CABAS: Context and Ability Based Adaptive System
When we interact with small screen devices, sometimes we make errors, due to our abilities/disabilities, contextual factors that distract our attention or problems related to the interface. Recovering from these errors may be time consuming or cause frustration. Predicting and learning these errors based on the previous user interaction and contextual factors, and adapting user interface to prevent from these errors can improve user performance and satisfaction. In this project, we aim to develop a system that monitors user performance and contextual changes and do adaptations based on the user performance by using machine learning techniques.
Type of Project
- PhD Project
People
- Elgin Akpınar (PhD Student)
- Assoc. Prof. Dr. Yeliz Yeşilada (Supervisor)
- Prof. Dr. Pınar Karagöz (Supervisor)
- Asst. Prof. Dr. Selim Temizer (Former Supervisor)
User Studies
User Study Datasets
Related Publications
- Elgin Akpinar, Yeliz Yeşilada, and Selim Temizer. 2020. The Effect of Context on Small Screen and Wearable Device Users’ Performance – A Systematic Review. ACM Comput. Surv. 53, 3, Article 52 (June 2020), 44 pages. DOI:https://doi.org/10.1145/3386370
- Elgin Akpınar, Yeliz Yesilada and Selim Temizer. Ability and Context Based Adaptive System: A Proposal for Machine Learning Approach, CHI’19 Workshop: Addressing the Challenges of Situationally-Induced Impairments and Disabilities in Mobile Interaction, 2019.