Science Learning

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explanation and argumentation
deanna kuhndavid dean jr.

knowledge organization and understanding
andrea a. disessa

angelo collins

michele spitulnikmarcia linn


The K12 U.S. science education standards, now published state by state, without exception cite competence in scientific investigation as an important curriculum goal from the early grades on. Students, it is claimed, should be able to formulate a question, design an investigation, analyze data, and draw conclusions. Reference to such skills in fact appears in discussions of curriculum objectives extending well beyond the discipline of science. The following description, for example, comes not from science education literature but from a description of language arts goals specified by the National Council of Teachers of English (NCTE): "Students conduct research on issues and interests by generating ideas and questions, and by posing problems. They gather, evaluate, and synthesize data from a variety of sources to communicate their discoveries in ways that suit their purpose and audience" (NCTE and International Reading Association website).

It is important that the cognitive skills involved in such activities be defined in a clear and rigorous enough way to make it possible to specify how they develop and how this development is best supported educationally. At the same time, to make the case that scientific thinking is a critical educational objective, it must be defined more broadly than "what professional scientists do." Scientific thinking is essential to science but not specific to it.

But are not children naturally inquisitive, it may be asked, observant and sensitive to the intricacies of the world around them and eager to discover more? Do inquiry skills really need to be developed? The image of the inquisitive preschool child, eager and energetic in her exploration of a world full of surprises, is a compelling one. But the image fades as the child grows older, most often becoming unrecognizable by middle childhood and certainly by adolescence. What happens to the "natural" inquisitiveness of early childhood? The answer is that it needs to be channeled into the development of the cognitive skills that make for effective inquiry. More needs to be done than keeping alive a "natural curiosity." The natural curiosity that infants and children show about the world around them needs to be enriched and directed by the tools of scientific thought.

Coordination of Theories and Evidence

One way to conceptualize these scientific thinking skills is as skills in the coordination of theories and evidence. Even very young children construct theories to help them make sense of the world, and they revise these theories in the face of new evidence. But they do so without awareness. Scientific thinking, in contrast, involves the intentional coordination of theories with new evidence. Another way to define scientific thinking, then, is as intentional knowledge seeking. Scientific thinkers intentionally seek evidence that will bear on their theories. Defined in this way, the developmental origins of scientific thinking lie in awareness of knowledge states as generating from human minds. Awareness of the possibility of false belief is thus a prerequisite to scientific thinking. If knowledge states are fallible, one's own knowledge may warrant revision in the face of new evidence.

Regarded in this way, scientific thinking is more closely aligned with argument than with experiment and needs to be distinguished from scientific under-standing (of any particular content). Scientific thinking is something one does, whereas scientific understanding is something one has. When conditions are favorable, the process of scientific thinking may lead to scientific understanding as its product. Indeed, it is the desire for scientific understandingfor explanationthat drives the process of scientific thinking. Enhanced understandings of scientific phenomena are certainly a goal of science education. But it is the capacity to advance these understandings that is reflected in scientific thinking.

Scientific thinking requires that evidence be represented in its own right, distinct from the theory, and that the implications of the evidence for the theory be contemplated. Although older children, adolescents, and even adults continue to have trouble in this respect, young children are especially insensitive to the distinction between theory and evidence when they are asked to justify simple knowledge claims.

Note that the outcome of the theory-evidence coordination process remains open. It is not necessary that the theory be revised in light of the evidence, nor certainly that theory be ignored in favor of evidence, which is a misunderstanding of what is meant by theory-evidence coordination. The criterion is only that the evidence be represented in its own right and its implications for the theory contemplated. Skilled scientific thinking always entails the coordination of theories and evidence, but coordination cannot occur unless the two are encoded and represented as distinguishable entities.

The following six criteria for genuine scientific thinking as a process (in contrast to scientific understanding as a knowledge state) can be stipulated:

  1. One's existing understanding (theory) is represented as an object of cognition.
  2. An intention exists to examine and potentially advance this understanding.
  3. The theory's possible falsehood and susceptibility to revision is recognized.
  4. Evidence as a source of potential support (or nonsupport) for a theory is recognized.
  5. Evidence is encoded and represented distinct from the theory.
  6. Implications of the evidence for the theory are identified (relations between the two are constructed).

The Epistemology of Scientific Learning

There is more to scientific thinking that needs to develop, however, than a set of procedures or strategies for coordinating theories with evidence. As hinted earlier, at its core this development is epistemological in nature, having to do with how one understands the nature of knowledge and knowing. An until recently largely neglected literature on the development of epistemological understanding shows a progression from an absolutist belief in knowledge as certain and disagreements resolvable by recourse to fact, to the multiplist's equation of knowledge with subjective opinion. Only at a final, evaluativist level is uncertainty acknowledged without foregoing the potential for evaluation of claims in a framework of alternatives and evidence.

If facts can be readily ascertained with certainty, as the absolutist understands, or if all claims are equally valid, as the multiplist understands, scientific inquiry has little purpose. There is little incentive to expend the intellectual effort it entails. Epistemological understanding thus informs intellectual values and hence influences the meta-level disposition (as opposed to the competence) to engage in scientific thinking.

Similarly, a strategic meta-level that manages strategy selection can be proposed. This metastrategic level entails explicit awareness of not so much what to do as why to do itthe understanding of why one strategy is the most effective strategy to achieve one's goals and why others are inferior. It is this meta-strategic understanding that governs whether an appropriate inquiry or inference strategy is actually applied when the occasion calls for it.

