Learning to Teach: The Next Generation of Intelligent Tutor Systems

这个项目的主要目标是开发新的方法来优化自动化教学代理以提高其教学效率，方法是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、记忆提取速度、长期保持、替代教学策略(如视觉和计算解决策略)的有效性以及与导师的投入程度，为学生定制教学。重点将放在使用机器学习和计算优化方法来自动化为新学科领域开发高效智能辅导系统(ITS)的过程。该方法有三个方面。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，以自动创建具有丰富的学生状态表示的学生模型，该方法基于从学生群体中收集的多个辅导情节的数据。第二，将制定和评估方法，以制定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，而且在长期内有效，目标是至少保留一个月。第三，对已知的和强大的潜在变量和教学变量通过将其纳入学生模型而对表现产生的作用进行系统的研究。认知和教育心理学的研究清楚地表明了短期记忆和投入等潜在变量在学习中的关键作用，以及过度学习和复习等教学变量以及大规模和分布式练习对材料学习速度的影响。研究人员共同拥有智能辅导、机器学习和优化以及认知、数学和教育心理学等领域的优势，这些优势是实现拟议中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。从智力的价值来看，他建议研究应该推进基础数学和更高级的代数和几何的学习和教学的基础知识。它应该是一套不断增长的统计和计算技术的补充，这些技术可以用来估计控制人类行为的复杂的隐藏等级结构。这项研究还应该通过处理基于数学教育的真实和具有挑战性的问题的学习场景，而不是目前通常研究的抽象场景，来显著扩展机器学习系统的能力。为了产生更广泛的影响，这项基础性教育研究将使代表性不足的群体，特别是妇女，更广泛地参与各种科学、技术、工程和数学(STEM)学科。它将促进对学习和参与的发现和理解，作为个体学习差异的预测指标，并将导致智能导师对个体差异更加敏感。它将揭示不同性别和认知能力的学生通过不同的教学形式更有效地学习的程度。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.