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A Method of Automatically Identifying Learners' Help-Seeking Behavior Classifications in Online Learning Environment

机译:在线学习环境中学习者求助行为分类的自动识别方法

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In the online learning environment, identifying learners' behaviors in the learning process can help them improve their learning effect autonomously. Firstly, we use K-Means algorithm to cluster the learner's help-seeking behavior data to get the classification label of the learner's help-seeking behavior. Secondly, we use the t-distributed Stochastic Neighbor Embedding(T-sne) algorithm to reduce the dimension of the data to visualize the clustering result. Finally, the learner's help-seeking behavior data and the help-seeking behavior classification labels are used as training data to train the Naive Bayesian model so as to automatically obtain the help-seeking behavior classification for the data generated by the new learner. Via the analysis and processing of the help-seeking behavior data using the method proposed in this paper, it shows that this method can effectively find online learners' help-seeking behavior classifications.
机译:在在线学习环境中,识别学习者在学习过程中的行为可以帮助他们自主提高学习效果。首先,我们使用K-Means算法对学习者的求助行为数据进行聚类,得到学习者的求助行为的分类标签。其次,我们使用t分布随机邻居嵌入(T-sne)算法来减小数据的维数,以可视化聚类结果。最后,将学习者的求助行为数据和求助行为分类标签作为训练数据来训练朴素贝叶斯模型,从而自动获得新学习者生成的数据的求助行为分类。通过本文提出的方法对求助行为数据进行分析和处理,表明该方法可以有效地找到在线学习者的求助行为分类。

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