Supporting Learners in Open Ended and Ill-Defined Domains

我的研究方向是“教育中的人工智能”(AIED)，这是一个计算机科学领域，探索如何创造深度支持学习者的学习环境。当然，AIED借鉴(并贡献)了人工智能的方法，但也借鉴了人机交互、数据挖掘、推荐系统、多代理系统、语义网和认知科学。一个关键的AIED问题是个性化：学习环境如何适应学习者之间的个体差异，并定制其互动，以最大化每个学习者的成就。个性化是计算机科学的另一个领域的核心问题：“用户建模、适应和个性化”(UMAP)。我对AED和UMAP的研究都有贡献。我和我的研究生们一直在探索的问题之一是模拟学习者和学习环境。我们使用生态方法创建了模拟来探索各种假设，生态方法是一种新型的学习环境架构(在我早期的研究中开发)，随着时间的推移，它可以根据在学习者行为中发现的模式来调整其行动。我们已经证明，模拟可以提供对学习环境的洞察，而不需要与实际的学习者进行实验。因此，模拟可以减少建立学习环境的时间和成本。从历史上看，AED一直专注于代数等领域，在这些领域有定义明确的知识体系。相比之下，我的兴趣被吸引到定义不明确的领域，那里需要新的技术。其中一个领域是职业道德，有两个项目。一个严肃的游戏已经建立起来，软件工程专业的学生扮演项目经理的角色，做出决定(涉及道德层面)并面临后果，从而阐明了他们在职业生涯中将遇到的微妙权衡。在另一个项目中，我们一直在开发一个系统，以支持学习者通过一种新颖的可视化(“开放式学习者模型”)分析伦理案例研究，向他们展示他们分析的广度和深度，以及这些分析与其他学习者的分析相比如何。这会刺激反思，这是许多定义不清的领域所必需的一种元认知技能。在另一个定义不明确的领域--语言，人们已经建立了一个系统来帮助人们学习俄语发音，并借助一个创新的“历史学习者模型”，使他们能够直观地看到自己随着时间的推移而取得的进步。我们一直在开发的另一个系统，在阅读理解方面，可以数据挖掘学习者在阅读文件和回答问题时的行为。已经发现了一些模式，可以准确预测他们回答这些问题的能力。然而，这些模式不会出现，除非首先对问题进行分类，以确定回答这些问题所需的知识深度(它们的“Bloom Level”)，这是教育数据挖掘中的一个新想法。展望未来，我们在模拟、伦理和阅读理解方面的工作正在进行新的迭代。我们还希望进一步进入定义不明确的领域，着眼于终身学习。终身学习是一个特殊的挑战，因为学习者必须经过多年的跟踪，学习必须作为人们忙碌生活的一部分得到主动和被动的支持。终身学习是AED和UMAP的一个新兴领域，鉴于信息时代个人和专业技能的不断提升，我们在这方面的努力应该会在研究影响和现实世界影响方面得到回报。我们一直在探索的技术，如生态方法和教育数据挖掘，有助于应对支持终身学习的挑战，而模拟对于探索影响和测试新的想法和技术至关重要。

My research is in “artificial intelligence in education” (AIED), an area of computer science that explores how to create learning environments that are deeply supportive of learners. AIED draws on (and contributes to) methodologies from artificial intelligence, of course, but also human-computer interaction, data mining, recommender systems, multi-agent systems, the semantic web, and cognitive science. One key AIED issue is personalization: how the learning environment can adapt to individual differences among learners and tailor its interactions to maximize each learner’s achievements. Personalization is the central concern of another area of computer science: “user modeling, adaptation, and personalization” (UMAP). I contribute to both AIED and UMAP research. One of the issues my graduate students and I have been exploring is simulation of learners and learning environments. We have created simulations to explore various hypotheses using the ecological approach, a novel learning environment architecture (developed in my earlier research) that, over time, can adapt its actions based on patterns discovered in learner behaviour. We have shown that simulations can provide insight into a learning environment without the need for experimentation with actual learners. Simulation could thus lead to reductions in the time and cost of building learning environments. Historically, AIED has been focused on domains like algebra, where there is a well-defined body of knowledge. In contrast, my interests have been drawn to ill-defined domains, where new techniques are needed. One such domain is professional ethics, with two projects. A serious game has been built where software engineering students take the role of a project manager and make decisions (with an ethical dimension) and face consequences, thus illuminating subtle trade-offs they will encounter in their careers. In another project we have been developing a system to support learners in analyzing ethics case studies through a novel visualization (an “open learner model”) that shows them the breadth and depth of their analyses, and how these compare to other learners’ analyses. This stimulates reflection, a metacognitive skill necessary in many ill-defined domains. In another ill-defined domain, language, a system has been built to help people learn Russian pronunciation, with the aid of an innovative “historic learner model” that allows them to visualize their progress over time. Another system we have been developing, in reading comprehension, data mines learners’ behaviour as they read documents and answer questions. Patterns have been found that allow accurate predictions of how well they will answer these questions. These patterns don’t emerge, however, without first classifying the questions as to the depth of knowledge needed to answer them (their “Bloom level”), a novel idea in educational data mining. Going forward we are undertaking new iterations of our work on simulation, ethics, and reading comprehension. We also wish to move further into ill-defined domains to look at lifelong learning. Lifelong learning is a special challenge because learners must be tracked over years, and learning must be supported both proactively and reactively as part of people’s busy lives. Lifelong learning is an emerging area of AIED and UMAP, and our efforts here should pay off both in research impact and real world impact given the need for constant upgrading of personal and professional skills in the information age. The techniques we have been exploring, such as the ecological approach and educational data mining, are helpful in tackling the challenges of supporting lifelong learning, and simulation is crucial to explore implications and test new ideas and techniques.