defining the specific targets of assessment. Ideally, models will highlight the main determinants of and obstacles to learning and include descriptions of students’ conceptual progressions as they develop competence and expertise.
Selected yet powerful examples of such models currently exist and demonstrate how cognitive theory can be applied to issues of curriculum, instruction, and assessment. Some integrate instruction and assessment and make it possible to assess students continuously as they work on problems. The following are three examples of attempts at such integration.
Some of the most productive research in cognitive science comes from efforts to understand thinking in a domain in enough detail to craft computerized systems known as intelligent tutors. These systems show that it is possible to assess components of students’ knowledge while they are working on problems on line. In principle, intelligent tutors could be used for assessment in a wide range of well-structured knowledge domains.
The intelligent tutors developed by Anderson and colleagues (Anderson, Boyle, Corbett, and Lewis, 1990) and VanLehn and Martin (1998) represent a well-developed integration of multiple methods of observation and inference about cognition. To design such tutors, these researchers have developed highly specific descriptions of thinking about school subjects typically taught at the secondary level, such as geometry, algebra, and physics. As further discussed in the next section, their task analysis and model-building efforts incorporate information from reaction-time measures, strategy diagnosis, eye-movement analysis, and knowledge assessment. The importance of their cognitive task analyses cannot be overstated. As Newell and Simon (1972, p. 8) point out, “If performance is not well understood, it is somewhat premature to study learning,” and it would certainly be premature to design complex instructional systems that attempt to integrate instruction and assessment to support student learning.
For instance, the systems designed by Anderson’s group seamlessly integrate specific cognitive objectives, such as being able to solve a certain kind of algebraic equation, with individualized assessment of student errors, or “bugs,” and specific instructional steps to remediate those bugs. When a student makes a mistake, the system provides advice and remediation to correct the error. Studies suggest that when individuals work with these tutors, there is a relatively direct relationship between the assessment of student learning and the research-based model of student thinking. On average, students learn more with the system than with traditional instruction (Koedinger and Anderson, 1999). Intelligent tutoring systems are discussed in more detail in subsequent chapters.