Science Learning

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 ideas–which 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 (1896–1980) 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 science–they 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 goal–developing 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.

Implications

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 might–and 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 learning–compared 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.