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Advances in the Sciences of Thinking and Learning

In the latter part of the 20th century, study of the human mind generated considerable insight into one of the most powerful questions of science: How do people think and learn? Evidence from a variety of disciplines— cognitive psychology, developmental psychology, computer science, anthropology, linguistics, and neuroscience, in particular—has advanced our understanding of such matters as how knowledge is organized in the mind; how children develop conceptual understanding; how people acquire expertise in specific subjects and domains of work; how participation in various forms of practice and community shapes understanding; and what happens in the physical structures of the brain during the processes of learning, storing, and retrieving information. Over the same time period, research in mathematics and science education has advanced greatly. In the 1999 volume How People Learn, the National Research Council (NRC) describes numerous findings from the research on learning and analyzes their implications for instruction. This chapter focuses on those findings that have the greatest implications for improving the assessment of school learning.

EXPANDING VIEWS OF THE NATURE OF KNOWING AND LEARNING

In the quest to understand the human mind, thinkers through the centuries have engaged in reflection and speculation; developed theories and philosophies of elegance and genius; conducted arrays of scientific experiments; and produced great works of art and literature—all testaments to the powers of the very entity they were investigating. Over a century ago, scientists began to study thinking and learning in a more systematic way, taking early steps toward what we now call the cognitive sciences. During the first



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Knowing What Students Know: The Science and Design of Eduacational Assessment 3 Advances in the Sciences of Thinking and Learning In the latter part of the 20th century, study of the human mind generated considerable insight into one of the most powerful questions of science: How do people think and learn? Evidence from a variety of disciplines— cognitive psychology, developmental psychology, computer science, anthropology, linguistics, and neuroscience, in particular—has advanced our understanding of such matters as how knowledge is organized in the mind; how children develop conceptual understanding; how people acquire expertise in specific subjects and domains of work; how participation in various forms of practice and community shapes understanding; and what happens in the physical structures of the brain during the processes of learning, storing, and retrieving information. Over the same time period, research in mathematics and science education has advanced greatly. In the 1999 volume How People Learn, the National Research Council (NRC) describes numerous findings from the research on learning and analyzes their implications for instruction. This chapter focuses on those findings that have the greatest implications for improving the assessment of school learning. EXPANDING VIEWS OF THE NATURE OF KNOWING AND LEARNING In the quest to understand the human mind, thinkers through the centuries have engaged in reflection and speculation; developed theories and philosophies of elegance and genius; conducted arrays of scientific experiments; and produced great works of art and literature—all testaments to the powers of the very entity they were investigating. Over a century ago, scientists began to study thinking and learning in a more systematic way, taking early steps toward what we now call the cognitive sciences. During the first

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Knowing What Students Know: The Science and Design of Eduacational Assessment few decades of the 20th century, researchers focused on such matters as the nature of general intellectual ability and its distribution in the population. In the 1930s, scholars started emphasizing such issues as the laws governing stimulus-and-response associations in learning. Beginning in the 1960s, advances in fields as diverse as linguistics, computer science, and neuroscience offered provocative new perspectives on human development and powerful new technologies for observing behavior and brain functions. The result during the past 40 years has been an outpouring of scientific research on the mind and brain—a “cognitive revolution” as some have termed it. With richer and more varied evidence in hand, researchers have refined earlier theories or developed new ones to explain the nature of knowing and learning. As described by Greeno, Pearson and Schoenfeld (1996b), four perspectives are particularly significant in the history of research and theory regarding the nature of the human mind: the differential, behaviorist, cognitive, and situative perspectives. Most current tests, and indeed many aspects of the science of educational measurement, have theoretical roots in the differential and behaviorist traditions. The more recent perspectives—the cognitive and the situative—are not well reflected in traditional assessments but have influenced several recent innovations in the design and use of educational assessments. These four perspectives, summarized below, are not mutually exclusive. Rather, they emphasize different aspects of knowing and learning with differing implications for what should be assessed and how the assessment process should be transacted (see e.g., Greeno, Collins, and Resnick, 1996a; Greeno et al., 1996b). The Differential Perspective The differential perspective focuses mainly on the nature of individual differences in what people know and in their potential for learning. The roots of research within this tradition go back to the start of the 20th century. “Mental tests” were developed to discriminate among children who were more or less suited to succeed in the compulsory school environment that had recently been instituted in France (Binet and Simon, 1980). The construction and composition of such tests was a very practical matter: tasks were chosen to represent a variety of basic knowledge and cognitive skills that children of a given age could be expected to have acquired. Inclusion of a task in the assessment was based on the how well it discriminated among children within and across various age ranges. A more abstract approach to theorizing about the capacities of the mind arose, however, from the practice of constructing mental tests and administering them to samples of children and adults. Theories of intelligence and mental ability emerged that were based entirely on analyses of the patterns of correlation among test scores. To pursue such work, elaborate statistical machinery was devel-

