Relating a piece to previously established works is crucial in creating and engaging with art, but AI interfaces tend to obscure such relationships, rather than helping users explore them. Embedding models present new opportunities to support spatially exploring and relating artwork. We built Artographer, an art-exploration system featuring a zoomable 2-D map, constructed from similarity-clustered embeddings of ~16,000 historical artworks. We used Artographer as a design probe to explore how alternative artwork distribution interface design can shape media engagement: we invited 20 participants, including 9 art history scholars, to traverse the map, collecting artworks for a goal-driven task and while freely exploring. We identify values enacted in spatial art discovery (Visibility, Agency, Serendipity, Friction) and consider how these values challenge dominant design paradigms—in particular, the recommendation systems governing contemporary media distribution platforms. We reimagine a curatorial approach to media distribution, within digital ecosystems where history and culture can thrive.

Creativity support tools (CSTs) increasingly include image-generation features. The underlying diffusion models enact a particular image diffusing process that AI CSTs tend to obscure within a black-box. Artists’ creative control is limited to indirect manipulation (prompting), chaining these “black-boxes” together, or using ML-engineering skills to build custom black-boxes. Seeking to maintain the low-threshold offered by prompting, while raising the ceiling of expressive interactions, we built Noise Pilot: a multi-layered approach to supporting diffusion-based creative processes at three levels of depth. We used Noise Pilot as a probe to study the artistic processes of 9 artists over a 2-week period. Artists engaged with diffusion at different levels of manipulative depth and crafted reusable artifacts to enact bespoke diffusion processes; some produced results impossible to achieve with prompting alone. We discuss how black-box AIs in CSTs limit creative power, and propose subverting this by favoring visibility over obscurity, and materiality over personification.

In this work, we explore explicit Large Language Model (LLM)powered support for the iterative design of computer programs.Program design, like other design activity, is characterized by navigating a space of alternative problem formulations and associated solutions in an iterative fashion.LLMs are potentially powerful tools in helping this exploration; however, by default, code-generation LLMs deliver code that represents a particular point solution.This obscures the larger space of possible alternatives, many of which might be preferable to the LLM's default interpretation and its generated code.We contribute an IDE that supports program design through generating and showing new ways to frame problems alongside alternative solutions, tracking design decisions, and identifying implicit decisions made by either the programmer or the LLM.In a user study, we find that with our IDE, users combine and parallelize design phases to explore a broader design space-but also struggle to keep up with LLM-originated changes to code and other information overload.These findings suggest a core challenge for future IDEs that support program design through higher-level instructions given to LLM-based agents: carefully managing attention and deciding what information agents should surface to program designers and when.

LLM-based chatbots enable students to get immediate, interactive help on homework assignments, but even a thoughtfully-designed bot may not serve all pedagogical goals. We report here on the development and deployment of a GPT-4-based interactive homework assistant ("61A Bot'') for students in a large CS1 course; over 2000 students made over 100,000 requests of our Bot across two semesters. Our assistant offers one-shot, contextual feedback within the command-line "autograder'' students use to test their code. Our Bot wraps student code in a custom prompt that supports our pedagogical goals and avoids providing solutions directly. Analyzing student feedback, questions, and autograder data, we find reductions in homework-related question rates in our course forum, as well as reductions in homework completion time when our Bot is available. For students in the 50th -80th percentile, reductions can exceed 30 minutes per assignment, up to 50% less time than students at the same percentile rank in prior semesters. Finally, we discuss these observations, potential impacts on student learning, and other potential costs and benefits of AI assistance in CS1.

intent is hard to communicat  Prompts need to be detailed and dataspecific  LLM behavior varies across document  Requires fine-grained decomposition A B C Docs & Outputs condition discomfort_level symptoms

