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Knowing What Students Know: The Science and Design of Eduacational Assessment 5 Implications of the New Foundations for Assessment Design This chapter describes features of a new approach to assessment design that is based on a synthesis of the cognitive and measurement foundations set forth in Chapters 3 and 4. Ways in which the three elements of the assessment triangle defined in Chapter 2—cognition, observation, and interpretation—must work together are described and illustrated with examples. This chapter does not aim to describe the entire assessment design process. A number of existing documents, most notably Standards for Educational and Psychological Testing (American Educational Research Association [AERA], American Psychological Association, and National Council of Measurement in Education, 1999), present experts’ consensus guidelines for test design. We have not attempted to repeat here all of the important guidance these sources provide, for instance, about standards for validity, reliability, and fairness in testing. Instead, this chapter focuses on ways in which assessment design and practice could be enhanced by forging stronger connections between advances in the cognitive sciences and new approaches to measurement. Three important caveats should be borne in mind when reading this chapter. First, the presentation of topics in this chapter corresponds to a general sequence of stages in the design process. Yet to be most effective, those stages must be executed recursively. That is, design decisions about late stages in the assessment process (e.g., reporting) will affect decisions about earlier stages (e.g., task design), causing assessment developers to revisit their choices and refine the design. All aspects of an assessment’s design, from identifying the targets of inference to deciding how results will be reported, must be considered—all within the confines of practical constraints—during the initial conceptualization.
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Knowing What Students Know: The Science and Design of Eduacational Assessment Second, the design principles proposed in this chapter apply to assessments intended to serve a variety of purposes. The different ways in which the principles play out in specific contexts of use and under different sets of constraints are illustrated with a diverse set of examples. In other words, it should not be assumed that the principles proposed in this chapter pertain only to formal, large-scale assessment design. These principles also apply to informal forms of assessment in the classroom, such as when a teacher asks students oral questions or creates homework assignments. All assessments will be more fruitful when based on an understanding of cognition in the domain and on the precept of reasoning from evidence. Finally, the features of assessment design described here represent an ideal case that is unlikely to be fully attained with any single assessment. The examples provided of actual assessments are approximations of this ideal. They illustrate how advances in the cognitive and measurement sciences have informed the development of many aspects of such an ideal design, and provide evidence that further efforts in this direction could enhance teaching and learning. In turn, these examples point to the limitations of current knowledge and technology and suggest the need for further research and development, addressed in Part IV. THE IMPORTANCE OF A MODEL OF COGNITION AND LEARNING Deciding what to assess is not as simple as it might appear. Existing guidelines for assessment design emphasize that the process should begin with a statement of the purpose for the assessment and a definition of the content domain to be measured (AERA et al., 1999; Millman and Greene, 1993). This report expands on current guidelines by emphasizing that the targets of inference should also be largely determined by a model of cognition and learning that describes how people represent knowledge and develop competence in the domain (the cognition element of the assessment triangle). Starting with a model of learning is one of the main features that distinguishes the committee’s proposed approach to assessment design from current approaches. The model suggests the most important aspects of student achievement about which one would want to draw inferences and provides clues about the types of assessment tasks that will elicit evidence to support those inferences. For example, if the purpose of an assessment is to provide teachers with a tool for determining the most appropriate next steps for arithmetic instruction, the assessment designer should turn to the research on children’s development of number sense (see also Chapter 3). Case, Griffin, and colleagues have produced descriptions of how young children develop understanding in various mathematical areas (Case, 1996; Case, Griffin, and
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Knowing What Students Know: The Science and Design of Eduacational Assessment Kelly, 1999; Griffin and Case, 1997). A summary of their cognitive theory for the development of whole-number sense is presented in Box 5–1. Drawing from their extensive research on how children develop mathematical understanding as well as the work of other cognitive development researchers— such as Gelman, Siegler, Fuson, and Piaget—Case, Griffin and colleagues have constructed a detailed theory of how children develop number sense. This theory describes the understandings that children typically exhibit at various stages of development, the ways they approach problems, and the processes they use to solve them. The theory also describes how children typically progress from the novice state of understanding to expertise. Case, Griffin, and colleagues have used their model of cognition and learning to design mathematics readiness programs for economically disadvantaged young children. The model has enabled them to (1) specify what knowledge is most crucial for early success in mathematics, (2) assess where any given population stands with regard to this knowledge, and (3) provide children who do not have all this knowledge with the experience they need to construct it (Case et al., 1999). These researchers have implemented their Rightstart program in different communities in Canada and the United States and have consistently found that children in the experimental program perform significantly better on a variety of measures of number sense than those in control groups (Griffin and Case, 1997; Griffin, Case, and Sandieson, 1992; Griffin, Case, and Siegler, 1994). Later in this chapter we present an assessment they have developed to assess student understanding relative to this theory. Features of the Model of Cognition and Learning The model of learning that informs assessment design should have several key features. First, it should be based on empirical studies of learners in the domain. Developing a model of learning such as the example in Box 5–1 requires an intensive analysis of the targeted performances, using the types of scientific methods described in Chapter 3. The amount of work requirfsed should not be underestimated. Research on cognition and learning has produced a rich set of descriptions of domain-specific performance that can serve as the basis for assessment design, particularly for certain areas of mathematics and science (e.g., National Research Council [NRC], 2001; American Association for the Advancement of Science, 2001) (see also the discussion of domain-based models of learning and performance in Chapter 3). Yet much more research is needed. The literature contains analyses of children’s thinking conducted by various types of professionals, including teachers, curriculum developers, and research psychologists, for a variety of purposes. Existing descriptions of thinking differ on a number of dimensions: some are highly detailed, whereas others are coarser-grained; some
