Generating personalized study guide for self-paced learning online

为在线自定进度学习生成个性化学习指南

• 批准号: RGPIN-2018-05545
• 负责人: Chang, Maiga
• 金额: $ 1.68万
• 依托单位: Athabasca University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=647167
• 关键词: Generating; personalized; study; guide; self

Students in online courses typically follow a pre-planned learning path, typically prescribed by a teacher in the form of a study guide. Students follow the study guide for readings, teaching materials, instructional video clips and animations, practice exercises, assignments, self-assessments and reflections, collaborating with peers via survey, discussion, team works and projects, and competing with others via simulation trials, quiz scores and game-play performances. However, a study guide cannot meet all students' needs. Every student has individual learning preferences, paces, and trajectories. Nor can a single, fixed, pre-designed study guide provide all students with the best way to learn effectively and efficiently.******This research aims to enable an open-source learning management system (i.e., Moodle and edX) to provide students with a personalized study guide for their online learning via the use of graph structures to store and represent the learning objects in an online course as well as the learning behaviours, strategies, and preferences of individual students studying online. The research first finds the relations among the learning objects in an entry level, online Physics course. This research then designs a learning analytics approach to process students' behaviours that are recorded by the learning management system and automatically categorize students into clusters according to the degrees of similarity of students' learning strategies and preferences while learning online. This research is expected to result in automatic re-planning/re-arrangement of not only learning objects (i.e., video clips, hands-on, simulations, mini-games, real-life problems, etc.), but also the learning trajectory (i.e., sequence of learning concepts, topics, and units) for the individual student according to the analysis result of the student's learning behaviours. ******More specifically the outcome of this research can be summarized as: (1) a graph structure that can be used to store and present the relations among learning objects in an online course; (2) a learning analytics approach that analyzes students' time-series learning behaviours in a learning management system/platform, finds students' behaviour patterns based on their learning strategies and preferences; (3) a method that generates a personalized learning trajectory for the individual student according to their student model and behaviour pattern; and, (4) personalized study guide plug-ins for open-source learning management systems and MOOC platforms (e.g., Moodle and edX) so that any university, college, or facility offering online and/or MOOC courses can adopt the plug-in into its system/platform and students can benefit from the results of this research.

在线课程的学生通常遵循预先计划的学习路径，通常由教师以学习指南的形式规定。学生遵循阅读，教学材料，教学视频剪辑和动画，练习，作业，自我评估和反思的学习指南，通过调查，讨论，团队工作和项目与同龄人合作，并通过模拟试验，测验分数和游戏表演与他人竞争。然而，学习指南不能满足所有学生的需求。每个学生都有自己的学习偏好、节奏和轨迹。一个单一的，固定的，预先设计的学习指南也不能为所有学生提供有效和高效学习的最佳方式。这项研究旨在实现一个开源的学习管理系统（即，Moodle和edX）通过使用图结构来存储和表示在线课程中的学习对象以及在线学习的单个学生的学习行为，策略和偏好，为学生提供个性化的在线学习指导。本研究首先在一个入门级的网路物理课程中找出学习对象之间的关系。本研究设计一种学习分析方法，处理学习管理系统记录的学生行为，并根据学生在线学习时学习策略和偏好的相似程度自动将学生分类。这项研究预计将导致不仅学习对象（即，视频剪辑、动手操作、模拟、小游戏、现实问题等），而且学习轨迹（即，根据学生学习行为的分析结果，为个别学生提供学习概念、主题和单元的顺序。** 更具体地说，本研究的成果可以概括为：（1）一个图形结构，可以用来存储和呈现在线课程中学习对象之间的关系;（2）一个学习分析方法，在学习管理系统/平台中分析学生的时间序列学习行为，根据学生的学习策略和偏好发现他们的行为模式;（3）根据学生模型和行为模式为学生个体生成个性化学习轨迹的方法;以及（4）用于开源学习管理系统和MOOC平台的个性化学习指导插件（例如，Moodle和edX），以便任何提供在线和/或MOOC课程的大学，学院或设施都可以将插件应用到其系统/平台中，学生可以从这项研究的结果中受益。