May 2015· PeerJ Computer Science· Vol 1, pp. e18· 63 citations· ⚡ 5 influential· 116 references
Computer Science
TL;DR
This paper conducts a qualitative interpretive study based on face-to-face open-ended interviews, in-field observations, and e-mail exchanges to construct a novel explanatory theory of the impact of affects on development performance.
Abstract
Affects---emotions and moods---have an impact on cognitive activities and the working performance of individuals. Development tasks are undertaken through cognitive processes, yet software engineering research lacks theory on affects and their impact on software development activities. In this paper, we report on an interpretive study aimed at broadening our understanding of the psychology of programming in terms of the experience of affects while programming, and the impact of affects on programming performance. We conducted a qualitative interpretive study based on: face-to-face open-ended interviews, in-field observations, and e-mail exchanges. This enabled us to construct a novel explanatory theory of the impact of affects on development performance. The theory is explicated using an established taxonomy framework. The proposed theory builds upon the concepts of events, affects, attractors, focus, goals, and performance. Theoretical and practical implications are given.
This paper reviews literature in Engineering, Economics, and Science and Technology Studies (STS) to generate a conceptual framework explaining the role and impact of Artificial Intelligence (AI) on engineering work. In recent years, with the growth of AI capabilities, labour analysts have raised concerns about job replacement and displacement in routine work. However, the extent to which AI impacts engineering work, and how social and technical factors influence these changes, is unclear. Findings from this literature review indicate four discrete impacts on engineers’ work—replacement, augmentation, transformation, and enhancement—which differ along four dimensions—the nature of the task (routine/non-routine; technical/social); competencies (high/low skillsets), mindsets (open/closed to AI), and organizational factors (hierarchical/flat; supports/constraints). We illustrate the relationships between these factors through a conceptual framework that will serve as the groundwork for further, empirical research.
Prarthona Paul, Cindy Rottmann· Proceedings of the Canadian...· 0 citations
Setting personal goals is a complex process dependent on appropriate skills, motivation, and context, making failure a common part of goal pursuit. While recent Human-Computer Interaction research has begun to address this complexity by exploring qualitative and long-term goals, little is known about how failure shapes users’ goals, emotions, and sense of identity. To address this, we conducted an in-depth study of failure to explore new approaches for supporting users when they fail. Through 17 semi-structured interviews, we examined how and why people fail in their personal goals. We found that long-term aspirations and identity strongly shape goals, creating challenges in choosing appropriate strategies to achieve them. In addition, this connection made failure emotionally intense. Goals that reflected deeply held values were highly motivated but also prompted severe negative reactions when they failed. Rather than adjusting their strategies, many ascribed failure to their personal shortcomings and reattempted the same ineffective approaches, creating cycles of repeated failure. Based on this understanding of the causes and impacts of failure, we discuss new design approaches for technologies to support goal setting, failure diagnostics, and adaptation by prompting reflection on emotions and external factors, making ties to identity explicit, and regularly re-evaluating current goals.
Rebecca Lietz, Steve Whittaker, N. Su· Behaviour & Information...· 0 citations
This study explores how students in an online EdD in Instructional and Performance Technology experience personal growth, focusing on shifts in autonomy, self-acceptance, and purpose in life. Using an explanatory sequential mixed methods design, researchers administered Anderson et al.’s (2019) Personal Growth and Development Scale (PGDS) to current students and alumni (N = 44), followed by thematic analysis of semi-structured interviews. Surveys indicated consistently high perceived growth across all PGDS domains, with the highest mean scores in self-acceptance, environmental mastery, and purpose in life. Participants described strengthened self-confidence, decision-making skills, professional relationships, and sense of purpose. Peer and faculty relationships and program structures were identified as key supports for growth. EdD programs can intentionally cultivate both personal and academic growth. Embedding reflective practices, milestone-based check-ins, and structured support may strengthen students’ sense of identity, purpose, and leadership confidence.
H. Handley, Lauren Adlof, Isabelle Knudsen et al.· Impacting Education Journal...· 1 citation
Computer-use AI agents struggle with multi-step workflows like email and customer support. Echoverse trains agents in realistic environments rather than simply providing more training tasks, helping them improve as the tasks, tests, and environments evolve. The post Echoverse: Deep, evolving environments for computer-use agents appeared first on Microsoft Research.