"There’s no way to keep up!": Diverse Motivations and Challenges Faced by Informal Learners of ML

Abstract

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 selfaware and effective in directing their learning.

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