KDI: Learning Through Writing Using Adaptive Tutoring Systems: Modeling Knowledge Representations from Open-Ended Questions

该项目结合了心理学，教育，人工智能和计算语言学在学习和知识表示方面的相关最新研究结果，并将其应用于智能教学系统（ITS）的开发。学习理论可以通过研究不同类型的知识表征以及程序表征的演变来扩展。 因此，本研究将研究学习，学生的知识表征的变化，因为他们整合了多种来源的信息。虽然ITS已经开发出来，以检查学习的定义明确的问题，需要有限的一组可能的答案，ITS有问题评估开放式的问题，需要学生提供自然语言的反应。然而，要求学生提供书面答案的作文问题可以提供一个更丰富的代表性的学生的知识，也提高了学生的写作能力。因此，本研究将表明，ITS可以开发扩展到简单的反应，这些ITS可以适应更容易地提供个性化的教学给学生。为了开发这些ITS，本研究使用计算语言学技术潜在语义分析（LSA）来模拟学生的知识表征，从基于作文的答案在辅导系统中。随着学生的知识表征的变化，所以也应该他们的作文回答，包括更多的知识和质量的提高。因此，这样的ITS可以提供必要的反馈，为学生的改进（无论是在领域知识和写作技能）。此外，ITS可以最初评估学生的能力，并随着学生获得更多的知识（例如，支架化）。因此，通过将LSA纳入ITS，个性化教学可以调整所提供的反馈水平和所呈现的内容。 因此，本研究的目标是通过将LSA（一种通过学生的论文来表示学生知识表征的方法）纳入智能辅导系统来研究复杂技能的获得。由于本研究关注知识丰富领域的复杂技能，该项目的结果通过提供关于如何将多种知识来源整合到学生的知识中的信息，扩展了心理学中的学习理论他们正在学习的表征。该研究也具有理论意义的教育心理学，包括两个内容领域的脚手架系统检查。此外，由于LSA是一种自动方法，用于获得知识表示，这项研究具有广泛的影响，在任何领域的内容知识的培训系统的开发。

This project combines relevant, recent findings from psychology, education, artificial intelligence, and computational linguistics in learning and knowledge representation and applies them to the development of intelligent tutoring systems (ITSs). Learning theory can be extended through the study of the evolution of different types of knowledge representations together with procedural representations. Thus, this research will examine learning, the change in students' knowledge representations, as they integrate multiple sources of information.Although ITSs have been developed to examine learning for well-defined problems requiring a limited set of possi-ble responses, ITSs have had problems evaluating open-ended questions that require the student to provide natural language responses. However, asking students to provide written answers to essay questions can provide a much richer representation of student knowledge and also improving students' writing abilities. Thus, this research will demonstrate that ITSs can be developed which extend beyond simple responses and that these ITSs can adapt more readily to provide individualized instruction to students. In order to develop these ITSs, this research uses the computational linguistic technique Latent Semantic Analysis (LSA) to model student knowledge representations from essay-based answers within tutoring systems.As students' knowledge representations change, so too should their essay responses, including more knowledge and improving in quality. Therefore such an ITS can provide the necessary feedback for student improvement (both in domain knowledge and in writing skill). In addition, the ITS can initially assess the students' ability and provide extra help to the students with help gradually fading as the student gains more knowledge (e.g., scaffold-ing). Thus, by incorporating LSA into an ITS, individualized instruction is possible for adjusting both the level of feedback provided and the content presented. The goal of this research is thus to study the acquisition of complex skills through incorporating LSA, a method for representing students' knowledge representations through their essays, into intelligent tutoring systems.Because this research focuses on complex skills from knowledge-rich domains, results from this project extend theories of learning in psychology by providing information about how multiple sources of knowledge are integrated into students' knowledge representations as they are learning . The research also has theoretical implications for educational psychology including the systematic examination of scaffolding for two content areas. In addition, because LSA is an automatic method for deriving knowledge representations, this research has broad implications for developing systems for training in any area of content knowledge.