A Lesson Generation Framework to Enable Massively Distributed Content Development

S. Sanand, S. Raghavan
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Abstract

Developing instructional materials to be used by e-learning systems is time consuming task. Systems -- especially those which do personalized lesson generation -- require the author to precisely follow a predefined pedagogic model while developing content. Careful coordination is required between contributors to ensure consistency and to prevent duplication of efforts. This slows down content development and also reduces the scope for reuse of existing digital content like video lectures. In this paper we propose a framework for a lesson generation system which supports massively distributed content development, by requiring only a minimum amount of prior planning and coordination between contributors. He/she only has to fill a simple metadata schema -- consisting of 3 sets of strings -- while adding the content to the repository. We show that, with a sufficiently large collection of such content in the repository, the system is able to perform the fundamental tasks of search, redundancy-elimination and personalized sequencing of content.
支持大规模分布式内容开发的课程生成框架
开发供电子学习系统使用的教学材料是一项耗时的任务。系统——尤其是那些个性化的课程生成系统——要求作者在开发内容时精确地遵循预定义的教学模型。捐助国之间需要仔细协调,以确保一致性和防止工作重复。这减慢了内容的开发,也减少了现有数字内容(如视频讲座)的重用范围。在本文中,我们提出了一个支持大规模分布式内容开发的课程生成系统框架,只需要最少的事先计划和贡献者之间的协调。他/她只需要填充一个简单的元数据模式——由3组字符串组成——同时将内容添加到存储库中。我们表明,在存储库中有足够大的此类内容集合,系统能够执行搜索,冗余消除和内容个性化排序的基本任务。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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