We deployed a feedback visualization tool to learn how students used the tool for interpreting feedback from peers and teaching assistants. The tool visualizes the topic and opinion structure in a collection of feedback and provides interaction for reviewing providers’ backgrounds. A total of 18 teams engaged with the tool to interpret feedback for course projects. We surveyed students (N = 69) to learn about their sensemaking goals, use of the tool to accomplish those goals, and perceptions of specific features. We interviewed students (N = 12) and TAs (N = 2) to assess the tool’s impact on students’ review processes and course instruction. Students discovered valuable feedback, assessed project quality, and justified design decisions to teammates by exploring specific icon patterns in the visualization. The interviews revealed that students mimicked strategies implemented in the tool when reviewing new feedback without the tool. Students found the benefits of the visualization outweighed the cost of labeling feedback.

Examples are powerful tools for creativity and can provide inspiration and structure. However, novices often don’t know when to seek examples or how to apply them. Showing real-time examples targeted to the current task (adaptive conceptual guidance) may help novices consider inspiration more often and better implement the ideas that examples illustrate. We explore this in a Wizard-of-Oz system, Shöwn, that presents examples based on the user’s current activity while drawing a comic strip. Shöwn’s design is informed by interviews with novice and expert comic artists (n=18). A between-subjects experiment (n=24) found that adaptive conceptual guidance improved the clarity and uniqueness of drawings and stories compared to non-adaptive examples. Users also found the examples more useful and inspirational than those without adaptive guidance. Our results present an initial exploration of the challenges and benefits of contextually showing examples in interactive creativity tools.

Freelancers increasingly earn their livelihood through online marketplaces. To attract new clients, freelancers continuously curate their online portfolios to convey their unique skills and style. However, many lack access to rapid, regular, and inexpensive feedback needed to improve their portfolios. Existing crowd feedback systems, which collect feedback on individual creative projects (i.e., decomposed approach), could fill this need, but it is unclear how they might support feedback on multiple projects (i.e., holistic approach). In a between-subjects study with 30 freelancers, we compared decomposed and holistic feedback collection approaches using CrowdFolio, a crowd feedback system for portfolios. The holistic approach helped freelancers discover new ways to describe their work, while the decomposed approach provided detailed insight about the visual attractiveness of projects. This study contributes evidence that portfolio feedback systems, regardless of collection approach, can positively support professional development by impacting how freelancers portray themselves online and reflect on their identity.

Many artists broadcast their creative process through live streaming platforms like Twitch and YouTube, and people often watch archives of these broadcasts later for learning and inspiration. Unfortunately, because live stream videos are often multiple hours long and hard to skim and browse, few can leverage the wealth of knowledge hidden in these archives. We present an approach for automatic temporal segmentation of creative live stream videos. Using an audio transcript and a log of software usage, the system segments the video into sections that the artist can optionally label with meaningful titles. We evaluate this approach by gathering feedback from expert streamers and comparing automatic segmentations to those made by viewers. We find that, while there is no one "correct" way to segment a live stream, our automatic method performs similarly to viewers, and streamers find it useful for navigating their streams after making slight adjustments and adding section titles.

We present a system that automatically transforms text articles into audio-visual slideshows by leveraging the notion of word concreteness, which measures how strongly a word or phrase is related to some perceptible concept. In a formative study we learn that people not only prefer such audio-visual slideshows but find that the content is easier to understand compared to text articles or text articles augmented with images. We use word concreteness to select search terms and find images relevant to the text. Then, based on the distribution of concrete words and the grammatical structure of an article, we time-align selected images with audio narration obtained through text-to-speech to produce audio-visual slideshows. In a user evaluation we find that our concreteness-based algorithm selects images that are highly relevant to the text. The quality of our slideshows is comparable to slideshows produced manually using standard video editing tools, and people strongly prefer our slideshows to those generated using a simple keyword-search based approach.

Feedback from diverse audiences can vary in focus, differ in structure, and contradict each other, making it hard to interpret and act on. While prior work has explored generating quality feedback, our work helps a designer interpret that feedback. Through a formative study with professional designers (N=10), we discovered that the interpretation process includes categorizing feedback, identifying valuable feedback, and prioritizing which feedback to incorporate in a revision. We also found that designers leverage feedback topic and sentiment, and the status of the provider to aid interpretation. Based on the findings, we created a new tool (Decipher) that enables designers to visualize and navigate a collection of feedback using its topic and sentiment structure. In a preliminary evaluation (N=20), we found that Decipher helped users feel less overwhelmed during feedback interpretation tasks and better attend to critical issues and conflicting opinions compared to using a typical document-editing tool.

