HCC: Medium: Collaborative Research: Formal Analysis of Choice-Adaptive Intelligent Learning Environments (FACILE) that support Future Learning

该奖项是根据2009年美国复苏和再投资法案（公法111-5）资助的。在过去的十年中，新的和令人兴奋的技术为开发丰富的开放式学习环境创造了机会，联合收割机结合了许多不同的学习范式和资源。 学生可以在游戏环境中完成任务，参与调查，与虚拟代理互动，运行科学模拟，参加测验，访问Web，以及更普遍地选择不同的学习活动。这些选择可以提供关于学生学习的信息，并诊断学生一旦离开高度照本宣科的课程可以学到什么。通过采用机器学习方法，如隐马尔可夫模型和其他序列分析算法来分析学生在相对开放的学习环境中的选择，并确定学生是否表现出（次）最佳行为模式，环境可以通过鼓励（替代）选择和更好的学习行为来智能地适应。主要假设是，帮助学生发展元认知能力，使学习选择将有很大的影响，他们随后的能力，在未来的学习在非结构化和无监督，但资源丰富的环境。该项目的目标是：a）选择适应性智能学习环境和计算方法，帮助学生制定策略，使他们能够自主学习; B）为教师和学生提供新颖的自动评估工具，将选择和学习行为与学习成绩联系起来;以及c）研究，将确定我们的干预措施，联合收割机的选择与指导相结合，是否有利于强和弱学习者在科学领域。工作跨越多个维度。首先，它提供了一个包容性的计算机科学框架，将许多技术丰富的交互式环境汇集在一起，这些环境正在为教育扩散到一个共同的选择填充和自适应架构中。其次，这种基于选择的框架提供了一个范式转变，在跟踪和理论化的选择是适用于学习的背景下，在过去，一直占主导地位的知识结构的表征。通过选择来描述学习不仅将学习研究与更大的社会科学研究联系起来，它也是一种描述和指导学习的全新方式，更接近于许多教学的目标，即智能未来选择。 第三，计算机环境应该允许许多研究人员收集和分析大型日志文件，并可以想象导致一个新的数据库的共同选择模式及其对学习的影响。我们将创建一个框架，使其他人能够将智能融入他们的虚拟世界，并帮助实现这些拟议的成果。

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).Over the past decade, new and exciting technologies have created opportunities for developing rich open-ended learning environments that combine a number of different learning paradigms and resources. Students can complete quests in game environments, engage in inquiry, interact with virtual agents, run science simulations, take quizzes, access the web, and more generally make choices about different learning activities. These choices can be extremely informative about student learning and diagnostic of what students can learn once they leave highly scripted curricula. By employing machine learning methods, such as hidden Markov Models and other sequence analysis algorithms to analyze student choices in relatively open learning environments and determine whether students are showing (sub) optimal behavior patterns, the environment can then adapt intelligently by encouraging (alternative) choices and better learning behaviors. The primary hypothesis is that helping students develop the metacognitive abilities to make learning choices will have strong effects on their subsequent abilities to learn in the future in unstructured and unsupervised but resource-rich environments. The goals for this project are to create: a) Choice adaptive intelligent learning environments and computational methodologies that help students develop strategies to enable them to learn on their own; b) Novel automated assessment tools for both teachers and students that link choice and learning behaviors to learning performance; and c) Research studies that will establish whether our interventions that combine choice with guidance is beneficial for both strong and weak learners in science domains.The broader impacts of this work span multiple dimensions. First, it provides an encompassing computer science framework for bringing together a number of technology-rich, interactive environments that are proliferating for education into a common choice filled and adaptive architecture. Second, this choice-based framework provides a paradigm shift in that tracking and theorizing about choice is applied in the context of learning, which, in the past, has been dominated by characterizations of the knowledge construct. Characterizing learning by choice not only connects learning research to a larger body of social science research, it is also a fundamentally new way to characterize and guide learning that is closer to the goal of much instruction, namely intelligent future choice. Third, the computer environment should permit the collection and analysis of large log files by many researchers, and conceivably lead to a new database of common choice patterns and their effects on learning. We will create the framework that enables others to incorporate intelligence into their virtual worlds and help achieve these proposed outcomes.