Supporting Learners in Open Ended and Ill-Defined Domains

支持开放式和不明确领域的学习者

• 批准号: RGPIN-2014-05546
• 负责人: McCalla, Gordon
• 金额: $ 1.89万
• 依托单位: University of Saskatchewan
• 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=634547
• 关键词: Supporting; Learners; Open; Ended; Ill

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.

我的研究方向是“教育中的人工智能”（AIED），这是计算机科学的一个领域，探索如何创造对学习者有深度支持的学习环境。当然，AIED借鉴（并贡献）了人工智能的方法，但也包括人机交互、数据挖掘、推荐系统、多代理系统、语义网和认知科学。AIED的一个关键问题是个性化：学习环境如何适应学习者的个体差异，并定制其互动，以最大限度地提高每个学习者的成就。个性化是计算机科学另一个领域的核心问题：“用户建模、适应和个性化”（UMAP）。我对AIED和UMAP的研究都有贡献。我和我的研究生一直在探索的一个问题是模拟学习者和学习环境。我们创建了模拟来探索使用生态方法的各种假设，生态方法是一种新的学习环境架构（在我早期的研究中开发的），随着时间的推移，它可以根据学习者行为中发现的模式调整其行为。我们已经证明，模拟可以在不需要与实际学习者进行实验的情况下提供对学习环境的洞察。因此，模拟可以减少构建学习环境的时间和成本。从历史上看，AIED一直专注于代数等领域，这些领域有一个定义良好的知识体系。相反，我的兴趣被吸引到那些需要新技术的定义不明确的领域。其中一个领域是职业道德，有两个项目。在一个严肃的游戏中，软件工程专业的学生扮演项目经理的角色，做出决定（从道德的角度）并面对后果，从而阐明他们在职业生涯中会遇到的微妙权衡。在另一个项目中，我们一直在开发一个系统，通过一种新颖的可视化（“开放学习者模型”）来支持学习者分析伦理学案例研究，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的比较。这刺激了反思，在许多不明确的领域，这是一种必要的元认知技能。在另一个定义不清的语言领域，一个帮助人们学习俄语发音的系统已经建立起来，在一个创新的“历史学习者模型”的帮助下，他们可以看到自己随着时间的推移所取得的进步。我们一直在开发的另一个系统是在阅读理解中，数据挖掘学习者在阅读文档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，如果不首先根据回答问题所需的知识深度（“Bloom水平”）对问题进行分类，这些模式就不会出现，这是教育数据挖掘中的一个新想法。展望未来，我们将在模拟、伦理和阅读理解方面进行新的工作迭代。我们还希望进一步深入定义不明确的领域，看看终身学习。终身学习是一项特殊的挑战，因为必须对学习者进行多年跟踪，并且必须主动和被动地支持学习，使其成为人们繁忙生活的一部分。终身学习是AIED和UMAP的一个新兴领域，鉴于在信息时代需要不断提升个人和专业技能，我们在这方面的努力将在研究和现实世界中产生影响。我们一直在探索的技术，如生态方法和教育数据挖掘，有助于解决支持终身学习的挑战，而模拟对于探索影响和测试新思想和技术至关重要。