Because policymakers tend to be unaware of the variety of purposes and products of government-sponsored research, the engineering community must coordinate and focus more effectively the many voices speaking for engineering. Both policymakers and the public need to better appreciate the important differences between scientific and engineering research, especially with regard to how quickly the two disciplines can address pressing national concerns. In general, the concept of engineering research is not readily understood.
In academic settings, its distinction from research in the basic sciences is even less well understood. Therefore, the next section of this report is devoted to an exposition of the nature and value of academic engineering research. In many ways, the methods of academic engineering research and the resulting insights into the nature of the physical world are indistinguishable from those of basic scientific research.
However, there are crucial differences between the two endeavors. Basic scientific research is concerned with the discovery of new phenomena and their integration into coherent.
By definition, the focus of greatest interest tends to be at the outer edges of present knowledge. Most scientific knowledge will, in a highly variable and unpredictable fashion, find technical applications of economic and social value, but in most cases the nature of such applications will not be apparent to the those who perform the original scientific research.
Basic research in engineering is by definition concerned with the discovery and systematic conceptual structuring of knowledge. Engineers develop, design, produce or construct, and operate devices, structures, machines, and systems of economic and societal value.
Virtually all engineering research is driven by the anticipated value of an application. However, not all potential applications can be anticipated, and occasionally the hoped-for application may not be nearly as important as one that turns up by serendipity. The time from research to production may be a few years, as in the development and application of the laser or in the progression from the integrated circuit to microprocessor, or it may be decades, as in the development of television.
Engineering, unlike science, is concerned not only with knowledge of natural phenomena, but also with how knowledge can serve humankind's needs and wants.
Such variables as cost, user compatibility, producibility, safety, and adaptability to various external operating conditions and environments must be taken into account in the design, development, operational support, and maintenance of the products and services that engineers create.
Although admittedly a simplification, the figure does identify three overarching categories of practices and shows how they interact. Engineering and science are similar in that both involve creative processes, and neither uses just one method. And just as scientific investigation has been defined in different ways, engineering design has been described in various ways. However, there is widespread agreement on the broad outlines of the engineering design process [ 24 , 25 ].
Like scientific investigations, engineering design is both iterative and systematic. It is iterative in that each new version of the design is tested and then modified, based on what has been learned up to that point. It is systematic in that a number of characteristic steps must be undertaken.
One step is identifying the problem and defining specifications and constraints. Another step is generating ideas for how to solve the problem; engineers often use research and group. Yet another step is the testing of potential solutions through the building and testing of physical or mathematical models and prototypes, all of which provide valuable data that cannot be obtained in any other way. With data in hand, the engineer can analyze how well the various solutions meet the given specifications and constraints and then evaluate what is needed to improve the leading design or devise a better one.
In contrast, scientific studies may or may not be driven by any immediate practical application. On the other hand, many scientific studies, such as the search for the planets orbiting distant stars, are driven by curiosity and undertaken with the aim of answering a question about the world or understanding an.
For science, developing such an explanation constitutes success in and of itself, regardless of whether it has an immediate practical application; the goal of science is to develop a set of coherent and mutually consistent theoretical descriptions of the world that can provide explanations over a wide range of phenomena, For engineering, however, success is measured by the extent to which a human need or want has been addressed.
Both scientists and engineers engage in argumentation, but they do so with different goals. In engineering, the goal of argumentation is to evaluate prospective designs and then produce the most effective design for meeting the specifications and constraints.
Instead, there are a number of possible solutions, and choosing among them inevitably involves personal as well as technical and cost considerations. Moreover, the continual arrival of new technologies enables new solutions. In contrast, theories in science must meet a very different set of criteria, such as parsimony a preference for simpler solutions and explanatory coherence essentially how well any new theory provides explanations of phenomena that fit with observations and allow predictions or inferences about the past to be made.
