Self- and Co-Regulating Anthropomorphic Pedagogical Agents

自我调节和共同调节的拟人化教学代理

• 批准号: 249872-2013
• 负责人: Kumar, Vivekanandan
• 金额: $ 1.09万
• 依托单位: Athabasca University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=635039
• 关键词: Self; Co; Regulating; Anthropomorphic; Pedagogical

Imagine a software agent that learns how to develop personalized education and training strategies for you based on your own habits and strengths, and based on the learning analytics patterns of thousands of other successful students that have come before. A trusted friend, partner, and ally that can help identify goals, and map out the most likely path to achieve those goals, based on your own learning styles, consistent with your own deepest values, personal, social, and other standards. This research aims to build and test a suite of such intelligent agents, called anthropomorphic agents, that mimic human-like traits and will assist learners in their regulatory tasks. Further these agents self-regulate their own anthropomorphic traits, such as initiative and subject matter competency, in order to inspire a high level of trust, shared perspective, and entitativity - thus increasing the learners' willingness to view them as competent partners in the educational process.Research shows that motivational strategies through mixed-initiatives can be used to scaffold students'emotional, cognitive, and competence experiences so as to increase the success of regulation. This is accomplished by circumventing unproductive mental states and/or maintaining states that are more conducive for carrying out regulation tasks. Other mediative acts include assistance with goal formulation and identifying strengths and weaknesses, data-mining to evaluate goal-progress as well as selection of optimal strategies and plans for adaptation proffered in a preferred format and manner most likely to inspire the required changes and adjustments. Current theories of regulation do not model the impact of anthropomorphic agents. The results of this research will provide new information that fills this gap in the existing body of knowledge. There are real reasons that prevent people from enjoying the benefits of regulation that include low working memory capacity, stress or depletion of regulatory resources, disorders of executive function (e.g., ADHD), poor time management skills, personality and temperament, which can be effectively addressed with anthropomorphic software agents.

想象一下，一个软件代理学习如何根据您自己的习惯和优势，以及基于之前成千上万名其他成功学生的学习分析模式，为您制定个性化的教育和培训策略。一个值得信赖的朋友、伙伴和盟友，可以帮助你确定目标，并根据你自己的学习风格，与你自己最深层次的价值观、个人、社会和其他标准保持一致，制定出实现这些目标的最有可能的途径。这项研究旨在建立和测试一套这样的智能代理，称为拟人化代理，它模仿人类的特征，将帮助学习者完成他们的调节任务。此外，这些主体通过自我调节自身的拟人化特征，如主动性和主题能力，以激发高度的信任、共享的观点和实体性，从而增加学习者将其视为教育过程中称职伙伴的意愿。研究表明，通过混合倡议的动机策略可以用来构建学生的情感、认知和能力体验，从而提高调节的成功率。这是通过绕过非生产性心理状态和/或保持更有利于执行调节任务的状态来实现的。其他调解行动包括协助制定目标和确定长处和短处，进行数据挖掘以评估目标进展情况，以及选择最有可能激发所需变化和调整的最佳形式和方式提供的适应的最佳战略和计划。目前的监管理论没有对拟人化因素的影响进行建模。这项研究的结果将提供新的信息，填补现有知识体系中的这一空白。阻碍人们享受监管好处的真正原因包括工作记忆容量低、监管资源紧张或耗尽、执行功能障碍(例如ADHD)、糟糕的时间管理技能、个性和气质，这些问题可以通过拟人化的软件代理有效地解决。