Mouse tracking measures and movement patterns with application for online surveys

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

16 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationMachine Learning and Knowledge Extraction - Second IFIP TC 5, TC 8/WG 8.4, 8.9, TC 12/WG 12.9 International Cross-Domain Conference, CD-MAKE 2018, Proceedings
EditorsPeter Kieseberg, Edgar Weippl, Andreas Holzinger, A Min Tjoa
Place of PublicationCham
PublisherSpringer
Pages28-42
Number of pages15
ISBN (Electronic)978-3-319-99740-7
ISBN (Print)978-3-319-99739-1
DOIs
Publication statusPublished - 2018
Event2nd International Cross-Domain Conference for Machine Learning and Knowledge Extraction, CD-MAKE 2018 - Hamburg, Germany
Duration: 27 Aug 201830 Aug 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer
Volume11015 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference2nd International Cross-Domain Conference for Machine Learning and Knowledge Extraction, CD-MAKE 2018
Country/TerritoryGermany
CityHamburg
Period27/08/1830/08/18

Keywords

  • Human-computer interaction
  • Knowledge extraction
  • Mouse behavior patterns
  • Mouse tracking
  • Survey
  • User

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