%0 Conference Proceedings
%T Mouse Tracking Measures and Movement Patterns with Application for Online Surveys
%+ Department of Internal Medicine [Zürich]
%+ Laboratorio de Instrumentaçao, Engenharia Biomédica e Fisica da Radiaçao (LIBPhys-UNL)
%+ Universität Zürich [Zürich] = University of Zurich (UZH)
%A Cepeda, Catia
%A Rodrigues, Joao
%A Dias, Maria, Camila
%A Oliveira, Diogo
%A Rindlisbacher, Dina
%A Cheetham, Marcus
%A Gamboa, Hugo
%Z Part 1: MAKE-Main Track
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 2nd International Cross-Domain Conference for Machine Learning and Knowledge Extraction (CD-MAKE)
%C Hamburg, Germany
%Y Andreas Holzinger
%Y Peter Kieseberg
%Y A Min Tjoa
%Y Edgar Weippl
%I Springer International Publishing
%3 Machine Learning and Knowledge Extraction
%V LNCS-11015
%P 28-42
%8 2018-08-27
%D 2018
%R 10.1007/978-3-319-99740-7_3
%K Knowledge extraction
%K Mouse behavior patterns
%K Mouse tracking
%K Human-computer interaction
%K User
%K Survey
%Z Computer Science [cs]
%Z Humanities and Social Sciences/Library and information sciencesConference papers
%X There is growing interest in the field of human-computer interaction in the use of mouse movement data to infer e.g. user’s interests, preferences and personality. Previous work has defined various patterns of mouse movement behavior. However, there is a paucity of mouse tracking measures and defined movement patterns for use in the specific context of data collection with online surveys. The present study aimed to define and visualize patterns of mouse movements while the user provided responses in a survey (with questions to be answered using a 5-point Likert response scale). The study produced a wide range of different patterns, including new patterns, and showed that these can easily be distinguished. The identified patterns may - in conjunction with machine learning algorithms - be used for further investigation toward e.g. the recognition of the user’s state of mind or for user studies.
%G English
%Z TC 5
%Z TC 8
%Z TC 12
%Z WG 8.4
%Z WG 8.9
%Z WG 12.9
%2 https://inria.hal.science/hal-02060040/document
%2 https://inria.hal.science/hal-02060040/file/472936_1_En_3_Chapter.pdf
%L hal-02060040
%U https://inria.hal.science/hal-02060040
%~ SHS
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-TC
%~ IFIP-TC5
%~ IFIP-WG
%~ IFIP-TC12
%~ IFIP-TC8
%~ IFIP-WG8-4
%~ IFIP-WG8-9
%~ IFIP-CD-MAKE
%~ IFIP-WG12-9
%~ IFIP-LNCS-11015