“根本没办法跟上!”非正式ML学习者的不同动机和面临的挑战

Rimika Chaudhury, Philip J. Guo, Parmit K. Chilana
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引用次数: 5

摘要

近年来,越来越多来自不同背景的人正在尝试使用大量的在线资源非正式地学习机器学习(ML),但我们对他们的动机和学习策略知之甚少。我们采访了22名非正式的机器学习学习者,他们来自不同的工作角色和背景,包括计算机科学、医学、金融等,以了解他们在寻找和互动不同资源以管理学习方面的方法、偏好和挑战。我们使用自主学习的框架分析了我们的研究结果,发现这些非正式学习者在自主学习的各个阶段都很挣扎,包括确定学习目标和选择资源,他们的挑战在衡量进展和评估结果的最后阶段最为严重。我们确定了未来研究的几个机会,以更好地理解和支持ML(以及其他复杂的技术技能)的非正式学习者。特别是,有必要培养更多的自我监督和自我反思技术,这些技术可以帮助非正式学习者在指导他们的学习方面变得更加自我意识和有效。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
"There’s no way to keep up!": Diverse Motivations and Challenges Faced by Informal Learners of ML
—In recent years, more people from different backgrounds are trying to informally learn Machine Learning (ML) using a plethora of online resources, yet we know little about their motivations and learning strategies. We carried out interviews with 22 informal learners of ML from diverse job roles and backgrounds, including Computer Science, Medicine, Finance, and others, to understand their approaches, preferences, and challenges in locating and interacting with different resources to manage their learning. We analyzed our findings using the framework of self-directed learning and found that these informal learners struggled in all stages of self-direction, including identifying learning goals and selecting resources, and that their challenges were most acute in the last stage of gauging progress and evaluating outcomes. We identify several opportunities for future research to better understand and support informal learners of ML (and other complex technical skills). In particular, there is a need to foster more self-monitoring and self-reflection techniques that can help informal learners become more self-aware and effective in directing their learning.
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