Moreover, the aim of science is to find a single coherent and comprehensive theory for a range of related phenomena. Multiple competing explanations are regarded as unsatisfactory and, if possible, the contradictions they contain must be resolved through more data, which enable either the selection of the best available explanation or the development of a new and more comprehensive theory for the phenomena in question. Although we do not expect K students to be able to develop new scientific theories, we do expect that they can develop theory-based models and argue using them, in conjunction with evidence from observations, to develop explanations.
Indeed, developing evidence-based models, arguments, and explanations is key to both developing and demonstrating understanding of an accepted scientific viewpoint.
The K practices described in this chapter are derived from those that scientists and engineers actually engage in as part of their work. We recognize that students cannot reach the level of competence of professional scientists and engineers, any more than a novice violinist is expected to attain the abilities of a virtuoso.
We consider eight practices to be essential elements of the K science and engineering curriculum:. In the eight subsections that follow, we address in turn each of these eight practices in some depth. The overall objective is that students develop both the facility and the inclination to call on these practices, separately or in combination, as needed to support their learning and to demonstrate their understanding of science and engineering.
In doing science or engineering, the practices are used iteratively and in combination; they should not be seen as a linear sequence of steps to be taken in the order presented. Asking questions is essential to developing scientific habits of mind. Even for individuals who do not become scientists or engineers, the ability to ask well-defined questions is an important component of science literacy, helping to make them critical consumers of scientific knowledge.
Scientific questions arise in a variety of ways. They can be driven by curiosity about the world e. Or they can result from the need to provide better solutions to a problem. For example, the question of why it is impossible to siphon water above a height of 32 feet led Evangelista Torricelli 17th-century inventor of the barometer to his discoveries about the atmosphere and the identification of a vacuum.
Questions are also important in engineering. Engineers must be able to ask probing questions in order to define an engineering problem. For example, they may ask: What is the need or desire that underlies the problem? What are the criteria specifications for a successful solution? What are the constraints? Other questions arise when generating possible solutions: Will this solution meet the design criteria?
Can two or more ideas be combined to produce a better solution? Students at any grade level should be able to ask questions of each other about the texts they read, the features of the phenomena they observe, and the conclusions they draw from their models or scientific investigations.
What are the possible trade-offs? And more questions arise when testing solutions: Which ideas should be tested? What evidence is needed to show which idea is optimal under the given constraints? Students also need to recognize the distinction between questions that can be answered empirically and those that are answerable only in other domains of knowledge or human experience. What do bees do? Why did that structure collapse? How is electric power generated?
What evidence supports that argument? For engineering, they should ask questions to define the problem to be solved and to elicit ideas that lead to the constraints and specifications for its solution. As they progress across the grades, their questions should become more relevant, focused, and sophisticated. Facilitating such evolution will require a classroom culture that respects and values good questions, that offers students opportunities to refine their questions and questioning strategies, and that incorporates the teaching of effective questioning strategies across all grade levels.
As a result, students will become increasingly proficient at posing questions that request relevant empirical evidence; that seek to refine a model, an explanation, or an engineering problem; or that challenge the premise of an argument or the suitability of a design.
Scientists construct mental and conceptual models of phenomena. Mental models are internal, personal, idiosyncratic, incomplete, unstable, and essentially functional. They serve the purpose of being a tool for thinking with, making predictions, and making sense of experience. Conceptual models, the focus of this section, are, in contrast, explicit representations that are in some ways analogous to the phenomena they represent. Conceptual models allow scientists and engineers to better visualize and understand a phenomenon under investigation or develop a possible solution to a design problem.
Used in science and engineering as either structural, functional, or behavioral analogs, albeit simplified, conceptual models include diagrams, physical replicas, mathematical representations, analogies, and computer simulations. Although they do not correspond exactly to the more complicated entity being modeled, they do bring certain features into focus while minimizing or obscuring others.
Because all models contain approximations and assumptions that limit the range of validity of their application and the precision of their predictive power, it is important to recognize their limitations. Conceptual models are in some senses the external articulation of the mental models that scientists hold and are strongly interrelated with mental models. Building an understanding of models and their role in science helps students to construct and revise mental models of phenomena.
