论文标题

大规模的随机实验揭示机器学习可以帮助人们学习和更有效地记住

Large-scale randomized experiment reveals machine learning helps people learn and remember more effectively

论文作者

Upadhyay, Utkarsh, Lancashire, Graham, Moser, Christoph, Gomez-Rodriguez, Manuel

论文摘要

机器学习通常专注于开发模型和算法,这些模型和算法最终将取代需要智能的任务。在这项工作中,我们专注于揭示机器学习的潜力,以改善人们学习和记住事实材料的潜力。为此,我们从移动性领域的一个受欢迎的学习应用程序中,对数千名学习者进行了大规模的随机对照试验。在控制了研究的长度和频率之后,我们发现使用机器学习优化学习课程的学习者要比使用两种替代启发式方法生成学习课程的内容超过$ \ sim $ 67%。我们的随机对照试验还表明,使用机器学习优化的学习课程的学习者在4-7天内返回该应用的可能性高50%。

Machine learning has typically focused on developing models and algorithms that would ultimately replace humans at tasks where intelligence is required. In this work, rather than replacing humans, we focus on unveiling the potential of machine learning to improve how people learn and remember factual material. To this end, we perform a large-scale randomized controlled trial with thousands of learners from a popular learning app in the area of mobility. After controlling for the length and frequency of study, we find that learners whose study sessions are optimized using machine learning remember the content over $\sim$67% longer than those whose study sessions are generated using two alternative heuristics. Our randomized controlled trial also reveals that the learners whose study sessions are optimized using machine learning are $\sim$50% more likely to return to the app within 4-7 days.

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