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Higher Education Check-Ins: Exploring the User Experience of Hybrid Location Sensing
Abstract
A large body of literature is dedicated to understanding people's check-in behavior when they use location sharing services to pair their location with a venue, e.g., a restaurant, a park, etc. Check-in behavior in higher education settings, e.g., where students and instructors have academic purposes for check-ins, is under-studied. In this work, we explore how university students apply two different mechanisms, i.e., automatic and manual location-sharing services, to conduct check-ins for an academic purpose (i.e., students sharing their class attendance with their instructor). More specifically, a Bluetooth Low Energy beacon-based technology is applied to enable automatic class check-ins. We conducted two field trials with a total of 141 university students. Our findings showed that several social, technological, and psychological factors impacted the use of auto and manual check-ins. Feedback from the student participants suggested that future higher education check-in systems may need to consider the integration of check-ins for a variety of purposes.
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Gig economy platforms facilitate the exchange of labor and money by allowing gig workers to showcase and sell their skills to a global market.
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From Socio‐technical issues in the platform‐mediated gig economy: A systematic literature review: An Annual Review of Information Science and Technology (ARIST) paper (2024) · cites this paper as Huang et al., 2019
Fiverr assigns a level to workers based on their activity, performance, and reputation, and workers with higher levels obtain certain benefits when bidding for jobs.
“Similarly, Fiverr assigns a ' level ' to workers based on their activity, performance, and reputation, and workers with higher levels obtain certain benefits when bidding for jobs (Huang et al., 2019).”
From Socio‐technical issues in the platform‐mediated gig economy: A systematic literature review: An Annual Review of Information Science and Technology (ARIST) paper (2024) · cites this paper as Huang et al., 2019
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Metadata
- Authors
- Huang, Y., Sang, Y., Wu, Q., & Yao, Y.
- Affiliations
- University of Illinois Urbana-Champaign (US)
- Syracuse University (US)
- Venue
- Proceedings of the ACM on Human-Computer Interaction
- Published
- 2019-11-07
- Volume
- 3
- Issue
- CSCW
- Pages
- 1-26
- ISSN
- 2573-0142 (ISSN-L)
- OpenAlex
- W2983436756
- Cited by
- 12
- References
- 102
- Citations by year
- 2025: 1 · 2024: 1 · 2023: 1 · 2022: 4 · 2021: 3 · 2020: 2
- FWCI
- 2.52
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Proceedings of the ACM on Human-Computer Interaction (publishedVersion)
- Funders
- Institute of Museum and Library Services (LG-80- 15-0212-15)
- Topics
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
- Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
- Keywords
- location sharing, Bluetooth Low Energy beacons, class attendance, higher education, university students, instructor-student interaction, technology adoption, social factors, psychological factors
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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