While designers increasingly leverage Generative AI for divergent exploration, current interaction is optimized for convergent refinement, forcing users to specify fixed targets rather than open-ended search spaces. Based on a formative study (N=7), we define the anatomy of Divergent Intent, comprising property, direction, and range, and identified two critical barriers: the lack of mechanisms to explicitly shape the parametric boundaries of exploration and the difficulty of reusing successful search strategies. We present IdeaBlocks, where users can modularize divergent intents into Exploration Blocks. Users can reuse prior intents at multiple levels (block, path, and project) with options for literal or context-adaptive reuse. In our comparative study (N=12), participants using IdeaBlocks explored 2.13 times more images with 12.5% greater visual diversity than the baseline, demonstrating how structured intent expression and reuse support divergent exploration. A three-day longitudinal study (N=6) further revealed how different reuse mechanisms allowed distinct creative strategies, offering design implications for future intent-aware design support tools.

Generative AI shifts interaction toward intent-based outcome specification, despite inherently vague, fluid, and evolving intents. While HCI research has proposed diverse interaction techniques to support this process, how key aspects of intent communication interplay to shape users’ workflows remains underexplored. To bridge this gap, we conduct a systematic literature review of 46 HCI papers and identify four core aspects of intent communication support: intent • Articulation, • Exploration, • Management, and • Synchronization. To investigate how these aspects interplay in practice, we developed IntentFlow, a research probe that embodies all four aspects for a writing task, and conducted a comparative study (N=12). Our action-level behavioral analysis reveals that comprehensive support enables verification-driven refinement and progressive intent curation, reduces cognitive effort, and improves users’ sense of control and understanding of intent–output alignment. We conclude with design implications for building generative AI systems that support intent communication as a dynamic, iterative process.

User interface (UI) design is an iterative process in which designers progressively refine their work with design software such as Figma or Sketch. Recent advances in vision–language models (VLMs) with tool invocation suggest these models can operate design software to edit a UI design through iteration. Understanding and enhancing this capacity is important, as it highlights VLMs’ potential to collaborate with designers within conventional software. However, as no existing benchmark evaluates tool-based design performance, the capacity remains unknown. To address this, we introduce CANVAS, a benchmark for VLMs on tool-based user interface design. Our benchmark contains 598 tool-based design tasks paired with ground-truth references sampled from 3.3K mobile UI designs across 30 function-based categories (e.g., onboarding, messaging). In each task, a VLM updates the design step-by-step through context-based tool invocations (e.g., create a rectangle as a button background), linked to design software. Specifically, CANVAS incorporates two task types: (i) design replication evaluates the ability to reproduce a whole UI screen; (ii) design modification evaluates the ability to modify a specific part of an existing screen. Results suggest that leading models exhibit more strategic tool invocations, improving design quality. Furthermore, we identify common error patterns models exhibit, guiding future work in enhancing tool-based design capabilities.

Video-based learning (VBL) has become a dominant method for learning practical skills, yet accessibility guidelines provide limited guidance for users with cognitive differences. In particular, challenges that individuals with Borderline Intellectual Functioning (BIF) encounter in video-based learning remain largely underexplored, despite VBL’s potential to support their learning through features like self-paced viewing and visual demonstration. To address this gap, we conducted a series of studies with BIF individuals and caretakers to comprehensively understand their VBL challenges. Our analysis revealed challenges stemming from misalignment between user cognitive characteristics and video elements (e.g., overwhelmed by pacing and density, difficulty inferring omitted content), and experiential factors intensifying challenges (e.g., low self-efficacy). While participants employed coping strategies such as repetitive viewing to address these challenges, these strategies could not overcome fundamental gaps with video. We further discuss the design implications on both content and UI-level features for BIF and broader groups with cognitive diversities.

Large language models (LLMs) are promising tools for scaffolding students’ English writing skills, but their effectiveness in real-time K-12 classrooms remains underexplored. Addressing this gap, our study examines the benefits and limitations of using LLMs as real-time learning support, considering how classroom constraints, such as diverse proficiency levels and limited time, affect their effectiveness. We conducted a deployment study with 157 eighth-grade students in a South Korean middle school English class over six weeks. Our findings reveal that while scaffolding improved students’ ability to compose grammatically correct sentences, this step-by-step approach demotivated lower-proficiency students and increased their system reliance. We also observed challenges to classroom dynamics, where extroverted students often dominated the teacher’s attention, and the system’s assistance made it difficult for teachers to identify struggling students. Based on these findings, we discuss design guidelines for integrating LLMs into real-time writing classes as inclusive educational tools.

Large language models (LLMs) are increasingly used as collaborative partners in writing. However, this raises a critical challenge of authorship, as users and models jointly shape text across interaction turns. Understanding authorship in this context requires examining users’ evolving internal states during collaboration, particularly self-efficacy and trust. Yet, the dynamics of these states and their associations with users’ prompting strategies and authorship outcomes remain underexplored. We examined these dynamics through a study of 302 participants in LLM-assisted writing, capturing interaction logs and turn-by-turn self-efficacy and trust ratings. Our analysis showed that collaboration generally decreased users’ self-efficacy while increasing trust. Participants who lost self-efficacy were more likely to ask the LLM to edit their work directly, whereas those who recovered self-efficacy requested more review and feedback. Furthermore, participants with stable self-efficacy showed higher actual and perceived authorship of the final text. Based on these findings, we propose design implications for understanding and supporting authorship in human-LLM collaboration.

Capturing professionals’ decision-making in creative workflows (e.g., UI/UX) is essential for reflection, collaboration, and knowledge sharing, yet existing methods often leave rationales incomplete and implicit decisions hidden. To address this, we present the Clear approach, which structures reasoning into cognitive decision steps—linked units of actions, artifacts, and explanations making decisions traceable with generative AI. Building on Clear, we introduce ClearFairy, a think-aloud AI assistant for UI design that detects weak explanations, asks lightweight clarifying questions, and infers missing rationales. In a study with twelve professionals, 85% of ClearFairy’s inferred rationales were accepted (as-is or with revisions). Notably, the system increased “strong explanations”—rationales providing sufficient causal reasoning—from 14% to 83% without adding cognitive demand. Furthermore, exploratory applications demonstrate that captured steps can enhance generative AI agents in Figma, yielding predictions better aligned with professionals and producing coherent outcomes. We release a dataset of 417 decision steps to support future research.

An interactive vignette is a popular and immersive visual storytelling approach that invites viewers to role-play a character and influences the narrative in an interactive environment. However, it has not been widely used by everyday storytellers yet due to authoring complexity, which conflicts with the immediacy of everyday storytelling. We introduce DiaryPlay, an AI-assisted authoring system for interactive vignette creation in everyday storytelling. It takes a natural language story as input and extracts the three core elements of an interactive vignette (environment, characters, and events), enabling authors to focus on refining these elements instead of constructing them from scratch. Then, it automatically transforms the single-branch story input into a branch-and-bottleneck structure using an LLM-powered narrative planner, which enables flexible viewer interactions while freeing the author from multi-branching. A technical evaluation (N=16) shows that DiaryPlay-generated character activities are on par with human-authored ones regarding believability. A user study (N=16) shows that DiaryPlay effectively supports authors in creating interactive vignette elements, maintains authorial intent while reacting to viewer interactions, and provides engaging viewing experiences.

From purchasing a gift to deciding on a hobby, unfamiliar decisions—decisions without domain knowledge and experience—are frequent and significant. The complexity and uncertainty of such decisions demand unique approaches to information seeking, understanding, and decision-making. Our formative study highlights that in the current workflow, users want to start by discovering broad and relevant domain information evenly and simultaneously, quickly address emerging inquiries, and gain personalized standards to assess information found. We present ChoiceMates, an interactive multi-agent system designed to address these needs by enabling users to engage with a dynamic set of LLM agents each presenting a unique experience in the domain. Unlike existing multi-agent systems that automate tasks with agents, the user orchestrates agents to assist their decision-making process in each turn, through chatting with all agents, with a tagged subset of agents, or calling in new agents into the space. By comparing ChoiceMates with a web search condition and a multi-agent framework (n=12), we show that ChoiceMates enables a more confident, satisfactory decision-making with better situation understanding than web search, and higher decision quality than a commercial multi-agent framework. We further illustrate how participants utilized ChoiceMates to make unfamiliar decisions, providing insights into designing a more controllable and collaborative multi-agent system.

Social platforms have expanded opportunities for deliberation with the comments being used to inform one’s opinion. However, using such information to form opinions is challenged by unsubstantiated or false content. To enhance the quality of opinion formation and potentially confer resistance to misinformation, we developed Iffy-Or-Not ( ION ), a browser extension that seeks to invoke critical thinking when reading texts. With three features guided by argumentation theory, ION highlights fallacious content, suggests diverse queries to probe them with, and offers deeper questions to consider and chat with others about. From a user study ( \(N=18\) ), we found that ION encourages users to be more attentive to the content, suggests queries that align with or are preferable to their own, and poses thought-provoking questions that expands their perspectives. However, some participants expressed aversion to ION due to misalignments with their information goals and thinking predispositions. Potential backfiring effects with ION are discussed.

Peer Reviewed

In-game team communication in online multiplayer games has shown the potential to foster efficient collaboration and positive social interactions. Yet players often associate communication within ad hoc teams with frustration and wariness. Though previous works have quantitatively analyzed communication patterns at scale, few have identified the motivations of how a player makes in-the-moment communication decisions. In this paper, we conducted an observation study with 22 League of Legends players by interviewing them during Solo Ranked games on their use of four in-game communication media (chat, pings, emotes, votes). We performed thematic analysis to understand players' in-context assessment and perception of communication attempts. We demonstrate that players evaluate communication opportunities on proximate game states bound by player expectations and norms. Our findings illustrate players' tendency to view communication, regardless of its content, as a precursor to team breakdowns. We build upon these findings to motivate effective player-oriented communication design in online games.

Hyungyu Shin, Jingyu Tang, Yoonjoo Lee, Nayoung Kim, Hyunseung Lim, Ji Yong Cho, Hwajung Hong, Moontae Lee, Juho Kim. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025.

Tutorial videos are a valuable resource for people looking to learn new tasks. People often learn these skills by viewing multiple tutorial videos to get an overall understanding of a task by looking at different approaches to achieve the task. However, navigating through multiple videos can be time-consuming and mentally demanding as these videos are scattered and not easy to skim. We propose VideoMix, a system that helps users gain a holistic understanding of a how-to task by aggregating information from multiple videos on the task. Insights from our formative study (N=12) reveal that learners value understanding potential outcomes, required materials, alternative methods, and important details shared by different videos. Powered by a Vision-Language Model pipeline, VideoMix extracts and organizes this information, presenting concise textual summaries alongside relevant video clips, enabling users to quickly digest and navigate the content. A comparative user study (N=12) demonstrated that VideoMix enabled participants to gain a more comprehensive understanding of tasks with greater efficiency than a baseline video interface, where videos are viewed independently. Our findings highlight the potential of a task-oriented, multi-video approach where videos are organized around a shared goal, offering an enhanced alternative to conventional video-based learning.

Peer Reviewed

Patent examination remains an ongoing challenge in the NLP literature even after the advent of large language models (LLMs), as it requires an extensive yet nuanced human judgment on whether a submitted $\textit{claim}$ meets the statutory standards of $\textit{novelty}$ and $\textit{non-obviousness}$ against previously granted claims—$\textit{prior art}$—in expert domains. Previous NLP studies have approached this challenge as a prediction task (e.g., forecasting grant outcomes) with high-level proxies such as similarity metrics or classifiers trained on historical labels. However, this approach often overlooks the step-by-step evaluations that examiners must make with profound information, including rationales for the decisions provided in $\textit{office actions}$ documents, which also makes it harder to measure the current state of techniques in patent review processes. To fill this gap, we construct PANORAMA, a dataset of 8,143 U.S. patent examination records that preserves the full decision trails, including original applications, all cited references, $\textit{Non-Final Rejections}$, and $\textit{Notices of Allowance}$. Also, PANORAMA decomposes the trails into sequential benchmarks that emulate patent professionals' patent review processes and allow researchers to examine large language models' capabilities at each step of them. Our findings indicate that, although LLMs are relatively effective at retrieving relevant prior art and pinpointing the pertinent paragraphs, they struggle to assess the novelty and non-obviousness of patent claims. We discuss these results and argue that advancing NLP, including LLMs, in the patent domain requires a deeper understanding of real-world patent examination. Our dataset is openly available at https://huggingface.co/datasets/LG-AI-Research/PANORAMA.

