Contextual dissonance

Lathia, Neal; Rachuri, Kiran K.; Mascolo, Cecilia; Rentfrow, Peter J.

doi:10.1145/2493432.2493452

Cited by 59 publications

(13 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This option was only available before the notification was pushed into notification bar. Additionally, the survey notifications were only allowed to pop up between 8:00 AM and 10:00 PM [45]. To reduce the workload of the participants, the time interval between two surveys was at least one and half hours.…”

Section: Methodsmentioning

confidence: 99%

How Busy Are You?

Yuan

Gao

Lindqvist

2017

Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems

View full text Add to dashboard Cite

Smartphones frequently notify users about newly available messages or other notifications. It can be very disruptive when these notifications interrupt users while they are busy. Our work here is based on the observation that people usually exhibit different levels of busyness at different contexts. This means that classifying users' interruptibility as a binary status, interruptible or not interruptible, is not sufficient to accurately measure their availability towards smartphone interruptions. In this paper, we propose, implement and evaluate a two-stage hierarchical model to predict people's interruptibility intensity. Our work is the first to introduce personality traits into interruptibility prediction model, and we found that personality data improves the prediction significantly. Our model bootstraps the prediction with similar people's data, and provides a good initial prediction for users whose individual models have not been trained on their own data yet. Overall prediction accuracy of our model can reach 66.1%.We present the following four major contributions: 1) We propose a two-stage hierarchical interruptibility prediction model. In the first stage, our model predicts (with 75% accuracy) whether a user will react to an interruption

show abstract

Section: Methodsmentioning

confidence: 99%

How Busy Are You?

Yuan

Gao

Lindqvist

2017

Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems

View full text Add to dashboard Cite

show abstract

“…The importance of survey timing depending on participant characteristics was highlighted in additional work by Lathia et. al [21]. They showed that a response bias caused by simplistic trigger design gives rise to contextual dissonance.…”

Section: ) Lack Of Participant Customisationmentioning

confidence: 99%

Jeeves - A visual programming environment for mobile experience sampling

Rough

Quigley

2015

2015 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC)

View full text Add to dashboard Cite

Abstract-The Experience Sampling Method (ESM) captures participants' thoughts and feelings in their everyday environments. Mobile and wearable technologies afford us opportunities to reach people using ESM in varying contexts. However, a lack of programming knowledge often hinders researchers in creating ESM applications. In practice, they rely on specialised tools for app creation. Our initial review of these tools indicates that most are expensive commercial services, and none utilise the full potential of sensors for creating context-aware applications. We present "Jeeves", a visual language to facilitate ESM application creation. Inspired by successful visual languages in literature, our block-based notation enables researchers to visually construct ESM study specifications. We demonstrate its applicability by replicating existing ESM studies found in medical and psychology literature. Our preliminary study with 20 participants demonstrates that both non-programmers and programmers are able to successfully utilise Jeeves. We discuss future work in extending Jeeves with alternative mobile technologies.

show abstract

“…The large majority of studies asking for participant input (e.g., via ESM) is following a randomised or interval-based time schedule [2]. Although these may initially appear to provide the most equal distribution of participant responses, response rates are often not equally distributed throughout the day -resulting in certain contexts being overrepresented in the collected data [20,21]. Building on the aforementioned ambition towards integrated active and passive sensing, contextual information can inform researchers when participants are likely to be available and able to provide reliable input [22,21].…”

Section: Research Agenda On Human Accuracy In Mobile Data Collectionmentioning

confidence: 99%

Human accuracy in mobile data collection

Berkel

Gonçalves

Wac

et al. 2020

International Journal of Human-Computer Studies

View full text Add to dashboard Cite

The collection of participant data 'in the wild' is widely employed by Human-Computer Interaction researchers. A variety of methods, including experience sampling, mobile crowdsourcing, and citizen science, rely on repeated participant contributions for data collection. Given this strong reliance on participant data, ensuring that the data is complete, reliable, timely, and accurate is key. Although previous work has made significant progress on ensuring that a sufficient amount of data is collected, the accuracy of human contributions has remained underexposed. In this article we argue for an emerging need for an increased focus on this aspect of human-labelled data. The articles published in this special issue demonstrate how a focus on the accuracy of the collected data has implications on all aspects of a study -ranging from study design to the analysis and reporting of results. We put forward a five-point research agenda in which we outline future opportunities in assessing and improving human accuracy in mobile data collection.

show abstract

Contextual dissonance

Cited by 59 publications

References 22 publications

How Busy Are You?

How Busy Are You?

Jeeves - A visual programming environment for mobile experience sampling

Human accuracy in mobile data collection

Contact Info

Product

Resources

About