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. (shrink)

The current research investigates a combination of two instructional approaches, tutored problem solving and worked examples. Tutored problem solving with automated tutors has proven to be an effective instructional method. Worked‐out examples have been shown to be an effective complement to untutored problem solving, but it is largely unknown whether they are an effective complement to tutored problem solving. Further, while computer‐based learning environments offer the possibility of adaptively transitioning from examples to problems while tailoring to an individual learner, the (...) effectiveness of such machine‐adapted example fading is largely unstudied. To address these research questions, one lab and one classroom experiment were conducted. Both studies compared a standard Cognitive Tutor with two example‐enhanced Cognitive Tutors, in which the fading of worked‐out examples occurred either in a fixed way or adaptively. Results indicate that the adaptive fading of worked‐out examples leads to higher transfer performance on delayed posttests than the other two methods. (shrink)

Previous research on the use of diagrams for argumentation instruction has highlighted, but not conclusively demonstrated, their potential benefits. We examine the relative benefits of using diagrams and diagramming tools to teach causal reasoning about public policy. Sixty-three Carnegie Mellon University students were asked to analyze short policy texts using either: 1) text only, 2) text and a pre-made, correct diagram representing the causal claims in the text, or 3) text and a diagramming tool with which to construct their own (...) causal diagram. After a pretest and training, we tested student performance on a new policy text and found that students given a correct diagram significantly outperformed the other groups. Finally, we compared learning by testing students on a third policy problem in which we removed all diagram or tool aids and found that students who constructed their own diagrams learned the most. We describe these results and interpret them in a way that foreshadows work we now plan for a cognitive-tutor on causal diagram construction. (shrink)

Conceptual understanding of representations and fluency in using representations are important aspects of expertise. However, little is known about how these competencies interact: does representational understanding facilitate learning of fluency, or does fluency enhance learning of representational understanding? We analyze log data obtained from an experiment that investigates the effects of intelligent tutoring systems support for understanding and fluency in connection-making between fractions representations. The experiment shows that instructional support for both representational understanding and fluency are needed for students to (...) benefit from the ITS. In analyzing the ITS log data, we contrast the understanding-first hypothesis and the fluency-first hypothesis, testing whether errors made during the learning phase mediate the effect of experimental condition. Finding that a simple statistical model does not the fit data, we searched over all plausible causal path analysis models. Our results support the understanding-first hypothesis but not the fluency-first hypothesis. (shrink)