Research Methods for Human-Computer Interaction

By Paul A. Cairns, Anna Cox. 2008

2,118 citations recorded by the source corpus.

Subfields: Developmental and Educational Psychology, Human-Computer Interaction

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In Developmental and Educational Psychology

In Human-Computer Interaction

Citations in the corpus

Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AI (2019, background): “A number of general discussions on developing systems from a human factors perspective are relevant (e.g., Conant and Ashby, 1970; Bellotti, et al., 2002; Landauer, 1988; Lazar, Feng, and Hochheiser, 2017; Rasmussen, 1983).”
A Roadmap for a Rigorous Science of Interpretability (2017, methodology): “This reasoning aligns with the methods of evaluation common in the human-computer interaction and visualization communities, where there exists a strong ethos around making sure that the system delivers on its intended task [Antunes et al., 2012, Lazar et al., 2010].”
The Design with Intent Method: a design tool for influencing user behaviour. (2010, background): “In some fields, such as security or health and safety, user adherence to intended behaviour is more critical than in others—as Cairns & Cox (2008) put it, " in safety critical systems, like air traffic control or medical monitors... the cost of failure is never acceptable.”
A practical guide to controlled experiments of software engineering tools with human participants (2013, background): “There are also sources of variation due to language and cultural differences that require special attention (Lazar et al. 2010).”
Evaluation Strategies for HCI Toolkit Research (2018, background): “Much of usage evaluation is informed by traditional user studies [24,53,81], and can help verify whether the toolkit is (1) conceptually clear, (2) easy to use, or (3) valuable to the audience.”
What Makes a Dark Pattern... Dark?: Design Attributes, Normative Considerations, and Measurement Methods (2021, background): “Instead, we refer interested readers to standard HCI textbooks [30, 41].”
Usability: Lessons Learned … and Yet to Be Learned (2014, neutral): “For recent book-level treatments of usability and UX issues, see Albert et al. (2010); Barnum (2010); Lazar, Feng, and Hochheiser (2010); MacKenzie (2014); Sauro (2010a); Sauro & Lewis (2012); Shneiderman & Plaisant (2010); and Tullis and Albert (2013).”
Human-Computer Interaction and Knowledge Discovery (HCI-KDD): What Is the Benefit of Bringing Those Two Fields to Work Together? (2013, positive): “From the early beginning, HCI research was focused on the measurement of human performance, heavily borrowing from the cognitive sciences [42],[35]; today HCI-research includes a large collection of various methods [43], [44], [45].”
Co-Designing a Real-Time Classroom Orchestration Tool to Support Teacher-AI Complementarity (2019, background): “Initial needs analysis and concept generation, including design activities such as generative card sorting exercises (Cairns & Cox, 2008), directed storytelling (Evenson, 2006), semi-structured interviews with teachers, and field observations (Hanington & Martin, 2012).”
Towards A Rigorous Science of Interpretable Machine Learning (2017, methodology): “This reasoning aligns with the methods of evaluation common in the human-computer interaction and visualization communities, where there exists a strong ethos around making sure that the system delivers on its intended task [Antunes et al., 2012, Lazar et al., 2010].”