理论驱动的学习分析仪表板能否增强写作学习中人机协作？来自实证实验的洞察
Can theory-driven learning analytics dashboard enhance human-AI collaboration in writing learning? Insights from an empirical experiment
AI总结：
本研究通过准实验验证了理论驱动的学习分析仪表板能提升写作学习中的人机协作，增强知识获取和SRL技能，但可能增加认知负荷。
摘要：
生成式人工智能（GenAI）融入教育引发了对其过度依赖和浅层学习的担忧，尤其是在高等教育的写作任务中。本研究探讨了理论驱动的学习分析仪表板（LAD）是否能够通过提升写作知识获取、培养自我调节学习（SRL）技能以及构建不同的人机对话特征，来增强学术写作任务中的人机协作。基于Zimmerman的SRL框架，该LAD对学习者的目标设定、写作过程和反思提供实时反馈，同时监控学习者与人工智能交互的质量。本研究开展了一项准实验，涉及52名研究生，他们被分为实验组（EG）和使用LAD进行人机协作写作任务的对照组（CG）。研究收集并分析了前后知识测试、测量SRL和认知负荷的问卷以及学生与GenAI的对话数据。结果显示，实验组在写作知识获取和SRL技能提升方面显著优于对照组，尤其是在自我效能和认知策略方面。然而，实验组也报告了更高的考试焦虑和认知负荷，这可能是由于元认知意识的增强。认知网络分析显示，实验组与GenAI的互动更具反思性和评价性，而对照组的互动则更侧重于事务性和信息寻求性交流。这些发现为关于GenAI教育应用的日益增多的文献做出了贡献，并强调了设计补充GenAI工具的干预措施的重要性，确保技术增强而非削弱学习过程。
英文摘要：
The integration of Generative AI (GenAI) into education has raised concerns about over-reliance and superficial learning, particularly in writing tasks in higher education. This study explores whether a theory-driven learning analytics dashboard (LAD) can enhance human-AI collaboration in the academic writing task by improving writing knowledge gains, fostering self-regulated learning (SRL) skills and building different human-AI dialogue characteristics. Grounded in Zimmerman's SRL framework, the LAD provided real-time feedback on learners' goal-setting, writing processes and reflection, while monitoring the quality of learner-AI interactions. A quasi-experiment was conducted involving 52 postgraduate students divided into an experimental group (EG) using the LAD to a control group (CG) without it in a human-AI collaborative writing task. Pre- and post- knowledge tests, questionnaires measuring SRL and cognitive load, and students' dialogue data with GenAI were collected and analyzed. Results showed that the EG achieved significantly higher writing knowledge gains and improved SRL skills, particularly in self-efficacy and cognitive strategies. However, the EG also reported increased test anxiety and cognitive load, possibly due to heightened metacognitive awareness. Epistemic Network Analysis revealed that the EG engaged in more reflective, evaluative interactions with GenAI, while the CG focused on more transactional and information-seeking exchanges. These findings contribute to the growing body of literature on the educational use of GenAI and highlight the importance of designing interventions that complement GenAI tools, ensuring that technology enhances rather than undermines the learning process.
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