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Knowing What Students Know: The Science and Design of Eduacational Assessment BOX 5–1Example of a Model of Cognition and Learning: How Children Come to Understand the Whole Number System Below is a brief summary of the theory of Case, Griffin, and colleagues of how children gain understanding of the whole-number system, based on empirical study of learners. For a more detailed discussion see Case (1996) or Griffin and Case (1997). Initial counting and quantity schemas. Four-year-olds generally possess a good deal of knowledge about quantity that permits them to answer questions about more and less (Starkey, 1992). Children by this age can also reliably count a set of objects and understand that the final number tag assigned to a set is the answer to the question, “How many objects are there in this group?” (Gelman, 1978). However, they appear to be incapable of integrating these competencies. Thus when asked, “Which is more—four or five?” they respond at chance level, even though they can successfully count to five and make relative quantity judgments about arrays containing five versus four objects. Mental counting line structure. As children move from age 4 to 6, they gradually become capable of answering such questions, suggesting that these two earlier structures have merged into a “mental number line.” Case and colleagues refer to the mental number line as an example of a central conceptual structure because of the pivotal role it assumes in children’s subsequent scientific and mathematical thought. Children’s knowledge representation is now such that forward and backward counting words are merged into a single set of entries that can be “read off” in either direction, whether or not a concrete set of objects is present. As children develop this unified conceptual structure, they come to realize through practice of ideas and other work that a question about addition or subtraction can be answered in the absence of any concrete set of objects, simply by counting forward or backward along the string of counting words. Also during this period, focus on procedures, whereas others emphasize conceptual understanding; and some focus on individual aspects of learning, whereas others empha size the social nature of learning. Differing theoretical descriptions of learning should not be viewed as competitive. Rather, aspects of existing theoretical descriptions can often be combined to create a more complete picture of student performance to better achieve the purposes of an assessment.
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Knowing What Students Know: The Science and Design of Eduacational Assessment children begin to learn the system of notation that is used for representing numbers on paper, further serving to bind together the elements of the new cognitive structure. Double mental counting line structure. Between the ages of 6 and 8, once children understand how mental counting works, they gradually form representations of multiple number lines, such as those for counting by 2s, 5s, 10s, and 100s. The construction of these representations gives new meaning to problems such as double-digit addition and subtraction, which can now be understood as involving component problems that require thinking in terms of different number lines. For instance, the relationship between the 10s column and the 1s in the base-10 number system becomes more apparent to them. Understanding of full system. With further growth and practice, by about age 10 children gain a generalized understanding of the entire whole-number system and the base-10 system on which it rests. Addition or subtraction with regrouping, estimation problems using large numbers, and mental mathematics problems involving compensation all are grasped at a higher level as this understanding gradually takes shape. Case and Griffin explain that although most children develop these competencies, there are always some who do not. This usually does not mean that they are incapable of achieving these understandings, but rather that there has not been a heavy emphasis on counting and quantity in their early environment. The researchers have designed educational interventions to help disadvantaged children develop these competencies because they are so important for later mathematical learning. Second, the model of cognition and learning should identify performances that differentiate beginning and expert learners in the domain. The nature of subject matter expertise has been the focus of numerous studies in human cognition (see also Chapter 3). From this type of research it is known that experts have acquired extensive knowledge in their disciplines, and that this knowledge affects what they notice and how they organize, represent,
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Knowing What Students Know: The Science and Design of Eduacational Assessment and interpret information. The latter characteristics in turn affect their ability to remember, reason, and solve problems. Most useful for assessment design are descriptions of how characteristics of expertise are manifested in particular school subject domains. Studies of expert performance describe what the results of highly successful learning look like, suggesting targets for instruction and assessment. It is not, however, the goal of education to make all school children experts in every subject area, and many would argue that “literacy” and “competency” are more appropriate goals. Ideally, then, a model of learning will also provide a developmental perspective, laying out one or more typical progressions from novice levels toward competence and then expertise, identifying milestones or landmark performances along the way. The model of learning might also describe the types of experiences that provoke change or learning. Models of learning for some content areas will depict children as starting out with little or no knowledge in the domain and through instruction gradually building a larger and larger knowledge base. An example is learning to represent large-scale space. Children’s drawings provide a starting point for cartography, but they need to learn how to represent position and direction (e.g., coordinate systems) to create maps of spaces. In other domains, such as physics, students start with a good deal of naive or intuitive knowledge based on observations of the world around them. Some of this knowledge includes deeply entrenched misconceptions or concepts that must be disentangled through instruction. Given a developmental description of learning, assessments can be designed to identify current student thinking, likely antecedent understandings, and next steps to move the student toward more sophisticated understandings. Developmental models are also the starting point for designing assessment systems that can capture growth in competence. There is no single way in which knowledge is represented by competent performers, and there is no single path to competence. But some paths are traveled more than others. When large samples of learners are studied, a few predominant patterns tend to emerge. For instance, as described in Box 5–2, the majority of students who have problems with subtraction demonstrate one or more of a finite set of common conceptual errors (Brown and Burton, 1978; Brown and VanLehn, 1980).1 The same is true with fractions (Resnick et al., 1989; Hart, 1984) and with physics (diSessa and Minstrell, 1998). Research conducted with populations of children speaking different languages shows that many of the difficulties children experience in comprehending and solving simple mathematics word problems apply consistently across a wide range of languages and instructional settings. The re- 1 While the range of bugs that students demonstrate is quite limited and predictable, this research also shows that students with incomplete subtraction skill will often show variability in the strategies they use from moment to moment and problem to problem.