Social media users employ a variety of methods to avoid unwanted content in their personalized feeds. Platforms like Instagram and YouTube offer the ''Not Interested'' feature, allowing users to signal their preference to see less of certain content or similar types. Despite its wide availability on social media platforms, the ''Not Interested'' feature has received little academic attention, leaving a gap in understanding how it is perceived, used, and interpreted. This study investigates (1) the types of content users mark as ''Not Interested'', (2) their expectations about its effects, and (3) how they interpret its outcome. We conducted semi-structured interviews with 28 Instagram users who had used the ''Not Interested'' button more than 12 times over the past year, focusing on their experiences with this feature. Users used the ''Not Interested'' feature to avoid different types of content, ranging from problematic and personally discomforting to unrewarding. ''Not Interested'' feedback was considered unique in that it would remove similar content from their feeds without harming the creator. Users had mixed expectations about whose feed they wished their feedback to have an impact on. After submitting the feedback and observing the changes in the personalized feed, they were often uncertain of how each of their interactions was reflected in the algorithm. We discuss user characteristics that social media platforms need to consider, including diverse motivations related to content avoidance, users' demands to influence a broad range of content curation, and the need for granular control and deeper understanding of personalized algorithms.

Celebrities have used their imitated voices in media content for various purposes to entertain audiences. However, it is unclear how their fans perceive media content using synthetic celebrity voices. Fans are dedicated to celebrities and actively communicate with them on social media, sometimes resulting in the establishment of parasocial relationships. Focusing on simulated voice messages that mimic celebrities' communication behaviors, we explored how fans form parasocial relationships through synthetic media content. We conducted semi-structured interviews with 15 fans who used AI Private Call, an AI-based voice message service that employed K-Pop idols' synthetic voices. Findings show that the nature of synthetic voices could not provide impressive experiences related to their idols. However, participants believe that simulated voice messages can help make up for the limited opportunities to interact directly with idols. Based on the theoretical framework of parasocial relationship development, participants engaged with simulated voice messages, exhibiting various cognitive and behavioral actions that dynamically advanced the relationship. These behaviors include aligning voice content with idols' vocal characteristics, adhering to social norms in interactions, and confirming idols' involvement in content creation to demonstrate a sense of authenticity. Additionally, their communities associated with fandoms affect their experiences throughout the process. Based on the findings, we discuss how simulated voices might impact the relationship between fans and celebrities, highlighting the responsibility of communities and platforms. We also propose design guidelines for creating simulated celebrity voice content that fans can entertain.

One common asset of metaverse is that users can freely explore places and actions without linear procedures. Thus, it is hard yet important to understand the divergent challenges each user faces when onboarding metaverse. Our formative study (N = 16) shows that first-time users ask questions about metaverse that concern 1) a short-term spatiotemporal context, regarding the user’s current location, recent conversation, and actions, and 2) a long-term exploration context regarding the user’s experience history. Based on the findings, we present PICAN, a Large Language Model-based pipeline that generates context-aware answers to users when onboarding metaverse. An ablation study (N = 20) reveals that PICAN’s usage of context made responses more useful and immersive than those generated without contexts. Furthermore, a user study (N = 21) shows that the use of long-term exploration context promotes users’ learning about the locations and activities within the virtual environment.

Clients often partner with AI experts to develop AI applications tailored to their needs. In these partnerships, careful planning and clear communication are critical, as inaccurate or incomplete specifications can result in misaligned model characteristics, expensive reworks, and potential friction between collaborators. Unfortunately, given the complexity of requirements ranging from functionality, data, and governance, effective guidelines for collaborative specification of requirements in client-AI expert collaborations are missing. In this work, we introduce AINeedsPlanner, a workbook that AI experts and clients can use to facilitate effective interchange of clear specifications. The workbook is based on (1) an interview of 10 completed AI application project teams, which identifies and characterizes steps in AI application planning and (2) a study with 12 AI experts, which defines a taxonomy of AI experts’ information needs and dimensions that affect the information needs. Finally, we demonstrate the workbook’s utility with two case studies in real-world settings.

Research promotion enables researchers to share advanced knowledge with pertinent academic communities. The question-and-answer (QA) style articles are effective for researchers to promote their research by enabling readers to understand research on complex subjects. Recent advances in large language models (LLMs) have opened avenues for supporting researchers in creating QA-style articles for research promotion. However, without the authors’ involvement, these models may only partially capture the researcher’s intention and voice. We developed AQUA, a research probe that enables researchers to co-create QA-style articles with LLMs to promote their research papers. A user study (n=12) reveals that LLMs reduced authors’ burden and helped them understand the readers’ perspectives. Nevertheless, LLMs failed to capture the unique intent of the authors, and their automated generation discouraged authors from carefully revising their answers. Based on our findings, we discuss human-LLM interaction design to enable authors to create QA-style articles that reflect their intention.

By simply composing prompts, developers can prototype novel generative applications with Large Language Models (LLMs). To refine prototypes into products, however, developers must iteratively revise prompts by evaluating outputs to diagnose weaknesses. Formative interviews (N=8) revealed that developers invest significant effort in manually evaluating outputs as they assess context-specific and subjective criteria. We present EvalLM, an interactive system for iteratively refining prompts by evaluating multiple outputs on user-defined criteria. By describing criteria in natural language, users can employ the system’s LLM-based evaluator to get an overview of where prompts excel or fail, and improve these based on the evaluator’s feedback. A comparative study (N=12) showed that EvalLM, when compared to manual evaluation, helped participants compose more diverse criteria, examine twice as many outputs, and reach satisfactory prompts with 59% fewer revisions. Beyond prompts, our work can be extended to augment model evaluation and alignment in specific application contexts.

Graphic designers often get inspiration through the recombination of references. Our formative study (N=6) reveals that graphic designers focus on conceptual keywords during this process, and want support for discovering the keywords, expanding them, and exploring diverse recombination options of them, while still having room for designers’ creativity. We propose CreativeConnect, a system with generative AI pipelines that helps users discover useful elements from the reference image using keywords, recommends relevant keywords, generates diverse recombination options with user-selected keywords, and shows recombinations as sketches with text descriptions. Our user study (N=16) showed that CreativeConnect helped users discover keywords from the reference and generate multiple ideas based on them, ultimately helping users produce more design ideas with higher self-reported creativity, compared to the baseline system without generative pipelines. While CreativeConnect was shown effective in ideation, we discussed how CreativeConnect can be extended to support other types of tasks in creativity support.

The lengthy monologue-style online lectures cause learners to lose engagement easily. Designing lectures in a “vicarious dialogue” format can foster learners’ cognitive activities more than monologue-style. However, designing online lectures in a dialogue style catered to the diverse needs of learners is laborious for instructors. We conducted a design workshop with eight educational experts and seven instructors to present key guidelines and the potential use of large language models (LLM) to transform a monologue lecture script into pedagogically meaningful dialogue. Applying these design guidelines, we created VIVID which allows instructors to collaborate with LLMs to design, evaluate, and modify pedagogical dialogues. In a within-subjects study with instructors (N=12), we show that VIVID helped instructors select and revise dialogues efficiently, thereby supporting the authoring of quality dialogues. Our findings demonstrate the potential of LLMs to assist instructors with creating high-quality educational dialogues across various learning stages.

This work investigates large language models (LLMs) as teachable agents for learning by teaching (LBT). LBT with teachable agents helps learners identify knowledge gaps and discover new knowledge. However, teachable agents require expensive programming of subject-specific knowledge. While LLMs as teachable agents can reduce the cost, LLMs’ expansive knowledge as tutees discourages learners from teaching. We propose a prompting pipeline that restrains LLMs’ knowledge and makes them initiate “why” and “how” questions for effective knowledge-building. We combined these techniques into TeachYou, an LBT environment for algorithm learning, and AlgoBo, an LLM-based tutee chatbot that can simulate misconceptions and unawareness prescribed in its knowledge state. Our technical evaluation confirmed that our prompting pipeline can effectively configure AlgoBo’s problem-solving performance. Through a between-subject study with 40 algorithm novices, we also observed that AlgoBo’s questions led to knowledge-dense conversations (effect size=0.71). Lastly, we discuss design implications, cost-efficiency, and personalization of LLM-based teachable agents.

We introduce VL2NL, a Large Language Model (LLM) framework that generates rich and diverse NL datasets using Vega-Lite specifications as input, thereby streamlining the development of Natural Language Interfaces (NLIs) for data visualization. To synthesize relevant chart semantics accurately and enhance syntactic diversity in each NL dataset, we leverage 1) a guided discovery incorporated into prompting so that LLMs can steer themselves to create faithful NL datasets in a self-directed manner; 2) a score-based paraphrasing to augment NL syntax along with four language axes. We also present a new collection of 1,981 real-world Vega-Lite specifications that have increased diversity and complexity than existing chart collections. When tested on our chart collection, VL2NL extracted chart semantics and generated L1/L2 captions with 89.4% and 76.0% accuracy, respectively. It also demonstrated generating and paraphrasing utterances and questions with greater diversity compared to the benchmarks. Last, we discuss how our NL datasets and framework can be utilized in real-world scenarios. The codes and chart collection are available at https://github.com/hyungkwonko/chart-llm.

With the rapid growth of scholarly archives, researchers subscribe to “paper alert’’ systems that periodically provide them with recommendations of recently published papers that are similar to previously collected papers. However, researchers sometimes struggle to make sense of nuanced connections between recommended papers and their own research context, as existing systems only present paper titles and abstracts. To help researchers spot these connections, we present PaperWeaver, an enriched paper alerts system that provides contextualized text descriptions of recommended papers based on user-collected papers. PaperWeaver employs a computational method based on Large Language Models (LLMs) to infer users’ research interests from their collected papers, extract context-specific aspects of papers, and compare recommended and collected papers on these aspects. Our user study (N=15) showed that participants using PaperWeaver were able to better understand the relevance of recommended papers and triage them more confidently when compared to a baseline that presented the related work sections from recommended papers.

Despite the growing demand for professional graphic design knowledge, the tacit nature of design inhibits knowledge sharing. However, there is a limited understanding on the characteristics and instances of tacit knowledge in graphic design. In this work, we build a comprehensive set of tacit knowledge characteristics through a literature review. Through interviews with 10 professional graphic designers, we collected 123 tacit knowledge instances and labeled their characteristics. By qualitatively coding the instances, we identified the prominent elements, actions, and purposes of tacit knowledge. To identify which instances have been addressed the least, we conducted a systematic literature review of prior system support to graphic design. By understanding the reasons for the lack of support on these instances based on their characteristics, we propose design guidelines for capturing and applying tacit knowledge in design tools. This work takes a step towards understanding tacit knowledge, and how this knowledge can be communicated.

Designers rely on visual search to explore and develop ideas in early design stages. However, designers can struggle to identify suitable text queries to initiate a search or to discover images for similarity-based search that can adequately express their intent. We propose GenQuery, a novel system that integrates generative models into the visual search process. GenQuery can automatically elaborate on users’ queries and surface concrete search directions when users only have abstract ideas. To support precise expression of search intents, the system enables users to generatively modify images and use these in similarity-based search. In a comparative user study (N=16), designers felt that they could more accurately express their intents and find more satisfactory outcomes with GenQuery compared to a tool without generative features. Furthermore, the unpredictability of generations allowed participants to uncover more diverse outcomes. By supporting both convergence and divergence, GenQuery led to a more creative experience.

Benjamin Newman, Yoonjoo Lee, Aakanksha Naik, Pao Siangliulue, Raymond Fok, Juho Kim, Daniel S Weld, Joseph Chee Chang, Kyle Lo. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. 2024.

Evaluation of Large Language Models (LLMs) is challenging because instruction-following necessitates alignment with human values and the required set of skills varies depending on the instruction. However, previous studies have mainly focused on coarse-grained evaluation (i.e. overall preference-based evaluation), which limits interpretability since it does not consider the nature of user instructions that require instance-wise skill composition. In this paper, we introduce FLASK (Fine-grained Language Model Evaluation based on Alignment Skill Sets), a fine-grained evaluation protocol for both human-based and model-based evaluation which decomposes coarse-level scoring to a skill set-level scoring for each instruction. We experimentally observe that the fine-graininess of evaluation is crucial for attaining a holistic view of model performance and increasing the reliability of the evaluation. Using FLASK, we compare multiple open-source and proprietary LLMs and observe a high correlation between model-based and human-based evaluations.

Large language models (LLMs) with chat-based capabilities, such as ChatGPT, are widely used in various workflows. However, due to a limited understanding of these large-scale models, users struggle to use this technology and experience different kinds of dissatisfaction. Researchers have introduced several methods, such as prompt engineering, to improve model responses. However, they focus on enhancing the model’s performance in specific tasks, and little has been investigated on how to deal with the user dissatisfaction resulting from the model’s responses. Therefore, with ChatGPT as the case study, we examine users’ dissatisfaction along with their strategies to address the dissatisfaction. After organizing users’ dissatisfaction with LLM into seven categories based on a literature review, we collected 511 instances of dissatisfactory ChatGPT responses from 107 users and their detailed recollections of dissatisfactory experiences, which we released as a publicly accessible dataset. Our analysis reveals that users most frequently experience dissatisfaction when ChatGPT fails to grasp their intentions, while they rate the severity of dissatisfaction related to accuracy the highest. We also identified four tactics users employ to address their dissatisfaction and their effectiveness. We found that users often do not use any tactics to address their dissatisfaction, and even when using tactics, 72% of dissatisfaction remained unresolved. Moreover, we found that users with low knowledge of LLMs tend to face more dissatisfaction on accuracy while they often put minimal effort in addressing dissatisfaction. Based on these findings, we propose design implications for minimizing user dissatisfaction and enhancing the usability of chat-based LLM.