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Knowing What Students Know: The Science and Design of Eduacational Assessment oped for determining the separate factors that define the structure of intellect (Carroll, 1993). At the core of this approach to studying the mind is the concept that individuals differ in their mental capacities and that these differences define stable mental traits—aspects of knowledge, skill, and intellectual competence—that can be measured. It is presumed that different individuals possess these traits in differing amounts, as measured by their performance on sample tasks that make up a test. Specific traits or mental abilities are inferred when the pattern of scores shows consistent relationships across different situations. The differential perspective was developed largely to assess aspects of intelligence or cognitive ability that were separate from the processes and content of academic learning. However, the methods used in devising aptitude tests and ranking individuals were adopted directly in the design of “standardized” academic achievement tests that were initially developed during the first half of the century. In fact, the logic of measurement was quite compatible with assumptions about knowing and learning that existed within the behaviorist perspective that came to dominate much of research and theory on learning during the middle of the century. The Behaviorist Perspective Behaviorist theories became popular during the 1930s (e.g., Hull, 1943; Skinner, 1938), about the same time that theories of individual differences in intellectual abilities and the mental testing movement were maturing. In some ways the two perspectives are complementary. In the behaviorist view, knowledge is the organized accumulation of stimulus-response associations that serve as the components of skills. Learning is the process by which one acquires those associations and skills (Thorndike, 1931). People learn by acquiring simple components of a skill, then acquiring more complicated units that combine or differentiate the simpler ones. Stimulus-response associations can be strengthened by reinforcement or weakened by inattention. When people are motivated by rewards, punishments, or other (mainly extrinsic) factors, they attend to relevant aspects of a situation, and this favors the formation of new associations and skills. A rich and detailed body of research and theory on learning and performance has arisen within the behaviorist perspective, including important work on the strengthening of stimulus-response associations as a consequence of reinforcement or feedback. Many behavioral laws and principles that apply to human learning and performance are derived from work within this perspective. In fact, many of the elements of current cognitive theories of knowledge and skill acquisition are more elaborate versions of stimulusresponse associative theory. Missing from this perspective, however, is any

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Knowing What Students Know: The Science and Design of Eduacational Assessment treatment of the underlying structures or representations of mental events and processes and the richness of thought and language. The influence of associationist and behaviorist theories can easily be discerned in curriculum and instructional methods that present tasks in sequence, from simple to complex, and that seek to ensure that students learn prerequisite skills before moving on to more complex ones. Many common assessments of academic achievement have also been shaped by behaviorist theory. Within this perspective, a domain of knowledge can be analyzed in terms of the component information, skills, and procedures to be acquired. One can then construct tests containing samples of items or assessment situations that represent significant knowledge in that domain. A person’s performance on such a test indicates the extent to which he or she has mastered the domain. The Cognitive Perspective Cognitive theories focus on how people develop structures of knowledge, including the concepts associated with a subject matter discipline (or domain of knowledge) and procedures for reasoning and solving problems. The field of cognitive psychology has focused on how knowledge is encoded, stored, organized in complex networks, and retrieved, and how different types of internal representations are created as people learn about a domain (NRC, 1999). One major tenet of cognitive theory is that learners actively construct their understanding by trying to connect new information with their prior knowledge. In cognitive theory, knowing means more than the accumulation of factual information and routine procedures; it means being able to integrate knowledge, skills, and procedures in ways that are useful for interpreting situations and solving problems. Thus, instruction should not emphasize basic information and skills as ends in themselves, but as resources for more meaningful activities. As Wiggins (1989) points out, children learn soccer not just by practicing dribbling, passing, and shooting, but also by actually playing in soccer games. Whereas the differential and behaviorist approaches focus on how much knowledge someone has, cognitive theory also emphasizes what type of knowledge someone has. An important purpose of assessment is not only to determine what people know, but also to assess how, when, and whether they use what they know. This information is difficult to capture in traditional tests, which typically focus on how many items examinees answer correctly or incorrectly, with no information being provided about how they derive those answers or how well they understand the underlying concepts. Assessment of cognitive structures and reasoning processes generally requires more complex tasks that reveal information about thinking patterns,

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Knowing What Students Know: The Science and Design of Eduacational Assessment reasoning strategies, and growth in understanding over time. As noted later in this chapter and subsequently in this report, researchers and educators have made a start toward developing assessments based on cognitive theories. These assessments rely on detailed models of the goals and processes involved in mental performances such as solving problems, reading, and reasoning. The Situative Perspective The situative perspective, also sometimes referred to as the sociocultural perspective, grew out of concerns with the cognitive perspective’s nearly exclusive focus on individual thinking and learning. Instead of viewing thought as individual response to task structures and goals, the situative perspective describes behavior at a different level of analysis, one oriented toward practical activity and context. Context refers to engagement in particular forms of practice and community. The fundamental unit of analysis in these accounts is mediated activity, a person’s or group’s activity mediated by cultural artifacts, such as tools and language (Wertsch, 1998). In this view, one learns to participate in the practices, goals, and habits of mind of a particular community. A community can be any purposeful group, large or small, from the global society of professional physicists, for example, to a local book club or school. This view encompasses both individual and collective activity. One of its distinguishing characteristics is attention to the artifacts generated and used by people to shape the nature of cognitive activity. Hence, from a traditional cognitive perspective, reading is a series of symbolic manipulations that result in comprehension of text. In both contrast and complement, from the perspective of mediated activity, reading is a social practice rooted in the development of writing as a model for speech (Olson, 1996). So, for example, how parents introduce children to reading or how home language supports language as text can play an important role in helping children view reading as a form of communication and sense making. The situative perspective proposes that every assessment is at least in part a measure of the degree to which one can participate in a form of practice. Hence, taking a multiple-choice test is a form of practice. Some students, by virtue of their histories, inclinations, or interests, may be better prepared than others to participate effectively in this practice. The implication is that simple assumptions about these or any other forms of assessment as indicators of knowledge-in-the-head seem untenable. Moreover, opportunities to participate in even deceptively simple practices may provide important preparation for current assessments. A good example is dinnertime conversations that encourage children to weave narratives, hold and defend

