Emerging Research-Empirical Research--An Integrated Model of Cognitive and Affective Scaffolding for Intelligent Tutoring Systems

一对一的人际辅导非常有效。开创性的研究表明，辅导明显比小组指导更有效，并可能提供无与伦比的学习机会。一个核心的、尚未解决的研究问题是，“专家导师如何在长期的教学互动过程中提供有效的认知和动机支持，以提高学习效果？”该项目以大学水平的计算机科学教育为课程重点，将设计和评估基于计算机的智能辅导系统JavaTutor，该系统利用人工智能提供认知和动机支持。该项目将在北卡罗莱纳州立大学与三个合作机构一起进行：梅雷迪思学院、肖大学和圣奥古斯丁学院。该项目有三大重点。首先，研究小组将对在全仪器在线辅导环境中与学生互动的专家真人导师提供的认知和情感辅导支持进行为期一个学期的观察研究。该环境将记录所有教程对话、解决问题的痕迹和情感数据流，包括生理信号、姿势和面部表情。其次，研究团队将利用包括隐马尔可夫建模在内的机器学习技术，开发一个基于经验的认知和情感脚手架集成模型。第三，他们将在JavaTutor智能辅导系统的一个学期的实验中验证认知和情感脚手架的集成模型。将部署并比较四个版本的JavaTutor系统。据推测，在一个学期的课程中，具有认知和动机支架综合模型的版本将在学生的认知和情感结果上优于其他模型，并在不同的学习者群体中产生不同的效果，特别是对表现不佳的学生和女学生产生显著的好处。该项目的产品包括将为未来智能辅导系统的发展提供信息的发现和技术。通过集成认知和动机支架促进丰富的学习互动，该项目将创造新的学习环境技术，促进高水平的成就，并在STEM教育中得到广泛应用。预计由此产生的智能辅导系统技术将成为补充和扩大课堂教师影响的下一代教育软件的基础。考虑到人工辅导的有效性和这些新技术支持学习的潜在力量，这种影响应该是显著的。

One-on-one human tutoring is remarkably effective. Seminal studies have shown that tutoring is significantly more effective than group instruction and may provide unparalleled opportunities for learning. A central, unanswered research question is, "How do expert tutors provide effective cognitive and motivational support over the course of long-term tutorial interactions to improve learning?" With a curricular focus of college-level computer science education, this project will see the design and evaluation of a computer-based intelligent tutoring system, JavaTutor, which leverages artificial intelligence to provide both cognitive and motivational support. The project will be conducted at North Carolina State University in conjunction with three partner institutions: Meredith College, Shaw University, and St. Augustine's College. The project has three major thrusts. First, the research team will conduct a semester-long observational study of cognitive and affective tutorial support provided by expert human tutors interacting with students in a fully-instrumented online tutoring environment. The environment will log all tutorial conversations, problem-solving traces, and affective data streams including physiological signals, posture, and facial expressions. Second, the research team will develop an empirically grounded, integrated model of cognitive and affective scaffolding using machine learning techniques including hidden Markov modeling. Third, they will validate the integrated model of cognitive and affective scaffolding in a semester-long experiment with the JavaTutor intelligent tutoring system. Four versions of the JavaTutor system will be deployed and compared. It is hypothesized that over the course of a semester, the version with an integrated model of cognitive and motivational scaffolding will outperform each of the other models on both cognitive and affective student outcomes and yield differential effects across learner groups, accruing particularly significant benefit to low-performing and female students.The products of this project include findings and technologies that will inform the future development of intelligent tutoring systems. By promoting rich learning interactions through integrated cognitive and motivational scaffolding, the project will create new learning environment technologies that promote high levels of achievement and find broad application in STEM education. It is anticipated that the resulting intelligent tutoring system technologies will serve as a foundation for the next generation of educational software that both complements and expands the impact of classroom teachers. The impact should be significant given the effectiveness of human tutoring and the potential power of these new technologies to support learning.