Statistical statements that refer to data to support narratives or claims are commonly used to inform readers about the magnitude of social issues. While contextualizing statistical statements with relevant data supports readers in building their own interpretation of statements, the complexity of finding contextual information on the web and linking statistical statements with it impedes readers’ efforts to do so. We present DataDive, an interactive tool for contextualizing statistical statements for the readers of online texts. Based on users’ selections of statistical statements, our tool uses an LLM-powered pipeline to generate candidates of relevant contexts and poses them as guiding questions to the user as potential contexts for exploration. When the user selects a question, DataDive employs visualizations to further help the user compare and explore contextually relevant data. A technical evaluation shows that DataDive generates important and diverse questions that facilitate exploration around statistical statements and retrieves relevant data for comparison. Moreover, a user study with 21 participants suggests that DataDive facilitates users to explore diverse contexts and to be more aware of how statistical data could relate to the text.

Label set construction—deciding on a group of distinct labels—is an essential stage in building a supervised machine learning (ML) application, as a badly designed label set negatively affects subsequent stages, such as training dataset construction, model training, and model deployment. Despite its significance, it is challenging for ML practitioners to come up with a well-defined label set, especially when no external references are available. Through our formative study (n=8), we observed that even with the help of external references or domain experts, ML practitioners still need to go through multiple iterations to gradually improve the label set. In this process, there exist challenges in collecting helpful feedback and utilizing it to make optimal refinement decisions. To support informed refinement, we present DynamicLabels, a system that aims to support a more informed label set-building process with crowd feedback. Crowd workers provide annotations and label suggestions to the ML practitioner’s label set, and the ML practitioner can review the feedback through multi-aspect analysis and refine the label set with crowd-made labels. Through a within-subjects study (n=16) using two datasets, we found that DynamicLabels enables better understanding and exploration of the collected feedback and supports a more structured and flexible refinement process. The crowd feedback helped ML practitioners explore diverse perspectives, spot current weaknesses, and shop from crowd-generated labels. Metrics and label suggestions in DynamicLabels helped in obtaining a high-level overview of the feedback, gaining assurance, and spotting surfacing conflicts and edge cases that could have been overlooked.

Informational videos serve as a crucial source for explaining conceptual and procedural knowledge to novices and experts alike. When producing informational videos, editors edit videos by overlaying text/images or trimming footage to enhance the video quality and make it more engaging. However, video editing can be difficult and time-consuming, especially for novice video editors who often struggle with expressing and implementing their editing ideas. To address this challenge, we first explored how multimodality—natural language (NL) and sketching, which are natural modalities humans use for expression—can be utilized to support video editors in expressing video editing ideas. We gathered 176 multimodal expressions of editing commands from 10 video editors, which revealed the patterns of use of NL and sketching in describing edit intents. Based on the findings, we present ExpressEdit, a system that enables editing videos via NL text and sketching on the video frame. Powered by LLM and vision models, the system interprets (1) temporal, (2) spatial, and (3) operational references in an NL command and spatial references from sketching. The system implements the interpreted edits, which then the user can iterate on. An observational study (N=10) showed that ExpressEdit enhanced the ability of novice video editors to express and implement their edit ideas. The system allowed participants to perform edits more efficiently and generate more ideas by generating edits based on user’s multimodal edit commands and supporting iterations on the editing commands. This work offers insights into the design of future multimodal interfaces and AI-based pipelines for video editing.

This paper explores the space of optimizing feedback mechanisms in complex domains such as data science, by combining two prevailing approaches: Artificial Intelligence (AI) and learnersourcing. Towards addressing the challenges posed by each approach, this work compares traditional learnersourcing with an AI-supported approach. We report on the results of a randomized controlled experiment conducted with 72 Master’s level students in a data visualization course, comparing two conditions: students writing hints independently versus revising hints generated by GPT-4. The study aimed to evaluate the quality of learnersourced hints, examine the impact of student performance on hint quality, gauge learner preference for writing hints with versus without AI support, and explore the potential of the student-AI collaborative exercise in fostering critical thinking about LLMs. Based on our findings, we provide insights for designing learnersourcing activities leveraging AI support and optimizing students’ learning as they interact with LLMs.

Personalized feedback, tailored to students' needs and prior knowledge, is essential for fostering mathematical problem-solving skills. However, personalized feedback is often limited to one-to-one tutoring or small classrooms as it requires instructors' in-depth diagnosis of cognitive processes employed in students' answers. We propose a large language model (LLM) pipeline that diagnoses students' problem-solving skills from their answers at scale in elementary school math word problems. Based on prior literature and an interview with a math education expert, we developed PERC, a framework composed of four problem-solving stages that students can follow: Parse, Extract, Retrieve, and Combine. The framework facilitates diagnosis by externalizing students' step-by-step problem-solving processes and allowing our pipeline to analyze each stage individually. Our LLM pipeline diagnoses each stage by (1) generating rubrics and (2) comparing students' answers with the rubrics. We fine-tuned our LLM pipeline with 71 math problem-rubric pairs and 128 problem-answer-grade triplets collected from elementary school students. We evaluated our pipeline's diagnosis accuracy against vanilla GPT-3.5 and vanilla GPT-4 with automatic and expert evaluations. The results showed the potential of our approach in improving the end-to-end diagnosis accuracy of LLMs, and expert evaluation provided specific aspects that should be improved.

aendees to leave the workshop with a practical understanding of how to engage with learnersourcing.Participants will get hands-on experience with current tools,

Nayeon Lee, Chani Jung, Junho Myung, Jiho Jin, Jose Camacho-Collados, Juho Kim, Alice Oh. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024.

Educational live streaming has become a complement to in-person teaching. While synchronous instructor-learner communication is useful, the technology-mediated nature of live streaming can obscure many interaction cues (e.g., learners' facial expressions and body language), which dampens the instructors' ability to respond to remote learners' needs. We explore the opportunity of leveraging real-time transcripts generated from instructors' audio as a basis for re-creating interaction cues. Transcripts can be leveraged to reveal the content of live streams in a form that learners can trace back and annotate, and such annotations can be further aggregated and presented to instructors as signals to assist them in tracking learners' engagement. By designing and evaluating our proof-of-concept prototype system, EduLive, we show that instructors benefited from the summative information extracted from learners' annotations, and the context provided by the transcript enhanced their ability to answer learners' questions. Our system contributes to the design space of social annotations in CSCW by employing social annotations in educational live streaming scenarios.

Subgoal-labeled code examples help learners understand code patterns and apply them to different problem contexts. Subgoal labels are multi-level in nature and based on goal structures that define the hierarchical functional units in code. Data-driven methods and experts can supply the goal structures, but they do not work in environments with scarce data and limited availability of experts. Previous research has shown that learnersourcing is effective for sourcing high-quality subgoal labels of given goal structures. We extend this research by learnersourcing goal structures themselves, thereby making the generation of subgoal-labeled materials fully learner-driven. We introduce CodeTree, a system that generates multi-level goal structures by aggregating learner-generated subgoals from two subgoal learning activities---Generation and Selection. In a between-subjects study, 45 novices studied three code examples with either CodeTree or code explanations alone. The results showed that CodeTree could learnersource high-quality goal structures and subgoal labels for all three examples with just five learners. Learners reported a significantly higher learning gain and satisfaction compared to the baseline.

Online harassment, especially networked harassment at scale, has become an increasingly serious issue that pervades many social media platforms. In this study, we investigated the nature and harms of networked harassment on Twitter through design workshops (n = 11) and developed a set of design goals focusing on empowering the individual to fight back against harassment. We designed Re:SPect, an anti-harassment tool promoting scalable and active responses to networked harassment. We evaluated Re:SPect through a simulated scenario-based study with Twitter users (n = 18) who had directly or indirectly experienced networked harassment. Our findings reveal that users felt safer and more empowered as Re:SPect enabled them to manage interactions with a larger audience. Users felt less anxious about the potential of being harassed, while the summarization features of Re:SPect allowed users to perceive the situation more objectively. Based on the findings, we discuss how Re:SPect's features could be utilized in promoting healthier online discussion, as well as theoretical implications in designing such systems.

Research suggests that automatic speech recognition (ASR) systems, which automatically convert speech to text, show different performances according to various input classes (e.g., accent, age), requiring attention to building fairer AI systems that would perform similarly across various input classes. However, would an AI system with the same performance regardless of input classes really be perceived as fair enough? To this end, we investigate how listeners perceive the ASR system of the same result differently according to whether the speaker is a native speaker (NS) or a non-native speaker (NNS), which may lead to unfair situations. We conducted a study (n = 420), where participants were given one of the ten speech recordings with various accents of the same script along with the same captions. We found that even with the same ASR output, listeners perceive the ASR results differently. They found captions to be more useful for NNS's speech and blamed NNS more for the errors than NS. Based on the findings, we present design implications suggesting that we should take a step further than just achieving the same performance across various input classes to build a fair ASR system.

Recent work has shown that when both the chart and caption emphasize the same aspects of the data, readers tend to remember the doubly-emphasized features as takeaways; when there is a mismatch, readers rely on the chart to form takeaways and can miss information in the caption text. Through a survey of 280 chart-caption pairs in real-world sources (e.g., news media, poll reports, government reports, academic articles, and Tableau Public), we find that captions often do not emphasize the same information in practice, which could limit how effectively readers take away the authors' intended messages. Motivated by the survey findings, we present EMPHASISCHECKER, an interactive tool that highlights visually prominent chart features as well as the features emphasized by the caption text along with any mismatches in the emphasis. The tool implements a time-series prominent feature detector based on the Ramer-Douglas-Peucker algorithm and a text reference extractor that identifies time references and data descriptions in the caption and matches them with chart data. This information enables authors to compare features emphasized by these two modalities, quickly see mismatches, and make necessary revisions. A user study confirms that our tool is both useful and easy to use when authoring charts and captions.

Online videos are a promising medium for older adults to learn. Yet, few studies have investigated what, how, and why they learn through online videos. In this study, we investigated older adults’ motivation, watching patterns, and difficulties in using online videos for learning by (1) running interviews with 13 older adults and (2) analyzing large-scale video event logs (N=41.8M) from a Korean Massive Online Open Course (MOOC) platform. Our results show that older adults (1) are motivated to learn practical topics, leading to less consumption of STEM domains than non-older adults, (2) watch videos with less interaction and watch a larger portion of a single video compared to non-older adults, and (3) face various difficulties (e.g., inconvenience arisen due to their unfamiliarity with technologies) that limit their learning through online videos. Based on the findings, we propose design guidelines for online videos and platforms targeted to support older adults’ learning.

In many creator economy platforms, algorithms significantly impact creators’ practices and decisions about their creative expression and monetization. Emerging research suggests that the opacity of the algorithm and platform policies often distract creators from their creative endeavors. To study how algorithmic platforms can be more ‘creator-friendly,’ we conducted a mixed-methods study: interviews (N=14) and a participatory design workshop (N=12) with YouTube creators. Through the interviews, we found how creators’ folk theories of the curation algorithm impact their work strategies — whether they choose to work with or against the algorithm — and the associated challenges in the process. In the workshop, creators explored solution ideas to overcome the aforementioned challenges, such as fostering diverse and creative expressions, achieving success as a creator, and motivating creators to continue their job. Based on these findings, we discuss design opportunities for how algorithmic platforms can support and motivate creators to sustain their creative work.

Video is an effective medium for learning procedural knowledge, such as surgical techniques. However, learning procedural knowledge through videos remains difficult due to limited access to procedural structures of knowledge (e.g., compositions and ordering of steps) in a large-scale video dataset. We present Surch, a system that enables structural search and comparison of surgical procedures. Surch supports video search based on procedural graphs generated by our clustering workflow capturing latent patterns within surgical procedures. We used vectorization and weighting schemes that characterize the features of procedures, such as recursive structures and unique paths. Surch enhances cross-video comparison by providing video navigation synchronized by surgical steps. Evaluation of the workflow demonstrates the effectiveness and interpretability (Silhouette score = 0.82) of our clustering for surgical learning. A user study with 11 residents shows that our system significantly improves the learning experience and task efficiency of video search and comparison, especially benefiting junior residents.

Children acquire an understanding of the world by asking “why” and “how” questions. Conversational agents (CAs) like smart speakers or voice assistants can be promising respondents to children’s questions as they are more readily available than parents or teachers. However, CAs’ answers to “why” and “how” questions are not designed for children, as they can be difficult to understand and provide little interactivity to engage the child. In this work, we propose design guidelines for creating interactive dialogues that promote children’s engagement and help them understand explanations. Applying these guidelines, we propose DAPIE, a system that answers children’s questions through interactive dialogue by employing an AI-based pipeline that automatically transforms existing long-form answers from online sources into such dialogues. A user study (N=16) showed that, with DAPIE, children performed better in an immediate understanding assessment while also reporting higher enjoyment than when explanations were presented sentence-by-sentence.