The phases of scientific thinking themselvesinquiry, analysis, inference, and argumentrequire that the process of theory-evidence coordination become explicit and intentional, in contrast to the implicit theory revision that occurs without awareness as young children's understandings come into contact with new evidence. Despite its popularity in educational circles, once one looks below the surface of inquiry learning, it is less than obvious what cognitive processes are entailed. Research suggests that children lack a mental model of multivariable causality that most inquiry learning assumes. They are not consistent over time in their causal attributions, attributing an outcome first to one factor and later to another, and infrequently do they see two factors as combining additively (much less interactively) to produce an outcome. A mature mental model of causality in which effects combine additively to produce an outcome is critical to adoption of the task goal of identifying effects of individual factors and to the use of the controlled comparison strategy (which has been the focus of research on scientific reasoning) to achieve that goal. If a single (not necessarily consistent) factor is responsible for any outcome (as reflected in the inferential reasoning of many young adolescents), what need is there to worry about controlling for the effects of other factors?

If it is this total structure (including meta-strategic, meta-cognitive, and epistemological understanding, as well as values) that needs to develop, where do educators start? They probably need to begin at multiple entry points. Opportunities should be plentiful for the frequent and regular exercise of skills of inquiry, analysis, inference, and argument, thereby enabling these skills to be practiced, elaborated, consolidated, and perfected. At the same time, meta-level awareness and understanding of skills should be promoted by helping students to reflect on what and particularly how they know and what they are doing as they acquire new knowledge. The two endeavors reinforce one another: understanding informs practice and practice enhances understanding.

The Social Context

Equally critical is the social context in which all of this needs to take place, the often neglected dispositional side of knowing. Educators want children to become skilled scientific thinkers because they believe that these skills will equip them for productive adult lives. But it is not enough that these adults believe it. If children are to invest the sustained effort that is required to develop and practice intellectual skills, they too must believe that learning and knowing are worthwhile. These values and beliefs can develop only through sustained participation in what Ann Brown in 1997 called a "community of learners." Here, scientific thinking skills stand the best chance of developing because they are needed and practiced and socially valued.

Returning scientific thinking to its real-life social context is one approach to strengthening the meta-level components of scientific thinking. When students find themselves having to justify claims and strategies to one another, normally implicit meta-level cognitive processes become externalized, making them more available. Social scaffolding (supporting), then, may assist less able collaborators to monitor and manage strategic operations in a way that they cannot yet do alone. A number of authors have addressed scientific thinking as a form of discourse. This is of course the richest and most authentic context in which to examine scientific thinking, as long as the mistake is not made of regarding these discourse forms as exclusive to science. Scientific discourse asks, most importantly, "How do you know?" or "What is the support for your statement?" When children participate in discourse that poses these questions, they acquire the skills and values that lead them to pose the same questions to themselves. Although central to science, this critical development extends far beyond the borders of traditional scientific disciplines.

See also: Discourse, subentries on Classroom Discourse, Cognitive Perspective; Learning, subentries on Conceptual Change, Knowledge Acquisition, Representation, and Organization; Reading, subentry on Content Areas; Science Education.


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Hatano, Giyoo, and Inagaki, Kayoko. 1991. "Sharing Cognition through Collective Comprehension Activity." In Perspectives on Socially Shared Cognition, ed. Lauren Resnick, John Levine, and Stephanie Teasley. Washington, DC: American Psychological Association.

Herrenkohl, Leslie, and Guerra, Marion. 1998. "Participant Structures, Scientific Discourse, and Student Engagement in Fourth Grade." Cognition and Instruction 16:431473.

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Kuhn, Deanna. 1993. "Science as Argument: Implications for Teaching and Learning Scientific Thinking." Science Education 77:319337.

Kuhn, Deanna; Amsel, Eric; and O'Loughlin, Michael. 1988. The Development of Scientific Thinking Skills. Orlando, FL: Academic Press.

Kuhn, Deanna; Black, John; Keselman, Alla; and Kaplan, Danielle. 2000. "The Development of Cognitive Skills That Support Inquiry Learning." Cognition and Instruction 18:495523.

Kuhn, Deanna, and Pearsall, Susan. 2000. "Developmental Origins of Scientific Thinking." Journal of Cognition and Development 1:113129.

Lehrer, Richard; Schauble, Leona; and Petrosino, Anthony. 2001. "Reconsidering the Role of Experiment in Science Education." In Designing for Science: Implications from Everyday, Classroom, and Professional Settings, ed. Kevin Crowley, Christian Schunn, and Takishi Okadapp. Mahwah, NJ: Erlbaum.

Olson, David, and Astington, Janet. 1993. "Thinking about Thinking: Learning How to Take Statements and Hold Beliefs." Educational Psychologist 28:723.

Perner, Josef. 1991. Understanding the Representational Mind. Cambridge, MA: MIT Press.

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internet resource

National Council of Teachers of English and International Reading Association. 1996. Standards for the English Language Arts. Urbana, IL: National Council of Teachers of English; Newark, DE: International Reading Association.

Deanna Kuhn

David Dean Jr.


