How Interest-Driven Content Creation Shapes Opportunities for Informal Learning in Scratch: A Case Study on Novices' Use of Data Structures

要旨

Through a mixed-method analysis of data from Scratch, we examine how novices learn to program with simple data structures by using community-produced learning resources. First, we present a qualitative study that describes how community-produced learning resources create archetypes that shape exploration and may disadvantage some with less common interests. In a second quantitative study, we find broad support for this dynamic in several hypothesis tests. Our findings identify a social feedback loop that we argue could limit sources of inspiration, pose barriers to broadening participation, and confine learners' understanding of general concepts. We conclude by suggesting several approaches that may mitigate these dynamics.

受賞
Honorable Mention
著者
Ruijia Cheng
University of Washington, Seattle, Washington, United States
Sayamindu Dasgupta
University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States
Benjamin Mako Hill
University of Washington, Seattle, Washington, United States
論文URL

https://dl.acm.org/doi/abs/10.1145/3491102.3502124

動画

会議: CHI 2022

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2022.acm.org/)

セッション: Programming and Coding Support

293
4 件の発表
2022-05-04 18:00:00
2022-05-04 19:15:00