Learning to Teach: The Next Generation of Intelligent Tutor Systems

该项目的主要目标是开发优化自动化教学代理的新方法，通过根据学生对个别问题的反应、学生的个体差异(如认知发展水平、空间能力、记忆检索速度、长期保留、替代教学策略（如视觉与计算解决策略）的有效性等信息，为个别学生定制教学代理，提高教学效率。以及与导师的互动程度。重点将放在使用机器学习和计算优化方法来自动化开发新学科领域的高效智能辅导系统（ITS）的过程。这种方法有三个方面。首先，将开发和评估一种基于分层图形模型和机器学习的方法，用于自动化创建具有丰富学生状态表示的学生模型，该模型基于从多个辅导集的学生群体中收集的数据。其次，将开发和评估方法，以获得有效和高效的教学决策策略，这些策略不仅在短期内（从一个数学问题到下一个数学问题）有效，而且在长期内（目标是至少一个月的保留时间）有效。第三，通过将已知的和强大的潜在变量和指导性变量纳入学生模型，系统地研究它们对绩效的作用。认知和教育心理学的研究清楚地表明，短期记忆和投入等潜在变量在学习中发挥着关键作用，而过度学习和复习、大量和分散的练习等教学变量对学习材料的速度有重要影响。研究人员在智能辅导、机器学习和优化、认知、数学和教育心理学等领域拥有共同的优势，这些优势是实现所提出的协同进步所必需的。我们的初步模拟和课堂实验表明，基于改进的教学决策，我们可以显著减少学生学习新材料所需的时间。对于智力价值，他建议研究应该推进基础数学和更高级的代数和几何的基本知识的学习和教学。它应该添加到一组不断增长的统计和计算技术中，这些技术可用于估计控制人类行为的复杂隐藏的层次结构。研究还应该通过解决基于真实和具有挑战性的数学教育问题的学习场景，而不是目前通常研究的抽象场景，来显著拓宽机器学习系统的能力。对于更广泛的影响，这一基础教育研究将导致扩大代表性不足的群体，特别是妇女，在各种科学、技术、工程和数学（STEM）学科的参与。它将促进对学习和投入的发现和理解，并将其作为学习个体差异的预测因素，并将产生对个体差异更敏感的智能导师。它将揭示不同性别和认知能力的学生在多大程度上通过不同的教学形式更有效地学习。这项研究将造福社会，因为机器学习方法为构建复杂系统提供了核心技术，将适用于各种教学系统。

The primary objective of this project is to develop new methods for optimizing an automated pedagogical agent to improve its teaching efficiency through customization to individual students based on information about their responses to individual problems, student individual differences such as level of cognitive development, spatial ability, memory retrieval speed, long-term retention, effectiveness of alternative teaching strategies (such as visual vs. computational solution strategies), and degree of engagement with the tutor. An emphasis will be placed on using machine learning and computational optimization methods to automate the process of developing efficient Intelligent Tutoring Systems (ITS) for new subject domains. The approach is threefold.First, a methodology based on hierarchical graphical models and machine learning will be developed and evaluated for automating the creation of student models with rich representations of student state based on data collected from populations of students over multiple tutoring episodes. Second, methods will be developed and evaluated for deriving pedagogical decision strategies that are effective and efficient not just over the short-term (from one math problem to the next one), but over the long-term where retention over a period of at least one month is the objective. Third, a systematic study will be conducted of the role that known and powerful latent and instructional variables can have on performance through their inclusion in student models. Research in cognitive and educational psychology clearly shows the critical role that latent variables such as short-term memory and engagement play in learning, and that instructional variables such as over-learning and review, and massed and distributed practice have on the rate at which material is learned. The investigators jointly have strengths in the areas of intelligent tutoring, machine learning and optimization, and cognitive, mathematical and educational psychology, strengths that are needed in order to make the synergistic advances that are being proposed. Our preliminary simulations and classroom experiments suggest that we can significantly reduce the time it takes students to learn new material based on improved pedagogical decisions.For intellectual merit, he proposed research should advance fundamental knowledge of the learning and teaching of basic mathematics and more advanced algebra and geometry. It should add to the set of growing statistical and computational techniques that are available to estimate the complex hidden hierarchical structures that govern human behavior. The research should also significantly broaden the capabilities of machine learning systems by addressing learning scenarios that are grounded on the real and challenging problem of mathematics education than the abstract scenarios typically studied at present. For broader impact, this foundational educational research will lead to the broadening of participation of underrepresented groups, especially women, in a variety of science, technology, engineering and mathematics (STEM) disciplines. It will advance discovery and understanding of learning and engagement as predictors of individual differences in learning and will result in intelligent tutors that are more sensitive to individual differences. It will unveil the extent to which students of different genders and cognitive abilities learn more efficiently with different forms of teaching. This research will benefit society as machine learning methods, which provide a core technology for building complex systems, will be applicable to a variety of teaching systems.