Educational research is frequently construed as focusing on how teachers should teach. However, before this question is addressed, it is important to ask what should be taught. One might ask if the problem of what to teach is really a problem. Why not just ask scientists or rely on existing textbooks? There are good reasons a serious inquiry cannot be sidestepped, however. A fundamental realization of cognitive science is that almost all of the competence of experts is tacit. Careful studies of what scientists actually do show a vast repertoire of invisible (to them) processes and structures. Furthermore, textbooks are at best secondary sources, and they are much more likely idiosyncratic products of a complex social history than trustworthy sources for the essence of science. Progress is being made, even if cognitive science (including history, philosophy, and sociology of science) has not definitively identified the essence of scientific knowledge.

Target Areas

The essence of "what to teach" can be divided into five target areas: content, process, meta-content and process, representational competence, and discourse and membership.

Content. Content concerns science concepts that students need to acquire. Content is of two different types: (1) central, difficult to learn ideas; and (2) concepts that are more peripheral and more amenable to straightforward instruction. Starting in the late 1970s, a huge literature emerged delineating certain misconceptions. The idea behind studying misconceptions is that difficult-to-acquire concepts are difficult not only (if at all) because of any intrinsic complexity, but because they are incompatible with well-developed and entrenched prior ideas. Conceptual change describes learning that involves substantial recrafting of prior ideas. While learning science concepts might, in principle, be difficult for many reasons, the preponderance of research suggests that conceptual change is a major factor. Conceptual change has been implicated in learning about force and motion, optics, electricity, heat and temperature, evolution, particulate theory of matter, and other topics.

Probably the most robust result of conceptual change research is that such change is not difficult for simple or accidental reasons, such as bad instruction. Instead, even the best instructional strategies require time and effort on the part of both students and teachers. This has major implications for selecting targets of instruction. Most notably, at the start of the twenty-first century, especially in the United States, curricula are dramatically overly ambitious in terms of coverage. If students are to understand any science deeply, then choices must be made about the things that are to be taught. Study of cross-national science instruction has come to a similar conclusion. The U.S. science curriculum seems to be lacking in comparison to the best science instruction in the world because it is too shallow; it has been called "a mile wide and an inch deep."

Another result of conceptual change research is that calculation does not seem to be strongly tied to conceptual change. Students can often calculate without understanding, and numerical exercises do not often promote conceptual change. Quantitative reasoning is a hallmark of scientific thought, yet its centrality to deep understanding is questionable.

Conceptual change researchers have suggested several promising instructional techniques. One notable suggestion is that the curriculum needs pedagogically specific intermediate models that abandon a direct aim at scientifically complete and correct ideas. Instead of trying to jump a wide stream directly, metaphorically speaking, one may need to hop to rocks midstream, and then to the far shore. While teaching intermediate ideaswhich are prone to be described as "wrong" or "incomplete"may be counterintuitive, the scientific rationale is sound, and results are encouraging.

Conceptual change research is developing a new and refined vocabulary for various types of knowledge and knowledge system organizations, such as concepts, theories, mental models, ontologies, and various forms of intuitive, inarticulate knowledge. Identifying which of these are central instructional targets helps to define curriculum, plausible instructional techniques, and assessments.

Process. The process of doing science is the traditional complement to content. For example, introspection of scientists and textbook descriptions of what scientists do led to the introduction of the scientific method as part of science instruction. Scientists supposedly (a) define problems carefully; (b) generate hypotheses; (c) design experiments to select among hypotheses; and (d) carry out those experiments to determine results. This sort of instructional goal has generally been discredited by cognitive and other researchers. It seems quite likely that no general skills exist for "defining problems carefully" or "generating hypotheses." Instead, these are knowledge-intensive activities that require knowing many specific things about the particular domain that is being investigated. This is an important cognitive principle, which may be called the virtual knowledge problem, meaning that naming a process does not entail a particular body of knowledge. Instead, the process might require different knowledge in different circumstances, hence it may not name a coherent instructional target.

Other formulations of process skills in science (e.g., careful observation) seem certain to suffer from the virtual knowledge problem. Even if a general skill is real, rather than virtual, it is often very weak and overwhelmed by domain-specific knowledge. Mathematical problem-solving research has found similar results.

Jean Piaget (18961980) began an important line of thinking about science process. However, his assumptions about broad changes in logic and reasoning (e.g., younger students can think only concretely) have proved generally unsupportable. Young students, given proper support, can engage in remarkably abstract and cogent scientific study. More specific skills from Piagetian studies, such as proportional reasoning (reasoning in ratios), and controlling variables (understanding that experiments that change many things at once are difficult to evaluate), have proven more productive, although their importance is uncertain.

An important trend in the late 1990s was to regard many process issues as matters of effective frameworks for action, rather than matters of knowledge or skills. For example, many educational researchers embed instruction in an inquiry cycle, where students formulate ideas, test them, and then iteratively refine them. However, the consequences of such activities may be robust content learning and epistemological sophistication, rather than learning science process. A concern for frameworks for action also reflects the realization that students' taking fuller responsibility for authentic activities has many advantages over exercising isolated skills. This parallels the well-supported result that remediation by practicing isolated skills fails to produce transferable, long-term improvement.

Meta-content and process. Starting about 1990 research focused increasingly on students' conceptions of knowledge, or, more specifically, scientific knowledge. Students have naive assumptions about the nature of knowledge, in somewhat the same way that they have naive conceptions about the content of science. Students may believe (falsely) that their own sense of what is sensible is irrelevant to sciencethey must be told everything that is true and should not expect to figure anything out on their own. Students may also believe (falsely) that knowledge of science is embodied in small, simple chunks (e.g., sentences or equations) that can be memorized and do not form a larger fabric. Researchers refer to this knowledge as student epistemologies (theories of knowledge).

