Designing and evaluating mobile self-reporting techniques: crowdsourcing for citizen science

  • Eman M. G. Younis
  • , Eiman Kanjo
  • , Alan Chamberlain*
  • *Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

21 Citations (Scopus)

Abstract

In recent years, mobile phone technology has taken tremendous leaps and bounds to enable all types of sensing applications and interaction methods, including mobile journaling and self-reporting to add metadata and to label sensor data streams. Mobile self-report techniques are used to record user ratings of their experiences during structured studies, instead of traditional paper-based surveys. These techniques can be timely and convenient when data are collected Bin the wild^. This paper proposes three new viable methods for mobile self-reporting projects and in real-life settings such as recording weather information or urban noise mapping. These techniques are Volume Buttons control, NFC-on-Body, and NFC-on-Wall. This work also provides an experimental and comparative analysis of various self-report techniques regarding user preferences and submission rates based on a series of user experiments. The statistical analysis of our data showed that pressing screen buttons and screen touch allowed for higher labelling rates, while Volume Buttons proved to be more valuable when users engaged in other activities, e.g. while walking. Similarly, based on participants’ preferences, we found that NFC labelling was also an easy and intuitive technique when used in the context of self-reporting and place-tagging. Our hope is that by reviewing current self-reporting interfaces and user requirements, we will be able to enable new forms of self-reporting technologies that were not possible before.

Original languageEnglish
Pages (from-to)329-338
Number of pages10
JournalPersonal and Ubiquitous Computing
Volume23
Issue number2
Early online date29 Mar 2019
DOIs
Publication statusPublished - 01 Apr 2019

Keywords

  • Citizen science
  • Mobile self-report
  • Mobile sensing
  • Pervasive computing
  • User interfaces

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