A model of the self-explanation effect.
A model of the self-explanation effect.
复制标题
自我解释效应模型。
DOI：
10.1207/s15327809jls0201_1
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发表时间：
1992
期刊：
影响因子：
--
通讯作者：
M. Chi
中科院分区：
文献类型：
--
作者：
K. VanLehn;Randolph M. Jones;M. Chi
一些研究人员采用了学生学习复杂技能的规程，比如物理解题和LISP编程，方法是研究例子和解决问题。这些调查揭示了自我解释效应：对自己解释例子的学生学得更好，对自己的理解做出更准确的自我评估，在解决问题时更经济地使用类比。我们描述了一个计算机模型Cascade来解释这些发现。解释一个例子使Cascade获得领域知识和派生知识。推导知识被类比地用于控制问题解决过程中的搜索。领域知识是在当前领域知识不完整并导致僵局的情况下获得的。如果僵局可以通过应用一个过于一般化的规则来解决，那么该规则的专门化就会成为一个新的领域规则。计算实验表明，Cascade的学习机制足以重现自解模型。
Several investigators have taken protocols of students learning sophisticated skills, such as physics problem solving and LISP coding, by studying examples and solving problems. These investigations uncovered the self-explanation effect: Students who explain examples to themselves learn better, make more accurate self-assessments of their understanding, and use analogies more economically while solving problems. We describe a computer model, Cascade, that accounts for these findings. Explaining an example causes Cascade to acquire both domain knowledge and derivational knowledge. Derivational knowledge is used analogically to control search during problem solving. Domain knowledge is acquired when the current domain knowledge is incomplete and causes an impasse. If the impasse can be resolved by applying an overly general rule, then a specialization of the rule becomes a new domain rule. Computational experiments indicate that Cascade's learning mechanisms are jointly sufficient to reproduce the self-expl...