Unlike most versions of science process, it appears in theory and practice that improving student epistemologies also improves science-content learning. However, the precise nature of student epistemologies is unsettled. Some researchers hold closely to epistemological ideas that characterize professional science, such as: "Scientific knowledge is contingent and always subject to revision." Others focus on general qualities of knowledge, like simplicity or modularity (as in the example beliefs stated above). Still others teach schemes abstracted from the history of science (e.g., evaluating the plausibility and productivity of competing theories) as part of inquiry-based science instruction.

Representational competence. A comparative newcomer to the repertoire of potential knowledge goals is representational competence. Representation competence entails knowing: How do representations (like pictures, graphs, or algebra) work? What are qualities of good representations? and How does one design effective, new, scientific representations? Older conceptions of representational competence were restricted to a narrower, less creative base, such as being able to generate and interpret a few standard representations. Promising characteristics of this new conception of representational competence are (a) students appear to have strong and productive intuitive ideas to build on; (b) concern for it parallels the broader move toward more authentic frames for action, rather than a focus on isolated skills; and (c) the rapid computerization of science evidently requires a more flexible representational competence than previously. This may entail interpreting dynamic, three-dimensional data displays or adjusting and interpreting color-coded visualizations.

Discourse and membership. Among the instructional trends in science learning is an increased reliance on social, rather than individual, methods, such as whole-class or small-group discussion. The parallel theoretical move is the realization that science is, in essence, a social process. Ways of speaking and interacting, and one's feeling of affiliation to various groups (membership ), are not only means to an end, but are, in fact, vital to scientific competence. Adherents to this view often hold apprenticeship to be a fundamental model for learning and instruction.

Viewed instrumentally (only as a means to another goaldeveloping robust conceptual or procedural competence), considerations of discourse and membership are particularly appropriate for understanding difficulties encountered by cultural or linguistic minorities. If one does not speak or have values aligned with privileged modes in schools, one will be at a disadvantage. On the other hand, interpreted essentially (i.e., particular discourse patterns are goals in themselves, the essence of science), study of discourse and membership suggests a radical shift in current instructional goals.


The potential practical impact of research on science learning goals is obvious and immense. The very things students should understand and be able to do are at stake. On the other hand, science is slow and arduous, and although research is progressing, definitive answers are not at hand.

An important social process to determine science-learning goals is to engage multiple stakeholders, particularly disciplinary scientists and teachers, and to establish common standards. While this approach has advantages, a review of existing standards suggests areas of concern.

Definition and learnability. Standards rely on common-sense meanings of understanding and knowing. Cognitive research suggests that there are many different ways of knowing; appropriate means of instruction (memorizing, discussing, experiencing) and assessment (verbal answers, competence in extended inquiry) depend strongly on which is involved. Standards do not systematically distinguish easy-to-accomplish goals from deep conceptual change. Not calibrating how much time it takes to master particular items perpetuates a failing mile-wide and inch-deep curriculum. Limited empirical testing of the feasibility of standards does not screen out virtual knowledge.

Focus. Current standards only minimally reflect topics that have emerged from cognitive research. Representational competence and student epistemologies are almost absent. Furthermore, intermediate models and goals tend to be screened out because they are unfamiliar to both disciplinary scientists and teachers. Lack of consideration of discourse and membership may perpetuate marginalization of cultural or linguistic minority students.

Sequence. Bad theories of sequencing, or no theory at all, prevent students from encountering ideas as early as they mightand they do not build optimally. For example, as previously mentioned, characterizing young science students' thinking as concrete seems to have inappropriately limited instruction.

Coherence. Long lists of goals (the bread and butter of most standards) encourage piecemeal instruction, which is at odds with a fundamental shift in thinking about learning, which is that coherent frames for activity almost always enhance learningcompared to rehearsing isolated facts or skills. A common strategy in standards for providing coherence via broad themes is likely to lead to the virtual knowledge problem.

Pitting standards against scientific research suggests a false dichotomy. Both are appropriate. However, bringing standards and the standards-producing process into better alignment with research will provide a great opportunity for advancement.

See also: Learning, subentry on Conceptual Change; Reading, subentry on Content Areas; Science Education.


Brown, Ann L., and Campione, Joseph C. 1986. "Psychological Theory and the Study of Learning Disabilities." American Psychologist 41:10591068.

Brown, David E., and Clement, John. 1989. "Overcoming Misconceptions Via Analogical Reasoning: Abstract Transfer Versus Explanatory Model Construction." Instructional Science 18:237261.

California State Board of Education. 2000. Science Content Standards for California Public Schools, Kindergarten through Grade Twelve. Sacramento: State of California Department of Education.

Cobb, Paul; Wood, Terry; and Yackel, Ernal. 1993. "Discourse, Mathematical Thinking, and Classroom Practice." In Education and Mind: Institutional, Social and Developmental Processes, ed. Norris Minick, Ellice Forman, and Addison Stone. New York: Oxford University Press.

