction that explicitly promotes creativity but is rigorously content-rich as well. A large body of research on the differences between novice and expert cognition indicates that creative thinking requires at least a minimal level of expertise and fluency within a knowledge domain ( Bransford et al. , 2000 ; Crawford and Brophy, 2006 ). What distinguishes experts from novices, in addition to their deeper knowledge of the subject, is their recognition of patterns in information, their ability to see relationships among disparate facts and concepts, and their capacity for organizing content into conceptual frameworks or schemata ( Bransford et al. , 2000 ; Sawyer, 2005 ).

Such expertise is often lacking in the traditional classroom. For students attempting to grapple with new subject matter, many kinds of problems that are presented in high school or college courses or that arise in the real world can be solved merely by applying newly learned algorithms or procedural knowledge. With practice, problem solving of this kind can become routine and is often considered to represent mastery of a subject, producing what Sternberg refers to as “pseudoexperts” ( Sternberg, 2003 ). But beyond such routine use of content knowledge the instructor's goal must be to produce students who have gained the HOCS needed to apply, analyze, synthesize, and evaluate knowledge ( Crowe et al. , 2008 ). The aim is to produce students who know enough about a field to grasp meaningful patterns of information, who can readily retrieve relevant knowledge from memory, and who can apply such knowledge effectively to novel problems. This condition is referred to as adaptive expertise ( Hatano and Ouro, 2003 ; Schwartz et al. , 2005 ). Instead of applying already mastered procedures, adaptive experts are able to draw on their knowledge to invent or adapt strategies for solving unique or novel problems within a knowledge domain. They are also able, ideally, to transfer conceptual frameworks and schemata from one domain to another (e.g., Schwartz et al. , 2005 ). Such flexible, innovative application of knowledge is what results in inventive or creative solutions to problems ( Crawford and Brophy, 2006 ; Crawford, 2007 ).

Promoting Creative Problem Solving in the College Classroom

In most college courses, instructors teach science primarily through lectures and textbooks that are dominated by facts and algorithmic processing rather than by concepts, principles, and evidence-based ways of thinking. This is despite ample evidence that many students gain little new knowledge from traditional lectures ( Hrepic et al. , 2007 ). Moreover, it is well documented that these methods engender passive learning rather than active engagement, boredom instead of intellectual excitement, and linear thinking rather than cognitive flexibility (e.g., Halpern and Hakel, 2003 ; Nelson, 2008 ; Perkins and Wieman, 2008 ). Cognitive flexibility, as noted, is one of the three core mental executive functions involved in creative problem solving ( Ausubel, 1963 , 2000 ). The capacity to apply ideas creatively in new contexts, referred to as the ability to “transfer” knowledge (see Mestre, 2005 ), requires that learners have opportunities to actively develop their own representations of information to convert it to a usable form. Especially when a knowledge domain is complex and fraught with ill-structured information, as in a typical introductory college biology course, instruction that emphasizes active-learning strategies is demonstrably more effective than traditional linear teaching in reducing failure rates and in promoting learning and transfer (e.g., Freeman et al. , 2007 ). Furthermore, there is already some evidence that inclusion of creativity training as part of a college curriculum can have positive effects. Hunsaker (2005) has reviewed a number of such studies. He cites work by McGregor (2001) , for example, showing that various creativity training programs including brainstorming and creative problem solving increase student scores on tests of creative-thinking abilities.

Model creativity—students develop creativity when instructors model creative thinking and inventiveness.

Repeatedly encourage idea generation—students need to be reminded to generate their own ideas and solutions in an environment free of criticism.

Cross-fertilize ideas—where possible, avoid teaching in subject-area boxes: a math box, a social studies box, etc; students' creative ideas and insights often result from learning to integrate material across subject areas.

Build self-efficacy—all students have the capacity to create and to experience the joy of having new ideas, but they must be helped to believe in their own capacity to be creative.

Constantly question assumptions—make questioning a part of the daily classroom exchange; it is more important for students to learn what questions to ask and how to ask them than to learn the answers.

Imagine other viewpoints—students broaden their perspectives by learning to reflect upon ideas and concepts from different points of view.

How Is Creativity Related to Critical Thinking and the Higher-Order Cognitive Skills?

It is not uncommon to associate creativity and ingenuity with scientific reasoning ( Sawyer, 2005 ; 2006 ). When instructors apply scientific teaching strategies ( Handelsman et al. , 2004 ; DeHaan, 2005 ; Wood, 2009 ) by using instructional methods based on learning research, according to Ebert-May and Hodder ( 2008 ), “we see students actively engaged in the thinking, creativity, rigor, and experimentation we associate with the practice of science—in much the same way we see students learn in the field and in laboratories” (p. 2). Perkins and Wieman (2008) note that “To be successful innovators in science and engineering, students must develop a deep conceptual understanding of the underlying science ideas, an ability to apply these ideas and concepts broadly in different contexts, and a vision to see their relevance and usefulness in real-world applications … An innovator is able to perceive and realize potential connections and opportunities better than others” (pp. 181–182). The results of Scott et al. (2004) suggest that nontraditional courses in science that are based on constructivist principles and that use strategies of scientific teaching to promote the HOCS and enhance content mastery and dexterity in scientific thinking ( Handelsman et al. , 2007 ; Nelson, 2008 ) also should be effective in promoting creativity and cognitive flexibility if students are explicitly guided to learn these skills.

Creativity is an essential element of problem solving ( Mumford et al. , 1991 ; Runco, 2004 ) and of critical thinking ( Abrami et al. , 2008 ). As such, it is common to think of applications of creativity such as inventiveness and ingenuity among the HOCS as defined in Bloom's taxonomy ( Crowe et al. , 2008 ). Thus, it should come as no surprise that creativity, like other elements of the HOCS, can be taught most effectively through inquiry-based instruction, informed by constructivist theory ( Ausubel, 1963 , 2000 ; Duch et al. , 2001 ; Nelson, 2008 ). In a survey of 103 instructors who taught college courses that included creativity instruction, Bull et al. (1995) asked respondents to rate the importance of various course characteristics for enhancing student creativity. Items ranking high on the list were: providing a social climate in which students feels safe, an open classroom environment that promotes tolerance for ambiguity and independence, the use of humor, metaphorical thinking, and problem defining. Many of the responses emphasized the same strategies as those advanced to promote creative problem solving (e.g., Mumford et al. , 1991 ; McFadzean, 2002 ; Treffinger and Isaksen, 2005 ) and critical thinking ( Abrami et al. , 2008 ).