Design space exploration (DSE) for Text-to-Image (TTI) models entails navigating a vast, opaque space of possible image outputs, through a commensurately vast input space of hyperparameters and prompt text. Perceptually small movements in prompt-space can surface unexpectedly disparate images. How can interfaces support end-users in reliably steering prompt-space explorations towards interesting results? Our design probe, DreamSheets, supports user-composed exploration strategies with LLM-assisted prompt construction and large-scale simultaneous display of generated results, hosted in a spreadsheet interface. Two studies, a preliminary lab study and an extended two-week study where five expert artists developed custom TTI sheet-systems, reveal various strategies for targeted TTI design space exploration—such as using templated text generation to define and layer semantic “axes” for exploration. We identified patterns in exploratory structures across our participants’ sheet-systems: configurable exploration “units” that we distill into a UI mockup, and generalizable UI components to guide future interfaces.

Feedback on user interface (UI) mockups is crucial in design. However, human feedback is not always readily available. We explore the potential of using large language models for automatic feedback. Specifically, we focus on applying GPT-4 to automate heuristic evaluation, which currently entails a human expert assessing a UI’s compliance with a set of design guidelines. We implemented a Figma plugin that takes in a UI design and a set of written heuristics, and renders automatically-generated feedback as constructive suggestions. We assessed performance on 51 UIs using three sets of guidelines, compared GPT-4-generated design suggestions with those from human experts, and conducted a study with 12 expert designers to understand fit with existing practice. We found that GPT-4-based feedback is useful for catching subtle errors, improving text, and considering UI semantics, but feedback also decreased in utility over iterations. Participants described several uses for this plugin despite its imperfect suggestions.

While recent work explores novel tools to make electronics and device design easier and more accessible, these tend to be either highly automated (great for novices, but limiting for more advanced users) or highly manual (suitable for experts, but imposes a higher skill barrier to entry). In this work, we examine a middle ground: user-guided design space exploration to bridge an intuitive-but-ambiguous high-level representation to a fully-specified, fabrication-ready circuit. Our system helps users understand and make design choices by sweeping the design space of alternatives for electronics parts (e.g., choice of microcontroller), marking invalid options, and plotting points to visualize trade-offs (e.g., for power and size). We discuss the overall system and its structure, report on the results of a small but in-depth user study with participants from a wide range of electronics backgrounds, and draw insights on future directions for improving electronics design for everyone.

Dictation enables efficient text input on mobile devices. However, writing with speech can produce disfluent, wordy, and incoherent text and thus requires heavy post-processing. This paper presents Rambler, an LLM-powered graphical user interface that supports gist-level manipulation of dictated text with two main sets of functions: gist extraction and macro revision. Gist extraction generates keywords and summaries as anchors to support the review and interaction with spoken text. LLM-assisted macro revisions allow users to respeak, split, merge, and transform dictated text without specifying precise editing locations. Together they pave the way for interactive dictation and revision that help close gaps between spontaneously spoken words and well-structured writing. In a comparative study with 12 participants performing verbal composition tasks, Rambler outperformed the baseline of a speech-to-text editor + ChatGPT, as it better facilitates iterative revisions with enhanced user control over the content while supporting surprisingly diverse user strategies.

Automated UI evaluation can be beneficial for the design process; for example, to compare different UI designs, or conduct automated heuristic evaluation. LLM-based UI evaluation, in particular, holds the promise of generalizability to a wide variety of UI types and evaluation tasks. However, current LLM-based techniques do not yet match the performance of human evaluators. We hypothesize that automatic evaluation can be improved by collecting a targeted UI feedback dataset and then using this dataset to enhance the performance of general-purpose LLMs. We present a targeted dataset of 3,059 design critiques and quality ratings for 983 mobile UIs, collected from seven designers, each with at least a year of professional design experience. We carried out an in-depth analysis to characterize the dataset’s features. We then applied this dataset to achieve a 55% performance gain in LLM-generated UI feedback via various few-shot and visual prompting techniques. We also discuss future applications of this dataset, including training a reward model for generative UI techniques, and fine-tuning a tool-agnostic multi-modal LLM that automates UI evaluation.