Confrey, Jere. 1990. "A Review of the Research On Student Conceptions in Mathematics, Science, and Programming." In Review of Research in Education 16, ed. Courtney Cazden. Washington, DC: American Educational Research Association.

diSessa, Andrea A. 1996. "What Do 'Just Plain Folk' Know About Physics?" In The Handbook of Education and Human Development: New Models of Learning, Teaching, and Schooling, ed. David R. Olson and Nancy Torrance. Oxford: Blackwell.

diSessa, Andrea A., and Minstrell, Jim. 1998. "Cultivating Conceptual Change with Benchmark Lessons." In Thinking Practices in Mathematics and Science Learning, ed. James G. Greeno and Shelly V. Goldman. Mahwah, NJ: Erlbaum.

diSessa, Andrea A., and Sherin, Bruce. 1998. "What Changes in Conceptual Change?" International Journal of Science Education 20:11551191.

diSessa, Andrea A., and Sherin, Bruce. 2000. "Meta-Representation: An Introduction." Journal of Mathematical Behavior 19 (4):385398.

Friedman, Jeff, and diSessa, Andrea A. 1999. "What Should Students Know About Technology? The Case of Scientific Visualization." International Journal of Technology and Science Education 9 (3):175196.

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internet resources

American Association for the Advancement of Science. "Project 2061: Science for All Americans Online." 2001. <>.

National Research Council. 2001. "National Science Education Standards." <>.

Andrea A. diSessa


The release in 1983 of A Nation At Risk: The Imperative for Educational Reform is a reasonable place to begin consideration of the standards movement in science education in the United States in the later twentieth and early twenty-first centuries. This document, prepared by the National Commission on Excellence in Education (NCEE), was a response to "the widespread public perception that something is seriously remiss in our educational system" (p. 1). The document contained sentiments that became slogans of the standards movement. Science education for all is foreshadowed: "All, regardless of race or class or economic status, are entitled to a chance and to the tools for developing their individual powers of mind and spirit to the utmost" (p. 4). Recommendations focused on content, standards and expectations, time, teaching and leadership, and fiscal support. Science content was defined as "(a) the concepts, laws and processes of the physical and biological sciences; (b) the methods of scientific inquiry and reasoning; (c) the application of science knowledge to everyday life; and (d) the social and environmental implications of scientific and technological development" (p. 25).

The science education community both anticipated and responded to this report with numerous efforts. The American Association for the Advancement of Science (AAAS) initiated Project 2061, which began by defining scientific literacy for all high school graduates. The National Science Teachers Association (NSTA) began its Scope, Sequence, and Coordination Project and ultimately, in 1992, published Scope, Sequence, and Coordination: The Content Core. Professional organizations and curriculum development corporations began to produce curriculum materials that emphasized hands-on science, another slogan of the period.

Mathematics Standards and National Education Goals

In 1989 two events occurred that would influence the development of national science education standards. The National Council of Teachers of Mathematics (NCTM) released Curriculum and Evaluation Standards for School Mathematics. The term standards assumed new prominence in education reform.

Also in 1989 the National Governors Association met with then U.S. President George H. W. Bush at an education summit. They endorsed six national education goals, which were articulated as America 2000 under the Bush administration and were enacted as the Goals 2000: Educate America Act in 1994, during the administration of Bill Clinton. Two of the national goals made specific reference to improving the knowledge and skills of students in science:

Goal 3: Student Achievement and Citizenship. By the year 2000, American students will leave grades four, eight and twelve having demonstrated competency in challenging subject matter, including English, mathematics, science, history and geography; and Goal 4: Science and Mathematics. By the year 2000, U.S. students will be first in the world in science and mathematics achievement. (Malcom, p. 4)

The National Council on Education Standards and Testing, instituted by the U.S. Congress, referred explicitly to the mathematics standards when they recommended in 1992 that standards for school subjects were a desirable and feasible vehicle for meeting the national education goals. This council noted that the mathematics standards had been developed by a professional society that included mathematicians and teachers. Further, the standards had been subjected to cycles of public review and feedback that encouraged consensus building about what students should know and be able to do. Development by a professional society and public review became two requirements as federal agencies began awarding grants to develop high, voluntary, national standards in school subjects including science.

While there was public consensus that educational standards were good and useful, there was no consensus on what standards were. Examining ordinary dictionaries, two apparently contradictory meanings are found. A standard is an object used as an emblem, symbol, and rallying point for a leader, people, or movement; standards are banners. A standard also is an established basis or rule of comparison used to measure quality or value; standards are bars. Further, three types of standards were identified: content standards, performance standards, and delivery standards. Shirley Malcom, in a 1993 report of the National Education Goals Panel, defined content standards as what students should know and be able to do and performance standards as specifying how good is good enough. Diane Ravitch, in the 1995 book National Standards in American Education: A Citizen's Guide, defined delivery standards, later called opportunity-to-learn standards, as conditions for schooling under which content and performance standards would be attained.

Two Key Documents: NSES and Benchmarks

When the U.S. Department of Education (DoE) began to deliberate about which association to consider to develop national education standards for science, two were immediately apparent: AAAS and NSTA. Each had reasons to assume leadership in the enterprise. Project 2061 was well underway at AAAS and in 1989 had produced Science for All Americans, which was having an impact on thinking and practice in curriculum and instruction in science. Work had begun on Benchmarks for Science Literacy, which parsed what students at different grade levels needed to understand if they were to attain science literacy by grade twelve. Alternatively, NSTA is the largest organization of science teachers in the country and is analogous to NCTM.