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Knowing What Students Know: The Science and Design of Eduacational Assessment BOX 5–2 Manifestations of Some Subtraction Bugs The student subtracts the smaller digit in each column from the larger digit regardless of which is on top. When the student needs to borrow, s/he adds 10 to the top digit of the current column without subtracting 1 from the next column to the left. When borrowing from a column whose top digit is 0, the student writes 9 but does not continue borrowing from the column to the left of the 0. Whenever the top digit in a column is 0, the student writes the bottom digit in the answer; i.e., 0 – N = N. Whenever the top digit in a column is 0, the student writes 0 in the answer; i.e., 0 – N = 0. When borrowing from a column where the top digit is 0, the student borrows from the next column to the left correctly, but writes 10 instead of 9 in this column. When borrowing into a column whose top digit is 1, the student gets 10 instead of 11. Once the student needs to borrow from a column, s/he continues to borrow from every column whether s/he needs to or not. The student always subtracts all borrows from the leftmost digit in the top number. SOURCE: Brown and Burton (1978, p. 163). Used with permission of the Cognitive Science Society and by permisson of the authors.
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Knowing What Students Know: The Science and Design of Eduacational Assessment search reveals that children’s difficulties are derived from underlying conceptual representation issues that transcend linguistic differences (Verschaffel, Greer, and DeCorte, 2000). Differences among learners should not be ignored. Thus a third key feature of a model of learning is that it should convey a variety of typical ways in which children come to understand the subject matter of interest. Children are exposed to different content depending on the curriculum and family environment they encounter, and this affects what they learn (see Chapter 3). When developing models of learning, one starting point for capturing such differences is to study a group of learners that reflects the diversity of the population to be instructed and assessed in terms of such factors as age, culture, socioeconomic status, gender, and region. Fourth, starting with a theory of how people learn the subject matter of interest, the designers of an assessment will need to select a slice or subset of the larger theory of cognition and learning as the assessment targets. That is, any given model of learning underlying an assessment will capture some, but not all, aspects of what is known about how students think and learn in the domain. That selection should depend on the purpose for the assessment. For instance, the purpose of an intelligent tutor is to determine the precise topic or skill area in which a student is struggling at the moment so that the student can be directed to further help. To develop this kind of assessment, a detailed description of how people at different levels of expertise use correct and incorrect rules during problem solving is often needed (such as that illustrated by the model of cognition underlying the Anderson tutor, described below). More typical classroom assessments, such as quizzes administered by teachers to a class several times each week or month, provide individual students with feedback about their learning and areas for improvement. They help the teacher identify the extent of mastery and appropriate next steps for instruction. To design such assessments, an extraction from the theory that is not quite so detailed, but closer to the level at which concepts are discussed in classroom discourse, is most helpful. The model of cognition and learning underlying a classroom assessment might focus on common preconceptions or incomplete understandings that students tend to have and that the teacher can identify and build on (as illustrated by the Facets example described below). If the purpose for the assessment is to provide summative information following a larger chunk of instruction, as is the case with statewide achievement tests, a coarser-grained model of learning that focuses on the development of central conceptual structures in the subject domain may suffice. Finally, a model of learning will ideally lend itself to being aggregated in a principled way so that it can be used for different assessment purposes. For example, a fine-grained description of cognition underlying an intelligent tutoring system should be structured so the information can be com-
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Knowing What Students Know: The Science and Design of Eduacational Assessment bined to report less detailed summary information for students, parents, and teachers. The model should, in turn, be compatible with a coarse-grained model of learning used as a basis for an end-of-year summative assessment. To be sure, there will always be school subjects for which models of cognition and learning have not yet been developed. Policies about what topics should be taught and emphasized in school change, and theories of how people learn particular content will evolve over time as understanding of human cognition advances. In such situations, the assessment developer may choose to start from scratch with a cognitive analysis of the domain. But when resources do not allow for that, basic principles of cognition and learning described in Chapter 3—such as the importance of how people organize knowledge, represent problems, and monitor