2020/03/13 by Andreas Komninos, Kyriakos Katsaris, Komninos, Andreas +7
Computer Science · #FOS: Computer and information sciences #H.5.2 #Human-Computer Interaction (cs.HC) #I.3.6 #Innovative Human-Technology Interaction #Mobile Crowdsensing and Crowdsourcing #Recommender Systems and Techniques
paper · pdf · doi:10.48550/arxiv.2003.06323
openalex publication_date 2020/03/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Text entry in smartphones remains a critical element of mobile HCI. It has been widely studied in lab settings, using primarily transcription tasks, and to a far lesser extent through in-the-wild (field) experiments. So far it remains unknown how well user behaviour during lab transcription tasks approximates real use. In this paper, we present a study that provides evidence that lab text entry behaviour is clearly distinguishable from real world use. Using machine learning techniques, we show that it is possible to accurately identify the type of study in which text entry sessions took place. The implications of our findings relate to the design of future studies in text entry, aiming to support input with virtual smartphone keyboards.