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Knowing What Students Know: The Science and Design of Eduacational Assessment
which it is presented, and whether an assessment task or situation is functioning as a test of near, far, or zero transfer.
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.
Contributions of Measurement and Statistical Modeling to Assessment
Advances in methods of educational measurement include the development of formal measurement (psychometric) models, which represent a particular form of reasoning from evidence. These models provide explicit, formal rules for integrating the many pieces of information drawn from assessment tasks. Certain kinds of assessment applications require the capabilities of formal statistical models for the interpretation element of the assessment triangle. These tend to be applications with one or more of the following features: high stakes, distant users (i.e., assessment interpreters without day-to-day interaction with the students), complex models of learning, and large volumes of data.
Measurement models currently available can support the kinds of inferences that cognitive science suggests are important to pursue. In particular, it is now possible to characterize student achievement in terms of multiple aspects of proficiency, rather than a single score; chart students’ progress over time, instead of simply measuring performance at a particular point in