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（LOs）的激增，教师和学生在寻找满足他们特定教学和学习需求的学习对象时经常感到沮丧。这个问题的一个关键组成部分是，目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，也不遵循通用标准。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应该如何使用，它是如何使用的，以及它是如何影响教学和学习的，这将导致学习和教学的根本改进。有了这种嵌入式智能，学习者和教师将能够识别与他们的需求、教育和经验背景以及学习或教学模式相匹配的LOs。学习管理系统也将能够更有效地排序学习对象来构建课程。为了朝着这一长期目标迈出重要的一步，该项目将以综合的多学科方法为指导，追求以下具体的技术和学习目标：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：修改并将本科计算机科学入门课程（CS1，已由内布拉斯加-林肯大学[UNL]开发）的在线课程材料转换为学习对象。学习目标1：确定显著的学习者属性和内容/教学特征，这些特征可以通过经验跟踪来影响学习。学习目标2：用学习对象衡量主动学习和精细化反馈对学生学习的影响。在技术创新方面，我们提议的项目将(1)增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)产生一个框架和软件系统（即iLOG），以赋予学习对象经验使用智能。(3)开发先进的基于计算机的跟踪和分析工具，为学生的理解和学习进度提供可靠的定量信息；(4)为CS1课程开发符合scorm的学习对象，这些学习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，我们提出的项目将提高我们在以下方面的知识：(1)学生概念学习过程；(2)创建使用现代技术为基础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)在不确定和动态的学习环境中与人类受试者交互的智能系统（如iLOG）异常诊断。