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Knowing What Students Know: The Science and Design of Eduacational Assessment positions, and otherwise articulate points of view. These forms of cultural capital are not evenly distributed among the population of test takers. Most current testing practices are not a good match with the situative perspective. Traditional testing presents abstract situations, removed from the actual contexts in which people typically use the knowledge being tested. From a situative perspective, there is no reason to expect that people’s performance in the abstract testing situation adequately reflects how well they would participate in organized, cumulative activities that may hold greater meaning for them. From the situative standpoint, assessment means observing and analyzing how students use knowledge, skills, and processes to participate in the real work of a community. For example, to assess performance in mathematics, one might look at how productively students find and use information resources; how clearly they formulate and support arguments and hypotheses; how well they initiate, explain, and discuss in a group; and whether they apply their conceptual knowledge and skills according to the standards of the discipline. Points of Convergence Although we have emphasized the differences among the four perspectives, there are many ways in which they overlap and are complementary. The remainder of this chapter provides an overview of contemporary understanding of knowing and learning that has resulted from the evolution of these perspectives and that includes components of all four. Aspects of the most recent theoretical perspectives are particularly critical for understanding and assessing what people know. For example, both the individual development of knowledge emphasized by the cognitive approach and the social practices of learning emphasized by the situative approach are important aspects of education (Anderson, Greeno, Reder, and Simon, 2000; Cobb, 1998). The cognitive perspective can help teachers diagnose an individual student’s level of conceptual understanding, while the situative perspective can orient them toward patterns of participation that are important to knowing in a domain. For example, individuals learn to reason in science by crafting and using forms of notation or inscription that help represent the natural world. Crafting these forms of inscription can be viewed as being situated within a particular (and even peculiar) form of practice—modeling—into which students need to be initiated. But modeling practices can also be profitably viewed within a framework of goals and cognitive processes that govern conceptual development (Lehrer, Schauble, Carpenter and Penner, 2000; Roth and McGinn, 1998).

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Knowing What Students Know: The Science and Design of Eduacational Assessment The cognitive perspective informs the design and development of tasks to promote conceptual development for particular elements of knowledge, whereas the situative perspective informs a view of the larger purposes and practices in which these elements will come to participate. Likewise, the cognitive perspective can help teachers focus on the conceptual structures and modes of reasoning a student still needs to develop, while the situative perspective can aid them in organizing fruitful participatory activities and classroom discourse to support that learning. Both perspectives imply that assessment practices need to move beyond the focus on individual skills and discrete bits of knowledge that characterizes the earlier associative and behavioral perspectives. They must expand to encompass issues involving the organization and processing of knowledge, including participatory practices that support knowing and understanding and the embedding of knowledge in social contexts. FUNDAMENTAL COMPONENTS OF COGNITION How does the human mind process information? What kinds of “units” does it process? How do individuals monitor and direct their own thinking? Major theoretical advances have come from research on these types of questions. As it has developed over time, cognitive theory has dealt with thought at two different levels. The first focuses on the mind’s information processing capabilities, generally considered to comprise capacities independent of specific knowledge. The second level focuses on issues of representation, addressing how people organize the specific knowledge associated with mastering various domains of human endeavor, including academic content. The following subsections deal with each of these levels in turn and their respective implications for educational assessment. Components of Cognitive Architecture One of the chief theoretical advances to emerge from cognitive research is the notion of cognitive architecture—the information processing system that determines the flow of information and how it is acquired, stored, represented, revised, and accessed in the mind. The main components of this architecture are working memory and long-term memory. Research has identified the distinguishing characteristics of these two types of memory and the mechanisms by which they interact with each other. Working Memory Working memory, sometimes referred to as short-term memory, is what people use to process and act on information immediately before them

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Knowing What Students Know: The Science and Design of Eduacational Assessment (Baddeley, 1986). Working memory is a conscious system that receives input from memory buffers associated with the various sensory systems. There is also considerable evidence that working memory can receive input from the long-term memory system. The key variable for working memory is capacity—how much information it can hold at any given time. Controlled (also defined as conscious) human thought involves ordering and rearranging ideas in working memory and is consequently restricted by finite capacity. The ubiquitous sign “Do not talk to the bus driver” has good psychological justification. Working memory has assumed an important role in studies of human intelligence. For example, modern theories of intelligence distinguish between fluid intelligence, which corresponds roughly to the ability to solve new and unusual problems, and crystallized intelligence, or the ability to bring previously acquired information to bear on a current problem (Carroll, 1993; Horn and Noll, 1994; Hunt, 1995). Several studies (e.g., Kyllonen and Christal, 1990) have shown that measures of fluid intelligence are closely related to measures of working memory capacity. Carpenter, Just, and Shell (1990) show why this is the case with their detailed analysis of the information processing demands imposed on examinees by Raven’s Progressive Matrix Test, one of the best examples of tests of fluid intelligence. The authors developed a computer simulation model for item solution and showed that as working memory capacity increased, it was easier to keep track of the solution strategy, as well as elements of the different rules used for specific problems. This led in turn to a higher probability of solving more difficult items containing complex rule structures. Other research on inductive reasoning tasks frequently associated with fluid intelligence has similarly pointed to the importance of working memory capacity in solution accuracy and in age differences in performance (e.g., Holzman, Pellegrino, and Glaser, 1983; Mulholland, Pellegrino, and Glaser, 1980). This is not to suggest that the needs of educational assessment could be met by the wholesale development of tests of working memory capacity. There is a simple argument against this: the effectiveness of an information system in dealing with a specific problem depends not only on the system’s capacity to handle information in the abstract, but also on how the information has been coded into the system. Early theories of cognitive architecture viewed working memory as something analogous to a limited physical container that held the items a person was actively thinking about at a given time. The capacity of working memory was thought to form an outer boundary for the human cognitive system, with variations according to task and among individuals. This was the position taken in one of the first papers emerging from the cognitive revolution—George Miller’s (1956) famous “Magic Number Seven” argument, which maintains that people can readily remember seven numbers or unrelated

