Recasting the feedback debate: benefits of tutoring error detection and correction skills
Recasting the feedback debate: benefits of tutoring error detection and correction skills
复制标题
重新审视反馈辩论：辅导错误检测和纠正技能的好处
DOI：
--
复制
发表时间：
2003
期刊：
影响因子：
--
通讯作者：
K. Koedinger
中科院分区：
文献类型：
--
作者：
S. Mathan;K. Koedinger
传统上，智能辅导系统基于所谓的专家模型提供反馈。专家模型导师结合了与无错误和高效的任务性能相关的产生式规则。一旦学生偏离解决方案路径，这些系统就会通过纠正反馈进行干预。
本文在一个所谓的智能新手认知模型的基础上，探讨了提供反馈的效果。一位智能的新手导师允许学生犯错误，并通过练习错误检测和纠正技能提供指导。这种导师的基本认知模型既包括与解决方案生成相关的规则，也包括与错误检测和纠正相关的规则。基于智能新手模型的反馈有两个教学动机。首先，新手的表现往往容易出错，学生可能需要错误检测和纠正技能才能在现实世界的任务中取得成功。其次，对错误的原因和后果进行推理的机会可能会让学生形成一个更好的域名运营商行为模型。
对与这两个模型相关的学习结果进行了实验评估。结果表明，与接受专家模型反馈的学生相比，接受智能新手反馈的学习者在总体上表现出更好的学习效果，包括更好的保持和迁移表现。
这里描述的研究的另一个重点是帮助学生在使用智能辅导系统进行程序性练习之前形成稳健而准确的陈述性知识编码。范例已被广泛用作陈述性教学的一个组成部分。然而，研究表明，例子的有效性受到这样一个事实的限制，即关于运算符可能适用的特定条件的推论在大多数例子中都是隐含的，如果没有自我解释，学生可能看不出来。本文以演练为例，探讨了本文中提到的一种技术的有效性。示例演练以交互方式指导学生学习示例。它们提供问题提示，帮助学生做出必要的推理，以选择将导致解决方案的问题解决运算符。学生通过回答多项选择提示做出这些推论。评估表明，示例演练可能会提供一种经济高效的方式来提高智能教学系统中的学习结果。
Traditionally, intelligent tutoring systems have provided feedback on the basis of a so-called expert model. Expert model tutors incorporate production rules associated with error free and efficient task performance. These systems intervene with corrective feedback as soon as a student deviates from a solution path.
This thesis explores the effects of providing feedback on the basis of a so-called intelligent novice cognitive model. An intelligent novice tutor allows students to make errors, and provides guidance through the exercise of error detection and correction skills. The underlying cognitive model in such a tutor includes both rules associated with solution generation, and rules relating to error detection and correction. There are two pedagogical motivations for feedback based on an intelligent novice model. First, novice performance is often error prone and students may need error detection and correction skills in order to succeed in real world tasks. Second, the opportunity to reason about the causes and consequences of errors may allow students to form a better model of the behavior of domain operators.
Learning outcomes associated with the two models were experimentally evaluated. Results show that learners who receive intelligent novice feedback demonstrate better learning overall, including better retention and transfer performance than students receiving expert model based feedback.
Another focus of the research described here has been to help students form a robust and accurate encoding of declarative knowledge prior to procedural practice with an intelligent tutoring system. Examples have been widely used as a component of declarative instruction. However, research suggests that the effectiveness of examples is limited by the fact that inferences concerning the specific conditions under which operators may be applicable are only implicit in most examples, and may not be apparent to students without self-explanation. This thesis explores the effectiveness of a technique referred to in this thesis as example walkthroughs. Example walkthroughs interactively guide students through the study of examples. They present question prompts that help students make the inferences necessary to select problem solving operators that will lead to a solution. Students make these inferences by responding to multiple choice prompts. Evaluations suggest that example walkthroughs may provide a cost effective way to boost learning outcomes in intelligent tutoring systems.