Buying time: enabling learners to become earners with a real-world paid task recommender system

Guanliang Chen, Dan Davis, Markus Krause, C. Hauff, G. Houben
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引用次数: 4

Abstract

Massive Open Online Courses (MOOCs) aim to educate the world, especially learners from developing countries. While MOOCs are certainly available to the masses, they are not yet fully accessible. Although all course content is just clicks away, deeply engaging with a MOOC requires a substantial time commitment, which frequently becomes a barrier to success. To mitigate the time required to learn from a MOOC, we here introduce a design that enables learners to earn money by applying what they learn in the course to real-world marketplace tasks. We present a Paid Task Recommender System (Rec-$ys), which automatically recommends course-relevant tasks to learners as drawn from online freelance platforms. Rec-$ys has been deployed into a data analysis MOOC and is currently under evaluation.
购买时间:通过现实世界的付费任务推荐系统,使学习者成为赚钱者
大规模在线开放课程(MOOCs)旨在教育全世界,尤其是来自发展中国家的学习者。虽然mooc当然对大众开放,但它们还没有完全开放。尽管所有课程内容只需点击鼠标即可获得,但深入学习MOOC需要投入大量时间,这往往成为成功的障碍。为了减少从MOOC中学习所需的时间,我们在这里介绍了一种设计,使学习者能够通过将他们在课程中学到的知识应用到现实世界的市场任务中来赚钱。我们提出了一个付费任务推荐系统(Rec-$ys),它自动从在线自由平台向学习者推荐与课程相关的任务。Rec-$ys已被部署到数据分析MOOC中,目前正在进行评估。
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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