论文标题
劳动力市场信息驱动，个性化的OER推荐系统针对终身学习者
Labour Market Information Driven, Personalized, OER Recommendation System for Lifelong Learners
论文作者
论文摘要
在本文中，我们建议一种新颖的方法，以帮助终身学习者访问相关的学习内容，以对劳动力市场要求的主技巧。我们的软件原型1）在空缺公告中应用文本分类和文本挖掘方法，将作业分解为有意义的技能组成部分，终生学习者应针对这些技能； 2）创建一个混合的推荐系统，为学习者提供个性化的学习内容，以使学习者朝着技能目标迈进。对于该原型的首次评估，我们关注两个工作领域：数据科学家和机械工程师。我们采用了技能提取器方法，并为针对这些工作的学习者提供了建议。我们对12位主题专家进行了深入的半结构化访谈，以了解我们的原型在其目标，逻辑和对学习的贡献方面的表现。产生了150多个建议，其中76.9％的建议被受访者视为有用。访谈显示，基于劳动力市场要求的技能，一个个性化的OER推荐系统具有改善终身学习者的学习经验。
In this paper, we suggest a novel method to aid lifelong learners to access relevant OER based learning content to master skills demanded on the labour market. Our software prototype 1) applies Text Classification and Text Mining methods on vacancy announcements to decompose jobs into meaningful skills components, which lifelong learners should target; and 2) creates a hybrid OER Recommender System to suggest personalized learning content for learners to progress towards their skill targets. For the first evaluation of this prototype we focused on two job areas: Data Scientist, and Mechanical Engineer. We applied our skill extractor approach and provided OER recommendations for learners targeting these jobs. We conducted in-depth, semi-structured interviews with 12 subject matter experts to learn how our prototype performs in terms of its objectives, logic, and contribution to learning. More than 150 recommendations were generated, and 76.9% of these recommendations were treated as useful by the interviewees. Interviews revealed that a personalized OER recommender system, based on skills demanded by labour market, has the potential to improve the learning experience of lifelong learners.