In a careful meta-analysis, Scott et al. (2004) examined 70 instructional interventions designed to enhance and measure creative performance. The results were striking. Courses that stressed techniques such as critical thinking, convergent thinking, and constraint identification produced the largest positive effect sizes. More open techniques that provided less guidance in strategic approaches had less impact on the instructional outcomes. A striking finding was the effectiveness of being explicit; approaches that clearly informed students about the nature of creativity and offered clear strategies for creative thinking were most effective. Approaches such as social modeling, cooperative learning, and case-based (project-based) techniques that required the application of newly acquired knowledge were found to be positively correlated to high effect sizes. The most clear-cut result to emerge from the Scott et al. (2004) study was simply to confirm that creativity instruction can be highly successful in enhancing divergent thinking, problem solving, and imaginative performance. Most importantly, of the various cognitive processes examined, those linked to the generation of new ideas such as problem finding, conceptual combination, and idea generation showed the greatest improvement. The success of creativity instruction, the authors concluded, can be attributed to “developing and providing guidance concerning the application of requisite cognitive capacities … [and] a set of heuristics or strategies for working with already available knowledge” (p. 382).

Many of the scientific teaching practices that have been shown by research to foster content mastery and HOCS, and that are coming more widely into use, also would be consistent with promoting creativity. Wood (2009) has recently reviewed examples of such practices and how to apply them. These include relatively small modifications of the traditional lecture to engender more active learning, such as the use of concept tests and peer instruction ( Mazur, 1996 ), Just-in-Time-Teaching techniques ( Novak et al. , 1999 ), and student response systems known as “clickers” ( Knight and Wood, 2005 ; Crossgrove and Curran, 2008 ), all designed to allow the instructor to frequently and effortlessly elicit and respond to student thinking. Other strategies can transform the lecture hall into a workshop or studio classroom ( Gaffney et al. , 2008 ) where the teaching curriculum may emphasize problem-based (also known as project-based or case-based) learning strategies ( Duch et al. , 2001 ; Ebert-May and Hodder, 2008 ) or “community-based inquiry” in which students engage in research that enhances their critical-thinking skills ( Quitadamo et al. , 2008 ).

Another important approach that could readily subserve explicit creativity instruction is the use of computer-based interactive simulations, or “sims” ( Perkins and Wieman, 2008 ) to facilitate inquiry learning and effective, easy self-assessment. An example in the biological sciences would be Neurons in Action ( http://neuronsinaction.com/home/main ). In such educational environments, students gain conceptual understanding of scientific ideas through interactive engagement with materials (real or virtual), with each other, and with instructors. Following the tenets of scientific teaching, students are encouraged to pose and answer their own questions, to make sense of the materials, and to construct their own understanding. The question I pose here is whether an additional focus—guiding students to meet these challenges in a context that explicitly promotes creativity—would enhance learning and advance students' progress toward adaptive expertise?

Assessment of Creativity

To teach creativity, there must be measurable indicators to judge how much students have gained from instruction. Educational programs intended to teach creativity became popular after the Torrance Tests of Creative Thinking (TTCT) was introduced in the 1960s ( Torrance, 1974 ). But it soon became apparent that there were major problems in devising tests for creativity, both because of the difficulty of defining the construct and because of the number and complexity of elements that underlie it. Tests of intelligence and other personality characteristics on creative individuals revealed a host of related traits such as verbal fluency, metaphorical thinking, flexible decision making, tolerance of ambiguity, willingness to take risks, autonomy, divergent thinking, self-confidence, problem finding, ideational fluency, and belief in oneself as being “creative” ( Barron and Harrington, 1981 ; Tardif and Sternberg, 1988 ; Runco and Nemiro, 1994 ; Snyder et al. , 2004 ). Many of these traits have been the focus of extensive research of recent decades, but, as noted above, creativity is not defined by any one trait; there is now reason to believe that it is the interplay among the cognitive and affective processes that underlie inventiveness and the ability to find novel solutions to a problem.

Although the early creativity researchers recognized that assessing divergent thinking as a measure of creativity required tests for other underlying capacities ( Guilford, 1950 ; Torrance, 1974 ), these workers and their colleagues nonetheless believed that a high score for divergent thinking alone would correlate with real creative output. Unfortunately, no such correlation was shown ( Barron and Harrington, 1981 ). Results produced by many of the instruments initially designed to measure various aspects of creative thinking proved to be highly dependent on the test itself. A review of several hundred early studies showed that an individual's creativity score could be affected by simple test variables, for example, how the verbal pretest instructions were worded ( Barron and Harrington, 1981 , pp. 442–443). Most scholars now agree that divergent thinking, as originally defined, was not an adequate measure of creativity. The process of creative thinking requires a complex combination of elements that include cognitive flexibility, memory control, inhibitory control, and analogical thinking, enabling the mind to free-range and analogize, as well as to focus and test.

More recently, numerous psychometric measures have been developed and empirically tested (see Plucker and Renzulli, 1999 ) that allow more reliable and valid assessment of specific aspects of creativity. For example, the creativity quotient devised by Snyder et al. (2004) tests the ability of individuals to link different ideas and different categories of ideas into a novel synthesis. The Wallach–Kogan creativity test ( Wallach and Kogan, 1965 ) explores the uniqueness of ideas associated with a stimulus. For a more complete list and discussion, see the Creativity Tests website ( www.indiana.edu/∼bobweb/Handout/cretv_6.html ).

The most widely used measure of creativity is the TTCT, which has been modified four times since its original version in 1966 to take into account subsequent research. The TTCT-Verbal and the TTCT-Figural are two versions ( Torrance, 1998 ; see http://ststesting.com/2005giftttct.html ). The TTCT-Verbal consists of five tasks; the “stimulus” for each task is a picture to which the test-taker responds briefly in writing. A sample task that can be viewed from the TTCT Demonstrator website asks, “Suppose that people could transport themselves from place to place with just a wink of the eye or a twitch of the nose. What might be some things that would happen as a result? You have 3 min.” ( www.indiana.edu/∼bobweb/Handout/d3.ttct.htm ).

In the TTCT-Figural, participants are asked to construct a picture from a stimulus in the form of a partial line drawing given on the test sheet (see example below; Figure 1 ). Specific instructions are to “Add lines to the incomplete figures below to make pictures out of them. Try to tell complete stories with your pictures. Give your pictures titles. You have 3 min.” In the introductory materials, test-takers are urged to “… think of a picture or object that no one else will think of. Try to make it tell as complete and as interesting a story as you can …” ( Torrance et al. , 2008 , p. 2).

Figure 1. Sample figural test item from the TTCT Demonstrator website ( www.indiana.edu/∼bobweb/Handout/d3.ttct.htm ).

