| Literature DB >> 34541585 |
Dorota Chapko1, Pedro Rothstein1, Lizzie Emeh, Pino Frumiento, Donald Kennedy, David Mcnicholas, Ifeoma Orjiekwe, Michaela Overton, Mark Snead, Robyn Steward, Jenny Sutton, Melissa Bradshaw, Evie Jeffreys, Will Gallia, Sarah Ewans, Mark Williams2, Mick Grierson1.
Abstract
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.Entities:
Keywords: Human-centered computing → Human computer interaction (HCI); Learning disability; co-design; survey; topic model
Year: 2021 PMID: 34541585 PMCID: PMC7611679 DOI: 10.1145/3461778.3462010
Source DB: PubMed Journal: DIS (Des Interact Syst Conf)