In spring 1991 the president of NSTA, supported by the unanimous vote of the board, asked the president of the National Academy of Sciences (NAS) with its operating arm, the National Research Council (NRC), to coordinate the development of national science education standards. The DoE encouraged NAS/NRC, a prestigious organization, to draw on expertise and experience from both AAAS and NSTA. Subsequently, by the early twenty-first century, two works were acknowledged at the national level as setting education standards for science: National Science Education Standards (NSES), which was produced by NRC in 1996; and Benchmarks for Science Literacy, which was published in 1993 by AAAS and is one product of Project 2061. A 1997 analysis of the science content in NSES and Benchmarks conducted by Project 2061 revealed that, although organized differently, there is greater than 90 percent overlap in what the two documents claim all students should understand and should be able to do.

NSES describes science content as fundamental and included as a standard if it: represents a central event or phenomena in the natural world; represents a central scientific idea and organizing principle; has rich explanatory power; guides fruitful investigations; applies to situations and contexts common to everyday experience; can be linked to meaningful learning experiences; and is developmentally appropriate for students at the grade levels specified. In NSES, the science content begins with the unifying concepts and process standard: systems, order, and organization; evidence, models, and explanations; change, constancy, and measurement; evolution and equilibrium; and form and function. These are not sorted by grade level but are applicable in some form to all students and all science disciplines. The other science content standards in NSES are displayed in Table 1. An array of the fundamental ideas in science that constitute the standards illustrates three points. The ideas build on one another from grade level to grade level. The ideas increase in complexity and abstractness across grade levels. There is an increase in the number of ideas across grade levels.

At the standard statement level the knowledge and abilities of students about inquiry and about technological design are similar for all students. Across grade levels, the ideas with which inquiry and design interact are increasingly complex and sophisticated. The increased complexity and sophistication of inquiry and design are captured in the guide to the standards. For example, grade K4 students are to "Ask a question about objects, organisms and events in the environment" (NRC, p. 122). Grade 58 students are to "Identify questions that can be answered through scientific investigations" (p. 145), while grade 912 students are to "Identify questions and concepts that guide scientific investigations" (p. 175).

The NSES went beyond the charge from DoE and developed standards for teaching and assessment, recognizing that change in content is not sufficient to produce change in teaching and learning. Further, NSES produced professional development standards, which focus on initial and continuing education of teachers; program standards, which focus on changes for schools and school districts; and system standards, which focus on changes in the entire educational system. Benchmarks extended science content to mathematics and to human society.


Although science education standards have been generally well received, their implementation has been difficult and uneven. Returning to an emphasis on local control in education, some states chose to keep the frameworks they had in place prior to 1989, some adopted the NSES or Benchmarks, others adapted either NSES or Benchmarks, while still others created their own state science standards. By 2000 most instructional materials claimed to be standards based. An analysis of many of them in 2001 by Project 2061, however, indicated that few actually are. Some of the questions that plague those implementing standards include: What is inquiry? What does it mean to understand a science idea? and Are the indicated grade levels appropriate? To answer these and other questions and to extend the influence and implementation of the NSES, the NRC has held numerous conferences and published more than fifteen documents addressed to teachers, parents, policymakers, and curriculum and assessment developers. Benchmarks is only one in a series of materials available or planned by Project 2061 to promote science literacy. The Atlas of Science Literacy, published by AAAS in 2001, graphically presents how the understanding of important science ideas is developed by students over time. Project 2061 also has developed a number of online tools such as Blueprints for Reform, which was published by AAAS in 2001, and conducts meetings and workshops for various stakeholders in science education.


Other Standards Documents

The picture of education standards in science would be incomplete without mentioning the Standards for Technological Literacy released by the International Technology Education Association in 2000. Also, in 1989 the National Board for Professional Teaching Standards produced standards for experienced science teachers, while the Interstate New Teacher Assessment and Support Consortium published Standards in Science for New Teachers: A Resource for State Dialogue in 2001. There are also standards for programs that educate science teachers and for instructors in such programs.

At the beginning of the twenty-first century, standards are seen alternatively as vision or hurdle, as influential or intrusive, as realistic or impractical. Beyond question, however, they have become an integral part of the science education enterprise.

See also: National Board for Professional Teaching Standards; Science Education; Science Learning, subentry on Knowledge Organization and Understanding; Standards for Student Learning; Standards Movement in American Education.


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American Association for the Advancement of Science. 1997. Resources for Science Literacy: Professional Development. New York: Oxford University Press.

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Association for the Education of Teachers of Science. 1997. "Professional Knowledge Standards for Science Teacher Educators." AETS Newsletter 31 (3) (suppl.):16.

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National Council of Teachers of Mathematics. 1989. Curriculum and Evaluation Standards for School Mathematics. Reston, VA: National Council of Teachers of Mathematics.

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National Research Council. 1996. National Science Education Standards. Washington, DC: National Academy Press.

National Science Teachers Association. 1992. Scope, Sequence, and Coordination: The Content Core: A Guide to Curriculum Designers. Washington, DC: National Science Teachers Association.

Ravitch, Diane. 1995. National Standards in American Education: A Citizen's Guide. Washington, DC: Brookings Institution.

Rutherford, F. James, and Ahlgren, Andrew. 1989. Science for All Americans: A Project 2061 Report on Literacy Goals in Science, Mathematics, and Technology. New York: Oxford University Press.

internet resources

American Association for the Advancement of Science. 2001. Blueprints for Reform. <>.