their own learning— can inform the translation of curriculum into instruction and assessment. The principle that learning must start with what students currently understand and know about a topic and build from there will always hold. Some existing assessments have been built on the types of models of learning described above. The following examples have been chosen to illustrate the variation in theories that underlie assessments for different purposes. First, we use the example of intelligent tutoring systems (used to illustrate a number of points in this volume). Existing intelligent tutoring systems are built on detailed cognitive theories of expert problem solving (Anderson, Boyle, Corbett, and Lewis, 1990; VanLehn and Martin, 1998). The tutors use assessment constantly to (1) provide continuous, individualized feedback to learners as they work problems; (2) offer help when appropriate or when requested by the learner; and (3) select and present appropriate next activities for learning. The second example describes a classroom assessment approach that teachers can use for diagnosing qualitatively different states of student understanding in physics. An important point of this report is that a model of learning can take different forms and encompass different research perspectives. Thus the third example illustrates a model of learning that focuses on the situative and participatory aspects of learning mathematics. The fourth example demonstrates how models of learning can be used as the basis for large-scale as well as classroom assessments. Underlying Models of Cognition and Learning: Examples PAT Algebra Tutor John Anderson’s ACT-R research group has developed intelligent tutoring systems for algebra and geometry that are being used successfully in a number of classrooms (Koedinger, Anderson, Hadley, and Mark, 1997). The cognitive models of learning at the core of their systems are based on the group’s more general theory of human cognition, ACT-R, which has many
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Knowing What Students Know: The Science and Design of Eduacational Assessment features consistent with the cognitive architecture and structure of knowledge as described in Chapter 3. ACT-R theory aims to describe how people acquire and organize knowledge and produce successful performance in a wide range of simple and complex cognitive tasks, and it has been subjected to rigorous scientific testing (Anderson et al., 1990). The model of learning is written as a system of “if-then” production rules that are capable of generating the multitude of solution steps and missteps typical of students. As a simple example, below is a small portion of an ACT-R production system for algebra: Rule: IF the goal is to solve a(bx + c) = d THEN rewrite this as bx + c = d/a Rule: IF the goal is to solve a(bx + c) = d THEN rewrite this as abx + ac = d Bug rule: IF the goal is to solve a(bx + c) = d THEN rewrite this as abx + c = d The cognitive model consists of many rules—some correct and some flawed—and their inclusion is based on empirical studies of student performance on a wide range of algebra problems. As the student is working, the tutor uses two techniques to monitor his or her activities: model tracing and knowledge tracing. Model tracing is used to monitor the student’s progress through a problem (Anderson et al., 1990). This tracing is done in the background by matching student actions to those the cognitive model might generate; the tutor is mostly silent through this process. However, when the student asks for help, the tutor has an estimate of where he or she is and can provide hints that are tailored to that student’s particular approach to the problem. Knowledge tracing is used to monitor students’ learning from problem to problem (Corbett and Anderson, 1992). A Bayesian estimation procedure, of the type described in Chapter 4, identifies students’ strengths and weaknesses by seeking a match against a subset of the production rules in the cognitive model that best captures what a student knows at that point in time. This information is used to individualize problem selection and pace students optimally through the curriculum. Facet-Based Instruction and Assessment The Facets program provides an example of how student performance can be described at a medium level of detail that emphasizes the progression or development toward competence and is highly useful for classroom assessment (Hunt and Minstrell, 1994; Minstrell, 2000). Developed through collaboration between Jim Minstrell (an experienced high school science teacher) and Earl Hunt (a cognitive psychologist), the assessment approach
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Knowing What Students Know: The Science and Design of Eduacational Assessment is based on models of learning termed facets of student thinking. The approach is predicated on the cognitive principle that students come to instruction with initial ideas and preconceptions that the teacher should identify and build on. The term facets refers to pieces of knowledge or reasoning, processes, beliefs, or constructions of pieces of knowledge that serve as a convenient unit of thought for analysis of student behavior. In many ways they behave like general rules that students have in their knowledge base about how the world works. Facets are derived from research and from teachers’ observations of student learning. For instance, students in introductory physics classes often enter instruction with the belief (or facet) that air pressure has something to do