Procedural content generation (PCG), the process of algorithmically creating game components instead of manually, has been a common tool of game development for decades. Recent advances in large language models (LLMs) enable the generation of game behaviors based on player input at runtime. Such code generation brings with it the possibility of entirely new gameplay interactions that may be difficult to integrate with typical game development workflows. We explore these implications through GROMIT, a novel LLM-based runtime behavior generation system for Unity. When triggered by a player action, GROMIT generates a relevant behavior which is compiled without developer intervention and incorporated into the game. We create three demonstration scenarios with GROMIT to investigate how such a technology might be used in game development. In a system evaluation we find that our implementation is able to produce behaviors that result in significant downstream impacts to gameplay. We then conduct an interview study with n=13 game developers using GROMIT as a probe to elicit their current opinion on runtime behavior generation tools, and enumerate the specific themes curtailing the wider use of such tools. We find that the main themes of concern are quality considerations, community expectations, and fit with developer workflows, and that several of the subthemes are unique to runtime behavior generation specifically. We outline a future work agenda to address these concerns, including the need for additional guardrail systems for behavior generation.

Due to the cumbersome nature of human evaluation and limitations of code-based evaluation, Large Language Models (LLMs) are increasingly being used to assist humans in evaluating LLM outputs. Yet LLM-generated evaluators simply inherit all the problems of the LLMs they evaluate, requiring further human validation. We present a mixed-initiative approach to “validate the validators”—aligning LLM-generated evaluation functions (be it prompts or code) with human requirements. Our interface, EvalGen, provides automated assistance to users in generating evaluation criteria and implementing assertions. While generating candidate implementations (Python functions, LLM grader prompts), EvalGen asks humans to grade a subset of LLM outputs; this feedback is used to select implementations that better align with user grades. A qualitative study finds overall support for EvalGen but underscores the subjectivity and iterative nature of alignment. In particular, we identify a phenomenon we dub criteria drift: users need criteria to grade outputs, but grading outputs helps users define criteria. What is more, some criteria appear dependent on the specific LLM outputs observed (rather than independent and definable a priori), raising serious questions for approaches that assume the independence of evaluation from observation of model outputs. We present our interface and implementation details, a comparison of our algorithm with a baseline approach, and implications for the design of future LLM evaluation assistants.

A common way to enable immersion in VR is to render a virtual body that mirrors the user’s physical movements. VR allows us to design interaction schemes that go beyond direct avatar embodiments. In particular, there is a growing body of literature investigating the simultaneous control of multiple bodies in VR. We contribute to this literature by investigating the important case where multiple bodies perform a coordinated interaction with each other. Such actions directly question what kind of embodiment users experience. Concretely, we investigate people’s abilities to perform coordinated bimanual selection and handoff tasks between a first-person and third-person body through a user study with 19 participants. Results provide quantitative & qualitative evidence for people’s ability to perform complex coordinated tasks through two bodies. Furthermore we characterize participant performance in different task and interaction configurations, summarize the strategies they employed, and discuss qualities of user’s proprioception.

Reporting on the Non-Fungible Token (NFT) ecosystem overwhelmingly focuses on the community that drove its growth and price volatility, gaining widespread media attention in 2021. This overlooks the communities developing novel creative practices on NFT platforms. Interviews with 16 creatives utilizing NFTs reveal a vast Art World: networks of distinct communities maturing into cooperative ecosystems with unique artistic subcultures, philosophies, and interactions. We observe unique qualities of these decentralized distribution platforms and identify patterns of activity comparable to those of traditional art worlds. We identify how aspects of these systems might subvert, or replicate, existing systems of power, value, and access. The impacts of policy and platform design on online creative communities in the NFT Art World carry valuable lessons for developers of digital interventions into the creative industry, exemplifying pertinent considerations for the future of creative labor and cooperation online.