Better mental models, in turn, lead to a deeper understanding of science and enhanced scientific reasoning. Some of the models used by scientists are mathematical; for example, the ideal gas law is an equation derived from the model of a gas as a set of point masses engaged in perfectly elastic collisions with each other and the walls of the container—which is a simplified model based on the atomic theory of matter.
For more complex systems, mathematical representations of physical systems are used to create computer simulations, which enable scientists to predict the behavior of otherwise intractable systems—for example, the effects of increasing atmospheric levels of carbon dioxide on agriculture in different regions of the world. Models can be evaluated and refined through an iterative cycle of comparing their predictions with the real world and then adjusting them, thereby potentially yielding insights into the phenomenon being modeled.
Engineering makes use of models to analyze existing systems; this allows engineers to see where or under what conditions flaws might develop or to test possible solutions to a new problem. Models, particularly modern computer simulations that encode relevant physical laws and properties of materials, can be especially helpful both in realizing and testing designs for structures, such as buildings, bridges, or aircraft, that are expensive to construct and that must survive extreme conditions that occur only on rare occasions.
Other types of engineering problems also benefit from use of specialized computer-based simulations in their design and testing phases. But as in science, engineers who use. Refine a model in light of empirical evidence or criticism to improve its quality and explanatory power. Students should be asked to use diagrams, maps, and other abstract models as tools that enable them to elaborate on their own ideas or findings and present them to others [ 15 ].
Young students should be encouraged to devise pictorial and simple graphical representations of the findings of their investigations and to use these models in developing their explanations of what occurred. More sophisticated types of models should increasingly be used across the grades, both in instruction and curriculum materials, as students progress through their science education.
Curricula will need to stress the role of models explicitly and provide students with modeling tools e. Scientists and engineers investigate and observe the world with essentially two goals: 1 to systematically describe the world and 2 to develop and test theories and explanations of how the world works.
In the first, careful observation and description often lead to identification of features that need to be explained or questions that need to be explored. The second goal requires investigations to test explanatory models of the world and their predictions and whether the inferences suggested by these models are supported by data.
Planning and designing such investigations require the ability to design experimental or observational inquiries that are appropriate to answering the question being asked or testing a hypothesis that has been formed. This process begins by identifying the relevant variables and considering how they might be observed, measured, and controlled constrained by the experimental design to take particular values.
Planning for controls is an important part of the design of an investigation. In laboratory experiments, it is critical to decide which variables are to be treated as results or outputs and thus left to vary at will and which are to be treated as input conditions and hence controlled. In many cases, particularly in the case of field observations, such planning involves deciding what can be controlled and how to collect different samples of data under different conditions, even though not all conditions are under the direct control of the investigator.
Decisions must also be made about what measurements should be taken, the level of accuracy required, and the kinds of instrumentation best suited to making such measurements. As in other forms of inquiry, the key issue is one of precision—the goal is to measure the variable as accurately as possible and reduce sources of error. The investigator must therefore decide what constitutes. Students need opportunities to design investigations so that they can learn the importance of such decisions as what to measure, what to keep constant, and how to select or construct data collection instruments that are appropriate to the needs of an inquiry.
They also need experiences that help them recognize that the laboratory is not the sole domain for legitimate scientific inquiry and that, for many scientists e. The plan of the investigation, what trials to make and how to record information about them, then needs to be refined iteratively as students recognize from their experiences the limitations of their original plan.
These investigations can be enriched and extended by linking them to engineering design projects—for example, how can students apply what they have learned about ramps to design a track that makes a ball travel a given distance, go around a loop, or stop on an uphill slope. From the earliest grades, students should have. Students should have opportunities to plan and carry out several different kinds of investigations during their K years.
At all levels, they should engage in investigations that range from those structured by the teacher—in order to expose an issue or question that they would be unlikely to explore on their own e.
As they become more sophisticated, students also should have opportunities not only to identify questions to be researched but also to decide what data are to be gathered, what variables should be controlled, what tools or instruments are needed to gather and record data in an appropriate format, and eventually to consider how to incorporate measurement error in analyzing data.