with weight, since air presses down on objects. Another widely held facet is that if two bodies of different sizes and speeds collide, larger, faster bodies exert more force than do smaller, slower bodies. Whereas neither of these facets is consistent with actual physical principles, both are roughly satisfactory explanations for understanding a variety of situations. Facets are gathered in three ways: by examining relevant research when it exists, by consulting experienced teachers, and by examining student responses to open-ended questions intended to reveal the students’ initial ideas about a topic. Facet clusters are sets of related facets, grouped around a physical situation, such as forces on interacting objects, or some conceptual idea, such as the meaning of average velocity. Within the cluster, facets are sequenced in an approximate order of development, and for recording purposes they are numerically coded. Those ending with 0 or 1 in the units digit tend to be appropriate, acceptable understandings for introductory physics; those ending in 9, 8, or 7 are more problematic facets that should be targeted with remedial instruction. An example of a facets cluster is presented in Box 5–3 (another example was presented earlier in Box 3–10). Starting with a model of learning expressed in terms of facets, Minstrell and Hunt have carefully crafted assessment tasks and scoring procedures to provide evidence of which facets a student is likely to be using (illustrated later in this chapter). Middle School Math Through Applications Project Greeno and colleagues have designed curriculum and assessment practices based on situative theories of cognition and learning (see Chapter 3) (Cole, Coffey, and Goldman, 1999; Greeno, 1991). From a situative perspective, one who knows mathematics is able to participate successfully in the mathematical practices that prevail in one or more of the communities where mathematical knowledge is developed, used, or simply valued. Learning mathematics is a process of becoming more effective, responsible, and au-
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Knowing What Students Know: The Science and Design of Eduacational Assessment BOX 5–8 Cognitive Complexity of Science Tasks Baxter and Glaser (1998) studied matches and mismatches between the intentions of test developers and the nature of cognitive activity elicited in an assessment situation. The Connecticut Common Core of Learning Assessment Project developed a number of content-rich, process-constrained tasks around major topics in science. Baxter and Glaser analyzed a task that asked high school students to write an explanation in response to the following: “For what would you want your blood checked if you were having a transfusion?” (Lomask, Baron, Greig, and Harrison, 1992). Concept maps were developed for scoring student explanations. The expert’s (teacher’s) concept map below served as a template against which students’ performances were evaluated.
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Knowing What Students Know: The Science and Design of Eduacational Assessment On the surface, concept maps appear to be an excellent way to showcase the differential quality of student responses for teachers and students because they explicitly attend to the organization and structure of knowledge. However, Baxter and Glaser found that an overestimate of students’ understanding stems from two features of the concept map: (1) the knowledge assumed, with half of the core concepts (e.g., HIV, disease, blood type) being learned in contexts outside science class, and (2) the relations among the concepts, 90 percent of which are at the level of examples or procedural links (such as, is checked for) rather than processes or underlying causal mechanisms. Unless proficient performance displayed by the concept map requires inferences or reasoning about subject matter relations or causal mechanisms reflective of principled knowledge, the concept map serves primarily as a checklist of words and misrepresents (overestimates in this case) students’ understanding. SOURCE: Lomask, Baron, Greig, and Harrison (1992, p. 27). Used with permission of the authors. as he or she completed the task, and also to elaborate retrospectively on certain aspects of the solution. Interviews were recorded, transcribed, and analyzed to determine whether the student had interpreted each task as intended and the task had elicited the intended processes. The judgments of the internal reviewers, along with the pilot data, were used to answer a series of questions related to the quality of the tasks: Does the task assess the skill/content it was designed to assess? Does the task assess the high-level cognitive processes it was designed to assess? Does the task elicit different representations and strategies? What are they, and how often do they occur in the pilot data? If the task asks for an explanation, are the students providing high-level conceptual explanations? If the task requires students to show their work, are they complete in providing the steps involved in their solutions? On the basis of the answers to these questions, a task was either discarded, revised and pilot tested again, or judged satisfactory and forwarded to the next stage of external review. External review was conducted by teams of outside expert mathematics educators, mathematicians, and psychometricians. This review served as a check on whether important mathematical content and processes were being assessed, and whether the tasks were free from bias and technically sound.