How would an instructor in a biology course judge the creativity of students' responses to such an item? To assist in this task, the TTCT has scoring and norming guides ( Torrance, 1998 ; Torrance et al. , 2008 ) with numerous samples and responses representing different levels of creativity. The guides show sample evaluations based upon specific indicators such as fluency, originality, elaboration (or complexity), unusual visualization, extending or breaking boundaries, humor, and imagery. These examples are easy to use and provide a high degree of validity and generalizability to the tests. The TTCT has been more intensively researched and analyzed than any other creativity instrument, and the norming samples have longitudinal validations and high predictive validity over a wide age range. In addition to global creativity scores, the TTCT is designed to provide outcome measures in various domains and thematic areas to allow for more insightful analysis ( Kaufman and Baer, 2006 ). Kim (2006) has examined the characteristics of the TTCT, including norms, reliability, and validity, and concludes that the test is an accurate measure of creativity. When properly used, it has been shown to be fair in terms of gender, race, community status, and language background. According to Kim (2006) and other authorities in the field ( McIntyre et al. , 2003 ; Scott et al. , 2004 ), Torrance's research and the development of the TTCT have provided groundwork for the idea that creative levels can be measured and then increased through instruction and practice.

SCIENTIFIC TEACHING TO PROMOTE CREATIVITY

How could creativity instruction be integrated into scientific teaching.

Guidelines for designing specific course units that emphasize HOCS by using strategies of scientific teaching are now available from the current literature. As an example, Karen Cloud-Hansen and colleagues ( Cloud-Hansen et al. , 2008 ) describe a course titled, “Ciprofloxacin Resistance in Neisseria gonorrhoeae .” They developed this undergraduate seminar to introduce college freshmen to important concepts in biology within a real-world context and to increase their content knowledge and critical-thinking skills. The centerpiece of the unit is a case study in which teams of students are challenged to take the role of a director of a local public health clinic. One of the county commissioners overseeing the clinic is an epidemiologist who wants to know “how you plan to address the emergence of ciprofloxacin resistance in Neisseria gonorrhoeae ” (p. 304). State budget cuts limit availability of expensive antibiotics and some laboratory tests to patients. Student teams are challenged to 1) develop a plan to address the medical, economic, and political questions such a clinic director would face in dealing with ciprofloxacin-resistant N. gonorrhoeae ; 2) provide scientific data to support their conclusions; and 3) describe their clinic plan in a one- to two-page referenced written report.

Throughout the 3-wk unit, in accordance with the principles of problem-based instruction ( Duch et al. , 2001 ), course instructors encourage students to seek, interpret, and synthesize their own information to the extent possible. Students have access to a variety of instructional formats, and active-learning experiences are incorporated throughout the unit. These activities are interspersed among minilectures and give the students opportunities to apply new information to their existing base of knowledge. The active-learning activities emphasize the key concepts of the minilectures and directly confront common misconceptions about antibiotic resistance, gene expression, and evolution. Weekly classes include question/answer/discussion sessions to address student misconceptions and 20-min minilectures on such topics as antibiotic resistance, evolution, and the central dogma of molecular biology. Students gather information about antibiotic resistance in N. gonorrhoeae , epidemiology of gonorrhea, and treatment options for the disease, and each team is expected to formulate a plan to address ciprofloxacin resistance in N. gonorrhoeae .

In this project, the authors assessed student gains in terms of content knowledge regarding topics covered such as the role of evolution in antibiotic resistance, mechanisms of gene expression, and the role of oncogenes in human disease. They also measured HOCS as gains in problem solving, according to a rubric that assessed self-reported abilities to communicate ideas logically, solve difficult problems about microbiology, propose hypotheses, analyze data, and draw conclusions. Comparing the pre- and posttests, students reported significant learning of scientific content. Among the thinking skill categories, students demonstrated measurable gains in their ability to solve problems about microbiology but the unit seemed to have little impact on their more general perceived problem-solving skills ( Cloud-Hansen et al. , 2008 ).

What would such a class look like with the addition of explicit creativity-promoting approaches? Would the gains in problem-solving abilities have been greater if during the minilectures and other activities, students had been introduced explicitly to elements of creative thinking from the Sternberg and Williams (1998) list described above? Would the students have reported greater gains if their instructors had encouraged idea generation with weekly brainstorming sessions; if they had reminded students to cross-fertilize ideas by integrating material across subject areas; built self-efficacy by helping students believe in their own capacity to be creative; helped students question their own assumptions; and encouraged students to imagine other viewpoints and possibilities? Of most relevance, could the authors have been more explicit in assessing the originality of the student plans? In an experiment that required college students to develop plans of a different, but comparable, type, Osborn and Mumford (2006) created an originality rubric ( Figure 2 ) that could apply equally to assist instructors in judging student plans in any course. With such modifications, would student gains in problem-solving abilities or other HOCS have been greater? Would their plans have been measurably more imaginative?

Figure 2. Originality rubric (adapted from Osburn and Mumford, 2006 , p. 183).

Answers to these questions can only be obtained when a course like that described by Cloud-Hansen et al. (2008) is taught with explicit instruction in creativity of the type I described above. But, such answers could be based upon more than subjective impressions of the course instructors. For example, students could be pretested with items from the TTCT-Verbal or TTCT-Figural like those shown. If, during minilectures and at every contact with instructors, students were repeatedly reminded and shown how to be as creative as possible, to integrate material across subject areas, to question their own assumptions and imagine other viewpoints and possibilities, would their scores on TTCT posttest items improve? Would the plans they formulated to address ciprofloxacin resistance become more imaginative?

Recall that in their meta-analysis, Scott et al. (2004) found that explicitly informing students about the nature of creativity and offering strategies for creative thinking were the most effective components of instruction. From their careful examination of 70 experimental studies, they concluded that approaches such as social modeling, cooperative learning, and case-based (project-based) techniques that required the application of newly acquired knowledge were positively correlated with high effect sizes. The study was clear in confirming that explicit creativity instruction can be successful in enhancing divergent thinking and problem solving. Would the same strategies work for courses in ecology and environmental biology, as detailed by Ebert-May and Hodder (2008) , or for a unit elaborated by Knight and Wood (2005) that applies classroom response clickers?

Finally, I return to my opening question with the fictional Dr. Dunne. Could a weekly brainstorming “invention session” included in a course like those described here serve as the site where students are introduced to concepts and strategies of creative problem solving? As frequently applied in schools of engineering ( Paulus and Nijstad, 2003 ), brainstorming provides an opportunity for the instructor to pose a problem and to ask the students to suggest as many solutions as possible in a brief period, thus enhancing ideational fluency. Here, students can be encouraged explicitly to build on the ideas of others and to think flexibly. Would brainstorming enhance students' divergent thinking or creative abilities as measured by TTCT items or an originality rubric? Many studies have demonstrated that group interactions such as brainstorming, under the right conditions, can indeed enhance creativity ( Paulus and Nijstad, 2003 ; Scott et al. , 2004 ), but there is little information from an undergraduate science classroom setting. Intellectual Ventures, a firm founded by Nathan Myhrvold, the creator of Microsoft's Research Division, has gathered groups of engineers and scientists around a table for day-long sessions to brainstorm about a prearranged topic. Here, the method seems to work. Since it was founded in 2000, Intellectual Ventures has filed hundreds of patent applications in more than 30 technology areas, applying the “invention session” strategy ( Gladwell, 2008 ). Currently, the company ranks among the top 50 worldwide in number of patent applications filed annually. Whether such a technique could be applied successfully in a college science course will only be revealed by future research.

