劳动力市场信息驱动的、个性化的、面向终身学习者的OER推荐系统
Labour Market Information Driven, Personalized, OER Recommendation System for Lifelong Learners
摘要：
在本文中，我们提出了一种新的方法，以帮助终身学习者访问相关的OER为基础的学习内容，掌握劳动力市场所需的技能。我们的软件原型1）将文本分类和文本挖掘方法应用于空缺通知，将工作分解为终身学习者应该瞄准的有意义的技能组件; 2）创建混合OER推荐系统，为学习者提供个性化的学习内容，以实现他们的技能目标。对于这个原型的第一次评估，我们专注于两个工作领域：数据科学家和机械工程师。我们应用了我们的技能提取器方法，并为针对这些工作的学习者提供了OER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多条建议，其中76.9%的建议被受访者视为有用。访谈显示，个性化的OER推荐系统，根据劳动力市场所需的技能，有可能改善终身学习者的学习体验。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