Prompting Large Language Models (LLMs) is an exciting new approach to designing chatbots. But can it improve LLM’s user experience (UX) reliably enough to power chatbot products? Our attempt to design a robust chatbot by prompting GPT-3/4 alone suggests: not yet. Prompts made achieving “80%” UX goals easy, but not the remaining 20%. Fixing the few remaining interaction breakdowns resembled herding cats: We could not address one UX issue or test one design solution at a time; instead, we had to handle everything everywhere all at once. Moreover, because no prompt could make GPT reliably say “I don’t know” when it should, the user-GPT conversations had no guardrails after a breakdown occurred, often leading to UX downward spirals. These risks incentivized us to design highly prescriptive prompts and scripted bots, counter to the promises of LLM-powered chatbots. This paper describes this case study, unpacks prompting’s fickleness and its impact on UX design processes, and discusses implications for LLM-based design methods and tools.

Pre-trained large language models (“LLMs”) like GPT-3 can engage in fluent, multi-turn instruction-taking out-of-the-box, making them attractive materials for designing natural language interactions. Using natural language to steer LLM outputs (“prompting”) has emerged as an important design technique potentially accessible to non-AI-experts. Crafting effective prompts can be challenging, however, and prompt-based interactions are brittle. Here, we explore whether non-AI-experts can successfully engage in “end-user prompt engineering” using a design probe—a prototype LLM-based chatbot design tool supporting development and systematic evaluation of prompting strategies. Ultimately, our probe participants explored prompt designs opportunistically, not systematically, and struggled in ways echoing end-user programming systems and interactive machine learning systems. Expectations stemming from human-to-human instructional experiences, and a tendency to overgeneralize, were barriers to effective prompt design. These findings have implications for non-AI-expert-facing LLM-based tool design and for improving LLM-and-prompt literacy among programmers and the public, and present opportunities for further research.

Presenters often collect audience feedback through practice talks to refine their presentations. In formative interviews, we find that although text feedback and verbal discussions allow presenters to receive feedback, organizing that feedback into actionable presentation revisions remains challenging. Feedback may lack context, be redundant, and be spread across various emails, notes, and conversations. To collate and contextualize both text and verbal feedback, we present SlideSpecs. SlideSpecs lets audience members provide text feedback (e.g., ‘font too small’) while attaching an automatically detected context, including relevant slides (e.g., ‘Slide 7’) or content tags (e.g., ‘slide design’). SlideSpecs also records and transcribes spoken group discussions that commonly occur after practice talks and facilitates linking text critiques to relevant discussion segments. Finally, presenters can use SlideSpecs to review all text and spoken feedback in a single contextually rich interface (e.g., relevant slides, topics, and follow-up discussions). We demonstrate the effectiveness of SlideSpecs by deploying it in eight practice talks with a range of topics and purposes and reporting our findings.

Vector graphics are an industry-standard way to represent and share visual designs. Designers frequently source and incorporate styles from existing designs into their work. Unfortunately, popular design tools are not well suited for this task. We present VST, Vector Style Transfer, a novel design tool for flexibly transferring visual styles between vector graphics. The core of VST lies in leveraging automation while respecting designers’ tastes and the subjectivity inherent to style transfer. In VST, designers tune a cross-design element correspondence and customize which style attributes to change. We report results from a user study in which designers used VST to control style transfer between several designs, including designs participants created with external tools beforehand. VST shows that enabling design correspondence tuning and customization is one way to support interactive, flexible style transfer.

UI designers often correct false affordances and improve the discoverability of features when users have trouble determining if elements are tappable. We contribute a novel system that models the perceived tappability of mobile UI elements with a vision-based deep neural network and helps provide design insights with dataset-level and instance-level explanations of model predictions. Our system retrieves designs from similar mobile UI examples from our dataset using the latent space of our model. We also contribute a novel use of an interpretability algorithm, XRAI, to generate a heatmap of UI elements that contribute to a given tappability prediction. Through several examples, we show how our system can help automate elements of UI usability analysis and provide insights for designers to iterate their designs. In addition, we share findings from an exploratory evaluation with professional designers to learn how AI-based tools can aid UI design and evaluation for tappability issues.

