智能辅导系统中基于特征感知的知识跟踪生成概念知识报表

Mithun Haridas, Nirmala Vasudevan, S. Gayathry, G. Gutjahr, R. Raman, Prema Nedungadi
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引用次数: 5

摘要

在许多印度学校,学生与教师的比例很高,这使得教师很难评估个别学生的知识水平和学生理解能力的不足。教师应该清楚地了解每个学生掌握了哪些概念,以及教师需要更详细地复习哪些概念。本文基于学生与智能辅导系统的互动,对学生的概念知识进行了调查。使用了特征感知学生知识跟踪(FAST)算法,因为该算法有助于将特定课程技能与概念知识分离开来。来自28所学校的2400名一年级学生的数据被用于分析。结果包括适度拟合模型和模型参数的简单解释。
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Feature-Aware Knowledge Tracing for Generation of Concept-Knowledge Reports in an Intelligent Tutoring System
In many Indian schools, a high student-teacher ratio makes it an uphill struggle for teachers to assess the knowledge of individual students and deficiencies in the students' understanding. Teachers should have a clear picture on what concepts each student has mastered, and which concepts the teacher needs to review in greater detail. This paper investigates the students' concept knowledge, based on the interaction of the students with an intelligent tutoring system. The Feature-Aware Student knowledge Tracing (FAST) algorithm was used, since the algorithm facilitates the separation of lesson-specific skills from concept knowledge. Data from 2400 first-grade students from 28 schools were used for the analysis. Findings include a moderate fit model and an easy interpretation of the model parameters.
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