Despite the common use of rule-based tools for online content moderation, human moderators still spend a lot of time monitoring them to ensure they work as intended. Based on surveys and interviews with Reddit moderators who use AutoModerator, we identified the main challenges in reducing false positives and false negatives of automated rules: not being able to estimate the actual effect of a rule in advance and having difficulty figuring out how the rules should be updated. To address these issues, we built ModSandbox, a novel virtual sandbox system that detects possible false positives and false negatives of a rule and visualizes which part of the rule is causing issues. We conducted a comparative, between-subject study with online content moderators to evaluate the effect of ModSandbox in improving automated rules. Results show that ModSandbox can support quickly finding possible false positives and false negatives of automated rules and guide moderators to improve them to reduce future errors.

How-to videos are rich in information—they not only give instructions but also provide justifications or descriptions. People seek different information to meet their needs, and identifying different types of information present in the video can improve access to the desired knowledge. Thus, we present a taxonomy of information types in how-to videos. Through an iterative open coding of 4k sentences in 48 videos, 21 information types under 8 categories emerged. The taxonomy represents diverse information types that instructors provide beyond instructions. We first show how our taxonomy can serve as an analytical framework for video navigation systems. Then, we demonstrate through a user study (n=9) how type-based navigation helps participants locate the information they needed. Finally, we discuss how the taxonomy enables a wide range of video-related tasks, such as video authoring, viewing, and analysis. To allow researchers to build upon our taxonomy, we release a dataset of 120 videos containing 9.9k sentences labeled using the taxonomy.

Large-scale Text-to-image Generation Models (LTGMs) (e.g., DALL-E), self-supervised deep learning models trained on a huge dataset, have demonstrated the capacity for generating high-quality open-domain images from multi-modal input. Although they can even produce anthropomorphized versions of objects and animals, combine irrelevant concepts in reasonable ways, and give variation to any user-provided images, we witnessed such rapid technological advancement left many visual artists disoriented in leveraging LTGMs more actively in their creative works. Our goal in this work is to understand how visual artists would adopt LTGMs to support their creative works. To this end, we conducted an interview study as well as a systematic literature review of 72 system/application papers for a thorough examination. A total of 28 visual artists covering 35 distinct visual art domains acknowledged LTGMs’ versatile roles with high usability to support creative works in automating the creation process (i.e., automation), expanding their ideas (i.e., exploration), and facilitating or arbitrating in communication (i.e., mediation). We conclude by providing four design guidelines that future researchers can refer to in making intelligent user interfaces using LTGMs.

The integration of generative AI in the field of education is actively being explored. In particular, ChatGPT has garnered significant interest, offering an opportunity to examine its effectiveness in English as a foreign language (EFL) education. To address this need, we present a novel learning platform called RECIPE (Revising an Essay with ChatGPT on an Interactive Platform for EFL learners). Our platform features two types of prompts that facilitate conversations between ChatGPT and students: (1) a hidden prompt for ChatGPT to take an EFL teacher role and (2) an open prompt for students to initiate a dialogue with a self-written summary of what they have learned. We deployed this platform for 213 undergraduate and graduate students enrolled in EFL writing courses and seven instructors. For this study, we collect students' interaction data from RECIPE, including students' perceptions and usage of the platform, and user scenarios are examined with the data. We also conduct a focus group interview with six students and an individual interview with one EFL instructor to explore design opportunities for leveraging generative AI models in the field of EFL education.

Large Language Models (LLMs) have become the backbone of numerous writing interfaces with the goal of supporting end-users across diverse writing tasks. While LLMs reduce the effort of manual writing, end-users may need to experiment and iterate with various generation configurations (e.g., inputs and model parameters) until results meet their goals. However, these interfaces are not designed for experimentation and iteration, and can restrict how end-users track, compare, and combine configurations. In this work, we present “cells, generators, and lenses”, a framework to designing interfaces that support interactive objects that embody configuration components (i.e., input, model, output). Interface designers can apply our framework to produce interfaces that enable end-users to create variations of these objects, combine and recombine them into new configurations, and compare them in parallel to efficiently iterate and experiment with LLMs. To showcase how our framework generalizes to diverse writing tasks, we redesigned three different interfaces—story writing, copywriting, and email composing—and, to demonstrate its effectiveness in supporting end-users, we conducted a comparative study (N=18) where participants used our interactive objects to generate and experiment more. Finally, we investigate the usability of the framework through a workshop with designers (N=3) where we observed that our framework served as both bootstrapping and inspiration in the design process.

End-users can potentially style and customize websites by editing them through in-browser developer tools. Unfortunately, end-users lack the knowledge needed to translate high-level styling goals into low-level code edits. We present Stylette, a browser extension that enables users to change the style of websites by expressing goals in natural language. By interpreting the user’s goal with a large language model and extracting suggestions from our dataset of 1.7 million web components, Stylette generates a palette of CSS properties and values that the user can apply to reach their goal. A comparative study (N=40) showed that Stylette lowered the learning curve, helping participants perform styling changes 35% faster than those using developer tools. By presenting various alternatives for a single goal, the tool helped participants familiarize themselves with CSS through experimentation. Beyond CSS, our work can be expanded to help novices quickly grasp complex software or programming languages.

Designing solution plans before writing code is critical for successful algorithmic problem-solving. Novices, however, often plan on-the-fly during implementation, resulting in unsuccessful problem-solving due to lack of mental organization of the solution. Research shows that subgoal learning helps learners develop more complete solution plans by enhancing their understanding of the high-level solution structure. However, expert-created materials such as subgoal labels are necessary to provide learning benefits from subgoal learning, which are a scarce resource in self-learning due to limited availability and high cost. We propose a learnersourcing workflow that collects high-quality subgoal labels from learners by helping them improve their label quality. We implemented the workflow into AlgoSolve, a prototype interface that supports subgoal learning for algorithmic problems. A between-subjects study with 63 problem-solving novices revealed that AlgoSolve helped learners create higher-quality labels and more complete solution plans, compared to a baseline method known to be effective in subgoal learning.

Webtoon is a type of digital comics read online where readers can leave comments to share their thoughts on the story. While it has experienced a surge in popularity internationally, people with visual impairments cannot enjoy webtoon with the lack of an accessible format. While traditional image description practices can be adopted, resulting descriptions cannot preserve webtoons’ unique values such as control over the reading pace and social engagement through comments. To improve the webtoon reading experience for BLV users, we propose Cocomix, an interactive webtoon reader that leverages comments into the design of novel webtoon interactions. Since comments can identify story highlights and provide additional context, we designed a system that provides 1) comments-based adaptive descriptions with selective access to details and 2) panel-anchored comments for easy access to relevant descriptive comments. Our evaluation (N=12) showed that Cocomix users could adapt the description for various needs and better utilize comments.

Mobile video-based learning attracts many learners with its mobility and ease of access. However, most lectures are designed for desktops. Our formative study reveals mobile learners’ two major needs: more readable content and customizable video design. To support mobile-optimized learning, we present FitVid, a system that provides responsive and customizable video content. Our system consists of (1) an adaptation pipeline that reverse-engineers pixels to retrieve design elements (e.g., text, images) from videos, leveraging deep learning with a custom dataset, which powers (2) a UI that enables resizing, repositioning, and toggling in-video elements. The content adaptation improves the guideline compliance rate by 24% and 8% for word count and font size. The content evaluation study (n=198) shows that the adaptation significantly increases readability and user satisfaction. The user study (n=31) indicates that FitVid significantly improves learning experience, interactivity, and concentration. We discuss design implications for responsive and customizable video adaptation.

Most video-based learning content is designed for desktops without considering mobile environments. We (1) investigate the gap between mobile learners’ challenges and video engineers’ considerations using mixed methods and (2) provide design guidelines for creating mobile-friendly MOOC videos. To uncover learners’ challenges, we conducted a survey (n=134) and interviews (n=21), and evaluated the mobile adequacy of current MOOCs by analyzing 41,722 video frames from 101 video lectures. Interview results revealed low readability and situationally-induced impairments as major challenges. The content analysis showed a low guideline compliance rate for key design factors. We then interviewed 11 video production engineers to investigate design factors they mainly consider. The engineers mainly focus on the size and amount of content while lacking consideration for color, complex images, and situationally-induced impairments. Finally, we present and validate guidelines for designing mobile-friendly MOOCs, such as providing adaptive and customizable visual design and context-aware accessibility support.

Online learners are hugely diverse with varying prior knowledge, but most instructional videos online are created to be one-size-fits-all. Thus, learners may struggle to understand the content by only watching the videos. Providing scaffolding prompts can help learners overcome these struggles through questions and hints that relate different concepts in the videos and elicit meaningful learning. However, serving diverse learners would require a spectrum of scaffolding prompts, which incurs high authoring effort. In this work, we introduce Promptiverse, an approach for generating diverse, multi-turn scaffolding prompts at scale, powered by numerous traversal paths over knowledge graphs. To facilitate the construction of the knowledge graphs, we propose a hybrid human-AI annotation tool, Grannotate. In our study (N=24), participants produced 40 times more on-par quality prompts with higher diversity, through Promptiverse and Grannotate, compared to hand-designed prompts. Promptiverse presents a model for creating diverse and adaptive learning experiences online.

Live streams usually last several hours with many viewers joining in the middle. Viewers who join in the middle often want to understand what has happened in the stream. However, catching up with the earlier parts is challenging because it is difficult to know which parts are important in the long, unedited stream while also keeping up with the ongoing stream. We present CatchLive, a system that provides a real-time summary of ongoing live streams by utilizing both the stream content and user interaction data. CatchLive provides viewers with an overview of the stream along with summaries of highlight moments with multiple levels of detail in a readable format. Results from deployments of three streams with 67 viewers show that CatchLive helps viewers grasp the overview of the stream, identify important moments, and stay engaged. Our findings provide insights into designing summarizations of live streams reflecting their characteristics.

Many people rely on tutorial videos when learning to perform tasks using complex software. Watching the video for instructions and applying them to target software requires frequent going back-and-forth between the two, which incurs cognitive overhead. Furthermore, users need to constantly compare the two to see if they are following correctly, as they are prone to missing out on subtle differences. We propose SoftVideo, a prototype system that helps users plan ahead before watching each step in tutorial videos and provides feedback and help to users on their progress. SoftVideo is powered by collective interaction data, as experiences of previous learners with the same goal can provide insights into how they learned from the tutorial. By identifying the difficulty and relatedness of each step from the interaction logs, SoftVideo provides information on each step such as its estimated difficulty, lets users know if they completed or missed a step, and suggests tips such as relevant steps when it detects users struggling. To enable such a data-driven system, we collected and analyzed video interaction logs and the associated Photoshop usage logs for two tutorial videos from 120 users. We then defined six metrics that portray the difficulty of each step, including the time taken to complete a step and the number of pauses in a step, which were also used to detect users’ struggling moments by comparing their progress to the collected data. To investigate the feasibility and usefulness of SoftVideo, we ran a user study with 30 participants where they performed a Photoshop task by following along a tutorial video with SoftVideo. Results show that participants could proactively and effectively plan their pauses and playback speed, and adjust their concentration level. They were also able to identify and recover from errors with the help SoftVideo provides.

With the rise of the gig economy, online language tutoring platforms are becoming increasingly popular. They provide temporary and flexible jobs for native speakers as tutors and allow language learners to have one-on-one speaking practices on demand. However, the lack of stable relationships hinders tutors and learners from building long-term trust. “Distributed tutorship”—temporally discontinuous learning experience with different tutors—has been underexplored yet has many implications for modern learning platforms. In this paper, we analyzed tutorship sequences of 15,959 learners and found that around 40% of learners change to new tutors every session; 44% learners change to new tutors while reverting to previous tutors sometimes; only 16% learners change to new tutors and then fix on one tutor. We also found suggestive evidence that higher distributedness—higher diversity and lower continuity in tutorship—is correlated to slower improvements in speaking performance scores with a similar number of sessions. We further surveyed 519 and interviewed 40 learners and found that more learners preferred fixed tutorship while some do not have it due to various reasons. Finally, we conducted semi-structured interviews with three tutors and one product manager to discuss the implications for improving the continuity in learning under distributed tutorship.

With the rise of the gig economy, online language tutoring platforms are becoming increasingly popular. These platforms provide temporary and flexible jobs for native speakers as tutors and allow language learners to have one-on-one speaking practices on demand, on which learners occasionally practice the language with different tutors. With such distributed tutorship, learners can hold flexible schedules and receive diverse feedback. However, learners face challenges in consistently tracking their learning progress because different tutors provide feedback from diverse standards and perspectives, and hardly refer to learners' previous experiences with other tutors. We present RLens, a visualization system for facilitating learners' learning progress reflection by grouping different tutors' feedback, tracking how each feedback type has been addressed across learning sessions, and visualizing the learning progress. We validate our design through a between-subjects study with 40 real-world learners. Results show that learners can successfully analyze their progress and common language issues under distributed tutorship with RLens, while most learners using the baseline interface had difficulty achieving reflection tasks. We further discuss design considerations of computer-aided systems for supporting learning under distributed tutorship.

