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，令人兴奋的新技术为开发丰富的开放式学习环境创造了机会，这些学习环境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，参与探究，与虚拟代理互动，进行科学模拟，参加测验，访问网络，更广泛地说，可以对不同的学习活动做出选择。我们的基本观点是，这些选择可以极大地了解学生的学习情况，并诊断学生在离开高度照本的课程后可以学到什么。我们可以使用机器学习方法，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）最优行为模式。然后，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。我们的主要假设是，帮助学生发展做出学习选择的元认知能力，将对他们未来在非结构化和无监督但资源丰富的环境中学习的后续能力产生强烈影响。我们这个项目的目标是创造：a)选择自适应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化评估工具，将选择和学习行为与学习绩效联系起来；c)研究将确定我们将选择与指导相结合的干预措施是否对科学领域的强和弱学习者都有益。这项工作的广泛影响跨越了多个维度。首先，它提供了一个包络性的计算机科学框架，将许多技术丰富的、交互式的环境（这些环境正在为教育而激增）汇集到一个共同的、充满选择的、自适应的体系结构中。其次，这种基于选择的框架提供了一种范式转变，即在学习的背景下应用选择的跟踪和理论化，而在过去，学习一直被知识结构的特征化所主导。通过选择表征学习不仅将学习研究与更大的社会科学研究联系起来，而且是一种全新的表征和指导学习的方式，更接近于多指导的目标，即智能的未来选择。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，并有可能建立一个关于常见选择模式及其对学习的影响的新数据库。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，并帮助实现这些提议的结果。

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. Our basic insight is that these choices can be extremely informative about student learning and diagnostic of what students can learn once they leave highly scripted curricula. We can use 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. Our 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. Our 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.