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New human-centered sensing capabilities are introduced in surface transportation, aviation, healthcare, as well as many other professional and recreational domains. These capabilities enable industrial systems and consumer products to monitor a variety of human states and behaviors, enabling machines to better understand the users and to provide sophisticated, natural user interactions, decision aiding, and behavioral guidance. This panel invites five researchers to discuss what the Human Factors community can contribute to advancing human-centered sensing, and what technical and ethical challenges can arise in research and applications.

People often capture photos or notes from their phones to integrate later into a document. But current mobile capture tools can make this hard, with the captured information ending up fragmented and decontextualized. This paper explores how to help document authors capture, contextualize, and use document-related information. A survey of 66 information workers reveals that document-focused information capture differs from other types of mobile information capture, and that while people capture a broad range of information types while mobile, most document-related capture comes in the form of photos, notes, and bookmarks. Based on this survey we built Scraps, which consists of two parts: 1) a mobile app that makes it easy for people to capture and add context to information from their phone, and 2) a Word sidebar that helps them later link that information to a document on their desktop. In a field study with 11 information workers, we find that Scraps streamlined the process of capturing and using document-related information, and enabled people to focus on writing over integrating captured information.

Virtual meetings are critical for remote work because of the need for synchronous collaboration in the absence of in-person interactions. In-meeting multitasking is closely linked to people's productivity and wellbeing. However, we currently have limited understanding of multitasking in remote meetings and its potential impact. In this paper, we present what we believe is the most comprehensive study of remote meeting multitasking behavior through an analysis of a large-scale telemetry dataset collected from February to May 2020 of U.S. Microsoft employees and a 715-person diary study. Our results demonstrate that intrinsic meeting characteristics such as size, length, time, and type, significantly correlate with the extent to which people multitask, and multitasking can lead to both positive and negative outcomes. Our findings suggest important best-practice guidelines for remote meetings (e.g., avoid important meetings in the morning) and design implications for productivity tools (e.g., support positive remote multitasking)., In ACM CHI 2021

Information workers perform jobs that demand constant multitasking, leading to context switches, productivity loss, stress, and unhappiness. Systems that can mediate task transitions and breaks have the potential to keep people both productive and happy. We explore a crucial initial step for this goal: finding opportune moments to recommend transitions and breaks without disrupting people during focused states. Using affect, workstation activity, and task data from a three-week field study (N=25), we build models to predict whether a person should continue their task, transition to a new task, or take a break. The R-squared values of our models are as high as 0.7, with only 15% error cases. We ask users to evaluate the timing of recommendations provided by a recommender that relies on these models. Our study shows that users find our transition and break recommendations to be well-timed, rating them as 86% and 77% accurate, respectively. We conclude with a discussion of the implications for intelligent systems that seek to guide task transitions and manage interruptions at work.

As AI technology advances, it offers promising opportunities to improve educational outcomes when integrated with an overall learning experience. We investigate forward-looking interactive reading experiences that leverage both automatic question generation and analysis of attention signals, such as gaze tracking, to improve short- and long-term learning outcomes. We aim to expand the known pedagogical benefits of adjunct questions to more general reading scenarios, by investigating the benefits of adjunct questions generated after participants attend to passages in an article, based on their gaze behavior. We also compare the effectiveness of manually-written questions with those produced by Automatic Question Generation (AQG). We further investigate gaze and reading patterns indicative of low vs. high learning in both short- and long-term scenarios (one-week followup). We show AQG-generated adjunct questions have promise as a way to scale to a wide variety of reading material where the cost of manually curating questions may be prohibitive.

While having a step-by-step breakdown for a task-an action plan-helps people complete tasks, prior work has shown that people prefer not to make action plans for their own tasks. Getting planning support from others could be beneficial, but it is limited by how much domain knowledge people have about the task and how available they are. Our goal is to incorporate the benefits of having action plans in the complex domain of writing, while mitigating the time and effort costs of creating plans. To mitigate these costs, we introduce a vocabulary-a finite set of functions pertaining to writing tasks-as a cognitive scaffold that enables people with necessary context (e.g. collaborators) to generate action plans for others. We develop this vocabulary by analyzing 264 comments, and compare plans created using it with those created without any aid, in an online study with 768 comments (N=145) and a lab study with 96 comments (N=8). We show that using a vocabulary reduces planning time and effort and improves plan quality compared to unstructured planning, and opens the door for automation and task sharing for complex tasks.