[Objective] This paper proposes a method automatically annotating the knowledge points of test questions from online education resources.[Methods] First, we introduced the concept of text semantics to establish new association rules. Then, considering the semantic matching degrees between the target questions and the rules, we proposed an automatic method for knowledge point annotation. Finally, we presented a personalized question recommendation mechanism.[Results] We examined the proposed method with test questions from middle school mathematics and high school history courses. We also compared our model’s labeling accuracy with naive Bayes, K nearest neighbor, random forest and support vector machine, and yielded better results.[Limitations] The understanding of the semantics of test questions and the labeling accuracy could be further improved.[Conclusions] The knowledge point annotation and the personalized question recommendation methods could improve smart teaching and online learning.
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