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Submitted: 31 December 2008 Revised: 14 May 2009 Accepted: 28 May 2009

© 2009 by The American Society for Cell Biology

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• v.13(1); Spring 2014

Guiding Students to Develop an Understanding of Scientific Inquiry: A Science Skills Approach to Instruction and Assessment

Associated data.

Teacher-driven action research in the high school biology classroom reveals effective instructional and assessment strategies for guiding students to integrate their ideas about the skills and practices necessary for scientific inquiry. Implications for inquiry-based teaching and research in undergraduate life sciences courses are discussed.

New approaches for teaching and assessing scientific inquiry and practices are essential for guiding students to make the informed decisions required of an increasingly complex and global society. The Science Skills approach described here guides students to develop an understanding of the experimental skills required to perform a scientific investigation. An individual teacher's investigation of the strategies and tools she designed to promote scientific inquiry in her classroom is outlined. This teacher-driven action research in the high school biology classroom presents a simple study design that allowed for reciprocal testing of two simultaneous treatments, one that aimed to guide students to use vocabulary to identify and describe different scientific practices they were using in their investigations—for example, hypothesizing, data analysis, or use of controls—and another that focused on scientific collaboration. A knowledge integration (KI) rubric was designed to measure how students integrated their ideas about the skills and practices necessary for scientific inquiry. KI scores revealed that student understanding of scientific inquiry increased significantly after receiving instruction and using assessment tools aimed at promoting development of specific inquiry skills. General strategies for doing classroom-based action research in a straightforward and practical way are discussed, as are implications for teaching and evaluating introductory life sciences courses at the undergraduate level.

INTRODUCTION

Instruction and assessment are often designed to teach and measure the science concepts students learn but are less likely to address the skills students must develop in order to answer meaningful scientific questions. To make informed decisions in modern society, students must routinely formulate questions, test ideas, collect and analyze data, support arguments with evidence, and collaborate with peers. To promote such skills, science educators have long recommended frequent experimental work and hands-on activities ( Dewey, 1916 ) and effective assessment methods for measuring critical scientific-thinking skills and evaluating performance in laboratory exercises ( National Research Council [NRC], 2000 ). It is crucial that we strive to meet the call to improve K–12 and undergraduate inquiry instruction and assessment set out by a number of national science education organizations ( NRC, 1996 , 2003 ; American Association for the Advancement of Science [AAAS], 1998 , 2009 ). Most recently, the Next Generation Science Standards (NGSS) provided guidelines for encouraging the practices that scientists and engineers engage in as they investigate and build models across the K–12 science curriculum and beyond ( NGSS, 2013 ).

Many educators have claimed that inquiry is especially important in urban environments and for engaging minority students in making math and science relevant for them ( Barnes et al. , 1989 ; Stigler and Heibert, 1999 ; Moses, 2001 ; Haberman, 2003 ; Tate et al. , 2008 ; Siritunga et al. , 2011 ). Inquiry-based instruction includes a variety of teaching strategies, such as questioning; focusing on language; and guiding students to make comparisons, analyze, synthesize, and model. Skills important for scientific thinking are often taught implicitly; that is, the instructor assumes students learn how to think like a scientist by simply engaging in frequent experimental work in the classroom. However, explicit approaches have been shown to be more effective, for example, in teaching nature of science concepts to both students and science teachers ( Abd-El-Khalick and Lederman, 2000 ; Lederman et al ., 2001 ). The classroom described in this action research study aims to create a learning environment that is explicit about these essential features of classroom inquiry.

An accumulation of evidence exists for how inquiry in the science classroom at both the undergraduate and K–12 levels is effective in promoting student understanding of various content areas in life sciences education. Examples include an increased understanding of a variety of key concepts in the life sciences ( Aronson and Silviera, 2009 ; Lau and Robinson, 2009 ; Rissing and Cogan, 2009 ; Ribarič and Kordaš, 2011 ; Siritunga et al. , 2011 ; Treacy et al. , 2011 ; Zion et al. , 2011 ; Ryoo and Linn, 2012 ). Moreover, Derting and Ebert-May (2010) have shown long-term improvements in learning for students who experience learning-centered inquiry in introductory biology classes. While several studies have correlated such inquiry-based curricula on specific science topics with improvements in general academic skills ( Lord and Orkwiszewski, 2006 ; Treacy et al. , 2011 ), more research is needed on how students develop and integrate their understanding of specific experimental and scientific inquiry skills, as well as what general strategies are effective for promoting and measuring this understanding.

The theoretical framework that lies at the foundation of this study is knowledge integration (KI). “The knowledge integration perspective … characterizes learners as developing a repertoire of ideas, adding new ideas from instruction, experience, or social interactions, sorting out these ideas in varied contexts, making connections among ideas at multiple levels of analysis, developing more and more nuanced criteria for evaluating ideas, and formulating an increasingly linked set of views about any phenomenon” ( Linn, 2006 , p. 243). KI lies at the heart of the curricular design for both concepts and skills taught in the classroom in this study, as well as the design of the research tools for the study itself.

A number of KI rubrics and scoring guides have been developed to measure the extent to which students connect ideas important for understanding key concepts in different content areas ( Linn et al. , 2006 ; Liu et al. , 2008 ). For example, Ryoo and Linn (2012) designed a rubric that captures how middle school students integrate their ideas about how light energy is transformed into chemical energy during photosynthesis. In this study, the KI framework is applied to issues of how students integrate their ideas about skills important for scientific inquiry. In particular, this framework goes beyond considering inquiry as an accumulation of compartmentalized ideas. Rather than examining discrete steps in the process of experimentation, such as analyzing data or reaching conclusions ( Casotti et al. , 2008 ), the KI construct described below allows for the integration of student ideas about different aspects of experimental work, such as how experimental design is connected to interpretation of data, accounting for the complex ways in which separate skills important for experimentation are interconnected.

The research question for this study was: How does the use of Science Skills instructional and assessment tools that encourage students to identify and explain the skills they are using in laboratory activities improve KI of student ideas about scientific inquiry and experimentation? A successful model for combining inquiry-based instruction with assessment tools for measuring student understanding of concepts related to scientific experimentation in a high school biology class is presented. While this study is set in a high school context, an argument is provided for how it could translate into introductory life sciences courses at the undergraduate level.

