KNOWLEDGE TRACING - MODELING THE ACQUISITION OF PROCEDURAL KNOWLEDGE
KNOWLEDGE TRACING - MODELING THE ACQUISITION OF PROCEDURAL KNOWLEDGE
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DOI：
10.1007/bf01099821
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发表时间：
1994-01-01
影响因子：
3.6
通讯作者：
ANDERSON, JR
中科院分区：
文献类型：
--
作者：
CORBETT, AT;ANDERSON, JR
本文描述了一种努力，以模拟学生在技能获取过程中不断变化的知识状态。本研究中的学生正在学习与ACT编程导师（APT）编写短程序。APT是围绕编程知识的产生式规则认知模型构建的，称为理想学生模型。这种模式允许导师沿着学生解决练习题，并在必要时提供帮助。在学生学习的过程中，导师还对学生学习理想模型中的每一条规则的概率进行估计，这一过程称为知识追踪。导师根据这些概率估计向学生提供个性化的练习序列，直到学生“掌握”每条规则。编程导师，认知模型和学习和性能的假设进行了说明。一系列的研究进行审查，审查知识追踪的经验有效性，并导致修改的过程中。目前，该模型在预测测试性能方面相当成功。在建模过程中的进一步修改进行了讨论，可以提高性能水平。
This paper describes an effort to model students' changing knowledge state during skill acquisition. Students in this research are learning to write short programs with the ACT Programming Tutor (APT). APT is constructed around a production rule cognitive model of programming knowledge, called the ideal student model. This model allows the tutor to solve exercises along with the student and provide assistance as necessary. As the student works, the tutor also maintains an estimate of the probability that the student has learned each of the rules in the ideal model, in a process called knowledge tracing. The tutor presents an individualized sequence of exercises to the student based on these probability estimates until the student has 'mastered' each rule. The programming tutor, cognitive model and learning and performance assumptions are described. A series of studies is reviewed that examine the empirical validity of knowledge tracing and has led to modifications in the process. Currently the model is quite successful in predicting test performance. Further modifications in the modeling process are discussed that may improve performance levels.