Achieving Motivational and Cognitive Outcomes in Mathematics Using Enhanced Intelligent Tutoring Technology

现在教育领域的许多研究都指出了认知和动机过程在决定学习结果方面的重要性。然而，我们还不清楚这些过程是如何随着时间的推移在个体学习者内部相互作用以产生特定的学习结果，以及不同形式的教学和辅导如何影响认知和动机的相互作用以促进学习。先进的学习技术，如智能辅导系统，通常专注于认知问题，但尚未解决动机问题。因此，在学习技术支持下的教育实践与人类学习的认知和动机方面的研究之间存在着巨大的差距。该项目的目标是调查学习者敬业度、认知过程和支架策略在产生特定的数学学习结果方面的关系。它汇集了一支由心理学、教育学和信息技术专家组成的跨学科团队来开展这项工作。该项目将使用教育软件，旨在使我们能够以系统的方式解决和衡量认可和激励因素。它将大大扩展以前在智能教学系统和基于计算机的学习助手方面的工作，以实时评估单个学习者的注意力和动机，推断他们的参与水平，并估计他们的领域知识。研究结果将在现实的学习条件下获得：洛杉矶的公立高中教室，学生人数反映了加州城市的种族、语言和经济多样性。该软件将根据专家人类导师使用的决策规则做出教学决策，这些导师考虑动机和认知目标，以优化学习结果。教学策略也将以响应的动画虚拟角色的形式实例化，以了解类似于人类的教学代理的存在是否会影响学习者的参与以及在数学上的学习结果。课堂教师将能够通过创新的工具定制教学，以查看不断更新的评估并改进软件做出的教学决策。丰富的学习者数据集将在学生解决数学问题时自动收集，并将提供与特定学习结果相联系的综合评估，包括解决文字问题和转移到嵌入现实世界STEM环境中的新的、具有挑战性的问题。为了实现这一目标，研究人员将在之前NSF支持下开发的两个项目的结果基础上发展：一个是经过经验验证的注意力焦点跟踪系统，另一个是智能辅导系统(ITS)，专门设计用于提高在STEM研究领域传统上代表性不足的学生的数学技能。该项目的结果将产生广泛的社会影响，通过提供与STEM相关的个性化、有效的网络教学，任何高中的学生都可以免费访问互联网。我们预计，影响最大的将是目前在数学方面存在动机困难的学生，包括女性和代表性不足的群体。该项目的智力价值将是通过详细和实时地模拟个别学习者的认知和参与的相互作用，并观察学习者知识的变化作为教学历史的功能，从而更深入地理解学习过程的本质。

Much research in education now points to the importance of both cognitive andmotivational processes in determining learning outcomes. Yet we do not yet understandexactly how these processes interact within the individual learner over time to producespecific learning outcomes, and how different forms of instruction and tutoring caninfluence the interaction of cognition and motivation to facilitate learning. Advancedlearning technologies such as intelligent tutoring systems typically focus on cognitiveissues, and do not yet address motivational issues. Thus there is a major gap betweeneducational practice, as supported by learning technologies, and research in cognitive andmotivational aspects of human learning.The goal of the proposed project is to investigate the relations of learnerengagement, cognitive processes, and scaffolding strategies in producing specificlearning outcomes in mathematics. It brings together an interdisciplinary team ofspecialists in psychology, education, and information technology to pursue this work. Theproject will employ educational software designed to enable us to address and measurecognitive and motivational factors in systematic ways. It will significantly extendprevious work on intelligent tutoring systems and computer-based learning assistants toassess in real time the attention and motivation of individual learners, infer their level ofengagement, and estimate their domain knowledge. The research results will be obtainedin realistic learning conditions: public high school classrooms in Los Angeles, withstudent populations reflecting the ethnic, linguistic and economic diversity of urbanCalifornia. The software will make instructional decisions based on decision rules usedby expert human tutors who consider motivational and cognitive goals to optimizelearning outcomes. Instructional strategies will also be instantiated in the form of aresponsive animated virtual character, to learn if the presence of a human-likepedagogical agent influences the learner.s engagement as well as learning outcomes inmathematics. Classroom teachers will be able to customize the instruction throughinnovative tools to view continually updated assessments and refine pedagogicaldecisions made by the software. Rich learner data sets will be automatically collected asstudents work on mathematics problems, and will provide an integrated evaluation linkedto specific learning outcomes, including word problem solving and transfer to novel,challenging problems embedded in real-world STEM contexts. To accomplish this goal,the researchers will build on the results of two projects developed with prior NSF support: anempirically validated focus of attention tracking system, and an intelligent tutoringsystem (ITS) designed specifically to enhance math skills in students who have beentraditionally under-represented in STEM fields of study.The results of the project will have broad societal impact through providingindividualized, effective web-based instruction in STEM-related mathematics that will befreely accessible to students in any high school with access to the Internet. We anticipatethat the impact will be greatest among students who currently have motivationaldifficulties with mathematics, including women and underrepresented groups. Theintellectual merit of the project will be a deeper understanding of the very nature oflearning processes through the ability to model in detail and in real time the interaction ofcognition and engagement in the individual learner, and to observe changes in thelearner.s knowledge as a function of instructional history.