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.

Many interactive learning environments (ILEs) offer on-demand help, intended to positively influence learning. Recent studies report evidence that although effective help-seeking behavior in ILEs is related to better learning outcomes, learners are not using help facilities effectively. This selective review (a) examines theoretical perspectives on the role of on-demand help in ILEs, (b) reviews literature on the relations between help seeking and learning in ILEs, and (c) identifies reasons for the lack of effective help use. We review the effect of system-related factors, of student-related factors, and of interactions between these factors. The interaction between metacognitive skills and cognitive factors is important for appropriate help seeking, as are a potentially large space of system-related factors as well as interactions among learner- and system-related factors. We suggest directions for future research.

Intelligent tutoring systems are highly interactive learning environments that have been shown to improve upon typical classroom instruction. Cognitive Tutors are a type of intelligent tutor based on cognitive psychology theory of problem solving and learning. Cognitive Tutors provide a rich problem-solving environment with tutorial guidance in the form of step-by-step feedback, specific messages in response to common errors, and ondemand instructional hints. They also select problems based on individual student performance. The learning benefits of these forms of interactivity are supported, to varying extents, by a growing number of results from experimental studies. As Cognitive Tutors have matured and are being applied in new subject-matter areas, they have been used as a research platform and, particularly, to explore interactive methods to support metacognition. We review experiments with Cognitive Tutors that have compared different forms of interactivity and we reinterpret their results as partial answers to the general question: How should learning environments balance information or assistance giving and withholding to achieve optimal student learning? How best to achieve this balance remains a fundamental open problem in instructional science. We call this problem the "assistance dilemma" and emphasize the need for further science to yield specific conditions and parameters that indicate when and to what extent to use information giving versus information withholding forms of interaction.

Our 1997 article in IJAIED reported on a study that showed that a new algebra curriculum with an embedded intelligent tutoring system (the Algebra Cognitive Tutor) dramatically enhanced high-school students' learning. The main motivation for the study was to demonstrate that intelligent tutors that have cognitive science research embedded in them could have real impact in schools. This study was one of the first large-scale classroom evaluations of the integrated use of an Intelligent Tutoring System (ITS) in high schools. A core challenge was figuring out how to embed this new technology into a curriculum and into the existing social context of schools. A key element of the study design was to include multiple kinds of assessments, including standardized test items and items measuring complex problem solving and use of representations. The results were powerful: BOn average the 470 students in experimental classes outperformed students in comparison classes by 15 % on standardized tests and 100 % on tests targeting the [course] objectives.^We suggested that the study was evidence Bthat laboratory tutoring systems can be scaled up and made to work, both technically and pedagogically, in real and unforgiving settings like urban high schools.^Since this study, many more classroom studies comparing instruction that includes an ITS against business as usual have been conducted, often showing advantages for the ITS-enhanced curricula. More rigorous randomized field trials are now more commonplace, but the approach of using multiple assessments in large-scale randomized field trials has not caught on. Cognitive task analysis will remain fundamental to the success of ITSs. A key remaining question for ITS is to find out how they can be used most effectively to support open-ended problem solving, either online or offline. Given all the recent excitement around Massive Open Online Courses (MOOCs), it is interesting to note that our field of

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