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Knowing What Students Know: The Science and Design of Eduacational Assessment BOX 5–9 Revising Tasks The Pattern Task was designed to assess reasoning skills for identifying the underlying regularities of a figural pattern, using these regularities to extend the pattern, and communicating these regularities effectively. The following is the original version of this task: For homework, Allan’s teacher asked him to look at the pattern below and draw the figure that should come next. Allan doesn’t know how to find the next figure. Write a description for Allan telling him which figure comes next. The pilot data showed that in response to this initial version, many students simply drew a fifth figure rather than providing a description of the pattern regularities, making it difficult to obtain a sense of their solution strategies. The task was therefore revised so it asked students to describe how they knew which figure comes next. This change increased The development process for the QUASAR Cognitive Assessment Instrument required continual interplay among the validation procedures of logical analysis, internal review, pilot testing, and external review. Sometimes a task would undergo several iterations of a subset of these procedures before it was considered ready for the next stage of development. An example is given in Box 5–9. REPORTING OF ASSESSMENT RESULTS Although reporting of results occurs at the end of an assessment cycle, assessments must be designed from the outset to ensure that reporting of the desired types of information will be possible. As emphasized at the beginning of this chapter, the purpose for the assessment and the kinds of inferences one wants to draw from the results should drive the design, including
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Knowing What Students Know: The Science and Design of Eduacational Assessment the cognitive complexity of the students’ responses. On the basis of further pilot testing and expert review, the task was revised to its present form. For homework Miguel’s teacher asked him to look at the pattern below and draw the figure that should come next. Miguel does not know how to find the next figure. A. Draw the next figure for Miguel. B. Write a description for Miguel telling him how you knew which figure comes next. SOURCE: Magone, Cai, Silver, and Wang (1994, p. 324). Reprinted with permission from Elsevier Science. the selection of an appropriate model of learning, the observations, and the interpretation model. The familiar distinction between norm-referenced and criterion-referenced testing is salient in understanding the central role of a model of learning in the reporting of assessment results. Traditionally, achievement tests have been designed to provide results that compare students’ performance with that of other students. The results are usually norm-referenced since they compare student performance with that of a norm group (that is, a representative sample of students who took the same test). Such information is useful, just as height and weight data are informative when placed in the context of such data on other individuals. Comparative test information can help parents, teachers, and others determine whether students are progressing at the same rate as their peers or whether they are above or below the average. Norm-referenced data are limited, however, because they do not
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Knowing What Students Know: The Science and Design of Eduacational Assessment show what a student actually can or cannot do. A score indicating that a student is in the 40th percentile in mathematics does not reveal what mathematics knowledge the student does or does not have. The student may have answered most items correctly if the norm group was high-performing, or may have answered many questions incorrectly if the norm group performed less well. Nor does the norm-referenced score indicate what a student needs to do to improve. In the 1960s, Glaser (1963) drew attention to the desirability of shifting to criterion-referenced testing so that a student’s performance would be reported in absolute terms, that is, in terms of what the student can or cannot do. …the specific behaviors implied at each level of proficiency can be identified and used to describe the specific tasks a student must be capable of performing before he achieves one of these knowledge levels…. Measures which assess student achievement in terms of a criterion standard thus provide information as to the degree of competence attained by a particular student which is independent of reference to the performance of others, (pp. 519–520) The notion of criterion-referenced testing has gained popularity in the last few decades, particularly with the advent of standards-based reforms in the 1990s. As a result of these reforms, many states are implementing tests designed to measure student performance against standards in core content areas. A number of states are combining these measures with more traditional norm-referenced reports to show how students’ performance compares with that of students from other states as well (Council of Chief State School Officers, 2000). Because criterion-referenced interpretations depend so directly on a clear explication of what students can or cannot do, well-delineated descriptions of learning in the domain are key to their effectiveness in communicating about student performance. Test results should be reported in relation to a model of learning. The ways people learn the subject matter and different states of competence should be displayed and made as recognizable as possible to educators, students, and the public to foster discussion and shared understanding of what constitutes academic achievement. Some examples of enhanced reporting afforded by models of learning (e.g., progress maps) are presented in Chapter 4. FAIRNESS Fairness in testing is defined in many ways (see AERA et al., 1999; NRC, 1999b), but at its core is the idea of comparable validity: a fair test is one that yields comparably valid inferences from person to person and group to group. An assessment task is considered biased if construct-irrelevant characteristics of the task result in different meanings for different subgroups.
