Design Research: Methodological Innovation Through Messiness

Blackler, Alethea; Zelenko, Оksana; Chamorro‐Koc, Marianella; Rittenbruch, Markus; Sade, Gavin

doi:10.1007/978-3-319-73374-6_8

Cited by 3 publications

(4 citation statements)

References 27 publications

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“…While the differentiation between three moments (Understanding, Defining, and Shaping) helped us to analyze and reflect on the challenges, it is worth noting that the boundaries between phases are blurry, and it is not possible to understand them as separate, linear tasks. As designers, we were responsible for leading a process that has been often described as messy (Akama, 2009;Blackler et al, 2018;Sanders & Stappers, 2008). Such messiness has an impact on collaboration since it makes communication and tracking decisions harder.…”

Section: Discussionmentioning

confidence: 99%

“…Design practice has been connected to action research due to the strong resemblance between both processes. As several scholars have noted, design practice and action research are both oriented towards change and transformation to improve a particular situation through the interplay of action and research moments in successive cycles (Blackler et al, 2018;Swann, 2002). In addition, both action and design research acknowledge the complex interrelations between humans and their context and thus embrace uncertainty and messiness as part of the process (Blackler et al, 2018).…”

Section: Methodsmentioning

confidence: 99%

“…As several scholars have noted, design practice and action research are both oriented towards change and transformation to improve a particular situation through the interplay of action and research moments in successive cycles (Blackler et al, 2018;Swann, 2002). In addition, both action and design research acknowledge the complex interrelations between humans and their context and thus embrace uncertainty and messiness as part of the process (Blackler et al, 2018). In this article, we inquire on the challenges faced by learning technology designers to implement democratic processes in their practice.…”

Section: Methodsmentioning

confidence: 99%

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Designing learning technology collaboratively: Analysis of a chatbot co-design

2022

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Collaborative design approaches have been increasingly adopted in the design of learning technologies since they contribute to develop pedagogically inclusive and appropriate learning designs. Despite the positive reception of collaborative design strategies in technology-enhanced learning, little attention has been dedicated to analyzing the challenges faced in design processes using a collaborative approach. In this paper, we disclose the collaborative design of a chatbot for self-regulated learning in higher education using an action research approach. We analyze the design process of EDUguia chatbot, which includes diverse evidence from questionnaires and workshops with students and lecturers, as well as intermediary design objects. Based on the qualitative analysis, we identify several challenges that are transversal to the co-design work, as well as specific to the design phases. We critically reflect on the strategies deployed to overcome these challenges and how they relate to decision-making processes, highlighting the need to make stakeholders’ tacit knowledge explicit, cultivate trust-building and support democratic decision-making in technology design processes. We believe that the recommendations we present in this paper contribute to developing best practices in the collaborative design of chatbots for the self-regulation of learning, as well as learning technology in general.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Designing learning technology collaboratively: Analysis of a chatbot co-design

2022

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“…Recording user interactions with technology in complex settings, such as workplaces, has been widely embraced (Heath and Luff, 2018; Blackler et al, 2018 ). Researchers can scrutinize the recorded behaviors pertinent to their investigations, employing lucid, observable criteria to ensure impartiality and mitigate bias ( Bakeman and Quera, 2012 ; Guo et al, 2015 ).…”

Section: Methodsmentioning

confidence: 99%

Advanced workstations and collaborative robots: exploiting eye-tracking and cardiac activity indices to unveil senior workers’ mental workload in assembly tasks

Pluchino,

Pernice,

Nenna

et al. 2023

Front. Robot. AI

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Introduction: As a result of Industry 5.0’s technological advancements, collaborative robots (cobots) have emerged as pivotal enablers for refining manufacturing processes while re-focusing on humans. However, the successful integration of these cutting-edge tools hinges on a better understanding of human factors when interacting with such new technologies, eventually fostering workers’ trust and acceptance and promoting low-fatigue work. This study thus delves into the intricate dynamics of human-cobot interactions by adopting a human-centric view.Methods: With this intent, we targeted senior workers, who often contend with diminishing work capabilities, and we explored the nexus between various human factors and task outcomes during a joint assembly operation with a cobot on an ergonomic workstation. Exploiting a dual-task manipulation to increase the task demand, we measured performance, subjective perceptions, eye-tracking indices and cardiac activity during the task. Firstly, we provided an overview of the senior workers’ perceptions regarding their shared work with the cobot, by measuring technology acceptance, perceived wellbeing, work experience, and the estimated social impact of this technology in the industrial sector. Secondly, we asked whether the considered human factors varied significantly under dual-tasking, thus responding to a higher mental load while working alongside the cobot. Finally, we explored the predictive power of the collected measurements over the number of errors committed at the work task and the participants’ perceived workload.Results: The present findings demonstrated how senior workers exhibited strong acceptance and positive experiences with our advanced workstation and the cobot, even under higher mental strain. Besides, their task performance suffered increased errors and duration during dual-tasking, while the eye behavior partially reflected the increased mental demand. Some interesting outcomes were also gained about the predictive power of some of the collected indices over the number of errors committed at the assembly task, even though the same did not apply to predicting perceived workload levels.Discussion: Overall, the paper discusses possible applications of these results in the 5.0 manufacturing sector, emphasizing the importance of adopting a holistic human-centered approach to understand the human-cobot complex better.

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A playwriting technique to engage on a shared reflective enquiry about the social sustainability of robotization and artificial intelligence

Casazza

Gioppo

2020

Journal of Cleaner Production

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Design Research: Methodological Innovation Through Messiness

Cited by 3 publications

References 27 publications

Designing learning technology collaboratively: Analysis of a chatbot co-design

Designing learning technology collaboratively: Analysis of a chatbot co-design

Advanced workstations and collaborative robots: exploiting eye-tracking and cardiac activity indices to unveil senior workers’ mental workload in assembly tasks

A playwriting technique to engage on a shared reflective enquiry about the social sustainability of robotization and artificial intelligence

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