师徒制
位于
知识管理
协作学习
课程
过程(计算)
心理学
公共关系
教育学
社会学
计算机科学
医学教育
政治学
人工智能
医学
操作系统
出处
期刊:Organization Science
[Institute for Operations Research and the Management Sciences]
日期:2023-07-18
卷期号:35 (3): 948-973
标识
DOI:10.1287/orsc.2020.14214
摘要
Existing research depicts occupational learning as predominantly happening through formal education, situated learning, or a combination of the two. How career switchers might develop occupational skills outside of these established learning pathways is understudied. This paper examines how novice outsiders break into a skilled occupation by looking at the case of aspiring software developers attending coding bootcamps. Drawing on 17 months of fieldwork in the San Francisco Bay area, I find that bootcamps did not resemble either schools or workplaces, the two institutions that facilitate occupational learning. Instead, bootcamps scaffolded learning collectives—groups composed of peers and near peers who learn collaboratively and purposefully to reach a shared goal. Within learning collectives, aspirants progressed from novice outsiders to hirable software developers, despite limited access to proximate experts to learn from or legitimate peripheral participation opportunities. Three scaffoldings facilitated learning at bootcamps. First, peer team structures turned what is normally a solitary activity—writing code—into a collaborative endeavor and facilitated peer-to-peer knowledge exchange. Second, near-peer role structures engaged recent graduates in teaching and mentorship relationships with novices so that aspirants could access knowledge quickly and easily. Third, bootcamps encouraged aspirants to self-learn by reaching out to the expertise of the broader occupational community. This third scaffolding prepared aspirants for learning beyond the bootcamp curriculum and socialized them for an occupation with high learning demands. The outcome of this process was that novices pursuing an alternative mode of occupational entry developed both occupational skills and new self-conceptions as software developers. Funding: This work was supported by a grant from Stanford Cyber Initiative.
科研通智能强力驱动
Strongly Powered by AbleSci AI