Programmers often rely on online resources—such as code examples, documentation, blogs, and Q&A forums—to compare similar libraries and select the one most suitable for their own tasks and contexts. However, this comparison task is often done in an ad-hoc manner, which may result in suboptimal choices. Inspired by Analogical Learning and Variation Theory, we hypothesize that rendering many concept-annotated code examples from different libraries side-by-side can help programmers (1) develop a more comprehensive understanding of the libraries’ similarities and distinctions and (2) make more robust, appropriate library selections. We designed a novel interactive interface, ParaLib, and used it as a technical probe to explore to what extent many side-by-side concepted-annotated examples can facilitate the library comparison and selection process. A within-subjects user study with 20 programmers shows that, when using ParaLib, participants made more consistent, suitable library selections and provided more comprehensive summaries of libraries’ similarities and differences.

Training deep neural networks can generate non-descriptive error messages or produce unusual output without any explicit errors at all. While experts rely on tacit knowledge to apply debugging strategies, non-experts lack the experience required to interpret model output and correct Deep Learning (DL) programs. In this work, we identify DL debugging heuristics and strategies used by experts, andIn this work, we categorize the types of errors novices run into when writing ML code, and map them onto opportunities where tools could help. We use them to guide the design of Umlaut. Umlaut checks DL program structure and model behavior against these heuristics; provides human-readable error messages to users; and annotates erroneous model output to facilitate error correction. Umlaut links code, model output, and tutorial-driven error messages in a single interface. We evaluated Umlaut in a study with 15 participants to determine its effectiveness in helping developers find and fix errors in their DL programs. Participants using Umlaut found and fixed significantly more bugs and were able to implement fixes for more bugs compared to a baseline condition.

In many engineering disciplines such as circuit board, chip, and mechanical design, a hardware description language (HDL) approach provides important benefits over direct manipulation interfaces by supporting concepts like abstraction and generator meta-programming. While several such HDLs have emerged recently and promised power and flexibility, they also present challenges – especially to designers familiar with current graphical workflows. In this work, we investigate an IDE approach to provide a graphical editor for a board-level circuit design HDL. Unlike GUI builders which convert an entire diagram to code, we instead propose generating equivalent HDL from individual graphical edit actions. By keeping code as the primary design input, we preserve the full power of the underlying HDL, while remaining useful even to advanced users. We discuss our concept, design considerations such as performance, system implementation, and report on the results of an exploratory remote user study with four experienced hardware designers.

APIs are becoming the fundamental building block of modern software and their usability is crucial to programming efficiency and software quality. Yet API designers find it hard to gather and interpret user feedback on their APIs. To close the gap, we interviewed 23 API designers from 6 companies and 11 open-source projects to understand their practices and needs. The primary way of gathering user feedback is through bug reports and peer reviews, as formal usability testing is prohibitively expensive to conduct in practice. Participants expressed a strong desire to gather real-world use cases and understand users' mental models, but there was a lack of tool support for such needs. In particular, participants were curious about where users got stuck, their workarounds, common mistakes, and unanticipated corner cases. We highlight several opportunities to address those unmet needs, including developing new mechanisms that systematically elicit users' mental models, building mining frameworks that identify recurring patterns beyond shallow statistics about API usage, and exploring alternative design choices made in similar libraries.