School Site and Participants

There has been an emphasis in recent years on creating “small schools” within large comprehensive schools that provide a more personalized education for students; build relationships among students, teachers, and parents; give teachers additional opportunities to collaborate; and focus on specific themes, such as health or the arts ( Feldman, 2010 ). Student participants were enrolled in a small school for visual and performing arts students within a large urban high school of more than 3000 students. The total enrollment for this small school was approximately 200 students distributed throughout grades 9 through 12. Arts, humanities, and science teachers collaborated to design an integrated science curriculum in which students learned cell biology, genetics, evolution, and ecology in a ninth-grade biology course, and applied this learning to an in-depth study of human anatomy and physiology, particularly those topics most relevant for visual and performing artists, in a 10th-grade human anatomy and physiology course. Participants in this study included all 10th-grade students for one academic year in two class periods, referred to here as groups 1 and 2.

Students in each of the two class periods for this research study represent a typical group of performing and visual arts high school students. As with any two class periods at most high schools, the two classes for this study differed from one another in some ways. De facto tracking existed in terms of students’ interests in specific performing arts, as performing and visual arts classes were scheduled to alternate with the science classes. Students were given a choice about which arts classes they could take; students who preferred drama ended up in group 1, and those who preferred dance ended up in group 2. Additionally, because there were fewer male dancers than female dancers, group 2 was predominantly female (89% compared with 53% for group 1). Students who identified as visual artists were in both groups 1 and 2. It is not clear whether the differences between the two groups may have had any effect on academic performance in a life sciences class, as the overall academic performance as reflected in the grades awarded to assignments in this class was roughly similar between the two groups, falling between 75% and 82% each semester. Student demographic data for this small school were roughly similar to those of the total student body at the high school for the academic year studied (see Supplemental Table S1; Education Data Partnership, 2010 ).

Permission to do this research was sought and obtained through the local school district; parents received a letter describing the teacher's plans for the research and had the option to give consent for their student's work to be published.

Course Curricula

The students who participated in this research were in two class periods of 10th-grade human anatomy and physiology. The yearlong curriculum was organized by body system, as are many courses in human anatomy and physiology, with the systems brought together under different “big ideas,” such as “The brain serves to control and organize all body functions” and “Structure determines function.” While there was a strong emphasis on hands-on experience in the course, a variety of instructional approaches were used in these classrooms; these included lecture, group discussions, collaborative research projects, laboratory experiments, and inquiry-driven computer-based curricula. Throughout the course, the instructor made it clear to students that she particularly valued student-driven questions, experimentation, and the excitement of discovery. Key teaching strategies included: guiding students to provide a rationale for their predictions and hypotheses for experiments; leaving data organization and analysis open-ended; discussing as a class the pros and cons of experimental choices made by different student groups; combining class data to expand sample size; grading lab reports on the quality of evidence-based arguments rather than experimental outcomes; highlighting modeling whenever there was an opportunity; having students present findings directly to other classmates, including in scientific conference-style poster sessions; and providing structure for frequent class discussions and scientific discourse between peers in which there was an expectation of challenging and defending ideas. The textbook ( Marieb, 2006 ) was supplemented with curricula designed, collected, and/or modified by the author (e.g., from Kalamuck et al. , 2000 ; National Institutes of Health [NIH], 2000 ; WISE, 2012 ; Tate et al. , 2008 ). Students in the course typically engaged in experimental work for approximately 40% of the instructional time each week, with a specific focus on different aspects of the scientific research process and introduction to associated specialized vocabulary on an ongoing basis. A key learning objective for this curriculum is for students to increase awareness of who they are as scientists and develop a more specific vocabulary for discussing experimentation and their strengths in science.

Science Skills Instruments

With the intent of making a number of scientific-thinking and problem-solving skills explicit for students, a list of “A to Z Science Skills” was developed (such as A nalyzing, B uilding in controls, G raphing, H ypothesizing; Figure 1 ). Not only was this list posted on bulletin boards throughout the classroom, but every student also had quick access to a copy in his or her class binder. The instructor referred to the list whenever a term or method was introduced or required special emphasis in a class activity. It was made clear that this list was not all-inclusive; indeed, the class would often focus on a skill not on the list. Thus, this tool can be used to focus on different terms or parts of the scientific process, depending on the lab or activity for the day.

A to Z Science Skills. Note that this list is, of course, not all-inclusive and can be modified to fit instruction for other disciplines and at other levels from elementary through graduate education. A to Z Science Skills was adapted from Math Alphabilities (P. Tucher, personal communication).

Students’ experimental work was formatively assessed with a postlab reflection, which asked students to use the A to Z Science Skills list to “Pick 3 skills that you think you did well during lab, and describe how you demonstrated this skill.” This simple assignment was given approximately one to two times per month to assess 1) how students understood the importance of these terms and methods in relation to their own work doing science investigations, and 2) how they viewed their own development in using such skills. Generally, students completed this self-assessment at the end of a lab exercise as they wrote up their conclusions or after a series of experiments to reflect on their work during the previous week or two, which had the added benefit of promoting metacognition for their science learning. Conclusions for lab work were structured and always followed a similar pattern; students were asked to report their findings, use their data as evidence to support their claims, discuss sources of error, and identify a next experiment that would extend their work. As students completed their conclusions and the postlab reflection, they were encouraged to talk to one another about skills they had used that week (which were frequently different from those employed by their peers), giving the instructor the opportunity to wander around the classroom, checking to make sure their self-assessment matched her own understanding of what they had accomplished.

Group Collaboration Instruments

One skill that is key to successful scientific research is collaboration, which is specifically named in the A to Z Science Skills list. Scientific collaboration was promoted in the classroom on a regular basis with the use of a Group Collaboration rubric (Supplemental Figure S1) and the corresponding reflection, designed to measure successful collaboration skills, such as sharing ideas, distributing work, using time efficiently, and decision making. The goal for using the Group Collaboration tools was to improve awareness of what it takes to collaborate successfully and to help students learn to value group work more as an effective way to accomplish a major project. The Group Collaboration rubric outlined expectations for student group work and was introduced to students with the first major group project. Specifically, it measured collaboration skills in five categories described in a way that is accessible to high school students: Contributing & Listening to Ideas, Sharing in Work Equally, Using Time Efficiently, Making Decisions, and Discussing Science (Supplemental Figure S1). Over the course of the academic year, there were four times that groups worked together on an extensive project that served to review, connect, and apply concepts from a particular unit. These projects generally corresponded with the end of each quarter and lasted 1–2 wk each. After the projects were complete, each student was instructed “Use the rubric to pick the level that you feel your group reached in its collaboration for each category, and list one or more specific examples in the evidence column for how you reached that level” on an individual Group Collaboration reflection, which was a self-assessment of his or her group's performance. The Group Collaboration tools were also used intermittently for smaller group projects. It was made clear to the students that they would not be evaluated negatively if they identified areas needing improvement, but instead would be evaluated on their ability to provide evidence for their choices and to explain how they would improve on these areas in the future. As with the Science Skills tools, outlining different categories important for collaboration in the Group Collaboration rubric allowed an explicit focus on a specific aspect of good group work depending on the day or project.