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Knowing What Students Know: The Science and Design of Eduacational Assessment items (plus or minus two either way), but cannot easily process more than that. Subsequent research developed an enriched concept of working memory to explain the large variations in capacity that were being measured among different people and different contexts, and that appeared to be caused by the interaction between prior knowledge and encoding. According to this concept, people extend the limits of working memory by organizing disparate bits of information into “chunks” (Simon, 1974), or groupings that make sense to them. Using chunks, working memory can evoke from long-term memory items of highly variable depth and connectivity. Simply stated, working memory refers to the currently active portion of long-term memory. But there are limits to such activity, and these limits are governed primarily by how information is organized. Although few people can remember a randomly generated string of 16 digits, anyone with a slight knowledge of American history is likely to be able to recall the string 1492– 1776–1865–1945. Similarly, while a child from a village in a developing country would be unlikely to remember all nine letters in the following string— AOL-IBM-USA—most middle-class American children would have no trouble doing so. But to conclude from such a test that the American children had more working memory capacity than their developing-country counterparts would be quite wrong. This is just one example of an important concept: namely, that knowledge stored in long-term memory can have a profound effect on what appears, at first glance, to be the capacity constraint of working memory. Recent theoretical work has further extended notions about working memory by viewing it not as a “place” in the cognitive system, but as a kind of cognitive energy level that exists in limited amounts, with individual variations (Miyake, Just, and Carpenter, 1994). In this view, people tend to perform worse when they try to do two tasks at once because they must allocate a limited amount of processing capacity to two processes simultaneously. Thus, performance differences on any task may derive not only from individual differences in prior knowledge, but also from individual differences in both the amount and allocation or management of cognitive resources (Just, Carpenter, and Keller, 1996). Moreover, people may vary widely in their conscious or unconscious control of these allocation processes. Long-Term Memory Long-term memory contains two distinct types of information—semantic information about “the way the world is” and procedural information about “how things are done.” Several theoretical models have been developed to characterize how information is represented in long-term memory. At present the two leading models are production systems and connectionist

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Knowing What Students Know: The Science and Design of Eduacational Assessment networks (also called parallel distributed processing or PDP systems). Under the production system model, cognitive states are represented in terms of the activation of specific “production rules,” which are stated as condition-action pairs. Under the PDP model, cognitive states are represented as patterns of activation or inhibition in a network of neuronlike elements. At a global level, these two models share some important common features and processes. Both rely on the association of contexts with actions or facts, and both treat long-term memory as the source of information that not only defines facts and procedures, but also indicates how to access them (see Klahr and MacWhinney, 1998, for a comparison of production and PDP systems). The production system model has the added advantage of being very useful for constructing “intelligent tutors” —computerized learning systems, described later in this chapter, that have promising applications to instruction and assessment in several domains. Unlike working memory, long-term memory is, for all practical purposes, an effectively limitless store of information. It therefore makes sense to try to move the burden of problem solving from working to long-term memory. What matters most in learning situations is not the capacity of working memory—although that is a factor in speed of processing—but how well one can evoke the knowledge stored in long-term memory and use it to reason efficiently about information and problems in the present. Cognitive Architecture and Brain Research In addition to examining the information processing capacities of individuals, studies of human cognition have been broadened to include analysis of mind-brain relations. This topic has become of increasing interest to both scientists and the public, especially with the appearance of powerful new techniques for unobtrusively probing brain function such as positron-emission tomography (PET) scans and functional magnetic resonance imaging (fMRI). Research in cognitive neuroscience has been expanding rapidly and has led to the development and refinement of various brain-based theories of cognitive functioning. These theories deal with the relationships of brain structure and function to various aspects of the cognitive architecture and the processes of reasoning and learning. Brain-based research has convincingly demonstrated that experience can alter brain states, and it is highly likely that, conversely, brain states play an important role in the potential for learning (NRC, 1999). Several discoveries in cognitive neuroscience are relevant to an understanding of learning, memory, and cognitive processing, and reinforce many of the conclusions about the nature of cognition and thinking derived from behavioral research. Some of the more important topics addressed by this research, such as hemispheric specialization and environmental effects on

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Knowing What Students Know: The Science and Design of Eduacational Assessment brain development, are discussed in Annex 3–1 at the end of this chapter. As noted in that discussion, these discoveries point to the need for caution so as not to overstate and overgeneralize current findings of neuroscience to derive direct implications for educational and assessment practices. Contents of Memory Contemporary theories also characterize the types of cognitive content that are processed by the architecture of the mind. The nature of this content is extremely critical for understanding how people answer questions and solve problems, and how they differ in this regard as a function of the conditions of instruction and learning. There is an important distinction in cognitive content between domain-general knowledge, which is applicable to a range of situations, and domain-specific knowledge, which is relevant to a particular problem area. In science education, for example, the understanding that unconfounded experiments are at the heart of good experimental design is considered domain-general knowledge (Chen and Klahr, 1999) because the logic underlying this idea extends into all realms of experimental science. In contrast, an understanding of the underlying principles of kinetics or inorganic chemistry, for example, constitutes domain-specific knowledge, often accompanied by local theories and particular types of notation. Similarly, in the area of cognitive development, the general understanding that things can be organized according to a hierarchy is a type of domain-general knowledge, while an understanding of how to classify dinosaurs is domain-specific (Chi and Koeske, 1983). Domain-General Knowledge and Problem-Solving Processes Cognitive researchers have studied in depth the domain-general procedures for solving problems known as weak methods. Newell and Simon (1972) identify a set of such procedures, including hill climbing; means-ends analysis; analogy; and, as a last resort, trial and error. Problem solvers use these weak methods to constrain what would otherwise be very large search spaces when they are solving novel problems. Because the weak methods, by definition, are not tied to any specific context, they may reveal (and predict) people’s underlying ability to solve problems in a wide range of novel situations. In that sense, they can be viewed as the types of processes that are frequently assessed by general aptitude tests such as the SAT I. In most domains of instruction, however, learners are expected to use strong methods: relatively specific algorithms, particular to the domain, that will make it possible to solve problems efficiently. Strong methods, when

