Personalized Intelligent Composition of Test Papers Model Based on Knowledge Point Weight and Error Rate
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    Abstract:

    The research of personalized learning model in big data environment is a hot research topic under large-scale network learning environment. In view of the shortcomings of the traditional intelligent test paper generating strategy, such as the lack of data training, personalized features are not prominent, and the uneven distribution of knowledge points, etc. This study puts forward a personalized practice model, optimizes the rules of paper organization, and makes the individual characteristics more accurate. To a certain extent, it helps student to understand and digest the weak points and blind spot. In this paper, in order to develop a personalized learning practice strategy for personal learning, the knowledge points of each chapter will be transformed into tree management, and add the knowledge point error rate element into the knowledge tree. Finally, this new research model is applied to teaching and education system for experimental research. Research shows that the improvement of this key point is more conducive to improve students' overall academic achievement in general.

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潘婷婷,詹国华,李志华.基于知识点与错误率关联的个性化智能组卷模型.计算机系统应用,2018,27(5):139-144

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History
  • Received:August 15,2017
  • Revised:September 06,2017
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  • Online: April 23,2018
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