Björn Hartmann

Bio: Björn Hartmann is an academic researcher from University of California, Berkeley. The author has contributed to research in topics: Crowdsourcing & User interface. The author has an hindex of 47, co-authored 114 publications receiving 8114 citations. Previous affiliations of Björn Hartmann include Microsoft & Stanford University.

Soylent: a word processor with a crowd inside

Abstract: This paper introduces architectural and interaction patterns for integrating crowdsourced human contributions directly into user interfaces. We focus on writing and editing, complex endeavors that span many levels of conceptual and pragmatic activity. Authoring tools offer help with pragmatics, but for higher-level help, writers commonly turn to other people. We thus present Soylent, a word processing interface that enables writers to call on Mechanical Turk workers to shorten, proofread, and otherwise edit parts of their documents on demand. To improve worker quality, we introduce the Find-Fix-Verify crowd programming pattern, which splits tasks into a series of generation and review stages. Evaluation studies demonstrate the feasibility of crowdsourced editing and investigate questions of reliability, cost, wait time, and work time for edits.

How bodies matter: five themes for interaction design

Abstract: Our physical bodies play a central role in shaping human experience in the world, understandingof the world, and interactions in the world. This paper draws on theories of embodiment - from psychology, sociology, and philosophy - synthesizing five themes we believe are particularly salient for interaction design: thinking through doing, performance, visibility, risk, and thick practice. We intro-duce aspects of human embodied engagement in the world with the goal of inspiring new interaction design ap-proaches and evaluations that better integrate the physical and computational worlds.

Design lessons from the fastest q&a site in the west

Abstract: This paper analyzes a Question & Answer site for programmers, Stack Overflow, that dramatically improves on the utility and performance of Q&A systems for technical domains. Over 92% of Stack Overflow questions about expert topics are answered - in a median time of 11 minutes. Using a mixed methods approach that combines statistical data analysis with user interviews, we seek to understand this success. We argue that it is not primarily due to an a priori superior technical design, but also to the high visibility and daily involvement of the design team within the community they serve. This model of continued community leadership presents challenges to both CSCW systems research as well as to attempts to apply the Stack Overflow model to other specialized knowledge domains.

Shepherding the crowd yields better work

Abstract: Micro-task platforms provide massively parallel, on-demand labor. However, it can be difficult to reliably achieve high-quality work because online workers may behave irresponsibly, misunderstand the task, or lack necessary skills. This paper investigates whether timely, task-specific feedback helps crowd workers learn, persevere, and produce better results. We investigate this question through Shepherd, a feedback system for crowdsourced work. In a between-subjects study with three conditions, crowd workers wrote consumer reviews for six products they own. Participants in the None condition received no immediate feedback, consistent with most current crowdsourcing practices. Participants in the Self-assessment condition judged their own work. Participants in the External assessment condition received expert feedback. Self-assessment alone yielded better overall work than the None condition and helped workers improve over time. External assessment also yielded these benefits. Participants who received external assessment also revised their work more. We conclude by discussing interaction and infrastructure approaches for integrating real-time assessment into online work.

Reflective physical prototyping through integrated design, test, and analysis

Abstract: Prototyping is the pivotal activity that structures innovation, collaboration, and creativity in design. Prototypes embody design hypotheses and enable designers to test them. Framin design as a thinking-by-doing activity foregrounds iteration as a central concern. This paper presents d.tools, a toolkit that embodies an iterative-design-centered approach to prototyping information appliances. This work offers contributions in three areas. First, d.tools introduces a statechart-based visual design tool that provides a low threshold for early-stage prototyping, extensible through code for higher-fidelity prototypes. Second, our research introduces three important types of hardware extensibility - at the hardware-to-PC interface, the intra-hardware communication level, and the circuit level. Third, d.tools integrates design, test, and analysis of information appliances. We have evaluated d.tools through three studies: a laboratory study with thirteen participants; rebuilding prototypes of existing and emerging devices; and by observing seven student teams who built prototypes with d.tools.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

How video production affects student engagement: an empirical study of MOOC videos

Abstract: Videos are a widely-used kind of resource for online learning. This paper presents an empirical study of how video production decisions affect student engagement in online educational videos. To our knowledge, ours is the largest-scale study of video engagement to date, using data from 6.9 million video watching sessions across four courses on the edX MOOC platform. We measure engagement by how long students are watching each video, and whether they attempt to answer post-video assessment problems.Our main findings are that shorter videos are much more engaging, that informal talking-head videos are more engaging, that Khan-style tablet drawings are more engaging, that even high-quality pre-recorded classroom lectures might not make for engaging online videos, and that students engage differently with lecture and tutorial videos.Based upon these quantitative findings and qualitative insights from interviews with edX staff, we developed a set of recommendations to help instructors and video producers take better advantage of the online video format. Finally, to enable researchers to reproduce and build upon our findings, we have made our anonymized video watching data set and analysis scripts public. To our knowledge, ours is one of the first public data sets on MOOC resource usage.

A tutorial on human activity recognition using body-worn inertial sensors

Abstract: The last 20 years have seen ever-increasing research activity in the field of human activity recognition. With activity recognition having considerably matured, so has the number of challenges in designing, implementing, and evaluating activity recognition systems. This tutorial aims to provide a comprehensive hands-on introduction for newcomers to the field of human activity recognition. It specifically focuses on activity recognition using on-body inertial sensors. We first discuss the key research challenges that human activity recognition shares with general pattern recognition and identify those challenges that are specific to human activity recognition. We then describe the concept of an Activity Recognition Chain (ARC) as a general-purpose framework for designing and evaluating activity recognition systems. We detail each component of the framework, provide references to related research, and introduce the best practice methods developed by the activity recognition research community. We conclude with the educational example problem of recognizing different hand gestures from inertial sensors attached to the upper and lower arm. We illustrate how each component of this framework can be implemented for this specific activity recognition problem and demonstrate how different implementations compare and how they impact overall recognition performance.

A Tutorial on Human Activity Recognition Using Body-Worn

Abstract: The last 20 years have seen ever-increasing research activity in the field of human activity recognition. With activity recognition having considerably matured, so has the number of challenges in designing, implementing, and evaluating activity recognition systems. This tutorial aims to provide a comprehensive hands-on introduction for newcomers to the field of human activity recognition. It specifically focuses on activity recognition using on-body inertial sensors. We first discuss the key research challenges that human activity recognition shares with general pattern recognition and identify those challenges that are specific to human activity recognition. We then describe the concept of an Activity Recognition Chain (ARC) as a general-purpose framework for designing and evaluating activity recognition systems. We detail each component of the framework, provide references to related research, and introduce the best practice methods developed by the activity recognition research community. We conclude with the educational example problem of recognizing different hand gestures from inertial sensors attached to the upper and lower arm. We illustrate how each component of this framework can be implemented for this specific activity recognition problem and demonstrate how different implementations compare and how they impact overall recognition performance.