Abstract

Learning analytics has been widely used in the field of education. Most studies have adopted a learning analytics dashboard to present data on learning processes or learning outcomes. However, only presenting learning analytics results was not sufficient and lacked personalised feedback. In response to these gaps, this study proposed a learning analytics-based personalised feedback approach and examined the effects of the proposed approach on collaborative knowledge building, emotional status, co-regulated behavioural patterns and cognitive load. The learning analytics-based personalised feedback approach adopted a deep neural network model, namely Bert (bidirectional encoder representations from transformers), to automatically classify discussion transcripts in online collaborative learning. In total, 60 undergraduate students participated in this exploratory study and were randomly assigned into experimental and control groups. The students in the experimental group learned with the learning analytics-based personalised feedback approach, and the students in the control group learned with the traditional online collaborative learning approach. The learning analytics-based approach was found to have significant impacts and no significant difference in cognitive load was noted between the experimental and control groups.

摘要

学习分析已广泛应用于教育领域。大多数研究都采用了学习分析仪表板来呈现有关学习过程或学习成果的数据。然而，仅仅展示学习分析结果是不够的，而且缺乏个性化的反馈。针对这些差距，本研究提出了一种基于学习分析的个性化反馈方法，并检验了该方法对协作知识构建、情绪状态、共同调节的行为模式和认知负荷的影响。基于学习分析的个性化反馈方法采用了一种深度神经网络模型，即 Bert（来自转换器的双向编码器表示），以自动对在线协作学习中的讨论记录进行分类。总共，60名本科生参加了这项探索性研究，并被随机分配到实验组和对照组。实验组学生采用基于学习分析的个性化反馈方法进行学习，对照组学生采用传统的在线协作学习方法进行学习。发现基于学习分析的方法具有显着影响，并且在实验组和对照组之间没有注意到认知负荷的显着差异。