Human Computer Interaction

Engineering Undergraduate Programs

Human Computer Interaction

We aim to research, understand, analyse and learn user behaviour to improve user interaction and experience. We currently pursue a number of research projects that look at different aspects of interaction analysis and modelling. Our work mainly involves experimentation and research related to human-centred web. We have research projects on web accessibility, mobile/ubiquities web, eye tracking, web page segmentation, web page transcoding, and intelligent systems to improve the web experience of users. Therefore, our work is within the Human Computer Interaction field broadly that aims to understand how users interact with the web and how the web, through its design, technology and infrastructure, enables users to interact with it.

We have Interaction Analysis and Modelling Lab (IAM) at METU NCC (IAM@METU NCC) which is equipped with an eye tracker and a user study lab that can be used to conduct user experiments. IAM Lab @ METU NCC and Interaction Analysis and Modelling Lab (IAM@Mcr) in the School of Computer Science at the University of Manchester, UK is a federated network of research labs pursuing similar research and development goals.

Faculty Members:

Yeliz Yesilada

Şükrü Eraslan

Related Projects:

  • eMine: Web Page Transcoding Based on Eye Tracking, TUBITAK
  • DDS: Data Driven Segmentation, METU NCC Post-Doc Project
  • Mohse: Multilingual Dynamic Linking of Web Resources, METU NCC BAP


Selected Related Publications:

  • Sukru Eraslan, Yeliz Yesilada, Victoria Yaneva and Le An Ha. 2020. "Keep it Simple!" An Eye-tracking Study for Exploring Complexity and Distinguishability of Web Pages for People with Autism. Universal Access in the Information Society.
  • Sukru Eraslan, Yeliz Yesilada, and Simon Harper. 2020. "The Best of Both Worlds!": Integration of Web Page and Eye Tracking Data Driven Approaches for Automatic AOI Detection. ACM Transactions on the Web, 14, 1, Article 1.
  • Sukru Eraslan, Serkan Karabulut, Mehmet Can Atalay and Yeliz Yesilada. 2019. Evaluation of Visualisation of Scanpath Trend Analysis (ViSTA) Tool. Balkan Journal of Electrical and Computer Engineering, 7, 4, 373-383.
  • Sukru Eraslan, Victoria Yaneva, Yeliz Yesilada and Simon Harper. 2019. Web users with autism: eye tracking evidence for differences, Behaviour & Information Technology, 38, 7, 678-700.
  • Elgin Akpinar and Yeliz Yesilada. 2017. Discovering Visual Elements of Web Pages and Their Roles: Users’ Perception. Interacting with Computers, 2017, 29, 6, 845-867.
  • Sukru Eraslan, Yeliz Yesilada and Simon Harper. 2017. Less Users More Confidence: How AOIs Don't Affect Scanpath Trend Analysis. Journal of Eye Movement Research, 10, 4, Article 6.
  • Sukru Eraslan, Yeliz Yesilada, and Simon Harper. 2016. Scanpath Trend Analysis on Web Pages: Clustering Eye Tracking Scanpaths. ACM Transactions on the Web, 10, 4, Article 20.
  • Sukru Eraslan, Yeliz Yesilada and Simon Harper. 2016. Eye Tracking Scanpath Analysis Techniques on Web Pages: A Survey, Evaluation and Comparison. Journal of Eye Movement Research, 9, 1, Article 2.
  • Sukru Eraslan and Yeliz Yesilada. 2015. Patterns in Eyetracking Scanpaths and the Affecting Factors. Journal of Web Engineering, 14, 5-6, 363-385.