Chapko Dorota, Rothstein Pedro, Emeh Lizzie, Frumiento Pino, Kennedy Donald, Mcnicholas David, Orjiekwe Ifeoma, Overton Michaela, Snead Mark, Steward Robyn, Sutton Jenny, Bradshaw Melissa, Jeffreys Evie, Gallia Will, Ewans Sarah, Williams Mark, Grierson Mick
Creative Computing Institute, University of the Arts London, London, United Kingdom.
Heart n Soul at the Hub, Heart n Soul, London, United Kingdom.
DIS (Des Interact Syst Conf). 2021 Jun;2021:1668-1681. doi: 10.1145/3461778.3462010. Epub 2021 Jun 28.
Through a process of robust co-design, we created a bespoke accessible survey platform to explore the role of co-researchers with learning disabilities (LDs) in research design and analysis. A team of co-researchers used this system to create an online survey to challenge public understanding of LDs [3]. Here, we describe and evaluate the process of remotely co-analyzing the survey data across 30 meetings in a research team consisting of academics and non-academics with diverse abilities amid new COVID-19 lockdown challenges. Based on survey data with >1,500 responses, we first co-analyzed demographics using graphs and art & design approaches. Next, co-researchers co-analyzed the output of machine learning-based structural topic modelling (STM) applied to open-ended text responses. We derived an efficient five-steps STM co-analysis process for creative, inclusive, and critical engagement of data by co-researchers. Co-researchers observed that by trying to understand and impact public opinion, their own perspectives also changed.
通过一个稳健的共同设计过程,我们创建了一个定制的无障碍调查平台,以探索有学习障碍的共同研究者在研究设计和分析中的作用。一组共同研究者使用这个系统创建了一个在线调查,以挑战公众对学习障碍的理解[3]。在此,我们描述并评估了在新的新冠疫情封锁挑战下,一个由不同能力的学者和非学者组成的研究团队在30次会议中对调查数据进行远程共同分析的过程。基于有超过1500份回复的调查数据,我们首先使用图表以及艺术与设计方法对人口统计学数据进行了共同分析。接下来,共同研究者对应用于开放式文本回复的基于机器学习的结构主题建模(STM)的输出结果进行了共同分析。我们为共同研究者创造性、包容性和批判性地参与数据推导了一个高效的五步STM共同分析过程。共同研究者观察到,通过试图理解并影响公众舆论,他们自己的观点也发生了变化。