Ecological and social software to support human learning

支持人类学习的生态和社交软件

• 批准号: 3592-2008
• 负责人: McCalla, Gordon
• 金额: $ 3.42万
• 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: 2009
• 资助国家: 加拿大
• 起止时间: 2009-01-01 至 2010-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=419755
• 关键词: Ecological; social; software; support; human

Fragmentation increasingly characterizes computer systems that support human learning. Social fragmentation occurs when learners are divided into disparate virtual learning communities. Technological fragmentation occurs when the learning technology is no longer a monolithic single system but consists of a loose collection of learning objects (the "content") and processes (called agents) gathered "just in time" to help learner(s) achieve their learning goals. With my graduate students and colleagues, I have been investigating a number of issues that arise when building fragmented learning technologies. One issue is allowing fragmented systems to adapt to individual differences among learners. In a fragmented system adaptivity to learners must be highly reactive to context and purpose, thus necessitating a new active approach to learner modelling. A second issue is to discover how agents can model each other and negotiate with each other to achieve various goals. A third issue is the social and information environment that surrounds learners. My past research has investigated characteristics affecting effective social and group interaction. My proposed research investigates two approaches to supporting learners in this environment. In one, the ecological approach, learner models and learner interaction data are automatically associated with the learning objects in the environment and then "data mined" for patterns that can help achieve various pedagogical purposes (such as finding individualized content for learners). In the other, a social computing approach, tools are provided for learners to annotate the learning objects themselves, and to share these annotations to facilitate their learning. These issues are being explored by building working prototypes and trying them with real learners. Research into these issues draws from, and can contribute to, many disciplines: artificial intelligence in education, user modelling and adaptive systems, data mining, artificial intelligence, multi-agent systems, the semantic web, and areas of social science. The ultimate goal is learning technology that can knowledgeably and flexibly support learners, both individually and in groups.

碎片化越来越成为支持人类学习的计算机系统的特征。当学习者被分成不同的虚拟学习社区时，就会发生社会分裂。当学习技术不再是一个单一的系统，而是由一个松散的学习对象（“内容”）和过程（称为代理）的集合组成时，就会出现技术碎片化，这些对象和过程“及时”地收集起来，以帮助学习者实现他们的学习目标。与我的研究生和同事们一起，我一直在研究构建碎片化学习技术时出现的一些问题。一个问题是允许分散的系统适应学习者之间的个体差异。在一个支离破碎的系统中，对学习者的适应性必须对上下文和目的做出高度反应，因此需要一种新的主动学习者建模方法。第二个问题是发现代理如何相互建模并相互协商以实现各种目标。第三个问题是围绕学习者的社会和信息环境。我过去的研究调查了影响有效的社会和群体互动的特征。我提出的研究调查两种方法来支持学习者在这种环境中。在一个，生态方法，学习者模型和学习者交互数据自动与环境中的学习对象相关联，然后“数据挖掘”的模式，可以帮助实现各种教学目的（如为学习者找到个性化的内容）。在另一个，社会计算方法，工具提供给学习者注释自己的学习对象，并分享这些注释，以促进他们的学习。这些问题正在通过构建工作原型并与真实的学习者一起尝试来探索。对这些问题的研究借鉴了许多学科，并可以为这些学科做出贡献：教育中的人工智能，用户建模和自适应系统，数据挖掘，人工智能，多代理系统，语义网和社会科学领域。最终目标是学习技术，可以知识和灵活地支持学习者，无论是个人还是团体。