Older students should be asked to develop a hypothesis that predicts a particular and stable outcome and to explain their reasoning and justify their choice. By high school, any hypothesis should be based on a well-developed model or theory.
In addition, students should be able to recognize that it is not always possible to control variables and that other methods can be used in such cases—for example, looking for correlations with the understanding that correlations do not necessarily imply causality. Once collected, data must be presented in a form that can reveal any patterns and relationships and that allows results to be communicated to others.
Because raw data as such have little meaning, a major practice of scientists is to organize and interpret data through tabulating, graphing, or statistical analysis. Such analysis can bring out the meaning of data—and their relevance—so that they may be used as evidence. Engineers, too, make decisions based on evidence that a given design will work; they rarely rely on trial and error.
Engineers often analyze a design by creating a model or prototype and collecting extensive data on how it performs, including under extreme conditions. Analysis of this kind of data not only informs design decisions and enables the prediction or assessment of performance but also helps define or clarify problems, determine economic feasibility, evaluate alternatives, and investigate failures.
Spreadsheets and databases provide useful ways of organizing data, especially large data sets. The identification of relationships in data is aided by a range of tools, including tables, graphs, and mathematics.
Tables permit major features of a large body of data to be summarized in a conveniently accessible form, graphs offer a means of visually summarizing data, and mathematics is essential for expressing relationships between different variables in the data set see Practice 5 for further discussion of mathematics.
Modern computer-based visualization tools often allow data to be displayed in varied forms and thus for learners to engage interactively with data in their analyses.
In addition, standard statistical techniques can help to reduce the effect of error in relating one variable to another. Students need opportunities to analyze large data sets and identify correlations. Increasingly, such data sets—involving temperature, pollution levels, and other scientific measurements—are available on the Internet. Moreover, information technology enables the capture of data beyond the classroom at all hours of the day.
Distinguish between causal and correlational relationships. At the elementary level, students need support to recognize the need to record observations—whether in drawings, words, or numbers—and to share them with others. As they engage in scientific inquiry more deeply, they should begin to collect categorical or numerical data for presentation in forms that facilitate interpretation, such as tables and graphs.
When feasible, computers and other digital tools should be introduced as a means of enabling this practice. In middle school, students should have opportunities to learn standard techniques for displaying, analyzing, and interpreting data; such techniques include different types of graphs, the identification of outliers in the data set, and averaging to reduce the effects of measurement error.
Students should also be asked to explain why these techniques are needed. As students progress through various science classes in high school and their investigations become more complex, they need to develop skill in additional techniques for displaying and analyzing data, such as x-y scatterplots or cross-tabulations to express the relationship between two variables.
Students should be helped to recognize that they may need to explore more than one way to display their data in order to identify and present significant features. They also need opportunities to use mathematics and statistics to analyze features of data such as covariation. Also at the high school level, students should have the opportunity to use a greater diversity of samples of scientific data and to use computers or other digital tools to support this kind of analysis.
Students should be expected to use some of these same techniques in engineering as well. When they do so, it is important that they are made cognizant of the purpose of the exercise—that any data they collect and analyze are intended to help validate or improve a design or decide on an optimal solution.
Mathematics and computational tools are central to science and engineering. Mathematics enables the numerical representation of variables, the symbolic representation of relationships between physical entities, and the prediction of outcomes.
Mathematics provides powerful models for describing and predicting such phenomena as atomic structure, gravitational forces, and quantum mechanics. Since the midth century, computational theories, information and computer technologies, and algorithms have revolutionized virtually all scientific and engineering fields.
These tools and strategies allow scientists and engineers to collect and analyze large data sets, search for distinctive patterns, and identify relationships and significant features in ways that were previously impossible.
They also provide powerful new techniques for employing mathematics to model complex phenomena—for example, the circulation of carbon dioxide in the atmosphere and ocean. Mathematics and computation can be powerful tools when brought to bear in a scientific investigation.
