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

该项目结合了心理学、教育学、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发现，并将其应用于智能辅导系统（ITS）的开发。学习理论可以通过研究不同类型的知识表示和过程表示的演变来扩展。 因此，这项研究将考察学习，即学生在整合多种信息源时知识表征的变化。虽然 ITS 的开发是为了检查需要有限的可能答案的明确定义问题的学习，但 ITS 在评估需要学生提供自然语言答案的开放式问题时遇到了问题。然而，要求学生提供论文问题的书面答案可以更丰富地表达学生的知识，也可以提高学生的写作能力。因此，这项研究将证明，ITS 的开发可以超越简单的响应，并且这些 ITS 可以更容易地适应为学生提供个性化指导。为了开发这些 ITS，本研究使用计算语言技术潜在语义分析 (LSA)，根据辅导系统中基于论文的答案对学生知识表示进行建模。随着学生知识表示的变化，他们的论文回答也应该发生变化，包括更多的知识和质量的提高。因此，这样的 ITS 可以为学生的进步（领域知识和写作技能）提供必要的反馈。此外，ITS可以初步评估学生的能力，并为学生提供额外的帮助，随着学生获得更多知识（例如脚手架），帮助逐渐消失。因此，通过将 LSA 合并到 ITS 中，个性化教学可以调整所提供反馈的级别和所呈现的内容。 因此，本研究的目标是通过将 LSA（一种通过论文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取。由于本研究重点关注知识丰富领域的复杂技能，因此该项目的结果通过提供有关在学生学习时如何将多种知识源整合到学生知识表征中的信息，扩展了心理学中的学习理论。该研究还对教育心理学具有理论意义，包括对两个内容领域的支架的系统检查。此外，由于 LSA 是一种用于导出知识表示的自动方法，因此这项研究对于开发任何内容知识领域的培训系统都具有广泛的影响。

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.