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Knowing What Students Know: The Science and Design of Eduacational Assessment used to solve the problem. That information remains to be discovered by researchers who analyze the protocol. (See Ericsson and Simon, 1984, for an elaboration of this fundamental point.) Verbal reports have been used effectively with a range of age groups, starting as early as kindergarten (Klahr and Robinson, 1981). Inter-rater reliabilities are often in the 0.6–0.7 range, depending on the complexity of the report and the training of the people who interpret it. There is a substantial trade-off between the reliability and richness of the record. Also, the analysis of verbal reports is extremely labor-intensive. An equally rich but potentially more problematic source of data is the analysis of verbal interactions when two or more people work on a series of problems (Okada and Simon, 1997; Palincsar and Magnusson, 2001; Teasley, 1995). Obvious difficulties arise when these data are used to evaluate individual performance. However, the communicative demands of group problem solving may reveal certain kinds of knowledge that might otherwise not easily be assessed. Although it might be difficult to apply group problem-solving situations to large-scale assessment, it could be informative to ask individuals to respond to—or interpret others’ responses to—such multiple-player contexts. Indeed, several studies of cognitive development have used the technique of asking children to explain why another child responded erroneously to a question (Siegler, in press). These probes often yield highly diagnostic information about how well the child doing the explaining understands a domain. Microgenetic Analysis An increasingly refined and popular method of investigating cognitive development is microgenetic analysis.1 In this kind of fine-grained analysis, researchers closely observe people at densely spaced time intervals to view minute processes that could be obscured during less-frequent and less-detailed assessments. The properties of microgenetic analysis include (1) observations that span as much as possible of the period during which rapid change in competence occurs; (2) a density of observations within this period that is high relative to the rate of change in the phenomenon; and (3) observations that are examined on an intensive, trial-by-trial basis, with the goal of understanding the process of change in detail. Microgenetic observations may span weeks or months and hundreds of problems. The process 1   This terminology is an artifact of Piaget’s view of his own focus of research as “genetic epistemology,” with “genetic” meaning simply growth over the life span. The method has no particular connection to or implications for the role of genetics in cognitive development. It could just as well be dubbed “microtemporal analysis” or “microdevelopmental analysis.”

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Knowing What Students Know: The Science and Design of Eduacational Assessment has been likened to high-speed stroboscopic photography of a drop of water forming and falling from a spigot or the famous photograph of a drop of milk splashing into a shallow dish of milk. The finer temporal grain reveals phenomena that would not be seen at normal speeds, thereby indicating new underlying processes. (See Siegler and Crowley [1991] for an extensive discussion of the method.) Investigators have examined such issues as a child’s development of concepts, with the goal of identifying when the child first used a new strategy, what the experience was like, what led to its discovery, and how it was generalized beyond its individual use. Research by Alibali and Goldin-Meadow (1993), for instance, suggests that a child’s gestures can be indicators of cognitive change; a mismatch between gesture and speech often indicates a point at which a child is poised to make a transition in understanding. As in the case of reaction-time measures, gestures provide yet another potential window on the mind. Ethnographic Analysis Long used by anthropologists and other social scientists to study cultural practices and social patterns, ethnographic analyses have also proven useful for analyzing cognitive processes. These techniques are aimed at gathering rich information about the day-to-day experiences of a community and its individual members. They have been used to study cognitive performance in many different settings, including classrooms, workplaces, and other environments. In the ethnographic approach, researchers immerse themselves in a particular situation to obtain a sense of its characteristics and its people. They make detailed observations and records of people engaging in normal tasks. They may also use interviews, surveys, videotape recordings, or other methods to elicit qualitative information. This approach has been adapted by cognitive scientists to conduct what Dunbar (1999) calls “in vivo” studies of complex, situated, scientific problem solving in contexts such as worldclass research laboratories. Implications for Assessment Many highly effective tools exist for probing and modeling a person’s knowledge and for examining the contents and contexts of learning. Some of these methods, such as tracking of eye movements and computational modeling, rely on sophisticated technology, while others, such as close observation of what problem solvers say and do over meaningful periods of time, are outgrowths of more traditional and lower-technology modes of research. Although several of these techniques have been designed for use in laboratory studies with one person at a time, they could potentially be