Mathematics serves pragmatic functions as a tool—both a communicative function, as one of the languages of science, and a structural function, which allows for logical deduction. Mathematics enables ideas to be expressed in a precise form and enables the identification of new ideas about the physical world. For example, the concept of the equivalence of mass and energy emerged from the mathematical analysis conducted by Einstein, based on the premises of special relativity.
Modern theoretical physics is so heavily imbued with mathematics that it would make no sense to try to divide it into mathematical and nonmathematical parts. In much of modern science, predictions and inferences have a probabilistic nature, so understanding the mathematics of probability and of statistically derived inferences is an important part of understanding science.
Computational tools enhance the power of mathematics by enabling calculations that cannot be carried out analytically. For example, they allow the development of simulations, which combine mathematical representations of. Computational methods are also potent tools for visually representing data, and they can show the results of calculations or simulations in ways that allow the exploration of patterns.
Engineering, too, involves mathematical and computational skills. For example, structural engineers create mathematical models of bridge and building designs, based on physical laws, to test their performance, probe their structural limits, and assess whether they can be completed within acceptable budgets.
Virtually any engineering design raises issues that require computation for their resolution. Although there are differences in how mathematics and computational thinking are applied in science and in engineering, mathematics often brings these two fields together by enabling engineers to apply the mathematical form of scientific theories and by enabling scientists to use powerful information technologies designed by engineers.
Both kinds of professionals can thereby accomplish investigations and analyses and build complex models, which might otherwise be out of the question. Mathematics including statistics and computational tools are essential for data analysis, especially for large data sets.
The abilities to view data from different perspectives and with different graphical representations, to test relationships between variables, and to explore the interplay of diverse external conditions all require mathematical skills that are enhanced and extended with computational skills.
As soon as students learn to count, they can begin using numbers to find or describe patterns in nature. At appropriate grade levels, they should learn to use such instruments as rulers, protractors, and thermometers for the measurement of variables that are best represented by a continuous numerical scale, to apply mathematics to interpolate values, and to identify features—such as maximum, minimum, range, average, and median—of simple data sets.
Students should have opportunities to explore how such symbolic representations can be used to represent data, to predict outcomes, and eventually to derive further relationships using mathematics. Students should gain experience in using computers to record measurements taken with computer-connected probes or instruments, thereby recognizing how this process allows multiple measurements to be made rapidly and recurrently.
Likewise, students should gain experience in using computer programs to transform their data between various tabular and graphical forms, thereby aiding in the identification of patterns. Students should thus be encouraged to explore the use of computers for data analysis, using simple data sets, at an early age.
For example, they could use spreadsheets to record data and then perform simple and recurring calculations from those data, such as the calculation of average speed from measurements of positions at multiple times. Later work should introduce them to the use of mathematical relationships to build simple computer models, using appropriate supporting programs or information and computer technology tools.
As students progress in their understanding of mathematics and computation, at. Because science seeks to enhance human understanding of the world, scientific theories are developed to provide explanations aimed at illuminating the nature of particular phenomena, predicting future events, or making inferences about past events.
Theories are not mere guesses, and they are especially valued because they provide explanations for multiple instances. A scientific hypothesis is neither a scientific theory nor a guess; it is a plausible explanation for an observed phenomenon that can predict what will happen in a given situation. A hypothesis is made based on existing theoretical understanding relevant to the situation and often also on a specific model for the system in question.
Scientific explanations are accounts that link scientific theory with specific observations or phenomena—for example, they explain observed relationships between variables and describe the mechanisms that support cause and effect inferences about them.
Very often the theory is first represented by a specific model for the situation in question, and then a model-based explanation is developed. For example, if one understands the theory of how oxygen is obtained, transported, and utilized in the body, then a model of the circulatory system can be developed and used to explain why heart rate and breathing rate increase with exercise. Scientific theories are developed to provide explanations aimed at illuminating the nature of particular phenomena, predicting future events, or making inferences about past events.
Engaging students with standard scientific explanations of the world—helping them to gain an understanding of the major ideas that science has developed—is a central aspect of science education. Asking students to demonstrate their own understanding of the implications of a scientific idea by developing their own explanations of phenomena, whether based on observations they have made or models they have developed, engages them in an essential part of the process by which conceptual change can occur.
