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.

想象一个软件代理，它学习如何根据您自己的习惯和优势，并基于之前数千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、合作伙伴和盟友，可以帮助您确定目标，并根据您自己的学习风格，符合​​您自己最深刻的价值观、个人、社会和其他标准，规划出实现这些目标的最有可能的路径。这项研究的目的是构建和测试一套这样的智能代理，称为拟人代理，模仿人类的特征，并将帮助学习者完成他们的监管任务。此外，这些代理人自我调节自己的拟人化特征，例如主动性和主题能力，以激发高度的信任、共同的观点和实体性——从而增加学习者将他们视为教育中称职的合作伙伴的意愿。研究表明，通过混合主动的激励策略可以用来支撑学生的情感、认知和能力体验，从而提高调节的成功率。这是通过规避非生产性心理状态和/或维持更有利于执行调节任务的状态来实现的。其他调解行为包括协助目标制定和识别优势和劣势、评估目标进展的数据挖掘以及选择最佳策略和适应计划，以最有可能激发所需变革和调整的首选格式和方式提供。当前的监管理论并未模拟拟人化代理人的影响。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。有一些真正的原因阻止人们享受监管带来的好处，包括工作记忆能力低下、监管资源的压力或耗尽、执行功能障碍（例如多动症）、时间管理技能差、个性和气质，这些可以有效地通过通过拟人化的软件代理来解决。