Science Skills Assessment and KI Rubric

The extent to which student understanding of science skills and scientific collaboration changed over the course of the year was measured with a simple assessment, the Science Skills assessment (Supplemental Figure S2). Students were told that the assessment was for the instructor's own use to improve the course and would solicit their feedback in different ways about what helped them learn in the class. The assessment was given to students as a pre-, mid- and postassessment during approximately the first few weeks of the first semester, the first week of the second semester, and the last week of the academic year, respectively, and took less than 15 min to administer and complete each time.

A KI framework was used to develop a rubric for scoring open-ended responses to question 2 (“Name three skills that you think are important for doing science well, and explain why you picked them”) and question 5 (“What are your strengths in doing science?”). The Science Skills Knowledge Integration (SSKI) rubric was developed for this study to measure the extent to which students made links between specific skills and their importance for science ( Figure 2 includes examples of student responses). The SSKI rubric, a five-level latent construct aligned with other KI rubrics ( Linn et al. , 2006 ; Liu et al ., 2008 ), maps onto students’ increasingly sophisticated understanding of the research process. All scoring levels were represented in student responses. Interrater reliability for the SSKI rubric was greater than 95% for both questions 2 and 5, with two raters, with a high agreement indicated by a Cohen's kappa of 0.976.

SSKI rubric, with student examples. Examples are responses to the question “Name three skills that you think are important for doing science well, and explain why you picked them.”

For assessing whether students had more awareness of what it takes to collaborate successfully, at midsemester and at the end of the year, an attempt was made to create a KI rubric for collaboration to analyze responses to question 4 on the Science Skills assessment. Unfortunately, the open-ended responses proved difficult to code and categorize, limiting further information about student understanding of scientific collaboration from this study.

While some of the items on the Science Skills assessment are self-assessments, the SSKI rubric does not measure how much the students perceive they learned about science skills nor their skill level, but instead measures the ability of the student to identify specific skills important for scientific experimentation and the extent to which they are able to connect different ideas about the scientific research process. See Table 1   for a complete list of the different instruments used and the purpose intended for each.

Goals of Science Skills instruments

a Science Skills tools.

b Group Collaboration tools.

Study Design

The study was designed to simultaneously test two treatments, Science Skills or Group Collaboration, with two different groups of students and each group serving as a “no-treatment” comparison for other ( Figure 3 ). This experimental design can be applied to any classroom situation in which the student population can be divided into two groups for two independent interventions. In this case, the groups represented two different periods of students taking the same course, a typical teaching assignment for high school science teachers. During the first half of the year, group 2 was instructed to use the Science Skills assessment tools (A to Z Science Skills and postlab reflection, Table 1 ); at the same time, group 1 was instructed to use the Group Collaboration assessment tools (Group Collaboration rubric and reflection, Table 1 ). Thus, group 1 served as a no-treatment comparison for the group 2 Science Skills treatment, and group 2 served as a no-treatment comparison for the group 1 scientific collaboration treatment. Throughout the second half of the year, both groups were encouraged to develop scientific thinking and collaboration by using both types of assessment tools and therefore received instruction in both areas by the end of the course. KI gains were measured by scoring the Science Skills assessment responses to question 2 using the SSKI rubric (see above and Figure 2 ).

The study design allows for reciprocal testing of two simultaneous treatments.

The work presented here began when the author participated in Project IMPACT, a university program that provides a professional learning community structure to take on action research with regular feedback from teacher colleagues ( Curry, 2008 ). Teachers involved in this professional learning community worked on individual research projects connected by the general theme of how to promote social justice in a science or mathematics classroom. Teachers worked in groups of five to six with guidance from one another and a trained facilitator, posing a research question, designing a study, and testing changes they implemented in their classrooms. The Science Skills and Group Collaboration tools were piloted by the author with 120–140 high school life sciences students over the 2-yr period prior to the academic year in which this study took place.

Statistical Methods

Statistical analysis was done using Stata Data Analysis and Statistical Software (2012) and the R Software Environment (2013) , as indicated in relevant figure legends and associated text (see Results ).

Snapshots of Inquiry in the Classroom Using the Science Skills Approach

Many science teachers in K–12 urban schools struggle to create a learning environment that contains the essential features of classroom inquiry. This includes a learning environment in which students engage in scientifically oriented questions, formulate explanations from evidence, and communicate and justify their proposed explanations ( NRC, 2000 ; NGSS, 2013 ). Secondary life sciences teachers also struggle to unify inquiry-based lessons that span the entire curriculum in a cohesive manner. To meet the goal of promoting student inquiry, a new approach was designed and tested in the author's classroom, called “Science Skills,” a term that was accessible for high school students.

To illustrate how student inquiry was built into the curriculum in this classroom, a snapshot for one particular inquiry lesson is described. This lesson, which was taught within a unit on the senses and the nervous system, was modified from The Brain: Understanding Neurobiology through the Study of Addiction , a unit within a published curricular series ( NIH, 2000 ). For this lesson, all students in this study observed the effect of caffeine on the human body, generating their own hypotheses about the influence caffeinated soda drinks might have on their heart rates. They were instructed to choose controls to address different variables they expected to be important, record data in tables, and make graphs to analyze their data. As students worked together to make conclusions and compare findings with those of other student teams, students in one of the groups in this study were also asked to complete a postlab reflection (group 2; see the following section and Methods for details). One student focused on “observing” as a skill she used in this investigation, and remarked,

When we did the caffeine lab I observed my heart rate and pulse every two minutes and made notes on it. Observing makes it easier for me to understand [the effects of caffeine].

Instruction for the routine skills required for all experimental work was integrated in a similar fashion into each of the different curricular units and content areas. Additional examples of student responses to the postlab reflection reveal the variety of scientific processes emphasized by different students and for other experiments and activities (see Supplemental Table S2). Student reflections were first assessed informally by checking in with each student individually before his or her completed reflection was accepted. This informal assessment served to corroborate students’ self-assessment or to ask them to revise their responses until they matched the instructor's assessment of their mastery. Individual attention to students as they wrote their reflections was easy to manage, could be done quickly, and helped push students further in their understanding. For example, another student, who also focused on observation for a different laboratory activity, said,

I think observing is a more nature skill everyone has [ sic ]. I personally specialize with that skill because I’m always check[ing] new things and experiments out whenever something seems interesting.

The student was asked to be more specific after the instructor checked in, at which time the student added,

For example, when I had to dissect a cow's eye. I had to first examine and observe its outside.

