{"title":"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","authors":"Xueqing FANG , Thomas K.F. CHIU","doi":"10.1016/j.caeo.2025.100254","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":100322,"journal":{"name":"Computers and Education Open","volume":"8 ","pages":"Article 100254"},"PeriodicalIF":4.1000,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Using Self-Determination Theory to Explain How Mind Mapping and Real-time Commenting Enhance Student Engagement and Learning Outcomes in Video Creation\",\"authors\":\"Xueqing FANG , Thomas K.F. CHIU\",\"doi\":\"10.1016/j.caeo.2025.100254\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>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.</div></div>\",\"PeriodicalId\":100322,\"journal\":{\"name\":\"Computers and Education Open\",\"volume\":\"8 \",\"pages\":\"Article 100254\"},\"PeriodicalIF\":4.1000,\"publicationDate\":\"2025-03-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computers and Education Open\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2666557325000138\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers and Education Open","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2666557325000138","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
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.