Many artists livestream their creative process, allowing viewers to learn and be inspired from the decisions -- and mistakes -- they make along the way. This paper presents the first broad look at the range of creative activities people stream. Through content analysis of livestream archives, interviews with 8 streamers, and online surveys with 165 viewers, we study current practices and challenges in creative livestream communities and compare them with prior observations of livestreaming in other domains. We observed four common types of creative livestreams: teaching, making, socializing, and performing. We identify three open questions for the research community around how to better support the goals of creative streamers and viewers: how to support richer audience interactions at scale, how to support all parts of the creative process, and how to support watching livestream archives.

Social media platforms offer people a variety of ways to interact, ranging from public broadcast posts, to comments on posts, to private messages, to paralinguistic interactions such as "liking" posts. In 2015, the commenting function "replies" was temporarily removed from Tumblr, providing a unique opportunity to study the deprivation of a standard social media feature. We administered a survey to investigate Tumblr users' perceptions and use of replies. Respondents reported that they used replies to simultaneously support others' performance and their own. Respondents compared replies to other digital interaction channels such as paralinguistic interactions, the sharing feature "reblogs", and "direct messages" (DMs), citing social considerations and norms around each. We used Goffman's performance theory to draw insights on the perceived semi-public / semi-private space of replies, which enabled users to perform supportive actions that did not belong in their main blogging identity frontstage but that were not backstage either. We discuss the limitation of performance theory to describe a presentation to a limited but unknown audience, and we describe how replies enabled new frontstages such as the delicate ramp up to the performance of intimacy in DMs. We discuss implications for performing support and identity on social media with audiences that are perceived as limited but are unknown.

Online creative communities allow creators to share their work with a large audience, maximizing opportunities to showcase their work and connect with fans and peers. However, sharing in-progress work can be technically and socially challenging in environments designed for sharing completed pieces. We propose an online creative community where sharing process, rather than showcasing outcomes, is the main method of sharing creative work. Based on this, we present Mosaic---an online community where illustrators share work-in-progress snapshots showing how an artwork was completed from start to finish. In an online deployment and observational study, artists used Mosaic as a vehicle for reflecting on how they can improve their own creative process, developed a social norm of detailed feedback, and became less apprehensive of sharing early versions of artwork. Through Mosaic, we argue that communities oriented around sharing creative process can create a collaborative environment that is beneficial for creative growth.

Crowdsourcing systems accomplish large tasks with scale and speed by breaking work down into independent parts. However, many types of complex creative work, such as fiction writing, have remained out of reach for crowds because work is tightly interdependent: changing one part of a story may trigger changes to the overall plot and vice versa. Taking inspiration from how expert authors write, we propose a technique for achieving interdependent complex goals with crowds. With this technique, the crowd loops between reflection, to select a high-level goal, and revision, to decompose that goal into low-level, actionable tasks. We embody this approach in Mechanical Novel, a system that crowdsources short fiction stories on Amazon Mechanical Turk. In a field experiment, Mechanical Novel resulted in higher-quality stories than an iterative crowdsourcing workflow. Our findings suggest that orienting crowd work around high-level goals may enable workers to coordinate their effort to accomplish complex work.

People from all over the world use social media to share thoughts and opinions about events, and understanding what people say through these channels has been of increasing interest to researchers, journalists, and marketers alike. However, while automatically generated summaries enable people to consume large amounts of data efficiently, they do not provide the context needed for a viewer to fully understand an event. Narrative structure can provide templates for the order and manner in which this data is presented to create stories that are oriented around narrative elements rather than summaries made up of facts. In this paper, we use narrative theory as a framework for identifying the links between social media content. To do this, we designed crowdsourcing tasks to generate summaries of events based on commonly used narrative templates. In a controlled study, for certain types of events, people were more emotionally engaged with stories created with narrative structure and were also more likely to recommend them to others compared to summaries created without narrative structure.

Current crowdsourcing workflows comprise of discrete tasks that guide the crowd towards predetermined goals. However, this approach is ill-suited towards supporting massively collaborative open-ended creative work, which often involves an exploration of possible end results and revision of creative goals. My dissertation explores how existing expert creative practice can inform new crowdsourcing techniques that allow the crowd to collaborate on complex creative tasks such as writing short stories.

Creative tools today strive to amplify our ability to create high-quality work. However, experiencing failure is also an important part of mastering creative skills. While experts have developed strategies for engaging in risky experiments and learning from mistakes, novices lack the experience and mindset needed to use failures as opportunities for growth. Current tools intimidate the unsure novice, as they are designed around showcasing success or critiquing finished work, rather than providing safe spaces for experimentation. To better support experiences of failure for novices, we instead propose flipping the value of failure in creativity tools from something to avoid to something to pursue actively. To do this, we develop a taxonomy of creative activities that people engage in when they aim to succeed. We then invert this taxonomy to derive a new set of creative activities where deliberate failure can provide a path towards creative confidence. Lastly, we envision possible creativity support tools as examples of the potential value of supporting activities where failure is encouraged and showcased.

