revise their thinking. An interesting example is the cognitively based scheme for computerized diagnosis of study skills (e.g., self-explanation) recently produced and tested by Conati and VanLehn (1999). The development of metacognitive skills is also an explicit part of the designs used in SMART.

Third, researchers and educators realized that to document the learning effects of these innovations for parents, policy makers, funding agencies, and other outside audiences, it would be necessary to have assessments that captured the complex knowledge and skills that inquiry-based learning environments are designed to foster. Not surprisingly, traditional assessments of mathematics and science typically reveal little about the benefits of these kinds of learning environments. Indeed, one can understand why there is often no evidence of benefit when typical standardized tests are used to evaluate the learning effects of many technology-based instructional programs. The use of such tests constitutes an instance of a poor fit between the observation and cognitive elements of the assessment triangle. The tasks used for typical standardized tests provide observations that align with a student model focused on specific types of declarative and procedural knowledge that may or may not have been acquired with the assistance of the technology-based programs. Thus, it should come as no surprise that there is often a perceived mismatch between the learning goals of many educational technology programs and the data obtained from standardized tests. Despite their inappropriateness, however, many persist in using such data as the primary basis for judging the effectiveness and value of investments in educational technology.

Unfortunately, this situation poses a significant assessment and evaluation challenge for the designers and implementers of technology-enhanced learning environments. For example, if such environments are to be implemented on a wider scale, evidence must be produced that students are learning things of value, and this evidence must be convincing and accepted as valid by outside audiences. In many technology-enhanced learning environments, the data provided are from assessments that are highly contextualized: assessment observations are made while students are engaged in learning activities, and the model used to interpret these observations is linked specifically to that project. Other concerns relate to the technical quality of the assessment information. Is it derived from a representative sample of learners? Are the results generalizable to the broad learning goals in that domain? Are the data objective and technically defensible? Such concerns often make it difficult to use assessment data closely tied to the learning environment to convince educators and the public of the value of these new kinds of learning environments. Without such data it is difficult to expand the audience for these programs so that they are used on a larger scale. This dilemma represents yet another example of the point, made earlier in



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