While this student struggled to express himself articulately, this practice helped him advance his understanding of what approaches are important in scientific experiments. All of the students, in both groups 1 and 2, engaged in the same inquiry lessons at the same point in the curriculum. For example, in the case of the caffeine experiment, all students were instructed similarly to choose controls to address different variables, record data in tables, and analyze data with graphs. The two groups only differed in their use of additional tools that supplemented each inquiry lesson (see the following section).

Evidence of Gains in SSKI

For determining whether the Science Skills tools designed for this study were effective in promoting student inquiry and scientific thinking, student responses were evaluated for the pre-, mid-, and postassessment question 2 on the Science Skills assessment (see Methods ). Two different groups of students participated in the study: group 2 was instructed to use the Science Skills tools in the first half of the year; during this same instructional time, group 1 was being taught to use the Group Collaboration tools ( Figure 3 ). The learning goals, sequence of content taught, group projects, experiments, and activities were otherwise identical in both groups. Thus, group 1 served as a no-treatment comparison for the Science Skills treatment group 2, until the second half of the year, when both groups used both types of instruction and assessment tools ( Table 1 ). Student responses for both groups were analyzed using the SSKI rubric ( Figure 2 ) to measure the extent to which students made links between specific skills and their importance for science.

Results of this analysis revealed that students who used the Science Skills tools are better able to name and explain scientific-thinking strategies than their peers who did not use the same tools ( Table 2 ). The average scores for responses from both groups started at a similar level, as a two-sample t test revealed there was no significant difference ( p = 0.55). When the average pre- and midassessment KI scores for each student were compared, the change in scores for the no-treatment comparison group 1 was not statistically significant, as revealed by a one-sided paired t test ( p = 0.88). However, for group 2, which received the Science Skills treatment, each student's score improved on average by 0.44 at midsemester, demonstrating a statistically significant difference in KI ( p = 0.021). By the end of the school year, after both groups of students had received the Science Skills treatment, the average improvement on the assessment for group 1 was 1.1, and for group 2, it was 0.67. The average improvement in KI for each group pre- to postassessment is highly statistically significant ( p values were 0.00053 and 0.0048, respectively).

Students increase KI after using Science Skills instruction and assessment tools a

a This analysis was done on student responses to question 2 on the Science Skills assessment (“Name three skills that you think are important for doing science well, and explain why you picked them.”). A one-sided paired t test with 18 (or 17) degrees of freedom was performed for group 1 and group 2 changes in average KI scores, respectively. The null hypothesis was that there was no change and the average difference from pre- to midassessment or from pre- to postassessment was 0.

Effect size, which helps determine the extent to which statistically significant changes are likely to be meaningful, was also calculated ( Cohen, 1992 ). Consistently, Cohen's d measurements revealed that the Science Skills treatment for group 2 had a modest effect size midyear ( d = 0.57). By the end of the year, when both groups had received the treatment, an even larger effect size was seen ( d = 1.0 and d = 0.70, respectively; see Table 2B ). Similar patterns were seen from analysis of question 5 responses, in which students clearly identified science skills when describing their own particular strengths in science, confirming the results obtained with question 2 (unpublished data).

Further analysis of student responses for the Science Skills assessment was done to gain additional insight on the distribution of the student scores for pre-, mid-, and postassessment of these two groups ( Figure 4 ). A box plot reveals that the distribution was similar for both groups at the beginning of the year and for group 1 at midyear, before receiving the Science Skills treatment. However, the distribution shifted higher for group 2 midyear and for both groups at the end of the year, after both received the Science Skills treatment, confirming that students show gains in KI only after using these instruction and assessment tools.

Box plots reveal differences in the distribution of KI scores after students use Science Skills instruction and assessment tools. Group 1 (white boxes) used the Science Skills tools only after they were assessed midyear ( n = 19); group 2 (gray boxes) used Science Skills tools from the beginning of the course, were assessed at midyear, and continued use of the tools through the end of the year ( n = 18). Note that the dark lines represent the median; the boxes include 50% of the data, representing the 1st to the 3rd quartile; 90% of the data is within the whiskers; and open circles represent outliers. Figure 4 shows the distribution of the same data set as is analyzed in Table 2 and Figure 5 .

Not only did students show increases in average KI scores for treatment groups, but comparison of individual students’ scores for different assessments using scatter plots ( Figure 5 ) revealed that most students showed an increase from a lower KI score to a higher score after they had used the Science Skills instructional and assessment tools. A comparison of pre- and midassessment scores ( Figure 5A ) shows that most students in the no-treatment group 1 tended to score the same or worse, whereas most students in the Science Skills treatment group 2 had immediately increased their understanding of science skills at the midyear point. In comparing pre- and postassessment scores, Figure 5B shows that students in both groups had improved KI scores at the end of the year. Thus, there is a clear relationship between increased student understanding of science skills and the use of the Science Skills tools.

Scatter plots reveal increases in individual student's KI scores after using Science Skills instruction and assessment tools. (A) Comparison of pre- and midassessment scores for each student. (B) Comparison of pre- and postassessment scores for each student. Data points were jittered in the R Software Environment so that all were visible. A red line with a slope of 1 indicates no improvement; points above the line indicate increased improvement; points below the line indicate decreased improvement. Note that the data set is the same as that analyzed in Table 2 and Figure 4 .

Comparing Science Skills and Group Collaboration Treatments

Among many skills important for successful inquiry is group collaboration because the outcome of scientific projects and experiments depends on how well groups in classrooms or research laboratories function. In addition to helping students see collaboration as an important skill for scientific research, a goal for the introduction of Group Collaboration tools was for students to learn to appreciate collaborative group work more as an effective way to accomplish a significant science project. For assessing whether this goal was met, the five-level Likert-scale responses to the simple query in question 3 on the Science Skills assessment were analyzed by comparing the average change in KI scores for each student. For group 1, who had received the Group Collaboration treatment from the beginning of the year, an average of only 21% of the students had immediately increased their appreciation of group work at the midyear point; by the end of the year, 37% of the students on average increased their appreciation of group work as they continued to use the tools (as seen by comparing pre- and midassessments, and pre- and postassessments, respectively). For the no-treatment group 2, a surprising average of 33% of the students appreciated group work more when midyear responses were compared with those from the beginning of the year, but by the end of the year, this number had on average regressed back to the preassessment average (i.e., 0% of the students showed an increased appreciation of group work, the reason for which cannot be explained at this time). Thus evaluation of pre-, mid-, and postassessment of the scientific collaboration treatment did not reveal any relationship between the Group Collaboration treatment and gains made in the appreciation students had for group work.