We introduce XDesign, a web-based interactive platform that guides learners through a multi-stage design process for creating user-centered explanations of AI models. Results from a course deployment show that students were able to identify concrete user needs in interacting with explanations, highlight user tasks to support the needs, and design a user interface that aids the tasks.

Simple up/downvotes, arguably the most widely used reaction design across social media platforms, allow users to efficiently express their opinions and quickly evaluate others’ opinions from aggregated votes. However, such design forces users to project their diverse opinions onto dichotomized reactions and provides limited information to readers on why a comment was up/downvoted. We explore user-generated labels (UGLs) as an alternative reaction design to capture the rich context of user reactions to comments. We conducted a between-subjects study with 218 participants to understand how people use and are influenced by UGLs compared to up/downvotes. Specifically, we examine how UGLs affect users’ ability to express and perceive diverse opinions. Participants generated 234 unique labels on diverse aspects of a comment. Leaving more reactions than participants in the up/downvotes condition, participants reported that the ability to express their opinions improved with UGLs. UGLs also enabled participants to better understand the multifacetedness of public evaluation of a comment.

Calling out, a phenomenon where people publicly broadcast their critiques of someone to a larger audience using, has become increasingly common on social media. However, there has been concerns that it could develop into harassment, deteriorating the quality of public discourse by over-punishing individuals for minor transgressions. To investigate this phenomenon, we interviewed 32 Twitter users who had been called out, had called out, or had witnessed a calling out on Twitter. We found that a key determining factor that distinguishes criticism from harassment was the callee's ability to respond to or engage with the criticism, and that different stakeholders hold different perspectives toward how online harassment is defined. We also discovered that the distinction between callers and callees was not absolute, and that there was high interchangeability of roles both within and across events. Through these findings, we discuss design implications for the platform in promoting healthy discourse while preventing toxic behavior on social media.

Directly manipulating the timeline, such as scrubbing for thumbnails, is the standard way of controlling how-to videos. However, when how-to videos involve physical activities, people inconveniently alternate between controlling the video and performing the tasks. Adopting a voice user interface allows people to control the video with voice while performing the tasks with hands. However, naively translating timeline manipulation into voice user interfaces (VUI) results in temporal referencing (e.g. “rewind 20 seconds”), which requires a different mental model for navigation and thereby limiting users’ ability to peek into the content. We present RubySlippers, a system that supports efficient content-based voice navigation through keyword-based queries. Our computational pipeline automatically detects referenceable elements in the video, and finds the video segmentation that minimizes the number of needed navigational commands. Our evaluation (N=12) shows that participants could perform three representative navigation tasks with fewer commands and less frustration using RubySlippers than the conventional voice-enabled video interface.

Team members commonly collaborate on visual documents remotely and asynchronously. Particularly, students are frequently restricted to this setting as they often do not share work schedules or physical workspaces. As communication in this setting has delays and limits the main modality to text, members exert more effort to reference document objects and understand others’ intentions. We propose Winder, a Figma plugin that addresses these challenges through linked tapes—multimodal comments of clicks and voice. Bidirectional links between the clicked-on objects and voice recordings facilitate understanding tapes: selecting objects retrieves relevant recordings, and playing recordings highlights related objects. By periodically prompting users to produce tapes, Winder preemptively obtains information to satisfy potential communication needs. Through a five-day study with eight teams of three, we evaluated the system’s impact on teams asynchronously designing graphical user interfaces. Our findings revealed that producing linked tapes could be as lightweight as face-to-face (F2F) interactions while transmitting intentions more precisely than text. Furthermore, with preempted tapes, teammates coordinated tasks and invited members to build on each others’ work.

“Study with me” videos contain footage of people studying for hours, in which social components like conversations or informational content like instructions are absent. Recently, they became increasingly popular on video-sharing platforms. This paper provides the first broad look into what “study with me” videos are and how people use them. We analyzed 30 “study with me” videos and conducted 12 interviews with their viewers to understand their motivation and viewing practices. We identified a three-factor model that explains the mechanism for shaping a satisfactory studying experience in general. One of the factors, a well-suited ambience, was difficult to achieve because of two common challenges: external conditions that prevent studying in study-friendly places and extra cost needed to create a personally desired ambience. We found that the viewers used “study with me” videos to create a personalized ambience at a lower cost, to find controllable peer pressure, and to get emotional support. These findings suggest that the viewers self-regulate their learning through watching “study with me” videos to improve efficiency even when studying alone at home.

How can educational platforms be instrumented to accelerate the use of research to improve students' experiences? We show how modular components of any educational interface - e.g. explanations, homework problems, even emails - can be implemented using the novel MOOClet software architecture. Researchers and instructors can use these augmented MOOClet components for: (1) Iterative Cycles of Randomized Experiments that test alternative versions of course content; (2) Data-Driven Improvement using adaptive experiments that rapidly use data to give better versions of content to future students, on the order of days rather than months. A MOOClet supports both manual and automated improvement using reinforcement learning; (3) Personalization by delivering alternative versions as a function of data about a student's characteristics or subgroup, using both expert-authored rules and data mining algorithms. We provide an open-source web service for implementing MOOClets (www.mooclet.org) that has been used with thousands of students. The MOOClet framework provides an ecosystem that transforms online course components into collaborative micro-laboratories, where instructors, experimental researchers, and data mining/machine learning researchers can engage in perpetual cycles of experimentation, improvement, and personalization.

We are delighted to present this issue of the Proceedings of the ACM on Human-Computer Interaction, which contains scholarship from the Computer-Supported Cooperative Work and Social Computing (CSCW) community. This issue has 190 papers, 177 submitted in June 2020 and 13 submitted in October 2020. It represents contributions from two Program Committees, including external reviewers, Associate Chairs, and Editors, who together have conducted a rigorous review process. As Papers Chairs, we are grateful for the community's collective efforts to continue shaping and sharing CSCW's tradition of high-quality scholarship during a global pandemic.

Collaborative Sequencing (CoSeq) is the process by which a group collaboratively constructs a sequence. CoSeq is ubiquitous, occurring across diverse situations like trip planning, course scheduling, or book writing. Building a consensus on a sequence is desirable to groups. However, accomplishing this requires groups to dedicate significant effort to comprehensively discuss preferences and resolve conflicts. Furthermore, as numerous decisions must be assessed to construct a sequence, this challenge can be exacerbated in CoSeq. However, little research has aimed to effectively support consensus building in CoSeq. As a first step to systematically understand and support consensus building in CoSeq, we conducted a formative study to gain insights into how visual awareness may facilitate the holistic recognition of preferences and the resolution of conflicts within a group. From the study, we identified design requirements to support consensus building and designed a novel visual awareness technique for CoSeq. We instantiated this design in a collaborative travel itinerary planning system, Twine, and conducted a summative study to evaluate its effects. We found that visual awareness could decrease the effort of communicating preferences by 21%, and participants' comments suggest that it also encouraged group members to behave more cooperatively when building a consensus. We discuss future research directions to further explore the needs and challenges in this unique context and to advance the development of support for CoSeq tasks.

Enabling the free and equal exchange of arguments on social issues in a respectful manner is an integral part of establishing the democratic ideal. However, the current manifestation of online spaces tends to facilitate the gathering of like-minded people, leading to the polarization of opinions. Such polarization inhibits the sharing of diverse opinions and deteriorates respect for disagreeing opinions. To tackle this issue, we present StarryThoughts, an online system that supports users to express and explore diverse perspectives on social issues. The system supports three types of exploration of the collected arguments online: navigating opinions based on the demographic identities of the posters, checking the the stereotypes users hold towards demographics in relation to given social issues, and engaging with opinions with semantically different point-of-views. By deploying the system to the public in co-operation with a nationwide broadcasting company, we collected 1,950 opinions with 144 free-form responses from 1,209 visitors as initial data and iterated on the design. Results from a user study with 56 participants showed that the system enables participants to explore a wide range of opinions on social issues, be more informed on the various arguments, and be more confident about their opinions. From our findings, we provide several design considerations for building online systems for supporting users to explore diverse opinions on social issues.

Artificial intelligence (AI) algorithms are making remarkable achievements even in creative fields such as aesthetics. However, whether those outside the machine learning (ML) community can sufficiently interpret or agree with their results, especially in such highly subjective domains, is being questioned. In this paper, we try to understand how different user communities reason about AI algorithm results in subjective domains. We designed AI Mirror, a research probe that tells users the algorithmically predicted aesthetic scores of photographs. We conducted a user study of the system with 18 participants from three different groups: AI/ML experts, domain experts (photographers), and general public members. They performed tasks consisting of taking photos and reasoning about AI Mirror's prediction algorithm with think-aloud sessions, surveys, and interviews. The results showed the following: (1) Users understood the AI using their own group-specific expertise; (2) Users employed various strategies to close the gap between their judgments and AI predictions overtime; (3) The difference between users' thoughts and AI pre-dictions was negatively related with users' perceptions of the AI's interpretability and reasonability. We also discuss design considerations for AI-infused systems in subjective domains.

Thanks to advanced sensing and logging technology, automatic personality assessment (APA) with users' behavioral data in the workplace is on the rise. While previous work has focused on building APA systems with high accuracy, little research has attempted to understand users' perception towards APA systems. To fill this gap, we take a mixed-methods approach: we (1) designed a survey (n=89) to understand users'social workplace behavior both online and offline and their privacy concerns; (2) built a research probe that detects personality from online and offline data streams with up to 81.3% accuracy, and deployed it for three weeks in Korea (n=32); and (3) conducted post-interviews (n=9). We identify privacy issues in sharing data and system-induced change in natural behavior as important design factors for APA systems. Our findings suggest that designers should consider the complex relationship between users' perception and system accuracy for a more user-centered APA design.

Online chat is an emerging channel for discussing community problems. It is common practice for communities to assign dedicated moderators to maintain a structured discussion and enhance the problem-solving experience. However, due to the synchronous nature of online chat, moderators face a high managerial overhead in tasks like discussion stage management, opinion summarization, and consensus-building support. To assist moderators with facilitating a structured discussion for community problem-solving, we introduce SolutionChat, a system that (1) visualizes discussion stages and featured opinions and (2) recommends contextually appropriate moderator messages. Results from a controlled lab study (n=55, 12 groups) suggest that participants' perceived discussion trackability was significantly higher with SolutionChat than without. Also, moderators provided better summarization with less effort and better managerial support using system-generated messages with SolutionChat than without. With SolutionChat, we envision untrained moderators to effectively facilitate chat-based discussions of important community matters.

Live streaming visual art such as drawing or using design software is gaining popularity. An important aspect of live streams is the direct and real-time communication between streamers and viewers. However, currently available text-based interaction limits the expressiveness of viewers as well as streamers, especially when they refer to specific moments or objects in the stream. To investigate the feasibility of using snapshots of streamed content as a way to enhance streamer-viewer interaction, we introduce Snapstream, a system that allows users to take snapshots of the live stream, annotate them, and share the annotated snapshots in the chat. Streamers can also verbally reference a specific snapshot during streaming to respond to viewers' questions or comments. Results from live deployments show that participants communicate more expressively and clearly with increased engagement using Snapstream. Participants used snapshots to reference part of the artwork, give suggestions on it, make fun images or memes, and log intermediate milestones. Our findings suggest that visual interaction enables richer experiences in live streaming.

This paper introduces Workflow graphs, or W-graphs, which encode how the approaches taken by multiple users performing a fixed 3D design task converge and diverge from one another. The graph's nodes represent equivalent intermediate task states across users, and directed edges represent how a user moved between these states, inferred from screen recording videos, command log data, and task content history. The result is a data structure that captures alternative methods for performing sub-tasks (e.g., modeling the legs of a chair) and alternative strategies of the overall task. As a case study, we describe and exemplify a computational pipeline for building W-graphs using screen recordings, command logs, and 3D model snapshots from an instrumented version of the Tinkercad 3D modeling application, and present graphs built for two sample tasks. We also illustrate how W-graphs can facilitate novel user interfaces with scenarios in workflow feedback, on-demand task guidance, and instructor dashboards.

Planning a solution before writing code is essential in algorithmic problem-solving. However, novices often skip planning and jump straight into coding. Even if they set up a plan, some do not connect to their plan when writing code. Learners solving algorithmic problems often struggle with high-level components such as solution techniques and sub-problems, but existing representations that guide learners in planning, such as flowcharts, focus on presenting lower-level details. We use subgoal diagrams -- diagrams made of subgoal labels and the relationships between them -- as a representation that guides learners to focus on high-level plans when they develop solutions. We introduce AlgoPlan, an interface that enables learners to build their own subgoal diagram and use it to guide their problem-solving process. A preliminary study with seven students shows that subgoal diagrams help learners focus on high-level plans and connect these plans to their code.