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Knowing What Students Know: The Science and Design of Eduacational Assessment For example, it is now common wisdom that a task used to observe mathematical reasoning should include words and expressions in general use and not those associated with particular cultures or regions; the latter might result in a lack of comparable score meanings across groups of examinees. Currently, bias tends to be identified through expert review of items. Such a finding is merely judgmental, however, and in and of itself may not warrant removal of items from an assessment. Also used are statistical differential item functioning (DIF) analyses, which identify items that produce differing results for members of particular groups after the groups have been matched in ability with regard to the attribute being measured (Holland and Thayer, 1988). However, DIF is a statistical finding and again may not warrant removal of items from an assessment. Some researchers have therefore begun to supplement existing bias-detection methods with cognitive analyses designed to uncover the reasons why items are functioning differently across groups in terms of how students think about and approach the problems (e.g., Lane, Wang, and Magone, 1996; Zwick and Ercikan, 1989). A particular set of fairness issues involves the testing of students with disabilities. A substantial number of children who participate in assessments do so with accommodations intended to permit them to participate meaningfully. For instance, a student with a severe reading and writing disability might be able to take a chemistry test with the assistance of a computer-based reader and dictation system. Unfortunately, little evidence currently exists about the effects of various accommodations on the inferences one might wish to draw about the performance of individuals with disabilities (NRC, 1997), though some researchers have taken initial steps in studying these issues (Abedi, Hofstetter, and Baker, 2001). Therefore, cognitive analyses are also needed to gain insight into how accommodations affect task demands, as well as the validity of inferences drawn from test scores obtained under such circumstances. In some situations, rather than aiming to design items that are culture-or background-free, a better option may be to take into account learner history in the interpretation of responses to the assessment. The distinction between conditional and unconditional inferences deserves attention because it may provide a key to resolving some of the thorniest issues in assessment today, including equity and student choice of tasks. Conditional Versus Unconditional Inferences To some extent in any assessment, given students of similar ability, what is relatively difficult for some students may be relatively easy for others, depending on the degree to which the tasks relate to the knowledge structures students have, each in their own way, constructed (Mislevy, 1996). From the traditional perspective, this is “noise,” or measurement error, and if
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Knowing What Students Know: The Science and Design of Eduacational Assessment excessive leads to low reliability (see Chapter 4). For inferences concerning overall proficiency in this sense, tasks that do not rank individuals in the same order are less informative than ones that do. Such interactions between tasks and prior knowledge are fully expected from modern perspectives on learning, however, since it is now known that knowledge typically develops first in context, then is extended and decontextualized so it can be applied more broadly to other contexts. An indepth project that dovetails with students’ prior knowledge provides solid information, but becomes a waste of time for students for whom this connection is lacking. The same task can therefore reveal either vital evidence or little at all, depending on the target of inference and the relationship of the information involved to what is known from other sources. Current approaches to assessment, particularly large-scale testing, rely on unconditional interpretation of student responses. This means that evaluation or interpretation of student responses does not depend on any other information the evaluator might have about the background of the examinee. This approach works reasonably well when there is little unique interaction between students and tasks (less likely for assessments connected with instruction than for those external to the classroom) or when enough tasks can be administered to average over the interactions (thus the SAT has 200 items). The disadvantage of unconditional scoring is that it precludes saying different things about a student’s performance in light of other information that might be known about the student’s instructional history. An alternative way to interpret evidence from students’ responses to tasks is referred to as conditional interpretation. Here the observer or scorer has additional background information about the student that affects the interpretation. This can be accomplished in one of three ways, each of which is illustrated using the example of all assessment of students’ understanding of control of variables in scientific experimentation (Chen and Klahr, 1999). Example: Assessment of Control-of-Variables Strategy In their study, Chen and Klahr (1999) exposed children to three levels of training in how to design simple unconfounded experiments. One group received explicit training and repeated probe questions. Another group received only probe questions and no direct training. Finally, the third group served as a control: they received equal exposure to the materials, but no instruction at all. Three different kinds of materials were used for subgroups of children in each training condition. Some children were initially exposed to ramps and balls, other to springs and weights, and still other to sinking objects. Children in each training condition were subsequently assessed on how
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Knowing What Students Know: The Science and Design of Eduacational Assessment well they could transfer their knowledge about the control-of-variables procedures. The three assessments were designed to be increasingly “distant” from the materials used during the training. “Very near transfer”: This assessment was in the same domain as was the initial exposure (e.g., children trained on ramps were asked to design additional experiments using ramps). “Near transfer”: In this assessment, children initially exposed to one domain (e.g., springs) were asked to design experiments in a different domain (e.g., ramps). “Far transfer”: Here, children were presented with a task that was amenable to the same control-of-variables strategy but had different surface features (e.g., paper-and-pencil assessments of good and bad experimental designs in domains outside physics). Two points are central to the present discussion: The targets of assessment were three factors: tendency to subsequently use the control-of-variables strategy in the instructional context, in near-transfer contexts, and in far-transfer contexts. All the tasks were carefully designed to make it possible to determine whether a child used the strategy. Whether a task was an example task, a near-transfer task, or a far-transfer task was not a property of the task, but of the match between the task and the student. Tasks were even counterbalanced within groups with regard to which was the teaching example and which was the near-transfer task. The results of the study showed clear differences among groups across the different kinds of tasks: negligible differences on the repeat of the task on which a child had been instructed; a larger difference on the near-transfer task, favoring children who had been taught the strategy; and a difference again favoring these children on far-transfer tasks, which turned out to be difficult for almost all the children. What is important here is that no such pattern could have emerged if the researchers had simply administered the post test task to all students without knowing either the training that constituted the first half of the experiment or the match between each child’s post test task and the training he or she had received. The evidence is not in the task performance data, but in the evaluation of those data in light of other information the researchers possessed about the students.