Programming tutorials are a pervasive, versatile medium for teaching programming. In this paper, we report on the content and structure of programming tutorials, the pain points authors experience in writing them, and a design for a tool to help improve this process. An interview study with 12 experienced tutorial authors found that they construct documents by interleaving code snippets with text and illustrative outputs. It also revealed that authors must often keep related artifacts of source programs, snippets, and outputs consistent as a program evolves. A content analysis of 200 frequently-referenced tutorials on the web also found that most tutorials contain related artifacts—duplicate code and outputs generated from snippets—that an author would need to keep consistent with each other. To address these needs, we designed a tool called Torii with novel authoring capabilities. An in-lab study showed that tutorial authors can successfully use the tool for the unique affordances identified, and provides guidance for designing future tools for tutorial authoring.

Virtual Reality (VR) users often need to work with other users, who observe them outside of VR using an external display. Communication between them is difficult; the VR user cannot see the external user's gestures, and the external user cannot see VR scene elements outside of the VR user's view. We carried out formative interviews with experts to understand these asymmetrical interactions and identify their goals and challenges. From this, we identify high-level system design goals to facilitate asymmetrical interactions and a corresponding space of implementation approaches based on the level of programmatic access to a VR application. We present TransceiVR, a system that utilizes VR platform APIs to enable asymmetric communication interfaces for third-party applications without requiring source code access. TransceiVR allows external users to explore the VR scene spatially or temporally, to annotate elements in the VR scene at correct depths, and to discuss via a shared static virtual display. An initial co-located user evaluation with 10 pairs shows that our system makes asymmetric collaborations in VR more effective and successful in terms of task time, error rate, and task load index. An informal evaluation with a remote expert gives additional insight on utility of features for real world tasks.

Mainstream board-level circuit design tools work at the lowest level of design --- schematics and individual components. While novel tools experiment with higher levels of design, abstraction often comes at the expense of the fine-grained control afforded by low-level tools. In this work, we propose a hardware description language (HDL) approach that supports users at multiple levels of abstraction from broad system architecture to subcircuits and component selection. We extend the familiar hierarchical block diagram with polymorphism to include abstract-typed blocks (e.g., generic resistor supertype) and electronics modeling (i.e., currents and voltages). Such an approach brings the advantages of reusability and encapsulation from object-oriented programming, while addressing the unique needs of electronics designers such as physical correctness verification. We discuss the system design, including fundamental abstractions, the block diagram construction HDL, and user interfaces to inspect and fine-tune the design; demonstrate example designs built with our system; and present feedback from intermediate-level engineers who have worked with our system.

Eye-tracking interfaces increase the communication bandwidth between humans and computers when using hands is not possible. For some, eyes are the only available input modality to control and interact with the various devices that enable their independence. The goal of this work is to develop and evaluate an eye-controlled wheelchair navigation interface that minimizes obstruction to the user's field of view by removing the conventional use of a computer screen as a feedback mechanism. We present See-Thru, an eye-tracking interface that provides feedback to the user without a screen while simultaneously providing a clear view of the path ahead. Our prototype is evaluated against a screen-based state of the art interface in a study with three navigation tasks completed by seven power wheelchair users. Our results show that a majority of the participants not only prefer using the See-Thru interface, but perform better at driving tasks when using it. This supports the notion that users favor minimally obstructive interfaces in navigational contexts.

Students and hobbyists build embedded systems that combine sensing, actuation and microcontrollers on solderless breadboards. To help students debug such circuits, experienced teachers apply visual inspection, targeted measurements, and circuit modifications to diagnose and localize the problem(s). However, experienced helpers may not always be available to review student projects in person. To enable remote debugging of circuit problems, we introduce Heimdall, a remote electronics workbench that allows experts to visually inspect a student's circuit; perform measurements; and to re-wire and inject test signals. These interactions are enabled by an actuated inspection camera; an augmented breadboard that enables flexible configuration of row connectivity and measurement/injection lines; and a web-based UI that teachers can use to perform measurements through interaction with the captured images. We demonstrate that common issues arising in embedded electronics classes can be successfully diagnosed remotely and report on preliminary user feedback from teaching assistants who frequently debug circuits.