While people primarily communicate with text in mobile chat applications, they are increasingly using visual elements such as images, emojis, and memes. Using such visual elements could help users communicate clearly and make chatting experience enjoyable. However, finding and inserting contextually appropriate images during the chat can be both tedious and distracting. We introduce MilliCat, a real-time image suggestion system that recommends images that match the chat content within a mobile chat application (i.e., autocomplete with images). MilliCat combines natural language processing (e.g., keyword extraction, dependency parsing) and mobile computing (e.g., resource and energy-efficiency) techniques to autonomously make image suggestions when users might want to use images. Through multiple user studies, we investigated the effectiveness of our design choices, the frequency and motivation of image usage by the participants, and the impact of MilliCat on mobile chat experiences. Our results indicate that MilliCat’s real-time image suggestion enables users to quickly and conveniently select and display images on mobile chat by significantly reducing the latency in the image selection process (3.19 × improvement) and consequently more frequent image usage (1.8 ×) than existing solutions. Our study participants reported that they used images more often with MilliCat as the images helped them convey information more effectively, emphasize their opinion, express emotions, and have fun chatting experience.

While mobile instant messaging (MIM) facilitates ubiquitous interpersonal communication, its constant connectivity could build the expectation of an immediate response to messages, and its notifications flood could cause interruptions at inopportune moments. We examine two design concepts for MIM-private status sharing and sender-controlled notifications-that aim to lower the pressure for an immediate reply and reduce unnecessary interruptions by untimely notifications. Private status sharing reactively reveals a customized status with a selected partner(s) only when the partner has sent a message. Sender-controlled notifications give senders the control of choosing whether to send a notification for their own messages. We built MyButler, an Android app prototype that instantiates these two concepts and integrated it with KakaoTalk, a commercial MIM app. During a two-week field study with 11 pairs (5 couples and 6 friend pairs), participants expressed themselves through a total of 210 different statuses, 64.3% of which indicated the current activity or task of the user. Participants reported that private status sharing enabled them to explain their unavailability and relieved the pressure and expectations for timely attendance. We reveal more findings on the types of privately shared statuses and their roles in MIM communication; the in-situ behaviors and patterns of using sender-controlled notifications; and the motivations of MIM users in choosing whether to alert their messages. In terms of message notifications, senders chose to send 25.4% of the messages without any notification. We found that senders' decisions to alert are affected by the receiver's status, their own status to chat, and the possibility of message content exposure to others through notifications. Based on our findings, we draw insights into how the concepts of private status sharing and sender-controlled notifications can be applied in future designs and explorations.

Similar to a design process for designing graphical user interfaces, conversation designers often apply an iterative design process by defining a conversation flow, testing with users, reviewing user data, and improving the design. While it is possible to iterate on conversation design with existing chatbot prototyping tools, there still remain challenges in recruiting participants on-demand and collecting structured feedback on specific conversational components. These limitations hinder designers from running rapid iterations and making informed design decisions. We posit that involving a crowd in the conversation design process can address these challenges, and introduce ProtoChat, a crowd-powered chatbot design tool built to support the iterative process of conversation design. ProtoChat makes it easy to recruit crowd workers to test the current conversation within the design tool. ProtoChat's crowd-testing tool allows crowd workers to provide concrete and practical feedback and suggest improvements on specific parts of the conversation. With the data collected from crowd-testing, ProtoChat provides multiple types of visualizations to help designers analyze and revise their design. Through a three-day study with eight designers, we found that ProtoChat enabled an iterative design process for designing a chatbot. Designers improved their design by not only modifying the conversation design itself, but also adjusting the persona and getting UI design implications beyond the conversation design itself. The crowd responses were helpful for designers to explore user needs, contexts, and diverse response formats. With ProtoChat, designers can successfully collect concrete evidence from the crowd and make decisions to iteratively improve their conversation design.

We are delighted to welcome you to this issue of the Proceedings of the ACM on Human-Computer Interaction, which contains scholarship from the Computer-Supported Cooperative Work and Social Computing (CSCW) community. This is the second issue that represents the new quarterly submission model. This issue has 91 papers accepted from the January 2020 round (16 previously accepted papers from the January 2020 round were published in a prior issue and another 19 accepted this round will be published in a later issue). This represents an overall 40.6% acceptance rate from the 310 submissions in January 2020. This issue represents the contributions of external reviewers, Associate Chairs, and the dedicated Editors, who were essential to carrying through the review process, especially during a global pandemic. As Papers Chairs, we are delighted to continue shaping and disseminating CSCW's tradition of high-quality scholarship.

No abstract available.

Crowdsourcing is a common means of collecting image segmentation training data for use in a variety of computer vision applications. However, designing accurate crowd-powered image segmentation systems is challenging, because defining object boundaries in an image requires significant fine motor skills and hand-eye coordination, which makes these tasks error-prone. Typically, special segmentation tools are created and then answers from multiple workers are aggregated to generate more accurate results. However, individual tool designs can bias how and where people make mistakes, resulting in shared errors that remain even after aggregation. In this article, we introduce a novel crowdsourcing approach that leverages tool diversity as a means of improving aggregate crowd performance. Our idea is that given a diverse set of tools, answer aggregation done across tools can help improve the collective performance by offsetting systematic biases induced by the individual tools themselves. To demonstrate the effectiveness of the proposed approach, we design four different tools and present FourEyes, a crowd-powered image segmentation system that uses aggregation across different tools. We then conduct a series of studies that evaluate different aggregation conditions and show that using multiple tools can significantly improve aggregate accuracy. Furthermore, we investigate the idea of applying post-processing for multi-tool aggregation in terms of correction mechanism. We introduce a novel region-based method for synthesizing more accurate bounds for image segmentation tasks through averaging surrounding annotations. In addition, we explore the effect of adjusting the threshold parameter of an EM-based aggregation method. Our results suggest that not only the individual tool’s design, but also the correction mechanism, can affect the performance of multi-tool aggregation. This article extends a work presented at ACM IUI 2018 [46] by providing a novel region-based error-correction method and additional in-depth evaluation of the proposed approach.

When watching how-to videos related to physical tasks, users' hands are often occupied by the task, making voice input a natural fit. To better understand the design space of voice interactions for how-to video navigation, we conducted three think-aloud studies using: 1) a traditional video interface, 2) a research probe providing a voice controlled video interface, and 3) a wizard-of-oz interface. From the studies, we distill seven navigation objectives and their underlying intents: pace control pause, content alignment pause, video control pause, reference jump, replay jump, skip jump, and peek jump. Our analysis found that users' navigation objectives and intents affect the choice of referent type and referencing approach in command utterances. Based on our findings, we recommend to 1) support conversational strategies like sequence expansions and command queues, 2) allow users to identify and refine their navigation objectives explicitly, and 3) support the seven interaction intents.

Collecting a sufficient amount of 3D training data for autonomous vehicles to handle rare, but critical, traffic events (e.g., collisions) may take decades of deployment. Abundant video data of such events from municipal traffic cameras and video sharing sites (e.g., YouTube) could provide a potential alternative, but generating realistic training data in the form of 3D video reconstructions is a challenging task beyond the current capabilities of computer vision. Crowdsourcing the annotation of necessary information could bridge this gap, but the level of accuracy required to obtain usable reconstructions makes this task nearly impossible for non-experts. In this paper, we propose a novel hybrid intelligence method that combines annotations from workers viewing different instances (video frames) of the same target (3D object), and uses particle filtering to aggregate responses. Our approach can leveraging temporal dependencies between video frames, enabling higher quality through more aggressive filtering. The proposed method results in a 33% reduction in the relative error of position estimation compared to a state-of-the-art baseline. Moreover, our method enables skipping (self-filtering) challenging annotations, reducing the total annotation time for hard-to-annotate frames by 16%. Our approach provides a generalizable means of aggregating more accurate crowd responses in settings where annotation is especially challenging or error-prone.

To help language learners achieve fluency, instructors often focus on teaching formulaic sequences (FS)--phrases such as idioms or phrasal verbs that are processed, stored, and retrieved holistically. Teaching FS effectively is challenging as it heavily involves instructors' intuition, prior knowledge, and manual efforts to identify a set of FSs with high utility. In this paper, we present FSIST, a tool that supports instructors for video-based instruction of FS. The core idea of FSIST is to utilize videos at scale to build a list of FSs along with videos that include example usages. To evaluate how FSIST can effectively support instructors, we conducted a user study with three English instructors. Results show that the browsing interactions provided in FSIST support instructors to efficiently find parts of videos that show example usages of FSs.-

We propose the design of MyButler, a sender-controlled notification management system that mitigates disruption caused by mobile instant messaging through sharing the receiver's activity information with the sender.

We present Reeboc that combines machine learning and k-means clustering to analyze the conversation of a chat, extract different emotions or topics of the conversation, and recommend emojis that represent various contexts to the user. Instead of simply analyzing a single input sentence, we consider recent sentences exchanged in a conversation. we performed a user study with 17 participants in 8 groups in a realistic mobile chat environment. Participants spent the least amount of time in identifying and selecting the emojis of their choice with Reeboc (38% faster than without emoji recommendation).

When crowdsourcing the creation of machine learning datasets, statistical distributions that capture diverse answers can represent ambiguous data better than a single best answer. Unfortunately, collecting distributions is expensive because a large number of responses need to be collected to form a stable distribution. Despite this, the efficient collection of answer distributions-that is, ways to use less human effort to collect estimates of the eventual distribution that would be formed by a large group of responses-is an under-studied topic. In this paper, we demonstrate that this type of estimation is possible and characterize different elicitation approaches to guide the development of future systems. We investigate eight elicitation approaches along two dimensions: annotation granularity and estimation perspective. Annotation granularity is varied by annotating i) a single "best" label, ii) all relevant labels, iii) a ranking of all relevant labels, or iv) real-valued weights for all relevant labels. Estimation perspective is varied by prompting workers to either respond with their own answer or an estimate of the answer(s) that they expect other workers would provide. Our study collected ordinal annotations on the emotional valence of facial images from 1,960 crowd workers and found that, surprisingly, the most fine-grained elicitation methods were not the most accurate, despite workers spending more time to provide answers. Instead, the most efficient approach was to ask workers to choose all relevant classes that others would have selected. This resulted in a 21.4% reduction in the human time required to reach the same performance as the baseline (i.e., selecting a single answer with their own perspective). By analyzing cases in which finer-grained annotations degraded performance, we contribute to a better understanding of the trade-offs between answer elicitation approaches. Our work makes it more tractable to use answer distributions in large-scale tasks such as ML training, and aims to spark future work on techniques that can efficiently estimate answer distributions.

Although Collaborative Information-Seeking (CIS) is becoming prevalent as people engage in shared decision-making, interface components adopted in the most commonly used information seeking tools (e.g., search, filter, select, and sort) are designed for individual use. To deepen our understanding of (1) how such single-user designs affect people's consensus building processes in CIS and (2) how to devise an alternative design to improve current practices, we conducted two 4-week diary studies and observed how groups seek out places together. Our studies focus on social event coordination as a case where CIS is necessary and important. In Study 1, we examined the major challenges people encounter when performing CIS using their preferred tools. These challenges include difficulties in capturing mutual preferences, high communication cost, and disparity of work depending on a group member's perceived role as an organizer or invitee. We discovered that improving a group's shared understanding of the target information they seek (e.g., places, products) could potentially address the challenges. In Study 2, we designed, deployed, and evaluated ComeTogether, a novel system that supports a group's social event coordination. ComeTogether adopts Collaborative Dynamic Queries (C-DQ), an interface designed to allow a group to share their preferences regarding potential destinations. Study 2 results indicate that using C-DQ increased users' awareness of other group members' preferences in performing CIS, making their coordination more transparent, more inviting, and fairer than what their current practice allows. Meanwhile, ComeTogether improved communication efficiency of groups while presenting opportunities to learn about others and to discover new places. We provide implications for design that explain considerations for adopting C-DQ and identify future research directions.

For cooking professionals and culinary students, understanding cooking instructions is an essential yet demanding task. Common tasks include categorizing different approaches to cooking a dish and identifying usage patterns of particular ingredients or cooking methods, all of which require extensive browsing and comparison of multiple recipes. However, no existing system provides support for such in-depth and at-scale analysis. We present RecipeScape, an interactive system for browsing and analyzing the hundreds of recipes of a single dish available online. We also introduce a computational pipeline that extracts cooking processes from recipe text and calculates a procedural similarity between them. To evaluate how RecipeScape supports culinary analysis at scale, we conducted a user study with cooking professionals and culinary students with 500 recipes for two different dishes. Results show that RecipeScape clusters recipes into distinct approaches, and captures notable usage patterns of ingredients and cooking actions.