Explanations in science are a natural for such pedagogical uses, given their inherent appeals to simplicity, analogy, and empirical data which may even be in the form of a thought experiment [ 26 , 27 ]. And explanations are especially valuable for the classroom because of, rather than in spite of, the fact that there often are competing explanations offered for the same phenomenon—for example, the recent gradual rise in the mean surface temperature on Earth.
Deciding on the best explanation is a matter of argument that is resolved by how well any given explanation fits with all available data, how much it simplifies what would seem to be complex, and whether it produces a sense of understanding.
Because scientists achieve their own understanding by building theories and theory-based explanations with the aid of models and representations and by drawing on data and evidence, students should also develop some facility in constructing model- or evidence-based explanations.
This is an essential step in building their own understanding of phenomena, in gaining greater appreciation of the explanatory power of the scientific theories that they are learning about in class, and in acquiring greater insight into how scientists operate.
In engineering, the goal is a design rather than an explanation. The process of developing a design is iterative and systematic, as is the process of developing an explanation or a theory in science. These elements include specifying constraints and criteria for desired qualities of the solution, developing a design plan, producing and testing models or prototypes, selecting among alternative design features to optimize the achievement of design criteria, and refining design ideas based on the performance of a prototype or simulation.
Early in their science education, students need opportunities to engage in constructing and critiquing explanations. Using their measurements of how one factor does or does not affect. For example, in investigating the conditions under which plants grow fastest, they may notice that the plants die when kept in the dark and seek to develop an explanation for this finding. They should be encouraged to revisit their initial ideas and produce more complete explanations that account for more of their observations.
By the middle grades, students recognize that many of the explanations of science rely on models or representations of entities that are too small to see or too large to visualize. In the later stages of their education, students should also progress to using mathematics or simulations to construct an explanation for a phenomenon.
In some ways, children are natural engineers. They spontaneously build sand castles, dollhouses, and hamster enclosures, and they use a variety of tools and materials for their own playful purposes. Thus a common elementary school activity is to challenge children to use tools and materials provided in class to solve a specific challenge, such as constructing a bridge from paper and tape and testing it until failure occurs.
Furthermore, design activities should not be limited just to structural engineering but should also include projects that reflect other areas of engineering, such as the need to design a traffic pattern for the school parking lot or a layout for planting a school garden box. In middle school, it is especially beneficial to engage students in engineering design projects in which they are expected to apply what they have recently learned in science—for example, using their now-familiar concepts of ecology to solve problems related to a school garden.
Middle school students should also. At the high school level, students can undertake more complex engineering design projects related to major local, national or global issues. Whether they concern new theories, proposed explanations of phenomena, novel solutions to technological problems, or fresh interpretations of old data, scientists and engineers use reasoning and argumentation to make their case. In science, the production of knowledge is dependent on a process of reasoning that requires a scientist to make a justified claim about the world.
Their arguments can be based on deductions from premises, on inductive generalizations of existing patterns, or on inferences about the best possible explanation. Argumentation is also needed to resolve questions involving, for example, the best experimental design, the most appropriate techniques of data analysis, or the best interpretation of a given data set.
In short, science is replete with arguments that take place both informally, in lab meetings and symposia, and formally, in peer review. Over time, ideas that survive critical examination even in the light of new data attain consensual acceptance in the community, and by this process of discourse and argument science maintains its objectivity and progress [ 28 ]. Becoming a critical consumer of science is fostered by opportunities to use critique and evaluation to judge the merits of any scientifically based argument.
For example, information theory was founded by Claude Shannon, who was known as a theoretical engineer. Though science and engineering are ultimately two separate fields, they do go hand-in-hand and are under the same family of study.
Therefore, those who are interested in science are probably going to be a fan of engineering, too. For more information, feel free to contact a member of the GET team by visiting our contact page or calling us on Toggle navigation. Engineering is a science, though there are several things that set it apart as an industry. Scientists observe the world, while engineers focus on creating.
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