聊天机器人
主动学习(机器学习)
个性化学习
计算机科学
平面图(考古学)
自主学习
心理学
合作学习
开放式学习
万维网
教学方法
数学教育
人工智能
历史
考古
作者
Michael Pin-Chuan Lin,Daniel Chang
标识
DOI:10.1016/j.caeai.2023.100167
摘要
The CHAT-ACTS pedagogical framework presented in this paper integrates personalized chatbots into active and self-regulated learning (SRL) to enhance student engagement, motivation, and learning outcomes. Employing three primary learning modes - Personalized Chatbot, Self-Regulated Learning, and Active Learning - the learner occupies the central position, symbolizing their active role in shaping their learning journey. Strategic actions such as Evaluation, Feedback, and Plan are crucial in the Personalized Chatbot mode, while the SRL mode emphasizes Goal Setting and Study Tactics. The Active Learning mode underscores Active-Based Learning and Teaching Strategies. Through these modes, bidirectional relationships are established, facilitating feedback, setting goals, and employing active learning techniques. By utilizing this framework, educators can maximize the impact of personalized chatbots in various educational settings.
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