Communication is critical in small group decision-making processes during which each member must be able to express preferences to reach consensus. Finding consensus can be difficult when each member in a group has a perspective that potentially conflicts with those of others. To support groups attempting to harmonize diverse preferences, we propose Collaborative Dynamic Queries (C-DQ), a UI component that enables a group to filter queries over decision criteria while being aware of others' preferences. To understand how C-DQ affects a group's behavior and perception in the decision-making process, we conducted 2 studies with groups who were prompted to make decisions together on mobile devices in a dispersed and synchronous situation. In Study 1, we found showing group preferences with C-DQ helped groups to communicate more efficiently and effectively. In Study 2, we found filtering candidates based on each member's own filter range further improved a groups' communication efficiency and effectiveness.

Distance Cartograms (DC) distort geographical features so that the measured distance between a single location and any other location on a map indicates absolute travel time. Although studies show that users can efficiently assess travel time with DC, distortion applied in DC may confuse users, and its usefulness "in the wild" is unknown. To understand how real world users perceive DC's benefits and drawbacks, we devise techniques that improve DC's presentation (preserving topological relationships among map features while aiming at retaining shapes) and scalability (presenting accurate live travel time). We developed a DC-enabled system with these techniques, and deployed it to 20 participants for 4 weeks. During this period, participants spent, on average, more than 50% of their time with DC as opposed to a standard map. Participants felt DC to be intuitive and useful for assessing travel time. They indicated intent in adopting DC in their real-life scenarios.

While video has become a widely adopted medium for online learning, existing video players provide limited support for navigation and learning. It is difficult to locate parts of the video that are linked to specific concepts. Also, most video players afford passive watching, thus making it difficult for learners with limited metacognitive skills to deeply engage with the content and reflect on their understanding. To support concept-driven navigation and comprehension of lecture videos, we present ConceptScape, a system that generates and presents a concept map for lecture videos. ConceptScape engages crowd workers to collaboratively generate a concept map by prompting them to externalize reflections on the video. We present two studies to show that (1) interactive concept maps can be useful tools for concept-based video navigation and comprehension, and (2) with ConceptScape, novice crowd workers can collaboratively generate complex concept maps that match the quality of those by experts.

Online instructional videos are ubiquitous, but it is difficult for instructors to gauge learners' experience and their level of comprehension or confusion regarding the lecture video. Moreover, learners watching the videos may become disengaged or fail to reflect and construct their own understanding. This paper explores instructor and learner perceptions of in-video prompting where learners answer reflective questions while watching videos. We conducted two studies with crowd workers to understand the effect of prompting in general, and the effect of different prompting strategies on both learners and instructors. Results show that some learners found prompts to be useful checkpoints for reflection, while others found them distracting. Instructors reported the collected responses to be generally more specific than what they have usually collected. Also, different prompting strategies had different effects on the learning experience and the usefulness of responses as feedback.

Continuous tracking young children's development is important for parents because early detection of developmental delay can lead to better treatment through early intervention. Screening tests, often based on questions answered by a parent, are used to assess children's development, but responses from only one parent can be subjective and even inaccurate due to limited memory and observations. In this work, we propose a collaborative child development tracking system, where screening test responses are collected through collaboration between parents or caregivers. We implement BebeCODE, a mobile system that encourages parents to independently answer all developmental questions for a given age and resolve disagreements through chatting, image/video sharing, or asking a third person. A 4-week deployment study of BebeCODE with 12 families found that parents had approximately 22% disagreements about questions regarding their children's developmental and BebeCODE helped them reach a consensus. Parents also reported that their awareness of their child's development, increased with BebeCODE.

Digital educational resources could enable the use of randomized experiments to answer pedagogical questions that instructors care about, taking academic research out of the laboratory and into the classroom. We take an instructor-centered approach to designing tools for experimentation that lower the barriers for instructors to conduct experiments. We explore this approach through DynamicProblem, a proof-of-concept system for experimentation on components of digital problems, which provides interfaces for authoring of experiments on explanations, hints, feedback messages, and learning tips. To rapidly turn data from experiments into practical improvements, the system uses an interpretable machine learning algorithm to analyze students' ratings of which conditions are helpful, and present conditions to future students in proportion to the evidence they are higher rated. We evaluated the system by collaboratively deploying experiments in the courses of three mathematics instructors. They reported benefits in reflecting on their pedagogy, and having a new method for improving online problems for future students.

Crowdsourcing is a common means of collecting image segmentation training data for use in a variety of computer vision applications. However, designing accurate crowd-powered image segmentation systems is challenging because defining object boundaries in an image requires significant fine motor skills and hand-eye coordination, which makes these tasks error-prone. Typically, special segmentation tools are created and then answers from multiple workers are aggregated to generate more accurate results. However, individual tool designs can bias how and where people make mistakes, resulting in shared errors that remain even after aggregation. In this paper, we introduce a novel crowdsourcing workflow that leverages multiple tools for the same task to increase output accuracy by reducing systematic error biases introduced by the tools themselves. When a task can no longer be broken down into more-tractable subtasks (the conventional approach taken by microtask crowdsourcing), our multi-tool approach can be used to further improve accuracy by assigning different tools to different workers. We present a series of studies that evaluate our multi-tool approach and show that it can significantly improve aggregate accuracy in semantic image segmentation.

Document authors commonly use tables to support arguments presented in the text. But, because tables are usually separate from the main body text, readers must split their attention between different parts of the document. We present an interactive document reader that automatically links document text with corresponding table cells. Readers can select a sentence (or tables cells) and our reader highlights the relevant table cells (or sentences). We provide an automatic pipeline for extracting such references between sentence text and table cells for existing PDF documents that combines structural analysis of tables with natural language processing and rule-based matching. On a test corpus of 330 (sentence, table) pairs, our pipeline correctly extracts 48.8% of the references. An additional 30.5% contain only false negatives (FN) errors -- the reference is missing table cells. The remaining 20.7% contain false positives (FP) errors -- the reference includes extraneous table cells and could therefore mislead readers. A user study finds that despite such errors, our interactive document reader helps readers match sentences with corresponding table cells more accurately and quickly than a baseline document reader.

In crowd-civic systems, citizens form groups and work towards shared goals, such as discovering social issues or reforming official policies. Unfortunately, many real-world systems have been unsuccessful in continually motivating large numbers of citizens to participate voluntarily, despite various approaches such as gamification and persuasion techniques. In this paper, we examine the influence of personalized messages designed to support motivation as asserted by the Self-Determination Theory (SDT). We designed a crowd-civic platform for collecting community issues with personalized motivation-supportive messages and conducted two studies: a pair-comparison experiment with 150 participants on Amazon's Mechanical Turk and a live deployment study with 120 university members. Results of the pair-comparison study indicate applicability of SDT's perspective in crowd-civic systems. While applying it in the live system surfaced several challenges, including recruiting participants without interfering with general motivations, the collected data exhibited similar promising trends.

A distance cartogram (DC) is a technique that alters distances between a user-specified origin and the other locations in a map with respect to travel time. With DC, users can weigh the relative travel time costs between the origin and potential destinations at a glance because travel times are projected in a linearly interpolated time space from the origin. Such glance-ability is known to be useful for travelers who are mindful of travel time when finding their travel destinations. When constructing DC, however, uneven urban traffic conditions introduce excessive distortion and challenge user intuition. In addition, there has been little research focusing on DC's user interaction design. To tackle these challenges and realize the potential of DC as an interactive decision-making support tool, we derive a set of useful interactions through two formative studies and devise two novel techniques called Geo-contextual Anchoring Projection and Scalable Road-network Construction. We develop an interactive map system using these techniques and evaluate this system by comparing it against an equidistant map (EM), a widely used conventional layout that preserves the geographical reality. Based on the analysis of user behavior and qualitative feedback, we identify several benefits of using DC itself and of the interaction techniques we derived. We also analyze the specific reasons behind these identified benefits.

Smartphone notifications provide application-specific information in real-time, but could distract users from in-person social interactions when delivered at inopportune moments. We explore breakpoint-based notification management, in which the smartphone defers notifications until an opportune moment. With a video survey where participants selected appropriate moments for notifications from a video-recorded social interaction, we identify four breakpoint types: long silence, a user leaving the table, others using smartphones, and a user left alone. We introduce a Social Context-Aware smartphone Notification system, SCAN, that uses build-in sensors to detect social context and identifies breakpoints to defer smartphone notifications until a breakpoint. We conducted a controlled study with ten friend groups who had SCAN installed on their smartphones while dining at a restaurant. Results show that SCAN accurately detects breakpoints (precision=92.0%, recall=82.5%), and reduces notification interruptions by 54.1%. Most participants reported that SCAN helped them to focus better on in-person social interaction and found selected breakpoints appropriate.

Randomized experiments in online educational environments are ubiquitous as a scientific method for investigating learning and motivation, but too rarely improve educational resources and produce practical benefits for learners. We suggest that software and tools for experimentally comparing resources are designed primarily through the lens of experiments as a scientific methodology, and therefore miss a tremendous opportunity for online experiments to serve as engines for dynamic improvement and personalization. We present the MOOClet requirements specification to guide the implementation of software or tools for experiments to ensure that whenever alternative versions of a resource can be experimentally compared (by randomly assigning versions), the resource can also be dynamically improved (by changing which versions are presented), and personalized (by presenting different versions to different people). The MOOClet specification was used to implement DEXPER, a proof-of-concept web service backend that enables dynamic experimentation and personalization of resources embedded in front-end educational platforms. We describe three use cases of MOOClets for dynamic experimentation and personalization of motivational emails, explanations, and problems.

In asynchronous online discussions, users actively reference visual materials (e.g., video, document) to provide supporting evidence and additional context. However, creating and comprehending complex references can be challenging, especially when there are multiple referents to refer, or when a referent is highly specific (e.g., specific sentences in a paper rather than the paper as a whole). To identify users' challenges in making references with multiple and specific referents while using existing discussion tools, we conducted an observational study and a preliminary interview. Based on the design lessons, we built Korero, a discussion interface that aims to facilitate complex referencing actions. For evaluation, we compared Korero against conventional interfaces in two user studies with referencing tasks of different referential difficulty. We found that Korero not only significantly reduces the time and effort in making references with multiple and specific referents, but also shows potential in increasing users' engagement with the discussion and referent materials.

The Internet has enabled learning at scale, from Massive Open Online Courses (MOOCs) to Wikipedia. But online learners may become passive, instead of actively constructing knowledge and revising their beliefs in light of new facts. Instructors cannot directly diagnose thousands of learners' misconceptions and provide remedial tutoring. This paper investigates how instructors can prompt learners to reflect on facts that are anomalies with respect to their existing misconceptions, and how to choose these anomalies and prompts to guide learners to revise incorrect beliefs without any feedback. We conducted two randomized experiments with online crowd workers learning statistics. Results show that prompts to explain why these anomalies are true drive revision towards correct beliefs. But prompts to simply articulate thoughts about anomalies have no effect on learning. Furthermore, we find that explaining multiple anomalies is more effective than explaining only one, but the anomalies should rule out multiple misconceptions simultaneously.

Despite recent efforts in opening up government data, developing tools for taxpayers to make sense of extensive and multi-faceted budget data remains an open challenge. In this paper, we present BudgetMap, an issue-driven classification and navigation interface for the budgets of government programs. Our novel issue-driven approach can complement the traditional budget classification system used by government organizations by reflecting time-evolving public interests. BudgetMap elicits the public to tag government programs with social issues by providing two modes of tagging. User-initiated tagging allows people to voluntarily search for programs of interest and classify each program with related social issues, while system-initiated tagging guides people through possible matches of issues and programs via microtasks. BudgetMap then facilitates visual exploration of the tagged budget data. Our evaluation shows that participants' awareness and understanding of budgetary issues increased after using BudgetMap, while they collaboratively identified issue-budget links with quality comparable to expert-generated links.

While explanations may help people learn by providing information about why an answer is correct, many problems on online platforms lack high-quality explanations. This paper presents AXIS (Adaptive eXplanation Improvement System), a system for obtaining explanations. AXIS asks learners to generate, revise, and evaluate explanations as they solve a problem, and then uses machine learning to dynamically determine which explanation to present to a future learner, based on previous learners' collective input. Results from a case study deployment and a randomized experiment demonstrate that AXIS elicits and identifies explanations that learners find helpful. Providing explanations from AXIS also objectively enhanced learning, when compared to the default practice where learners solved problems and received answers without explanations. The rated quality and learning benefit of AXIS explanations did not differ from explanations generated by an experienced instructor.

Educators have developed an effective technique to get feedback after in-person lectures, called "muddy cards." Students are given time to reflect and write the "muddiest" (least clear) point on an index card, to hand in as they leave class. This practice of assigning end-of-lecture reflection tasks to generate explicit student feedback is well suited for adaptation to the challenge of supporting feedback in online video lectures. We describe the design and evaluation of Mudslide, a prototype system that translates the practice of muddy cards into the realm of online lecture videos. Based on an in-lab study of students and teachers, we find that spatially contextualizing students' muddy point feedback with respect to particular lecture slides is advantageous to both students and teachers. We also reflect on further opportunities for enhancing this feedback method based on teachers' and students' experiences with our prototype.

