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Learning Dynamic Feedback in Intelligent Tutoring Systems

学习智能辅导系统中的动态反馈

基本信息

批准号：
200292319
负责人：
Professorin Dr. Barbara Hammer
金额：
--
依托单位：
Arbeitsgruppe Maschinelles Lernen
依托单位国家：
德国
项目类别：
Priority Programmes
财政年份：
2011
资助国家：
德国
起止时间：
2010-12-31 至 2018-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/200292319?language=en
关键词：
Learning Dynamic Feedback Intelligent Tutoring

项目摘要

The rapidly increasing availability of online educational resources and the recent attention gained by distance education methods such as massive open online courses (MOOCs) show the importance of intelligent tutoring systems (ITS) as adaptive educational technologies that can personalize e-Learning. Classical ITSs require an exact formalization of the learning task and learner-system interactions. Hence their applicability is typically limited to well-defined domains. In addition, their labor-intensive preparation restricts their use to static, large-scale applications where development costs do not play a significant role. Within the first period of the FIT project, we have developed a FIT ITS infrastructure which allows the construction of ITSs in ill-defined domains based on machine learning techniques. In particular, we have developed prototype-based machine learning models for structures and structure-metric adaptation techniques which enable an autonomous organization of an ITS solution space in ill-defined domains, based on which feedback provision strategies can be grounded. So far, the developed machine learning techniques are restricted to single tasks, and feedback provision is not tailored to individual users and their progress. The goal of DynaFIT is to (i) develop machine learning models which can generalize across different tasks and user behaviors and, based thereon, (ii) to enhance FIT ITSs via dynamic user-adaptive feedback and open learner models in ill-defined domains. More specifically, we will develop cross-task dimensionality reduction (DR) techniques for structures which generate a task-independent representation of different solution spaces in one common latent space. This enables autonomous information transfer across tasks and a generalization from possibly singular user behavior to relevant underlying principles. This representation constitutes a key prerequisite for obtaining the following central components of DynaFIT: a visualization of relevant characteristics of solution spaces and learner behavior (open learner models); a representation of user behavior across tasks as low dimensional time series, for which classical data analysis techniques as well as relevance learning as developed in the first period of the FIT project are available; finally, based thereon, dynamic feedback provision strategies adjusted to this time series data. In this realm, we will exemplarily investigate dynamic peering strategies (highly relevant for larger online courses) in detail.In addressing information transfer by cross-task dimensionality reduction, DynaFIT contributes to a central topic of the SPP: the autonomous development of suitable representations for learning. Further, the envisioned enrichment of ITSs in ill-defined domains by dynamic feedback provision and open learner models bears great potential for highly dynamic large-scale educational technology facilities such as MOOCs.

在线教育资源的快速增长和最近的远程教育方法，如大规模开放式在线课程（MOOC）获得的关注显示了智能辅导系统（ITS）作为自适应教育技术的重要性，可以个性化的电子学习。经典的ITS需要学习任务和学习者与系统交互的精确形式化。因此，它们的适用性通常仅限于定义明确的领域。此外，它们的劳动密集型制备限制了它们在开发成本不起重要作用的静态大规模应用中的使用。在FIT项目的第一阶段，我们开发了一个FIT ITS基础设施，允许基于机器学习技术在定义不清的领域中构建ITS。特别是，我们已经开发了基于原型的机器学习模型的结构和结构度量适应技术，使一个自治组织的ITS解决方案空间在定义不清的域，基于此反馈提供策略可以接地。到目前为止，开发的机器学习技术仅限于单一任务，反馈提供并不针对个人用户及其进度。 DynaFIT的目标是（i）开发可以概括不同任务和用户行为的机器学习模型，并在此基础上，（ii）通过动态用户自适应反馈和定义不明确的领域中的开放学习器模型来增强FIT ITS。更具体地说，我们将开发跨任务降维（DR）技术的结构，产生一个任务独立的表示不同的解决方案空间在一个共同的潜在空间。这使得跨任务的自主信息传输和从可能的单一用户行为到相关的基本原则的概括成为可能。这种表示构成了获得DynaFIT以下核心组件的关键先决条件：解决方案空间和学习者行为的相关特征的可视化（开放式学习模型）;将用户在任务中的行为表示为低维时间序列，可使用经典的数据分析技术以及FIT项目第一阶段开发的相关性学习;最后，基于该时间序列数据调整动态反馈提供策略。在这一领域，我们将详细研究动态对等策略（与大型在线课程高度相关）。在通过跨任务降维解决信息传递方面，DynaFIT有助于SPP的中心主题：自主开发适合学习的表征。此外，通过动态反馈提供和开放式学习者模型，在定义不清的领域中丰富ITS的设想，对MOOC等高度动态的大型教育技术设施具有巨大的潜力。