Stop questioning me!: towards optimizing user involvement during data collection on mobile devices

Nicholas Micallef; Michael Just; Lynne Baillie; Hilmi Gunes Kayacik

doi:10.1145/2493190.2494426

Stop questioning me!: towards optimizing user involvement during data collection on mobile devices

Nicholas Micallef, Michael Just, Lynne Baillie, Hilmi Gunes Kayacik

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Citations (Scopus)

Abstract

Current methods of behavioral data collection from mobile devices either require significant involvement from participants to verify the 'ground truth' of the data, or approximations that involve post-experiment comparisons to seed data. In this paper we argue that user involvement can be gracefully reduced by performing more intelligent seed comparisons. We aim to reduce the participant involvement to the 'most interesting' temporal slots, both during the experiment and in post-experiment verification. We carried out a 2 week study with 4 users, consisting of an initial opportunistic gathering of mobile sensor data. Our findings suggest that by using such a method we can significantly reduce user involvement.

Original language	English
Title of host publication	MobileHCI 2013
Subtitle of host publication	Proceedings of the 15th international conference on Human-computer interaction with mobile devices and services
Publisher	Association for Computing Machinery (ACM)
Pages	588-593
Number of pages	6
ISBN (Print)	9781450322737
DOIs	https://doi.org/10.1145/2493190.2494426
Publication status	Published - 27 Aug 2013

Keywords

mobile devices
data collection methods
mobile sensor data
user involvement

Documents and Links

10.1145/2493190.2494426

Cite this

Micallef, N., Just, M., Baillie, L., & Kayacik, H. G. (2013). Stop questioning me!: towards optimizing user involvement during data collection on mobile devices. In MobileHCI 2013: Proceedings of the 15th international conference on Human-computer interaction with mobile devices and services (pp. 588-593). Association for Computing Machinery (ACM). https://doi.org/10.1145/2493190.2494426

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abstract = "Current methods of behavioral data collection from mobile devices either require significant involvement from participants to verify the 'ground truth' of the data, or approximations that involve post-experiment comparisons to seed data. In this paper we argue that user involvement can be gracefully reduced by performing more intelligent seed comparisons. We aim to reduce the participant involvement to the 'most interesting' temporal slots, both during the experiment and in post-experiment verification. We carried out a 2 week study with 4 users, consisting of an initial opportunistic gathering of mobile sensor data. Our findings suggest that by using such a method we can significantly reduce user involvement.",

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author = "Nicholas Micallef and Michael Just and Lynne Baillie and Kayacik, {Hilmi Gunes}",

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doi = "10.1145/2493190.2494426",

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booktitle = "MobileHCI 2013",

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Micallef, N, Just, M, Baillie, L & Kayacik, HG 2013, Stop questioning me!: towards optimizing user involvement during data collection on mobile devices. in MobileHCI 2013: Proceedings of the 15th international conference on Human-computer interaction with mobile devices and services. Association for Computing Machinery (ACM), pp. 588-593. https://doi.org/10.1145/2493190.2494426

Stop questioning me!: towards optimizing user involvement during data collection on mobile devices. / Micallef, Nicholas; Just, Michael; Baillie, Lynne et al.

MobileHCI 2013: Proceedings of the 15th international conference on Human-computer interaction with mobile devices and services. Association for Computing Machinery (ACM), 2013. p. 588-593.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

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AU - Micallef, Nicholas

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AU - Baillie, Lynne

AU - Kayacik, Hilmi Gunes

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N2 - Current methods of behavioral data collection from mobile devices either require significant involvement from participants to verify the 'ground truth' of the data, or approximations that involve post-experiment comparisons to seed data. In this paper we argue that user involvement can be gracefully reduced by performing more intelligent seed comparisons. We aim to reduce the participant involvement to the 'most interesting' temporal slots, both during the experiment and in post-experiment verification. We carried out a 2 week study with 4 users, consisting of an initial opportunistic gathering of mobile sensor data. Our findings suggest that by using such a method we can significantly reduce user involvement.

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KW - data collection methods

KW - mobile sensor data

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Stop questioning me!: towards optimizing user involvement during data collection on mobile devices

Abstract

Keywords

Documents and Links

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Cite this