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Evaluating and Designing Computing Systems for the Future of Work
Evaluating and Designing Computing Systems for the Future of Work
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211152120
- ISBN
- 9798384345534
- DDC
- 621.384
- 저자명
- Cao, Hancheng.
- 서명/저자
- Evaluating and Designing Computing Systems for the Future of Work
- 발행사항
- [Sl] : Stanford University, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 152 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-03, Section: A.
- 주기사항
- Advisor: Bernstein, Michael;McFarland, Daniel.
- 학위논문주기
- Thesis (Ph.D.)--Stanford University, 2024.
- 초록/해제
- 요약From collaborative software to generative AI, computing technologies are reshaping communication, collaboration, and productivity in the workplace. Yet with the growing complexities of computing platforms at the workplace, it becomes increasingly challenging to foresee their impacts on workers and organization. This can lead to not only poor user experience but also sometimes problematic applications that mirror and exacerbate societal issues. How can we better understand user behavior over workplace computing platforms? How can we build applications for better future of work that align with our needs and values with emerging computing technologies? Inspired by Herbert Simon's vision towards building the science of the artificial, this dissertation aims to shed light on these questions through the development of novel empirical measurements, technical methods, and designs for studying workplace computing systems enabled by recent advances in computing technologies. Specifically, this dissertation present three works demonstrating these approaches, including an analysis of remote meeting multitasking behavior through mining millions of online meetings, emails and file edits; the development of an AI algorithm for predicting team fractures; and a design and evaluation study on a generative AI-based scientific feedback system for researchers. These projects exemplify the opportunities to leverage computation to better understand, support and augment work practices.
- 일반주제명
- Telemetry
- 일반주제명
- Multitasking
- 일반주제명
- Semantics
- 일반주제명
- Logic
- 기타저자
- Stanford University.
- 기본자료저록
- Dissertations Abstracts International. 86-03A.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a621.384
■1001 ▼aCao, Hancheng.
■24510▼aEvaluating and Designing Computing Systems for the Future of Work
■260 ▼a[Sl]▼bStanford University▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a152 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-03, Section: A.
■500 ▼aAdvisor: Bernstein, Michael;McFarland, Daniel.
■5021 ▼aThesis (Ph.D.)--Stanford University, 2024.
■520 ▼aFrom collaborative software to generative AI, computing technologies are reshaping communication, collaboration, and productivity in the workplace. Yet with the growing complexities of computing platforms at the workplace, it becomes increasingly challenging to foresee their impacts on workers and organization. This can lead to not only poor user experience but also sometimes problematic applications that mirror and exacerbate societal issues. How can we better understand user behavior over workplace computing platforms? How can we build applications for better future of work that align with our needs and values with emerging computing technologies? Inspired by Herbert Simon's vision towards building the science of the artificial, this dissertation aims to shed light on these questions through the development of novel empirical measurements, technical methods, and designs for studying workplace computing systems enabled by recent advances in computing technologies. Specifically, this dissertation present three works demonstrating these approaches, including an analysis of remote meeting multitasking behavior through mining millions of online meetings, emails and file edits; the development of an AI algorithm for predicting team fractures; and a design and evaluation study on a generative AI-based scientific feedback system for researchers. These projects exemplify the opportunities to leverage computation to better understand, support and augment work practices.
■590 ▼aSchool code: 0212.
■650 4▼aTelemetry
■650 4▼aMultitasking
■650 4▼aSemantics
■650 4▼aLogic
■690 ▼a0800
■690 ▼a0395
■71020▼aStanford University.
■7730 ▼tDissertations Abstracts International▼g86-03A.
■790 ▼a0212
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17162989▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
