MOOC中OERs的推荐系统

2017 IEEE 17th International Conference on Advanced Learning Technologies (ICALT) Pub Date : 2017-07-03 DOI:10.1109/ICALT.2017.89

Hiba Hajri, Yolaine Bourda, Fabrice Popineau

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引用次数: 7

摘要

个性化在技术促进学习(TEL)领域是一个备受研究者关注的话题。与此同时，越来越多的开放教育资源(OER)按照W3C标准建立了索引。相关的OERs可以有效地补充在线课程中提供给学习者的内容。计算在课程的每个点提供给学习者的最佳OERs是我们在本文中讨论的个性化的一个方面。在大规模在线开放课程(MOOC)的背景下，我们设计了MORS系统来解决这个问题。本文中描述的MORS系统是基于学习者概要、描述课程的元数据以及为OERs查询SparQL端点的精心设计的过程。

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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MORS: A System for Recommending OERs in a MOOC

Personalization in the field of Technology Enhanced Learning (TEL) is a topic that received a lot of concern by researchers. At the same time, there is a growing amount of Open Educational Resources (OER) indexed according to the W3C standards. Relevant OERs can usefully complement the contents delivered to a learner during an online course. Computing the best OERs to offer to the learner at each point of his course is an aspect of personalization that we address in this paper. We designed our MORS system to solve this problem in the context of Massive Open Online Courses (MOOC). Our MORS system described in this paper, is based on a learner profile, on metadata describing the course and on a carefully crafted process to query the SparQL endpoints for OERs.

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2017 IEEE 17th International Conference on Advanced Learning Technologies (ICALT)

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