Creating personal narratives helps people build meaning around their experiences. However, novices lack the knowledge and experience to create stories with strong narrative structure. Current storytelling tools often structure novice work through templates, enforcing a linear creative process that asks novices for materials they may not have. In this paper, we propose scaffolding creative work using storytelling patterns extracted from stories created by experts. Patterns are modular sets of related camera shots that expert videographers commonly use to achieve a specific narrative function. After identifying a set of patterns from high-quality storytelling videos, we created Motif, a mobile video storytelling application that allows users to construct video stories by combining these patterns. By making existing solutions used by experts available to novices, we encourage capturing shots with story structure and narrative goals in mind. In a controlled study where we asked participants to create travel video stories, videos created with patterns conveyed stronger narrative structure and were considered higher quality by expert evaluators than videos created without patterns.

In story writing, the diverse perspectives of the crowd could support an author's search for the perfect character, setting, or plot. However, structuring crowd collaboration is challenging. Too little structure leads to unfocused, sprawling narratives, and too much structure stifles creativity. Motivated by the idea that individual creative leaders and the crowd have complementary creative strengths, we present an approach where a leader directs the high-level vision for a story and articulates creative constraints for the crowd. This approach is embodied in Ensemble, a novel collaborative story-writing platform. In a month-long short story competition, over one hundred volunteer users on the web started over fifty short stories using Ensemble. Leaders used the platform to direct collaborator work by establishing creative goals, and collaborators contributed meaningful, high-level ideas to stories through specific suggestions. This work suggests that asymmetric creative contributions may support a broad new class of creative collaborations.

Conventional video games today rely on visual cues to drive user interaction, and as a result, there are few games for blind and low-vision people. To address this gap, we created an accessible and mobile casual game for Android called TapBeats, a musical rhythm game based on audio cues. In addition, we developed a gesture system that utilizes text-to-speech and haptic feedback to allow blind and low-vision users to interact with the game's menu screens using a mobile phone touchscreen. A graphical user interface is also included to encourage sighted users to play as well. Through this game, we aimed to explore how both blind and sighted users can share a common game experience.

Video and image quality are often objectively measured using peak signal-to-noise ratio (PSNR), but for sign language video, human comprehension is most important. Yet the relationship of human comprehension to PSNR has not been studied. In this survey, we determine how well PSNR matches human comprehension of sign language video. We use very low bitrates (10-60 kbps) and two low spatial resolutions (192×144 and 320×240 pixels) which may be typical of video transmission on mobile phones using 3G networks. In a national online video-based user survey of 103 respondents, we found that respondents preferred the 320×240 spatial resolution transmitted at 20 kbps and higher; this does not match what PSNR results would predict. However, when comparing perceived ease/difficulty of comprehension, we found that responses did correlate well with measured PSNR. This suggests that PSNR may not be suitable for representing subjective video quality, but can be reliable as a measure for comprehensibility of American Sign Language (ASL) video. These findings are applied to our experimental mobile phone application, MobileASL, which enables real-time sign language communication for Deaf users at low bandwidths over the U.S. 3G cellular network.

MobileASL is a video compression project for two-way, real-time video communication on cell phones, allowing Deaf people to communicate in the language most accessible to them, American Sign Language. Unfortunately, running MobileASL quickly depletes a full battery charge in a few hours. Previous work on MobileASL investigated a method called variable frame rate (VFR) to increase the battery duration. We expand on this previous work by creating two new power saving algorithms, variable spatial resolution (VSR), and the application of both VFR and VSR. These algorithms extend the battery life by altering the temporal and/or spatial resolutions of video transmitted on MobileASL. We found that implementing only VFR extended the battery life from 284 minutes to 307 minutes; implementing only VSR extended the battery life to 306 minutes, and implementing both VFR and VSR extended the battery life to 315 minutes. We evaluated all three algorithms by creating a linguistically accessible online survey to investigate Deaf people's perceptions of video quality when these algorithms were applied. In our survey results, we found that VFR produces perceived video choppiness and VSR produces perceived video blurriness; however, a surprising finding was that when both VFR and VSR are used together, they largely ameliorate the choppiness and blurriness perceived, i.e., they each improve the use of the other. This is a useful finding because using VFR and VSR together saves the most battery life.