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Learning styles' recognition in e-learning environments with feed-forward neural networks

机译:使用前馈神经网络在电子学习环境中学习风格的识别

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People have unique ways of learning, which may greatly affect the learning process and, therefore, its outcome. In order to be effective, e-learning systems should be capable of adapting the content of courses to the individual characteristics of students. In this regard, some educational systems have proposed the use of questionnaires for determining a student learning style; and then adapting their behaviour according to the students' styles. However, the use of questionnaires is shown to be not only a time-consuming investment but also an unreliable method for acquiring learning style characterisations. In this paper, we present an approach to recognize automatically the learning styles of individual students according to the actions that he or she has performed in an e-learning environment. This recognition technique is based upon feed-forward neural networks.
机译:人们有独特的学习方式,这可能会极大地影响学习过程,从而影响学习结果。为了提高效率,电子学习系统应能够使课程内容适应学生的个人特点。在这方面,一些教育系统建议使用问卷调查表来确定学生的学习方式。然后根据学生的风格调整他们的行为。但是,调查表的使用不仅是一项耗时的投资,而且还是获取学习风格特征的不可靠方法。在本文中,我们提出了一种方法,该方法可以根据学生在电子学习环境中所执行的动作自动识别他们的学习风格。该识别技术基于前馈神经网络。

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