Data and code: Customizable and Evolving Avatars to Improve Worker Engagement and Foster a Sense of Community in Online Crowd Work

This folder contains the data and code related to the paper below:<br>de Groot, E. C. S., &amp; Gadiraju, U. (2024, May). " Are we all in the same boat?" Customizable and Evolving Avatars to Improve Worker Engagement and Foster a Sense of Community in Online Crowd Work. In Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems (pp. 1-26).<br>Paper abstract:Human intelligence continues to be essential in building ground-truth data, training sets, and for evaluating a plethora of systems. The democratized and distributed nature of online crowd work — an attractive and accessible feature that has led to the proliferation of the paradigm — has also meant that crowd workers may not always feel connected to their remote peers. Despite the prevalence of collaborative crowdsourcing practices, workers on many microtask crowdsourcing platforms work on tasks individually and are seldom directly exposed to other crowd workers. In this context, improving worker engagement on microtask crowdsourcing platforms is an unsolved challenge. At the same time, fostering a sense of community among workers can improve the sustainability and working conditions in crowd work. This work aims to increase worker engagement in conversational microtask crowdsourcing by leveraging evolving avatars that workers can customize as they progress through monotonous task batches. We also aim to improve group identification in individual tasks by creating a community space where workers can share their avatars and feelings on task completion. To this end, we carried out a preregistered between-subjects controlled study (N = 680) spanning five experimental conditions and two task types. We found that evolving and customizable worker avatars can increase worker retention. The prospect of sharing worker avatars and task-related feelings in a community space did not consistently affect group identification. Our exploratory analysis indicated that workers who identify themselves as crowd workers experienced greater intrinsic motivation, subjective engagement, and perceived workload. Furthermore, we discuss how task differences shape the relative effectiveness of our interventions. Our findings have important theoretical and practical implications for designing conversational crowdsourcing tasks and in shaping new directions for research to improve crowd worker experiences.

本文件夹包含与下述论文相关的数据与代码：<br>de Groot, E. C. S. 与 Gadiraju, U.（2024年5月）。《“我们都在同一条船上吗？”：可定制且可演进的虚拟形象，用于提升在线众包工作中的工人参与度并培育社区归属感》。收录于2024年计算机系统人因学会议（CHI Conference on Human Factors in Computing Systems）论文集（第1至26页）。<br>论文摘要：人类智能在构建真实标注数据（ground-truth data）、训练集以及对各类系统进行评估方面始终不可或缺。在线众包工作（crowd work）具有大众化与分布式的特性，这一兼具吸引力与易用性的特征推动了该范式的广泛普及，但同时也导致众包工人往往难以与远程同行建立联结。尽管协作式众包实践已相当普遍，但诸多微任务众包（microtask crowdsourcing）平台上的工人均为独立完成任务，极少直接接触其他众包工人。在此背景下，提升微任务众包平台上的工人参与度仍是一项尚未解决的挑战。<br>与此同时，在工人群体中培育社区归属感，能够改善众包工作的可持续性与从业环境。本研究旨在通过引入工人可在完成单调任务批次过程中进行定制的可演进虚拟形象，提升对话式微任务众包（conversational microtask crowdsourcing）中的工人参与度。此外，我们还希望通过打造一个工人可在任务完成后分享其虚拟形象与工作感受的社区空间，提升个体任务中的群体认同感（group identification）。<br>为此，我们开展了一项预注册被试间对照实验（preregistered between-subjects controlled study），样本量N=680，涵盖5种实验条件与2种任务类型。研究发现，可演进且可定制的工人虚拟形象能够提升工人留存率。在社区空间中分享工人虚拟形象及任务相关感受这一设计，并未对群体认同感产生稳定的影响。我们的探索性分析表明，自视为众包工人的参与者会表现出更高的内在动机（intrinsic motivation）、主观投入度（subjective engagement）与感知工作负荷（perceived workload）。此外，我们还讨论了任务差异如何影响我们所采用干预措施的相对有效性。本研究结果对于设计对话式众包任务，以及为改善众包工人体验的研究开辟新方向，均具有重要的理论与实践价值。