Analysis of Group Collaboration Reflection Responses

Another goal for promoting scientific collaboration with the use of Group Collaboration tools was to improve awareness of what it takes to collaborate successfully, including that successful collaboration means sharing ideas, distributing work, using time efficiently, and decision making. Responses to the Group Collaboration reflection revealed that students’ self-assessment was both reflective and honest (see Table 3 for illustrative examples from each of these categories). The accuracy of their responses matched that of the teacher's own assessment, which frequently aligned well with students’ self-assessments. For example, Student E discussed a common issue for group work, referring to the group needing to pace itself in order to better meet the project deadline. Additionally, different students on the same team often responded similarly, even though they had completed their reflections independently. Almost every student discussed some things they did well, at the exemplary level, and some things they could improve on, at the developing or beginner level. Interestingly, different groups of students answered differently, emphasizing that the categories outlined on the rubric are each important for collaborative work in a science class. Moreover, each assessment revealed that only about half of the students indicated they had reached what they considered to be the exemplary level on any of the five categories.

Example Group Collaboration reflection responses

a Note that misspelled words were corrected for clarity.

New approaches for teaching and assessing scientific skills and practices are critical for producing scientifically literate citizens ( NGSS, 2013 ). This work shows that student understanding of scientific inquiry can be significantly increased by using instruction and assessment tools aimed at promoting development of specific inquiry skills. The success of the Science Skills approach can be attributed to being explicit with students about what skills are particularly important for progress in science, introducing specific terminology for experimentation, encouraging student self-assessment, and assessing scientific thinking in addition to content. Such strategies also place an emphasis on developing the academic language necessary for communicating in science and improving literacy ( Snow, 2010 ).

Overall the analysis of responses to the pre-, mid-, and postassessments revealed that students expressed an increasing awareness of who they are as scientists and developed a more specific vocabulary for discussing experimentation and their strengths in science compared with students who had not used the same tools, meeting one of the key learning outcome goals for this classroom. Moreover, this new assessment approach enabled the teacher to work individually with struggling students to help them master critical skills; it was these students who often showed the greatest gains in learning how to talk about their experimental work (unpublished data). Not only do such assessments evaluate the extent to which students understand experimental skills, but they also serve as a tool for learning the skills and vocabulary themselves; assessments that accomplish both goals simultaneously have been dubbed “learning tests” by Linn and Chiu (2011) .

While clear learning gains were made for KI of scientific inquiry skills, the tools designed to promote a better understanding of group collaboration were not as successful. Several interpretations could account for why the collaboration treatment was not as effective, including: 1) in contrast to science skills, group work is something with which students already have a lot of experience, as well as the vocabulary for describing strengths and limitations of good collaboration; 2) the maturity level of high school students makes the social interactions required for negotiating tasks like sharing work and making decisions difficult; 3) the measure was not optimal, for example, the pre–post questions did not fully elicit what students understood about scientific collaboration; and 4) implementation of the Group Collaboration rubric was not ideal. Plans for improving the measure and its implementation in the future include probing student understanding of group work with other questions, guiding students to be more specific in their responses, and performing more formative assessment and collecting suggestions for improvement from the students during the project group work. Despite limited success with these measures, from the teacher's perspective, the author found that listening to students discuss the Group Collaboration rubric and reading student responses for the Group Collaboration reflection were useful for understanding class patterns of what was working and not working for the students during collaborative work, as well as for uncovering problems with group dynamics for particular student teams. Although classroom group work is known to be difficult to implement effectively ( Cohen et al. , 1999 ), it is also an important component of successful teaching and is thus worthy of further investigation.

Action Research on Classroom Practices

When introducing a new teaching strategy, it is often difficult to determine its impact in isolation from other instructional approaches used in the classroom. While many instructors are interested in testing new teaching approaches in their own classrooms, questions of ethics quickly arise when one considers exposing some students to new strategies designed to improve learning, while a control group may not benefit from those same experimental strategies. Moreover, randomized field trials, the current gold standard for educational research, are impractical for the typical K–12 or college classroom instructor. Nonetheless, there is a need for improvement of scientific approaches to science education ( Wieman, 2007 ; Asai, 2011 ). Teacher research, also known as teacher inquiry or action research, is an intentional and systematic approach to educational research in which data are collected and analyzed by individual teachers in their own classrooms to improve their teaching practices ( Cochran-Smith and Lytle, 1993 ). This teacher-driven action research project served to improve the author's own teaching practice and is an example for other instructors on how to manage effective educational research while teaching high school, undergraduate, or graduate classes. Not only did these findings provide evidence for the educational benefits of the Science Skills approach to promoting scientific inquiry, but research in the context of the author's own classroom also allowed her to question the Group Collaboration approach and plan next steps for making it more effective. Being involved in an action research group can be a valuable professional development opportunity for any instructor, as it provides an opportunity to reflect on teaching strategies, engage in data analysis of student work, learn from colleagues, and consequently improve teaching practice.

Applications for Undergraduate Life Sciences Education

While this study is set in a high school context, lessons learned can easily transfer to introductory life sciences courses at the undergraduate level. The experimental design shown in Figure 3 allows the instructor to simultaneously test two treatments with two different groups of students, with each group serving as a no-treatment comparison for the other. This experimental design can be applied to any classroom situation in which the student population can be divided into two groups for two independent interventions. Examples of other contexts for which this design could be useful are parallel discussion or laboratory sections for the same undergraduate course or a large lecture format that can be divided into two groups to test the impact of implementing two independent teaching strategies. The KI perspective described here is a promising framework with which to evaluate the effectiveness of both K–12 and undergraduate student learning in life sciences education.

Supplementary Material

Acknowledgments.

I thank Marcia Linn and her research group; Marnie Curry, Jessica Quindel, and other teacher colleagues with Project IMPACT; and Nicci Nunes, Heeju Jang, Michelle Sinapuelas, Jack Kamm, Deborah Nolan, and others who have inspired the work, encouraged me to write an article describing the study, and generously given me feedback.

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Integrated Education and Learning pp 337–354 Cite as

Education of Integrated Science: Discussions on Importance and Teaching Approaches

• Kaan Bati 3  
• First Online: 02 January 2023

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Part of the book series: Integrated Science ((IS,volume 13))

The economic and technological development of societies depends on the training of students who can make connections between daily life and science issues and have problem-solving skills. Integrated science education supports the holistic development of the student’s personality by establishing a relationship between school and real life. Although there are different approaches, it is understood that all approaches to integrated science are more effective than the traditional single discipline-based approach for the student to learn. This chapter discusses the importance of integrated science education, teaching approaches at the K-12 level, and the skills that need to be emphasized to answer this question. An integrated science teaching program based on the transdisciplinary approach is exemplified as well.

Graphical Abstract/Art Performance

Transdisciplinary teaching process.

Science is the only true guide in life . Mustafa Kemal Atatürk

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Bati, K. (2022). Education of Integrated Science: Discussions on Importance and Teaching Approaches. In: Rezaei, N. (eds) Integrated Education and Learning. Integrated Science, vol 13. Springer, Cham. https://doi.org/10.1007/978-3-031-15963-3_19

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