Virtual Reality painting is a form of 3D-painting done in a Virtual Reality (VR) space. Being a relatively new kind of art form, there is a growing interest within the creative practices community to learn it. Currently, most users learn using community posted 2D-videos on the internet, which are a screencast recording of the painting process by an instructor. While such an approach may suffice for teaching 2D-software tools, these videos by themselves fail in delivering crucial details that required by the user to understand actions in a VR space. We conduct a formative study to identify challenges faced by users in learning to VR-paint using such video-based tutorials. Informed by results of this study, we develop a VR-embedded tutorial system that supplements video tutorials with 3D and contextual aids directly in the user's VR environment. An exploratory evaluation showed users were positive about the system and were able to use the proposed system to recreate painting tasks in VR.

Printed Circuit Board (PCB) design tools are critical in helping users build non-trivial electronics devices. While recent work recognizes deficiencies with current tools and explores novel methods, little has been done to understand modern designers and their needs. To gain better insight into their practices, we interview fifteen electronics designers of a variety of backgrounds. Our open-ended, semi-structured interviews examine both overarching design flows and details of individual steps. One major finding was that most creative engineering work happens during system architecture, yet current tools operate at lower abstraction levels and create significant tedious work for designers. From that insight, we conceptualize abstractions and primitives for higher-level tools and elicit feedback from our participants on clickthrough mockups of design flows through an example project. We close with our observation on opportunities for improving board design tools and discuss generalizability of our findings beyond the electronics domain.

Remotely instructing and guiding users in physical tasks has offered promise across a wide variety of domains. While it has been the subject of many research projects, current approaches are often limited in the communication bandwidth (lacking context, spatial information) or interactivity (unidirectional, asynchronous) between the expert and the learner. Systems that use Mixed-Reality systems for this purpose have rigid configurations for the expert and the learner. We explore the design space of bi-directional mixed-reality telepresence systems for teaching physical tasks, and present Loki, a novel system which explores the various dimensions of this space. Loki leverages video, audio and spatial capture along with mixed-reality presentation methods to allow users to explore and annotate the local and remote environments, and record and review their own performance as well as their peer's. The system design of Loki also enables easy transitions between different configurations within the explored design space. We validate its utility through a varied set of scenarios and a qualitative user study.

Computer vision is applied in an ever expanding range of applications, many of which require custom training data to perform well. We present a novel interface for rapid collection of labeled training images to improve CV-based object detectors. LabelAR leverages the spatial tracking capabilities of an AR-enabled camera, allowing users to place persistent bounding volumes that stay centered on real-world objects. The interface then guides the user to move the camera to cover a wide variety of viewpoints. We eliminate the need for post hoc labeling of images by automatically projecting 2D bounding boxes around objects in the images as they are captured from AR-marked viewpoints. In a user study with 12 participants, LabelAR significantly outperforms existing approaches in terms of the trade-off between detection performance and collection time.

Using existing APIs properly is a key challenge in programming, given that libraries and APIs are increasing in number and complexity. Programmers often search for online code examples in Q&A forums and read tutorials and blog posts to learn how to use a given API. However, there are often a massive number of related code examples and it is difficult for a user to understand the commonalities and variances among them, while being able to drill down to concrete details. We introduce an interactive visualization for exploring a large collection of code examples mined from open-source repositories at scale. This visualization summarizes hundreds of code examples in one synthetic code skeleton with statistical distributions for canonicalized statements and structures enclosing an API call. We implemented this interactive visualization for a set of Java APIs and found that, in a lab study, it helped users (1) answer significantly more API usage questions correctly and comprehensively and (2) explore how other programmers have used an unfamiliar API.