American Association for the Advancement of Science. 2001. Textbook Analysis. <>.

Angelo Collins


Research on the use of technology to support science learning reveals promise to improve learning and potential pitfalls. Technology offers promise for increasing science inquiry and is a major component of science education reforms and standards. In inquiry activities, students intentionally address challenging science questions by engaging in complex, sustained, reflective reasoning to design solutions, test ideas, revise solutions, critique ideas, and collaborate with others.

The focus here is on technologies including scientific visualizations, statistical modeling, real time data collection, dynamic modeling software, and collaborative environments that support inquiry practices. This discussion highlights uses of technology that are integrated into an inquiry-based, science curriculum and often delivered using a learning environment, to help students engage in substantial scientific reasoning. Technology research seeks applications that help students develop a coherent understanding of science rather than fragmented ideas, and that set students on a path toward lifelong learning.

Many researchers and software designers have identified pitfalls of technology use. Often technology distracts learners with glitzy animations or colorful photographs that not only do not connect to the ideas that students hold, but also reinforce perceptions of science as inaccessible, irrelevant to personal concerns, or inscrutable. Internet sites and software designed to transmit information can deter students from viewing scientific sources critically. Applications like word processors or spreadsheets designed for business may require valuable classroom time to learn yet not contribute to understanding of science. Software designers are just beginning to develop robust applications that contribute to students' understanding by capitalizing on both late twentieth-century research on learning and iterative design studies conducted in settings where learning takes place.

Consistent with the rapid change in educational technologies, this article presents criteria for selecting promising technological tools that are synthesized from research on effective uses of technology and describes applications that exemplify these criteria and have supporting empirical research to demonstrate effectiveness in the classroom. The purposes is to seek benefits in terms of student learning gains, student engagement in scientific practice, or teacher professional development using a range of methodologies, and the criteria are based on reviews of studies featuring general comparisons, studies based on iterative design, and case studies of student learning.

Engaging Students in Scientific Inquiry Activities

Technological resources can help students in inquiry activities, such as researching a complex question, building explanations, testing ideas, and refining understanding of the world. Applications that support modeling phenomena, visualizing, or collecting data also support inquiry. Emphasized are highlight modeling and simulation, visualization, and real time data collection.

Modeling and simulation environments. Modeling and simulation environments allow students to perform "what if" experiments or simulate experiments that would be difficult, impossible or dangerous to perform using real-world materials. Learners typically manipulate computer-based objects to see how they react under different conditions. Models represent complex scientific situations like an ecosystem or the world of Newtonian physics. Students construct or manipulate models to make conjectures, test ideas, and explore rules underlying scientific phenomena. Modeling and simulation environments generally fall within two types, either content based or open ended.

The software program Interactive Physics is an example of a content-based modeling environment. Interaction Physics provides a simulation environment and libraries of simulations for physics curricula. This program allows students to conduct controlled, simulated experiments without costs in time and materials. Students readily repeat experiments, change values of variables, and explore parameters of experiments. Students interact with their simulations in real time, and display measurements graphically in a variety of ways. Research demonstrates that students improve their physics understanding when interacting with modeling tools.

Open-ended, simplified modeling environments include Model-It, which is based on a more complex precursor, STELLA (Structural Thinking Experimental Learning Laboratory with Animation). Using Model-It, students can readily construct qualitative and quantitative models. Students define objects and factors within a system and build relationships between factors. Students "run" their models and monitor changes by viewing indicators or graphs. The design and use of Model-It in high school and middle school science classrooms is the focus of research at the University of Michigan. Model-It supports learners by allowing students to use personally meaningful images, providing information in qualitative, quantitative and graphical form, and prompting students for explanations. Case studies show that students use several higher order cognitive tasks when creating models with Model-It, including identifying causal relationships and elaborating upon explanations. Students learn the scientific content that forms the basis of their models as well as ideas related to the nature of science, including purposes of modeling.

Visualization software. Visualization software provides students with access to scientific visualizations such as molecular models or geographic information systems. For example, World Watcher uses scientific visualization software and historical data to help students recognize patterns in weather data by translating numerical data, such as temperature, to a palette of colors and displaying results on a world map. The software allows students to annotate data, make predictions, and perform sophisticated analysis by overlaying data sets. A Global Warming Project, an eight-to-ten-week unit intended for students in grades seven to ten, involves teams of students advising world leaders on issues countries may face due to global warming. The World Watcher formative classroom research reveals the challenges that students face in interpreting complex data and suggests ways to reduce complexity to support inquiry.

Real-time data collection software. An important technological support for science learning connects sensors to a computer, calculator, or handheld Personal Digital Assistant (like a Palm Pilot or Visor), and allows students to record real-time data about their environment. Common probes include temperature, voltage, and motion sensors. Researchers have studied the use of probes in computer-based labs and microcomputer-based labs, showing how real-time graphing helps students understand complex scientific phenomena. The use of probes in an inquiry environment assists student in distinguishing between important scientific concepts, such as heat and temperature.

Complex Science Content and Integrated Understanding

Technology can help students make sense of standards-based complex topics and provide a window on science in the making to illustrate science inquiry. To enable students to gather, organize, and display information, technology can combine visualization, modeling, and real-time data collection with a full curriculum. Ideally science instruction encourages students to build a more coherent understanding of science and to apply ideas from one domain to the next. Processed applications such as simulations depend on the curriculum and the teacher to emphasize connections. Whole curricula can support integrated understanding when well designed and complemented by a thoughtful teacher.

