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Hybrid CAT Using Bayes Classification and Two-Parameter Model

机译：使用贝叶斯分类和双参数模型的混合猫

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Much research and implementation has been done in the field of adaptive learning, while many such platforms exist almost none of them have tackled the problem of maintainability of such high demand systems. This paper proposes a new system using naive Bayes classifier and two-parameter model of IRT to develop a low cost, easy to maintain, self-evolving test platform. The proposed model harnesses the knowledge of the community while implementing powerful test theory. The paper discusses in detail the major modules of the system along with the related theory. The proposed model incorporates machine learning and IRT to provide a state of the art system while still being a community powered platform. The scope of the proposed model is visited. This paper provides a direction and precedent for the development of a new breed of low maintenance high capability test platforms.
机译：在自适应学习领域已经完成了许多研究和实施，而许多这样的平台几乎没有它们存在解决这些高需求系统的可维护性问题。本文提出了一种新系统，采用Naive Bayes分类器和IRT的两参数模型，开发出低成本，易于维护，自我不断发展的测试平台。建议的模型在实施强大的测试理论时利用社区的知识。本文详细讨论了系统的主要模块以及相关理论。该拟议的模型包括机器学习和IRT，以提供最先进的系统，同时仍然是社区供电的平台。访问了所提出的模型的范围。本文为开发新品种低维护高能力测试平台提供了方向和先例。

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