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Improving Time Management in Academia through Better Time Estimation Support
This project investigates time management challenges in academia focusing on the extensive time spent on planning tasks and highlighting the need for effective time estimation tools.
The project’s objective is to identify and investigate the effectiveness of planning support tools that can help academics manage their time better. Initial studies involved diaries and interviews with academics, revealing that current AI tools are often underutilised, and that manual planning is still common. Indicating a need for more precise time estimation support.
A literature and technology review identified existing strategies for accurate time estimation. This informed the design of a time monitoring intervention. Overall, the research aims to develop and refine tools that support proactive and precise time management, enhancing productivity in academic environments.
People
This project is being developed by Yoana Ahmetoglu, supervised by Anna Cox and Duncan Brumby. Supported by MSc students Shermin Teoh, Andy Ying, and Akeisha Iskandar.
Publications
Y Ahmetoglu, DP Brumby, AL Cox (2024) Bridging the Gap Between Time Management Research and Task Management App Design: A Study on the Integration of Planning Fallacy Mitigation Strategies CHIWORK2024
Ahmetoglu, Y., Brumby, D. P., & Cox, A. L. (2021). Disengaged from planning during the lockdown? an interview study in an academic setting. IEEE Pervasive Computing, 20(4), 18-25.
Ahmetoglu, Y., Brumby, D. P., & Cox, A. L. (2021). To plan or not to plan? A mixed-methods diary study examining when, how and why knowledge work planning is inaccurate. Proceedings of the ACM on Human-Computer Interaction, 4(CSCW3), 1-20.
Ahmetoglu, Y., Brumby, D., & Cox, A. (2020, August). A Longitudinal Interview Study on Work Planning During COVID-19 Lockdown. Microsoft.
Ahmetoglu, Y., Brumby, D. P., & Cox, A. L. (2020, April). Time Estimation Bias in Knowledge Work: Tasks With Fewer Time Constraints Are More Error-Prone. In Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1-8).
