This article reviews the 10-year history of tutor development based on the advanced computer tutoring theory (J. R. Anderson, 1983R. Anderson, , 1993. We developed production system models in ACT of how students solved problems in LISP, geometry, and algebra. Computer tutors were developed around these cognitive models. Construction of these tutors was guided by a set of eight principles loosely based on the ACT theory. Early evaluations of these tutors usually, but not always, showed significant achievement gains. Best case evaluations showed that students could achieve at least the same level of proficiency as conventional instruction in one third of the time. Empirical studies showed that students were learning skills in production-rule units and that the best tutorial interaction style was one in which the tutor provides immediate feedback, consisting of short and directed error messages. The tutors appear to work better if they present themselves to students as nonhuman tools to assist learning rather than as emulations of human tutors. Students working with these tutors display transfer to other environments to the degree that they can map the tutor environment into the test environment. These experiences have coalesced into a new system for developing and deploying tutors. This system involves selecting a problem-solving interface, constructing a curriculum under the guidance of a domain expert, designing a cognitive model for solving problems in that environment, building instruction around the productions in that model, and deploying the tutor in the classroom. New tutors are being built in this system to achieve the National Council of Teachers of Mathematics (NCTM) standards for high-school mathematics in an urban setting.O v e r the past 10 years, our research group (the Advanced Computer Tutoring Prqject a t Carnegie Mellon University) has been developing a type o f com-Requests for reprints should be sent to

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Recent studies have shown that self-explanation is an effective metacognitive strategy, but how can it be leveraged to improve students' learning in actual classrooms? How do instructional treatments that emphasizes self-explanation affect students' learning, as compared to other instructional treatments? We investigated whether self-explanation can be scaffolded effectively in a classroom environment using a Cognitive Tutor, which is intelligent instructional software that supports guided learning by doing. In two classroom experiments, we found that students who explained their steps during problem-solving practice with a Cognitive Tutor learned with greater understanding compared to students who did not explain steps. The explainers better explained their solutions steps and were more successful on transfer problems. We interpret these results as follows: By engaging in explanation, students acquired better-integrated visual and verbal declarative knowledge and acquired less shallow procedural knowledge. The research demonstrates that the benefits of self-explanation can be achieved in a relatively simple computer-based approach that scales well for classroom use.

Despite the accumulation of substantial cognitive science research relevant to education, there remains confusion and controversy in the application of research to educational practice. In support of a more systematic approach, we describe the Knowledge‐Learning‐Instruction (KLI) framework. KLI promotes the emergence of instructional principles of high potential for generality, while explicitly identifying constraints of and opportunities for detailed analysis of the knowledge students may acquire in courses. Drawing on research across domains of science, math, and language learning, we illustrate the analyses of knowledge, learning, and instructional events that the KLI framework affords. We present a set of three coordinated taxonomies of knowledge, learning, and instruction. For example, we identify three broad classes of learning events (LEs): (a) memory and fluency processes, (b) induction and refinement processes, and (c) understanding and sense‐making processes, and we show how these can lead to different knowledge changes and constraints on optimal instructional choices.