Teachers in conventional classrooms often ask learners to express themselves and show their thought processes by speaking out loud, drawing on a whiteboard, or even using physical objects. Despite the pedagogical value of such activities, interactive exercises available in most online learning platforms are constrained to multiple-choice and short answer questions. We introduce RIMES, a system for easily authoring, recording, and reviewing interactive multimedia exercises embedded in lecture videos. With RIMES, teachers can prompt learners to record their responses to an activity using video, audio, and inking while watching lecture videos. Teachers can then review and interact with all the learners' responses in an aggregated gallery. We evaluated RIMES with 19 teachers and 25 students. Teachers created a diverse set of activities across multiple subjects that tested deep conceptual and procedural knowledge. Teachers found the exercises useful for capturing students' thought processes, identifying misconceptions, and engaging students with content.

While a government budget determines how taxpayers' money is allocated to various programs and stakeholders that compete for limited resources, the extensiveness and complexity of the budget and its process hinder taxpayers from understanding the budget information and participating in the public discussion. To engage taxpayers in the public discussion around budgetary issues, we leverage news articles containing budgetary information for design opportunities. We present Factful, a web-based annotative article reading interface that enhances the article with fact-checking support and contextual budgetary information by processing open government data. In our lab study, participants using Factful discussed more critically with more fact-based supporting statements. They built a rich context surrounding the relevant budget facts beyond what was presented in the article. Factful presents a simple yet powerful model for supporting fact-oriented budgetary discussions online by leveraging open government data.

Prototyping allows designers to quickly iterate and gather feedback, but the time it takes to create even a Wizard-of-Oz prototype reduces the utility of the process. In this paper, we introduce crowdsourcing techniques and tools for prototyping interactive systems in the time it takes to describe the idea. Our Apparition system uses paid microtask crowds to make even hard-to-automate functions work immediately, allowing more fluid prototyping of interfaces that contain interactive elements and complex behaviors. As users sketch their interface and describe it aloud in natural language, crowd workers and sketch recognition algorithms translate the input into user interface elements, add animations, and provide Wizard-of-Oz functionality. We discuss how design teams can use our approach to reflect on prototypes or begin user studies within seconds, and how, over time, Apparition prototypes can become fully-implemented versions of the systems they simulate. Powering Apparition is the first self-coordinated, real-time crowdsourcing infrastructure. We anchor this infrastructure on a new, lightweight write-locking mechanism that workers can use to signal their intentions to each other.

Websites like YouTube host millions of how-to videos, but their interfaces are not optimized for learning. Previous research suggests that people learn more from how-to videos when the videos are accompanied by outlines showing individual steps and labels for groups of steps (subgoals). We envision an alternative video player where the steps and subgoals are displayed alongside the video. To generate this information for existing videos, we introduce learnersourcing, an approach in which intrinsically motivated learners contribute to a human computation workflow as they naturally go about learning from the videos. To demonstrate this method, we deployed a live website with a workflow for constructing subgoal labels implemented on a set of introductory web programming videos. For the four videos with the highest participation, we found that a majority of learner-generated subgoals were comparable in quality to expert-generated ones. Learners commented that the system helped them grasp the material, suggesting that our workflow did not detract from the learning experience.

In this work-in-progress, we present our preliminary findings from an exploratory study on understanding learners' general behavior and perception towards learning with classmates in MOOCs. One-on-one semi-structured interview designed with grounded theory method was conducted with seven MOOC learners. Initial analysis of the interview data revealed several interesting insights on learners' behavior in working with other learners in MOOCs. We intend to expand the findings in future work to derive design implications for incorporating collaborative features into MOOCs.

Most education and workplace learning takes place in classroom contexts far removed from laboratories or field sites with special arrangements for scientific research. But digital online resources provide a novel opportunity for large-scale efforts to bridge the real-world and laboratory settings which support data collection and randomized A/B experiments comparing different versions of content or interactions [2]. However, there are substantial technological and practical barriers in aligning instructors and researchers to use learning technologies like blended lessons/exercises & MOOCs as both a service for students and a realistic context to conduct research. This paper explains how the concept of a "MOOClet" can facilitate research-practitioner collaborations. MOOClets [3] are defined as modular components of a digital resource that can be implemented in technology to: (1) allow modification to create multiple versions, (2) allow experimental comparison and personalization of different versions, (3) reliably specify what data are collected. We suggest a framework in which instructors specify what kinds of changes to lessons, exercises, and emails they would be willing to adopt, and what data they will collect and make available. Researchers can then: (1) specify or design experiments that compare the effects of different versions on quantifiable outcomes. (2) Explore algorithms for maximizing particular outcomes by choosing alternative versions of a MOOClet based on the input variables available. We present a prototype survey tool for instructors intended to facilitate practitioner-researcher matches and successful collaborations.

In contrast to typical laboratory experiments, the everyday use of online educational resources by large populations and the prevalence of software infrastructure for A/B testing leads us to consider how platforms can embed in vivo experiments that do not merely support research, but ensure practical improvements to their educational components. Examples are presented of randomized experimental comparisons conducted by subsets of the authors in three widely used online educational platforms -- Khan Academy, edX, and ASSISTments. We suggest design principles for platform technology to support randomized experiments that lead to practical improvements -- enabling Iterative Improvement and Collaborative Work -- and explain the benefit of their implementation by WPI co-authors in the ASSISTments platform.

Organizing conference sessions around themes improves the experience for attendees. However, the session creation process can be difficult and time-consuming due to the amount of expertise and effort required to consider alternative paper groupings. We present a collaborative web application called Frenzy to draw on the efforts and knowledge of an entire program committee. Frenzy comprises (a) interfaces to support large numbers of experts working collectively to create sessions, and (b) a two-stage process that decomposes the session-creation problem into meta-data elicitation and global constraint satisfaction. Meta-data elicitation involves a large group of experts working simultaneously, while global constraint satisfaction involves a smaller group that uses the meta-data to form sessions.

Millions of learners today use how-to videos to master new skills in a variety of domains. But browsing such videos is often tedious and inefficient because video player interfaces are not optimized for the unique step-by-step structure of such videos. This research aims to improve the learning experience of existing how-to videos with step-by-step annotations.

Constructing a good conference schedule for a large multi-track conference needs to take into account the preferences and constraints of organizers, authors, and attendees. Creating a schedule which has fewer conflicts for authors and attendees, and thematically coherent sessions is a challenging task. Cobi introduced an alternative approach to conference scheduling by engaging the community to play an active role in the planning process. The current Cobi pipeline consists of committee-sourcing and author-sourcing to plan a conference schedule. We further explore the design space of community-sourcing by introducing attendee-sourcing -- a process that collects input from conference attendees and encodes them as preferences and constraints for creating sessions and schedule. For CHI 2014, a large multi-track conference in human-computer interaction with more than 3,000 attendees and 1,000 authors, we collected attendees’ preferences by making available all the accepted papers at the conference on a paper recommendation tool we built called Confer, for a period of 45 days before announcing the conference program (sessions and schedule). We compare the preferences marked on Confer with the preferences collected from Cobi’s author-sourcing approach. We show that attendee-sourcing can provide insights beyond what can be discovered by author-sourcing. For CHI 2014, the results show value in the method and attendees’ participation. It produces data that provides more alternatives in scheduling and complements data collected from other methods for creating coherent sessions and reducing conflicts.

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.

With thousands of learners watching the same online lecture videos, analyzing video watching patterns provides a unique opportunity to understand how students learn with videos. This paper reports a large-scale analysis of in-video dropout and peaks in viewership and student activity, using second-by-second user interaction data from 862 videos in four Massive Open Online Courses (MOOCs) on edX. We find higher dropout rates in longer videos, re-watching sessions (vs first-time), and tutorials (vs lectures). Peaks in re-watching sessions and play events indicate points of interest and confusion. Results show that tutorials (vs lectures) and re-watching sessions (vs first-time) lead to more frequent and sharper peaks. In attempting to reason why peaks occur by sampling 80 videos, we observe that 61% of the peaks accompany visual transitions in the video, e.g., a slide view to a classroom view. Based on this observation, we identify five student activity patterns that can explain peaks: starting from the beginning of a new material, returning to missed content, following a tutorial step, replaying a brief segment, and repeating a non-visual explanation. Our analysis has design implications for video authoring, editing, and interface design, providing a richer understanding of video learning on MOOCs.

With an unprecedented scale of learners watching educational videos on online platforms such as MOOCs and YouTube, there is an opportunity to incorporate data generated from their interactions into the design of novel video interaction techniques. Interaction data has the potential to help not only instructors to improve their videos, but also to enrich the learning experience of educational video watchers. This paper explores the design space of data-driven interaction techniques for educational video navigation. We introduce a set of techniques that augment existing video interface widgets, including: a 2D video timeline with an embedded visualization of collective navigation traces; dynamic and non-linear timeline scrubbing; data-enhanced transcript search and keyword summary; automatic display of relevant still frames next to the video; and a visual summary representing points with high learner activity. To evaluate the feasibility of the techniques, we ran a laboratory user study with simulated learning tasks. Participants rated watching lecture videos with interaction data to be efficient and useful in completing the tasks. However, no significant differences were found in task performance, suggesting that interaction data may not always align with moment-by-moment information needs during the tasks.

Long documents are abundant on the web today, and are accessed in increasing numbers from touchscreen devices such as mobile phones and tablets. Navigating long documents with small screens can be challenging both physically and cognitively because they compel the user to scroll a great deal and to mentally filter for important content. To support navigation of long documents on touchscreen devices, we introduce content-aware kinetic scrolling, a novel scrolling technique that dynamically applies pseudo-haptic feedback in the form of friction around points of high interest within the page. This allows users to quickly find interesting content while exploring without further cluttering the limited visual space. To model degrees of interest (DOI) for a variety of existing web pages, we introduce social wear, a method for capturing DOI based on social signals that indicate collective user interest. Our preliminary evaluation shows that users pay attention to items with kinetic scrolling feedback during search, recognition, and skimming tasks.

Creating sessions of related papers for a large conference is a complex and time-consuming task. Traditionally, a few conference organizers group papers into sessions manually. Organizers often fail to capture the affinities between papers beyond created sessions, making incoherent sessions difficult to fix and alternative groupings hard to discover. This paper proposes committeesourcing and authorsourcing approaches to session creation (a specific instance of clustering and constraint satisfaction) that tap into the expertise and interest of committee members and authors for identifying paper affinities. During the planning of ACM CHI'13, a large conference on human-computer interaction, we recruited committee members to group papers using two online distributed clustering methods. To refine these paper affinities — and to evaluate the committeesourcing methods against existing manual and automated approaches — we recruited authors to identify papers that fit well in a session with their own. Results show that authors found papers grouped by the distributed clustering methods to be as relevant as, or more relevant than, papers suggested through the existing in-person meeting. Results also demonstrate that communitysourced results capture affinities beyond sessions and provide flexibility during scheduling.

Effectively planning a large multi-track conference requires an understanding of the preferences and constraints of organizers, authors, and attendees. Traditionally, the onus of scheduling the program falls on a few dedicated organizers. Resolving conflicts becomes difficult due to the size and complexity of the schedule and the lack of insight into community members' needs and desires. Cobi presents an alternative approach to conference scheduling that engages the entire community in the planning process. Cobi comprises (a) communitysourcing applications that collect preferences, constraints, and affinity data from community members, and (b) a visual scheduling interface that combines communitysourced data and constraint-solving to enable organizers to make informed improvements to the schedule. This paper describes Cobi's scheduling tool and reports on a live deployment for planning CHI 2013, where organizers considered input from 645 authors and resolved 168 scheduling conflicts. Results show the value of integrating community input with an intelligent user interface to solve complex planning tasks.

We describe a social visualization system that monitors the vocal arousal levels of the participants in a simulated two-party employment negotiation. In a 3x2 factorial experiment (N = 84), we manipulate two variables of interest for social visualization systems: the feedback configuration of the system's display (participants receive self feedback vs. partner feedback vs. no feedback) and the status of the interactants (high vs. low). Receiving feedback about one's own arousal level has negative consequences for performance in and feelings about the negotiation. Receiving feedback about one's partner's arousal level interacts with status: high-status individuals benefit from the visualization, while low-status individuals do not.

We developed DataBoard, a freeform spatial interface, to support users in simple problem solving tasks. To develop a deeper understanding of the role of space and the tradeoffs between freeform and structured interaction styles in problem solving tasks, we conducted a controlled user study comparing the DataBoard with a spreadsheet and analyzed video data in detail. Beyond improvements in task performance and memory recall, our observations reveal that freeform interfaces can support users in a variety of ways: representing problems flexibly, developing strategies, executing strategies incrementally, tracking problem state easily, reducing mental computation, and verifying solutions perceptually. The spreadsheet also had advantages, and we discuss the tradeoffs.