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Knowing What Students Know: The Science and Design of Eduacational Assessment modified to meet the more demanding constraints of everyday assessment, especially assessment in the context of classrooms. More generally, the methods used in cognitive science to design tasks linked to underlying models of knowledge and cognitive processing, observe and analyze cognitive performance, and draw inferences about what a person knows are directly applicable to many of the challenges involved in educational assessment. Furthermore, these methods can be used across a variety of assessment contexts and purposes. As developed in subsequent chapters of this report, the crux of the assessment process is the integration of empirically based models of student learning and cognition with methods for designing tasks and carefully observing student performance, and with procedures for interpreting the meaning of those observations. In the next chapter we look at how these three elements come together in the many situations in which a statistical method is needed to help interpret the observational data. CONCLUSIONS Contemporary theories of learning and knowing emphasize the way knowledge is represented, organized, and processed in the mind. Emphasis is also given to social dimensions of learning, including social and participatory practices that support knowing and understanding. This body of knowledge strongly implies that assessment practices need to move beyond a focus on component skills and discrete bits of knowledge to encompass the more complex aspects of student achievement. Among the fundamental elements of cognition is the mind’s cognitive architecture, which includes working or short-term memory, a highly limited system, and long-term memory, a virtually limitless store of knowledge. What matters in most situations is how well one can evoke the knowledge stored in long-term memory and use it to reason efficiently about current information and problems. Therefore, within the normal range of cognitive abilities, estimates of how people organize information in long-term memory are likely to be more important than estimates of working memory capacity. Understanding the contents of long-term memory is especially critical for determining what people know; how they know it; and how they are able to use that knowledge to answer questions, solve problems, and engage in additional learning. While the contents include both general and specific knowledge, much of what one knows is domain- and task-specific and organized into structures known as schemas. Assessments should evaluate what schemas an individual has and under what circumstances he or she regards the information as relevant. This evaluation should include how a person organizes acquired information, encompassing both strategies for problem solving and ways of chunking relevant information into manageable units.

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Knowing What Students Know: The Science and Design of Eduacational Assessment The importance of evaluating knowledge structures comes from research on expertise. Studies of expert-novice differences in subject domains illuminate critical features of proficiency that should be the targets for assessment. Experts in a subject domain typically organize factual and procedural knowledge into schemas that support pattern recognition and the rapid retrieval and application of knowledge. One of the most important aspects of cognition is metacognition—the process of reflecting on and directing one’s own thinking. Metacognition is crucial to effective thinking and problem solving and is one of the hallmarks of expertise in specific areas of knowledge and skill. Experts use metacognitive strategies for monitoring understanding during problem solving and for performing self-correction. Assessment should therefore attempt to determine whether an individual has good metacognitive skills. Not all children learn in the same way and follow the same paths to competence. Children’s problem-solving strategies become more effective over time and with practice, but the growth process is not a simple, uniform progression, nor is there movement directly from erroneous to optimal solution strategies. Assessments should focus on identifying the specific strategies children are using for problem solving, giving particular consideration to where those strategies fall on a developmental continuum of efficiency and appropriateness for a particular domain of knowledge and skill. Children have rich intuitive knowledge of their world that undergoes significant change as they mature. Learning entails the transformation of naive understanding into more complete and accurate comprehension, and assessment can be used as a tool to facilitate this process. To this end, assessments, especially those conducted in the context of classroom instruction, should focus on making students’ thinking visible to both their teachers and themselves so that instructional strategies can be selected to support an appropriate course for future learning. Practice and feedback are critical aspects of the development of skill and expertise. One of the most important roles for assessment is the provision of timely and informative feedback to students during instruction and learning so that their practice of a skill and its subsequent acquisition will be effective and efficient. As a function of context, knowledge frequently develops in a highly contextualized and inflexible form, and often does not transfer very effectively. Transfer depends on the development of an explicit understanding of when to apply what has been learned. Assessments of academic achievement need to consider carefully the knowledge and skills required to understand and answer a question or solve a problem, including the context in which it is presented, and whether an assessment task or situation is functioning as a test of near, far, or zero transfer.

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Knowing What Students Know: The Science and Design of Eduacational Assessment Much of what humans learn is acquired through discourse and interaction with others. Thus, knowledge is often embedded in particular social and cultural contexts, including the context of the classroom, and it encompasses understandings about the meaning of specific practices such as asking and answering questions. Assessments need to examine how well students engage in communicative practices appropriate to a domain of knowledge and skill, what they understand about those practices, and how well they use the tools appropriate to that domain. Models of cognition and learning provide a basis for the design and implementation of theory-driven instructional and assessment practices. Such programs and practices already exist and have been used productively in certain curricular areas. However, the vast majority of what is known has yet to be applied to the design of assessments for classroom or external evaluation purposes. Further work is therefore needed on translating what is already known in cognitive science to assessment practice, as well as on developing additional cognitive analyses of domain-specific knowledge and expertise. Many highly effective tools exist for probing and modeling a person’s knowledge and for examining the contents and contexts of learning. The methods used in cognitive science to design tasks, observe and analyze cognition, and draw inferences about what a person knows are applicable to many of the challenges of designing effective educational assessments. ANNEX 3–1: COGNITION AND BRAIN SCIENCE There is an ever-increasing amount of information about how the brain develops and processes information and how this is linked to various aspects of cognition, development, and learning. Here we briefly consider two areas of special concern—hemispheric specialization and the effects of enriched environments on brain development—because of the way they have been treated in the popular literature, especially as regards educational practices. Hemispheric Specialization: Realities and Myths The notion that the left and right hemispheres of the brain serve specialized functions emerged some years ago from studies of people whose speech was impaired after damage to the left hemisphere. A study by Sperry (1984) of split-brain humans popularized this notion. Essentially, these studies indicated that in most humans, the right hemisphere has become specialized for spatial and synthetic tasks and the left for verbal, analytic, and sequential tasks. Careful laboratory studies of normal humans show clear hemispheric advantages in reaction times when information such as words or spatial

