Algorithms and Applications of Learned Student Models for Adaptive Learning Systems

自适应学习系统的学习学生模型的算法和应用

• 批准号: 121923-2012
• 负责人: Desmarais, Michel
• 金额: $ 2.48万
• 依托单位: École Polytechnique de Montréal
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=569241
• 关键词: Algorithms; Applications; Learned; Student; Models

In the last few years, a number of innovative applications have emerged to guide a user through a study process that can lead to an adequate preparation for an exam, a diploma, a new job, or simply a self-paced, self-motivated learning agenda. Such applications are designed to guide the user into a large body of learning material, according to specific learning objectives, and to the user's personal needs. They take full advantage of the one-on-one, personalized tutoring that a computer can offer. These applications rely on a detailed cognitive skills assessment that determines what the user has mastered and what topics should be the focus over the next hours, or the next days of study. The current research program's primary objective is to develop probabilistic models for skills assessment that are automatically built from test data. We aim to construct skill assessment models solely from small data sets, yet with fine grained representations of skills. The approach we advocate, item-to-item models, was shown to outperform current approaches in terms of predicting a user's success to unanswered items. However, it also raises issues such as model update and the mapping of items to skills. This project addresses these issues, in addition to looking at new means to improve the predictive performance of existing models.

在过去的几年中，出现了许多创新的应用程序来指导用户完成学习过程，从而为考试、文凭、新工作做好充分的准备，或者只是一个自定进度、自我激励的学习议程。 此类应用程序旨在根据特定的学习目标和用户的个人需求引导用户学习大量的学习材料。 他们充分利用计算机提供的一对一的个性化辅导。 这些应用程序依赖于详细的认知技能评估，该评估确定用户已掌握的内容以及接下来几个小时或接下来几天的学习重点应关注哪些主题。 当前研究计划的主要目标是开发根据测试数据自动构建的技能评估概率模型。 我们的目标是仅从小型数据集构建技能评估模型，但具有细粒度的技能表示。 我们提倡的方法（项目到项目模型）在预测用户对未回答项目的成功方面表现优于当前方法。 然而，它也引发了模型更新以及物品与技能映射等问题。除了寻找提高现有模型预测性能的新方法之外，该项目还解决了这些问题。