CAREER: Learning about Learning

拟议的项目将研究建立基于网络的智能辅导系统，教他们评估的方法。该项目的重点是创建一个系统来帮助八年级学生学习数学，以参加马萨诸塞州综合评估调查（MCAS）。该系统提供了PI和他在CMU的同事们所称的辅助教学：将联合收割机学习评估与脚手架辅助相结合的计算机生成的个别辅导，这样当他们接受个性化的计算机帮助时，教师可以在真实的时间内准确地监控学生遇到的困难以及他们取得的进展。该项目将有5个主要研究方向：在第一个研究方向，设计认知模型，PI将使用难度因素分析和学习因素分析等方法，以创建能够预测学生学习和迁移的良好拟合模型。在第二个研究重点中，推断学生知道什么和正在学习什么，PI将研究新的模型学习和推理机制，以尝试预测学习和迁移何时发生。这项工作将需要结合心理测量方法与智能辅导系统。在第三个重点，优化学习，PI将集中在发现教师使用的教学策略导致学生更好的学习。通过对模型的迭代改进，他将设计出一种机制，来选择最大化预期测试分数增益与完成所需预期时间之比的问题。在第四个重点中，通知教育工作者，PI将研究如何最好地将来自细粒度模型的信息呈现给教师。在最后的推力，允许用户适应，PI将试图提供一种手段，让K-12教师设计的教学方法中使用的智能辅导系统。在他的网站上，他将提供研究团队目前对理想的8年级智能辅导系统的“最佳假设”。教师将能够调整系统为自己的用途，改变现有的助理或创造新的问题，他们的学生。希望这样的系统最终能够提供给大量的教师。

The proposed project will investigate methods of building web-based intelligent tutoring systems that teach as they assess. The project focuses upon the creation of a system to help 8th grade students learn math for the Massachusetts Comprehensive Assessment Survey (MCAS). The system provides what the PI and his colleagues at CMU call assistments: individual pieces of computer-generated tutoring that combine assessment of learning with scaffolded assistance to the students such that as they receive individualized computer help, teachers can monitor, in real time, precisely the kinds of difficulty students are having and what progress they are making. The project will have 5 main research thrusts: In the first research thrust, designing cognitive models, the PI will use methodologies such as difficulty factors analysis and learning factors analysis in order to create good-fitting models that can predict student learning and transfer. In the second research thrust, inferring what students know and are learning, the PI will study novel model learning and inference mechanisms in order to try to predict when learning and transfer occur. This effort will entail combining psychometric methods with intelligent tutoring systems. In the third thrust, optimizing learning, the PI will focus on discovering what pedagogical strategies used by teachers lead to better learning by students. Through the iterative refining of his models, he will devise mechanisms that select problems that maximize the ratio of expected test-score gain to the expected time needed for completion. In the fourth thrust, informing educators, the PI will study how best to present information from the finer-grained model to teachers. In the final thrust, allowing user adaptation, the PI will attempt to provide a means for allowing K-12 teachers to design the pedagogy used in an intelligent tutoring system. On his website, he will provide the research team's current "best hypothesis" of an ideal 8th grade intelligent tutoring system. Teachers will be able to adapt the system for their own uses, changing existing assistments or creating new problems for their students. The hope is that such systems could eventually be made available to large numbers of teachers.