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Knowing What Students Know: The Science and Design of Eduacational Assessment objects is presented to only one hemisphere or the other (Hellige, 1993; Springer and Deutsch, 1993). Brain imaging studies reveal extraordinary degrees of hemispheric specialization (Thompson, 2000). Spatial navigation involves the right hippocampus; attention shift involves the right parietal lobe; attention processes also involve the right anterior cingulate gyrus and right anterior medial frontal lobe; and visual attention processes also activate areas in the left cerebellum. Verbal short-term memory involves the left parietal and frontal areas; spatial short-term memory involves the right parietal, occipital, and frontal areas and the superior frontal sulcus bilaterally; and face working memory predominantly involves the left precentral sulcus, the left middle frontal gyrus, and the left inferior frontal gyrus. The left prefrontal cortex is more involved in retrieval of information from semantic memory, whereas the right prefrontal cortex is more involved in episodic memory retrieval. In short, hemispheric specialization is the norm for cognitive processes. But from an educational standpoint, this is of little consequence. While there may be some educational implications, those claimed most often (e.g., that a teacher should address the left and right hemispheres separately) are ill founded. In normal humans, the two hemispheres communicate seamlessly. Information projected to one hemisphere is immediately transferred to the other as needed. During most cognitive operations, both hemispheres are activated. Enriched Environments and Brain Development: Realities and Myths Another strand of neuroscientific research has examined the effects of enriched environments on the development of the brain and behavior (Greenough, 1976). Various studies have concluded that rats raised together in a complex environment (“rich” rats) have a significantly thicker cerebral cortex and many more dendritic spines (synapses) on their cortical neurons than rats raised alone in plain cages. Similar results have been found with monkeys. Enhanced cortical development can occur in adult rats, but in rich rats it regresses if the animals are placed in poor environments. Rich rats also perform better than poor rats on learning tasks, but we do not yet know whether the cortical changes relate to learning experiences per se or to other processes, such as arousal. There is a major problem, however, in the way this literature has been interpreted and applied to humans, such that parents believe they should expose their infants to super-rich environments filled with bells, whistles, and moving objects. A particular example of this phenomenon is the attention given to “the Mozart effect” (see Annex Box 3–1). In fact, the animal literature suggests that the effects of a rich environment on brain develop-

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Knowing What Students Know: The Science and Design of Eduacational Assessment ment are simply the effects of a normal environment; the abnormal condition is isolation, resulting in impaired development, as is seen with children raised in extreme isolation. Indeed, wild rats and laboratory rats raised in semiwild environments (which may be rich in stress) have the same cortical development as rich rats. Thus, the available evidence suggests that the normal environment provided by caring parents or other caregivers is sufficient for normal brain development. A common misconception is that the brain grows in spurts and is particularly sensitive to specific educational procedures at these critical growth times. This is not the case. Critical periods—periods in development during which brain systems are especially vulnerable—are indeed real, as demonstrated by the literature on visual deprivation. These periods are important, however, only in abnormal or extreme circumstances. Nor is it true that no new nerve cells form after birth. Studies in rats indicate that particular learn- ANNEX BOX 3–1The Mozart Effect Several years ago, great excitement arose over a report published in Nature that claimed listening to the music of Mozart enhanced intellectual performance, increasing IQ by the equivalent of 8 to 9 points as measured by portions of the Stanford-Binet Intelligence Scale (Rauscher, Shaw, and Ky, 1993). Dubbed “the Mozart effect,” this claim was widely disseminated by the popular media. Articles encouraged parents to play classical music to infants and children and even to listen to such music during pregnancy. Companies responded by selling Mozart effect kits including tapes and players. (An aspect of the Nature account overlooked by the media is that the Mozart effect is reported to last about 10 to 15 minutes.) The authors of the Nature report subsequently offered a neurophysiological rationale for their claim (Rauscher, Shaw, and Ky, 1995). This rationale essentially held that exposure to complex musical compositions excites cortical firing patterns similar to those used in spatial-temporal reasoning, so that performance on spatial-temporal tasks is positively affected. Several groups attempted to replicate the Mozart effect, with consistently negative results (Carstens, Huskins, and Hounshell, 1995; Kenealy and Monsef, 1994; Newman et al., 1995; Steele, Ball, and Runk, 1997;. In a careful study, Steele, Bass and Crook (1999) precisely replicated the conditions described by Rauscher and Shaw as critical. Yet the results were entirely negative, even though subjects were

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Knowing What Students Know: The Science and Design of Eduacational Assessment ing experiences can enhance the proliferation of new neurons, specifically, the hippocampal dentate gyrus used in hippocampal-dependent tasks. Implications In general, applications of brain-based theories to education and assessment are relatively limited at this time, though that may not be the case in the future. As Bruer (1997, 1999) and others have noted, brain research by itself currently provides limited guidance for understanding or modifying complex higher-order cognitive processes. Although neuroimaging or neurophysiological measures may reveal limits to cognitive abilities at the behavioral level, in most cases additional understanding and cognitive theory are necessary to translate these observations into instructional and assessment practices. Rushing to conclusions about the educational implications “significantly happier” listening to silence or Mozart than they were listening to a control piece of postmodern music by Philip Glass. One recent report (Nantais and Schellenberg, 1999) indicates a very slight but significant improvement in performance after listening to music by Mozart and Schubert as compared with silence. When listening to Mozart was compared with listening to a story, however, no effect was observed, a finding that negates the brain model. Mood appeared to be the critical variable in this study. Why did the Mozart effect receive so much media play, particularly when the effect, if it exists at all, lasts only minutes? One might speculate that this was the case in part because the initial positive result was published in Nature, a journal routinely viewed by the media as being highly prestigious in science. Another factor, no doubt, is that exposing one’s child to music appears to be an easy way of making her or him smarter—much easier than reading to the child regularly. Moreover, the so-called neurophysiological rationale provided for the effect probably enhanced its scientific credibility in the eyes of the media. Actually, this rationale is not neurophysiological at all: there is no evidence whatsoever to support the argument that music excites cortical firing patterns similar to those used in spatial-temporal tasks.

