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

使用增强型智能辅导技术实现数学的动机和认知成果

• 批准号: 0411886
• 负责人: William Johnson
• 金额: --
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-07-01 至 2008-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0411886&HistoricalAwards=false
• 关键词: Achieving; Motivational; Cognitive; Outcomes; Mathematics

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.

现在，许多教育研究都指出认知和动机过程在决定学习成果方面的重要性。然而，我们尚不清楚这些过程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成果，以及不同形式的指导和辅导如何影响认知和动机的相互作用以促进学习。智能辅导系统等先进学习技术通常关注认知问题，但尚未解决动机问题。因此，学习技术支持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重大差距。拟议项目的目标是研究学习者参与度、认知过程和支架策略在产生特定数学学习成果时的关系。它汇集了心理学、教育和信息技术领域的跨学科专家团队来开展这项工作。该项目将采用旨在使我们能够以系统的方式处理和测量认知和动机因素的教育软件。它将显着扩展之前在智能辅导系统和基于计算机的学习助手方面的工作，以实时评估个体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，学生群体反映了加州城市的种族、语言和经济多样性。该软件将根据专家导师使用的决策规则做出教学决策，专家导师会考虑动机和认知目标来优化学习成果。教学策略也将以响应式动画虚拟角色的形式实例化，以了解类人教学代理的存在是否会影响学习者的参与度以及数学学习成果。课堂教师将能够通过创新工具定制教学，查看不断更新的评估并完善软件做出的教学决策。当学生解决数学问题时，将自动收集丰富的学习者数据集，并将提供与特定学习成果相关的综合评估，包括应用题解决和转移到嵌入现实世界 STEM 环境中的新颖、具有挑战性的问题。为了实现这一目标，研究人员将在 NSF 先前支持下开发的两个项目的成果基础上进行开发：经过经验验证的注意力焦点跟踪系统和智能辅导系统 (ITS)，该系统专门用于提高传统上在 STEM 研究领域中代表性不足的学生的数学技能。该项目的结果将通过提供 STEM 相关数学方面的个性化、有效的基于网络的教学来产生广泛的社会影响。 任何可以访问互联网的高中的学生都可以免费访问。我们预计，对于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）来说，影响最大。该项目的智力价值将是通过详细、实时地建模个体学习者认知和参与的相互作用的能力，以及观察学习者知识随教学历史的变化而发生的变化，从而更深入地理解学习过程的本质。