论文标题
基于技能要求的开放教育视频的推荐系统
A Recommender System For Open Educational Videos Based On Skill Requirements
论文作者
论文摘要
在本文中，我们建议一种新颖的方法，以帮助学习者找到相关的开放教育视频，以掌握在劳动力市场上要求的技能。我们已经建立了一个原型，其中1）将文本分类和文本挖掘方法应用于工作空缺公告，以匹配工作及其所需技能； 2）预测视频的质量； 3）创建一个开放的教育视频推荐系统，向学习者建议个性化的学习内容。 对于该原型的首次评估，我们重点介绍了与数据科学相关的工作领域。我们的原型通过深入的半结构化访谈进行了评估。 15主题专家提供了反馈，以评估我们的推荐原型在其目标，逻辑和对学习的贡献方面的表现。建议使用250多个视频，其中82.8％的建议被受访者视为有用。此外，访谈显示，我们个性化的视频推荐系统有可能改善学习经验。
In this paper, we suggest a novel method to help learners find relevant open educational videos to master skills demanded on the labour market. We have built a prototype, which 1) applies text classification and text mining methods on job vacancy announcements to match jobs and their required skills; 2) predicts the quality of videos; and 3) creates an open educational video recommender system to suggest personalized learning content to learners. For the first evaluation of this prototype we focused on the area of data science related jobs. Our prototype was evaluated by in-depth, semi-structured interviews. 15 subject matter experts provided feedback to assess how our recommender prototype performs in terms of its objectives, logic, and contribution to learning. More than 250 videos were recommended, and 82.8% of these recommendations were treated as useful by the interviewees. Moreover, interviews revealed that our personalized video recommender system, has the potential to improve the learning experience.
