iLOG: Embedding and Validating Empirical Usage Intelligence in Learning Objects

iLOG：在学习对象中嵌入和验证经验使用智能

• 批准号: 0632642
• 负责人: Leen-Kiat Soh
• 金额: $ 39.77万
• 依托单位: University of Nebraska-Lincoln
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-15 至 2012-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0632642&HistoricalAwards=false
• 关键词: iLOG; Embedding; Validating; Empirical; Usage

ABSTRACTWith the proliferation of learning objects (LOs) online, both teachers and students are often frustrated in locating those that will meet their specific instructional and learning needs. A key component of this problem is that learning objects that are currently available typically are not based on learning research, do not contain embedded guidance on how they should be used, and do not adhere to common standards. Thus, the long-range goal of the project research team is to augment LOs with empirical usage intelligence how an LO should be used, how it has been used, and how it has impacted instruction and learning that will result in radical improvements in learning and instruction. With this embedded intelligence, learners and teachers will be able to identify the LOs that match their needs, educational and experiential backgrounds, and mode of learning or teaching. Learning management systems will also be able to more effectively sequence learning objects to build courses. To take a significant step toward this long-range goal, this project will be guided by an integrated and multidisciplinary approach in pursuit of the following specific technology and learning goals:Technology Goal 1: Create an Intelligent Learning Object Guide (iLOG) that tracks, diagnoses, and tags the empirical usage intelligence of learning objects.Technology Goal 2: Revise and convert the online course materials for an undergraduate introductory CS course (CS1, already developed at the University of Nebraska-Lincoln [UNL]) into learning objects.Learning Goal 1: Identify the salient learner attributes and content/pedagogical characteristics that can be empirically tracked to impact learning. Learning Goal 2: Measure the impact of active learning and elaborative feedback on student learning with learning objects.In terms of technical innovations, our proposed project will (1) add to the Shareable Content Object Reference Model (SCORM) metadata standard to include empirical usage history and statistics on each learning object, (2) result in a framework and a software system (i.e., iLOG) to empower learning objects with empirical usage intelligence, (3) develop advanced computer-based tracking and analysis tools that provide robust quantitative information on student understanding and learning progress and (4) develop SCORM-compliant learning objects for the CS1 course that are interoperable across a variety of platforms and Learning Management Systems. In terms of research advances, our proposed project will advance our knowledge of (1) student conceptual learning processes, (2) creation of new strategies for using contemporary technology-based instructional approaches, (3) matching of instruction to meet specific needs and preferences of learners, and (4) anomaly diagnosis for intelligent systems such as iLOG interacting with human subjects in uncertain and dynamic learning environments.

摘要随着在线学习对象 (LO) 的激增，教师和学生在寻找能够满足其特定教学和学习需求的对象时常常感到沮丧。这个问题的一个关键组成部分是，当前可用的学习对象通常不是基于学习研究，不包含关于如何使用它们的嵌入式指导，并且不遵守通用标准。因此，项目研究团队的长期目标是通过经验使用智能来增强 LO，了解 LO 应该如何使用、如何使用以及它如何影响教学和学习，从而从根本上改善学习和教学。借助这种嵌入式智能，学习者和教师将能够识别符合他们需求、教育和经验背景以及学习或教学模式的 LO。 学习管理系统还能够更有效地对学习对象进行排序以构建课程。 为了向这一长期目标迈出重要一步，该项目将以综合和多学科方法为指导，以追求以下具体技术和学习目标：技术目标 1：创建智能学习对象指南 (iLOG)，跟踪、诊断和标记学习对象的经验使用智能。技术目标 2：修改和转换本科计算机科学入门课程（CS1，已在纽约大学开发）的在线课程材料。 内布拉斯加州林肯大学 [UNL]）进入学习对象。学习目标 1：识别显着的学习者属性和内容/教学特征，可以根据经验进行跟踪以影响学习。学习目标 2：衡量主动学习和精细反馈对学习对象学习的影响。在技术创新方面，我们提出的项目将 (1) 添加可共享内容对象参考模型 (SCORM) 元数据标准，以包括每个学习对象的经验使用历史和统计数据，(2) 形成一个框架和软件系统（即 iLOG），以赋予学习对象以经验使用智能，(3) 开发先进的基于计算机的 跟踪和分析工具，提供有关学生理解和学习进度的可靠定量信息，并且 (4) 为 CS1 课程开发符合 SCORM 的学习对象，这些对象可跨各种平台和学习管理系统互操作。 在研究进展方面，我们提出的项目将增进我们对以下方面的认识：(1)学生概念学习过程；(2)创建使用当代基于技术的教学方法的新策略；(3)匹配教学以满足学习者的特定需求和偏好；(4)智能系统的异常诊断，例如在不确定和动态的学习环境中与人类受试者交互的iLOG。