For example, Constructing Physics Understanding (CPU), a National Science Foundation-funded project, encourages robust physics understanding by connecting laboratory and computer-based materials to elicit students' ideas, guide students to modify ideas, and help students apply target ideas to new situations by using simulations.

The Virtual High School (VHS) allows teachers in a consortium to use online materials and collaborative tools to create specialty Net Courses online for students at other schools that belong to the consortium. VHS offers a wide range of courses, but science selections include ethnobotany, evolutionary genetics, paleontology, astronomy, and bioethics. VHS research helps teachers redesign courses and enhance inquiry by supporting inquiry-based teaching online.

Supporting Peer Learning

Research shows benefits when students productively specialize and tutor their peers. To support peer learning, software offers some group and individual activities, specifies how groups should work together, and accommodates the contributions of individuals to the group.

For example, software can support geographically separated students in sharing quantitative and qualitative data that are location dependent and/or time-sensitive, including weather, astronomical, or water quality data. Synchronized collaborative programs provide the tools and curricula to organize students over large distances. Synchronized collaborative programs range from days to weeks to a semester, and work best when several classrooms use them simultaneously and share findings. The programs use communication technologies including email and discussion forums to coordinate activity and discussion. For example, in One Sky Many Voices students serve as "resident experts" and communicate with other sites to compare local environments. Participation encourages scientific discussion and debate, asking questions, and presenting evidence to students in distant classrooms.

Several programs use software, including electronic probes to facilitate group data collection and analysis. For example, the Global Learning and Observations to Benefit the Environment (GLOBE) project has three major goals: "to enhance the environmental awareness of individuals throughout the world; to contribute to scientific understanding of the earth; and to help all students reach higher levels of achievement in science and mathematics" (GLOBE Teacher's Guide, Program Overview). Through GLOBE, elementary to high school students around the world investigate earth science, including atmosphere, hydrosphere, land use, and soil. Scientists and students partner to collect and use data to gain a better understanding of global environmental processes. Providing online tools and materials, GLOBE uses high quality satellite photos and graphical representations of temperature, climate, and land use data. Students add findings to the Student Data Archive and use an Internet-based forum called GLOBEMail to communicate with schools and scientists. GLOBE is effective in improving student achievement in key mathematics, science, and geography skills. Students and teachers also report more interest and awareness of environmental issues and believe their data contributes to scientific research.

Recognizing Relevant Experiences, Diverse Contributions to Science, and Autonomy

Effective instruction should connect students' complex and varied ideas from prior observation and instruction, and introduce new scientific ideas. Technology can help students make connections, test their ideas against normative ones, and sort out varied perspectives on a topic.

To promote independent inquiry, technology-enhanced environments can prompt students to reflect on their progress and critique solutions proposed by others. Effective software should invite diverse students to engage in science by providing a variety of ways to learn (discussion, projects, reading, designing, debating), and by using students' views and experiences as a springboard to further learning. Software can support students in refining ideas, developing interest in new scientific topics, and carrying out sustained, complex projects.

In the early twenty-first century, learning environments are emerging to meet this challenging criteria. Computer-based learning environments combine curricula, classroom activities, and assessments into packages designed to improve teacher effectiveness and to provide cognitive and social supports for students who are conducting inquiry projects. Learning environments incorporate results of cognitive research including hint giving, prompts for reflection, and connections to online discussions. They free teachers to tutor individuals, identify common theories, and monitor progress.

The Web-based Integrated Science Environment (WISE), a browser-based application, offers a library of middle school and high school activities that enable students to critique real-world "evidence" from the Internet, compare scientific arguments, and design solutions to scientific problems. WISE projects offer inquiry activities that are personally relevant to students. These activities present multifaceted, interdisciplinary scientific issues, introduce scientific methodology, and encourage students to gain lifelong learning skills, including the ability to critique websites and support conclusions with appropriate evidence. The WISE technology provides an organizational structure helping students to reflect upon their learning, take notes, sort evidence, and discuss arguments online with peers. Many WISE projects involve hands-on data collection, online modeling of observations, or design activities. WISE provides scientific evidence, differing points of view, and visualizations (e.g., images, diagrams, animations or models). Students perform all work collaboratively, and are assessed in terms of their notes, arguments, models, and designs. WISE draws upon extensive cognitive and educational research, summarized by Marcia Linn and Sherry Hsi, to explore how computer technology can guide and support students' understanding.


There is widespread agreement that students benefit from learning with and about technology in science. Nevertheless, effective incorporation of information technologies into the curriculum has been controversial, difficult, and demanding. Finding ideal uses of technology in science instruction remains an active research area, and the technology itself is a "moving target," as new projects emerge on a regular basis. The recommendations in this entry capture current practices and research findings, and require regular revision as new tools and new research results become available. The best gift science teachers can give this generation of students is to offer them courses and tools that enable them to become life-long science learners and to add new technological resources regularly to their repertoire.

See also: Mathematics Learning, subentry on Learning Tools; Peer Relations and Learning; Reading, subentry on Content Areas; Science Education; Science Learning subentry on Standards; Technology Education; Technology in Education, subentry on Current Trends.


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Michele Spitulnik

Marcia Linn

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