运用自我决定理论解释思维导图和实时评论如何在视频创作中提高学生的参与度和学习成果

IF 4.1 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Xueqing FANG , Thomas K.F. CHIU
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引用次数: 0

摘要

视频创作为学生提供了参与真实学习体验的机会,同时发展了不同学科的知识和21世纪的技能。学生自创的视频活动可以成为人工智能时代当代高等教育教学的有效手段。然而,它的全部潜力尚未实现,需要更多的研究来探索可以提高其有效性的学习方法。思维导图(MM)和实时评论(RTC)是两种被证明可以提高学生参与度的策略。本研究探讨了视频制作策略(有/没有)和视频制作策略(有/没有)对学生需求满意度、参与度、创造力和协作的影响,并运用自我决定理论(SDT)解释了这两种策略如何影响视频创作活动中的参与度和学习成果。我们对138名中国大学生进行了为期8周的干预研究,采用2 × 2受试者间因子设计,分为四个实验组:视频创作(VC),视频创作与MM (VC-MM),视频创作与RTC (VC-RTC),视频创作与MM和RTC (VC- mmrtc)。分析结果显示:(1)MM显著满足学生的自主性、胜任性和关联性需求,RTC显著满足学生的关联性需求;(ii) MM显著提高了学生的行为投入、认知投入和主体投入,RTC显著提高了学生的情感投入;(iii) MM显著提高了学生的协作能力;(iv) MM和RTC都没有显著提高学生的创造力。研究结果显示,整合多媒体与即时学习策略在满足学生的三种心理需求、增强四种学生参与以及改善视频学习活动中的协作方面具有有效性。在生成式人工智能工具的帮助下,教师和学生可以轻松地采用这些策略进行有效的学习。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using Self-Determination Theory to Explain How Mind Mapping and Real-time Commenting Enhance Student Engagement and Learning Outcomes in Video Creation
Video creation provides students with opportunities to engage in authentic learning experiences while developing knowledge and 21st-century skills across various subjects. The student-created video activity could be an effective pedagogical approach for contemporary higher education teaching in the artificial intelligence (AI) Era. However, its full potential has yet to be realized, and more research is needed to explore learning methodologies that can enhance its effectiveness. Mind mapping (MM) and real-time commenting (RTC) are two strategies that have been shown to enhance student engagement. This study investigated the effects of MM (with vs. without) and RTC (with vs. without) on students’ need satisfaction, engagement, creativity, and collaboration, using Self-Determination Theory (SDT) to explain how the two strategies influence engagement and learning outcomes in video creation activities. We conducted an eight-week intervention study with 138 Chinese university students, using a 2 × 2 between-subjects factorial design, with four experimental groups: video creation (VC), video creation with MM (VC-MM), video creation with RTC (VC-RTC), and video creation with both MM and RTC (VC-MMRTC). Our analysis revealed that: (i) MM significantly satisfied students’ needs for autonomy, competence, and relatedness, while RTC significantly fulfilled their need for relatedness; (ii) MM significantly improved students’ behavioral, cognitive, and agentic engagement, while RTC significantly enhanced their emotional engagement; (iii) MM significantly improved students’ collaboration; and (iv) neither the MM nor RTC significantly improved students’ creativity. The results highlight the effectiveness of integrating MM and RTC strategies in satisfying students’ three psychological needs, enhancing four types of student engagement, and improving collaboration in video-based learning activities. With the help of generative AI tools, instructors and students can easily adopt these strategies for effective learning.
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