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Knowing What Students Know: The Science and Design of Eduacational Assessment of neuroscientific observations could lead to misguided instructional practices, as illustrated by reactions to press reports of the Mozart effect. The exceptions are limited to situations in which cognitive capacities are far below the normal range. For example, the design of a rehabilitation program following brain damage may indeed benefit from neuroimaging or neurophysiological measures. A less extreme example is emergent neural imaging research on dyslexia (see Annex Box 3–2, above). At present, however, both the theoretical basis and the methodology for applying these ANNEX BOX 3–2 Neural Bases of Dyslexia Recent studies using brain imaging techniques suggest that dyslexia is in some degree due to specific abnormalities in the way the brain processes visual and verbal language information (see Thompson, 2000). Guenevere Eden and associates at the National Institute of Mental Health used functional magnetic resonance imaging (fMRI) to examine the extent of brain activation in area V5/MT—an area particularly involved in the perception of movement—in response to moving stimuli in dyslexic men and normal control subjects. The control group showed substantial activation in this area, while the dyslexic subjects did not. In contrast, presenting the subjects with stationary patterns resulted in equivalent activations in other visuocortical areas in each group. A key point here is that area V5/MT is a part of the magnocellular visual system, which is critical to normal perception of motion. Perceptual studies suggest that dyslexics are deficient in motion detection. A study at the National Institute on Aging used positron emission tomography (PET) to study the degree of activation of the angular gyrus, relative to occipital regions, during reading in normal and dyslexic men. In the normal subjects, there was a strong correlation between activation (i.e., increased blood flow) in the angular gyrus and occipital regions. In the dyslexic group, by contrast, there appeared to be a disconnection between the angular gyrus and the occipital regions; there was no correlation between changes in blood flow in the two regions. Additional PET studies of reading tasks (Shaywitz et al., 2000) also found striking differences between dyslexic and nondyslexic subjects in the degree of activation of different brain areas. Studies conducted by Merzenich, Tallal, and colleagues showed that

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Knowing What Students Know: The Science and Design of Eduacational Assessment measures to education or training within the normal range remain to be developed. Even in situations in which methods from neuroscience can be used to diagnose learning needs—for example, in imaging diagnosis of dyslexia—behavioral methods are much simpler to use. children who have trouble understanding spoken language have major deficits in their ability to recognize some rapidly successive phonetic elements in speech and similar impairments in detecting rapid changes in nonspeech sounds. The investigators trained a group of these children in computer “games” designed to cause improvement in auditory temporal processing skills. Following 8 to 16 hours of training over a 20-day period, the children improved markedly in their ability to recognize fast sequences of speech stimuli. In fact, their language was notably enhanced. (See Buonomano and Merzenich [1998] and Fitch, Miller, and Tallal [1997] for extensive discussion of issues of brain plasticity and language, and Merzenich et al. [1996] and Tallal et al. [1996] for initial findings on their procedures for treating language-learning-impaired children.) This appears to be one of the few cases in which basic neuroscience knowledge has led to an effective treatment for a learning disorder.* *   The conventional view of dyslexia is that the children have speech-specific deficits in phonological representation rather than in auditory temporal processing. This view finds considerable support in the literature. For example, Mody, Studdert-Kennedy, and Brady (1997) studied groups of second-grade children who were good and poor readers, matched for age and intelligence. The children were selected to differ on a temporal task used by Tallal as diagnostic (e.g., / ba / - / da / temporal order judgement task). The children were tested on several auditory tasks, including rapid changes in nonspeech sine wave analogues of speech sounds. The results supported the view that the perceptual problem for these poor readers was confusion between phonetically similar, though phonologically contrastive, syllables rather than difficulty in perceiving rapid auditory spectral changes, i.e., speech-specific rather than general auditory deficits. There are, of course, procedural differences between this and other studies supporting the phonological hypothesis and studies supporting the auditory perception hypothesis. Nonetheless, the work by Tallal and Merzenich offers a possible example of how basic research in neuroscience may have practical application to learning in a particular disadvantaged group.

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Knowing What Students Know: The Science and Design of Eduacational Assessment Using three themes, this chapter reviews broad categories of formal measurement models and the principles of reasoning from evidence that underlie them: Formal measurement models are a particular form of reasoning from evidence. They provide explicit, formal rules for how to integrate the many pieces of information that may be relevant to a particular inference. Effectively, they are statistical examples of ways to articulate the relationships between the cognition and observation elements of the assessment triangle described in Chapter 2. The current array of psychometric models and methods is the result of an evolutionary progression shaped, in part, by changes in the kinds of inferences teachers and policy makers want to draw, the ways people have thought about learning and schooling, and the technologies that have been available for gathering and using test data. Work on measurement models has progressed from (1) developing models that are intended to measure general proficiency and/or to rank examinees (referred to here as standard models); to (2) adding enhancements to a standard psychometric model to make it more consistent with changing conceptions of learning, cognition, and curricular emphasis; to (3) incorporating cognitive elements, including a model of learning and curriculum directly into psychometric models as parameters; to (4) creating a family of models that are adaptable to a broad range of contexts. Each model and adaptation has its particular uses, strengths, and limitations. Measurement models now exist that can address specific aspects of cognition. An example is the choice of problem-solving strategies and the strategy changes that occur from person to person, from task to task for an individual, and within a task for an individual. Developments in statistical methods have made it possible to create and work with models more flexibly than in the past, opening the door to a wider array of assessment data and uses. To do so, however, requires closer attention to the interplay between the statistical and cognitive aspects of assessment than has been customary.