An effective metacognitive strategy: learning by doing and explaining with a computer-based Cognitive Tutor

被引:443
作者
Aleven, VAWMM [1 ]
Koedinger, KR [1 ]
机构
[1] Carnegie Mellon Univ, Sch Comp Sci, Human Comp Interact Inst, Pittsburgh, PA 15213 USA
基金
美国国家科学基金会; 美国安德鲁·梅隆基金会;
关键词
self-explanation; metacognition; computer-based learning environments; intelligent tutoring systems; cognitive tutors; problem solving; learning; instruction; implicit and explicit learning;
D O I
10.1016/S0364-0213(02)00061-7
中图分类号
B84 [心理学];
学科分类号
04 ; 0402 ;
摘要
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. (C) 2002 Cognitive Science Society, Inc. All rights reserved.
引用
收藏
页码:147 / 179
页数:33
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