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Knowing What Students Know: The Science and Design of Eduacational Assessment Methods of Conditional Inference The first method of conditional inference is that the observer influences the observational setting (the assessment task presented to the student) or the conditions that precede the observation in ways that ensure a certain task-examinee matchup. This method is demonstrated by the design of the control-of-variables study just described. A second way to condition the extraction of information from student performances is to obtain relevant background information about students from which to infer key aspects of the matchups. In the Klahr et al. (in press) example, this approach would be appropriate if the researchers could only give randomly selected post-test tasks to students, but could try to use curriculum guides and teacher interviews to determine how each student’s post-test happened to correspond with his or her past instruction (if at all). A third method is to let students choose among assessment tasks in light of what they know about themselves—their interests, their strengths, and their backgrounds. In the control-of-variables study, students might be shown several tasks and asked to solve one they encountered in instruction, one a great deal like it, and one quite dissimilar (making sure the student identified which was which). A complication here is that some students will likely be better at making such decisions than others. The forms of conditional inference described above offer promise for tackling persisting issues of equity and fairness in large-scale assessment. Future assessments could be designed that take into account students’ opportunity to learn what is being tested. Similarly, such approaches could help address issues of curriculum fairness, that is, help protect against external assessments that favor one curriculum over another. Issues of opportunity to learn and the need for alignment among assessment, curriculum, and instruction are taken up further in Chapter 6. CONCLUSIONS The design of high-quality classroom and large-scale assessments is a complex process that involves numerous components best characterized as iterative and interdependent, rather than linear and sequential. A design decision made at a later stage can affect one occurring earlier in the process. As a result, assessment developers must often revisit their choices and refine their designs. One of the main features that distinguishes the committee’s proposed approach to assessment design from current approaches is the central role of a model of cognition and learning, as emphasized above. This model may be fine-grained and very elaborate or more coarsely grained, depending on the purpose of the assessment, but it should always be based on empirical
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Knowing What Students Know: The Science and Design of Eduacational Assessment studies of learners in a domain. Ideally, the model of learning will also provide a developmental perspective, showing typical ways in which learners progress toward competence. Another essential feature of good assessment design is an interpretation model that fits the model of learning. Just as sophisticated interpretation techniques used with assessment tasks based on impoverished models of learning will produce limited information about student competence, assessments based on a contemporary and detailed understanding of how students learn will not yield all the information they otherwise might if the statistical tools available to interpret the data, or the data themselves, are not sufficient for the task. Observations, which include assessment tasks along with the criteria for evaluating students’ responses, must be carefully designed to elicit the knowledge and cognitive processes that the model of learning suggests are most important for competence in the domain. The interpretation model must incorporate this evidence in the results in a manner consistent with the model of learning. Validation that tasks tap relevant knowledge and cognitive processes, often lacking in assessment development, is another essential aspect of the development effort. Starting with hypotheses about the cognitive demands of a task, a variety of research techniques, such as interviews, having students think aloud as they work problems, and analysis of errors, can be used to analyze the mental processes of examinees during task performance. Conducting such analyses early in the assessment development process can help ensure that assessments do, in fact, measure what they are intended to measure. Well-delineated descriptions of learning in the domain are key to being able to communicate effectively about the nature of student performance. Although reporting of results occurs at the end of an assessment cycle, assessments must be designed from the outset to ensure that reporting of the desired types of information will be possible. The ways in which people learn the subject matter, as well as different types or levels of competence, should be displayed and made as recognizable as possible to educators, students, and the public. Fairness is a key issue in educational assessment. One way of addressing fairness in assessment is to take into account examinees’ histories of instruction—or opportunities to learn the material being tested—when designing assessments and interpreting students’ responses. Ways of drawing such conditional inferences have been tried mainly on a small scale, but hold promise for tackling persistent issues of equity in testing. Some examples of assessments that approximate the above features already exist. They are illustrative of the new approach to assessment the committee advocates, and they suggest principles for the design of new assessments that can better serve the goals of learning.
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Knowing What Students Know: The Science and Design of Eduacational Assessment Four themes guide the discussion in this chapter of how advances in the cognitive sciences and new approaches to measurement have created opportunities, not yet fully realized, for assessments to be used in ways that better serve the goals of learning. One type of assessment does not fit all. The purpose and context of an assessment set priorities and constraints on the design. The power of classroom assessment resides in its close connections to instruction and teachers’ knowledge of their students’ instructional histories. Largescale, standardized assessments can communicate across time and place, but by so constraining the content and timeliness of the message that they often have limited utility in the classroom. These kinds of trade-offs are an inescapable aspect of assessment design. It is in the context of classroom assessment that the most significant benefit can be gained from advances in cognitive theory. Learning is enhanced by assessment that provides feedback to students about particular qualities of their work and what they can do to improve their understanding. To provide this kind of information, teachers must have a foundation of knowledge about how students learn the subject matter. Large-scale assessments are further removed from instruction, but can still benefit learning if well designed and properly used. They can signal worthy goals and display to the public what competency in a domain looks like, along with typical learning pathways. They can also play an important role in communicating and fostering public dialogue about educational goals. However, fully capitalizing on a merger of cognitive and measurement principles will require relaxing some of the constraints that drive current large-scale assessment practices. Multiple measures are needed to serve the assessment needs of an educational system. Currently, however, conflicts between classroom and large-scale assessments in terms of both goals and feedback cause confusion for educators, students, and parents. We describe a vision of coordinated systems of assessment in which multiple assessments work together, along with curriculum and instruction, to support a shared set of learning goals. In this vision, a greater portion of the investment in assessment is shifted toward the classroom, where it can be used most directly to assist learning.