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<p>Online courses have a large number of learners with large individual differences, which makes it difficult for teachers to accurately analyse their learning, and there are problems such as low course completion rate and low learning outcomes. This paper proposes a subjective and objective two-class analysis method to analyse learners' learning behaviour. The method divides online learning behaviour data into objective factual data and subjective value data. The method first assesses the objective factual data to determine whether the objective facts are learning behaviours, and then evaluates the learners' subjective data to understand their motivations and attitudes, and assesses the learners' subjective value in performing the behaviours. Through empirical analysis, the results show that the method can effectively analyse students' online learning behaviour data and provide a comprehensive and in-depth understanding of students' learning behaviours and needs, thus providing strong support for personalised teaching and learning advice.</p>

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Construction and empirical research of a subjective-and-objective-analysis model of the online learning behavior

  • 期刊论文

  • 发表日期：

    2025年3月1日

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  • Ke Wang,
  • Kun Zheng,
  • Ying Wang

摘要

Online courses have a large number of learners with large individual differences, which makes it difficult for teachers to accurately analyse their learning, and there are problems such as low course completion rate and low learning outcomes. This paper proposes a subjective and objective two-class analysis method to analyse learners' learning behaviour. The method divides online learning behaviour data into objective factual data and subjective value data. The method first assesses the objective factual data to determine whether the objective facts are learning behaviours, and then evaluates the learners' subjective data to understand their motivations and attitudes, and assesses the learners' subjective value in performing the behaviours. Through empirical analysis, the results show that the method can effectively analyse students' online learning behaviour data and provide a comprehensive and in-depth understanding of students' learning behaviours and needs, thus providing strong support for personalised teaching and learning advice.