Programmers frequently learn from examples produced and shared by other programmers. However, it can be challenging and time-consuming to produce concise, working code examples. We conducted a formative study where 12 participants made examples based on their own code. This revealed a key hurdle: making meaningful simplifications without introducing errors. Based on this insight, we designed a mixed-initiative tool, CodeScoop, to help programmers extract executable, simplified code from existing code. CodeScoop enables programmers to "scoop" out a relevant subset of code. Techniques include selectively including control structures and recording an execution trace that allows authors to substitute literal values for code and variables. In a controlled study with 19 participants, CodeScoop helped programmers extract executable code examples with the intended behavior more easily than with a standard code editor.

Our perception of our surrounding environment is limited by the constraints of human biology. The field of augmented perception asks how our sensory capabilities can be usefully extended through computational means. We argue that spatial awareness can be enhanced by exploiting recent advances in computer vision which make high-accuracy, real-time object detection feasible in everyday settings. We introduce HindSight, a wearable system that increases spatial awareness by detecting relevant objects in live 360-degree video and sonifying their position and class through bone conduction headphones. HindSight uses a deep neural network to locate and attribute semantic information to objects surrounding a user through a head-worn panoramic camera. It then uses bone conduction headphones, which preserve natural auditory acuity, to transmit audio notifications for detected objects of interest. We develop an application using HindSight to warn cyclists of approaching vehicles outside their field of view and evaluate it in an exploratory study with 15 users. Participants reported increases in perceived safety and awareness of approaching vehicles when using HindSight.

The rise in prevalence of Internet of Things (IoT) technologies has encouraged more people to prototype and build custom internet connected devices based on low power microcontrollers. While well-developed tools exist for debugging network communication for desktop and web applications, it can be difficult for developers of networked embedded systems to figure out why their network code is failing due to the limited output affordances of embedded devices. This paper presents WiFröst, a new approach for debugging these systems using instrumentation that spans from the device itself, to its communication API, to the wireless router and back-end server. WiFröst automatically collects this data, displays it in a web-based visualization, and highlights likely issues with an extensible suite of checks based on analysis of recorded execution traces.

Although many software libraries and hardware modules support reading data from sensors, makers of interactive systems often struggle to extract higher-level information from raw sensor data. Available general-purpose machine learning (ML) libraries remain difficult to use for non-experts. Prior research has sought to bridge this gap through domain-specific user interfaces for particular types of sensors or algorithms. Our ESP (Example-based Sensor Prediction) system introduces a more general approach in which interactive visualizations and control interfaces are dynamically generated from augmented code examples written by experts. ESP's augmented examples allow experts to write logic that guides makers through important steps such as sensor calibration, parameter tuning, and assessing signal quality and classification performance. Writing augmented examples requires additional effort. ESP leverages a fundamental dynamic of online communities: experts are often willing to invest such effort to teach and train novices. Thus support for particular sensing domains does not have to be hard-wired a priori by system authors, but can be provided later by its community of users. We illustrate ESP's flexibility by detailing pipelines for four distinct sensors and classification algorithms. We validated the usability and flexibility of our example-based approach through a one-day workshop with 11 participants.

Automatic program transformation tools can be valuable for programmers to help them with refactoring tasks, and for Computer Science students in the form of tutoring systems that suggest repairs to programming assignments. However, manually creating catalogs of transformations is complex and time-consuming. In this paper, we present REFAZER, a technique for automatically learning program transformations. REFAZER builds on the observation that code edits performed by developers can be used as input-output examples for learning program transformations. Example edits may share the same structure but involve different variables and subexpressions, which must be generalized in a transformation at the right level of abstraction. To learn transformations, REFAZER leverages state-of-the-art programming-by-example methodology using the following key components: (a) a novel domain-specific language (DSL) for describing program transformations, (b) domain-specific deductive algorithms for efficiently synthesizing transformations in the DSL, and (c) functions for ranking the synthesized transformations. We instantiate and evaluate REFAZER in two domains. First, given examples of code edits used by students to fix incorrect programming assignment submissions, we learn program transformations that can fix other students' submissions with similar faults. In our evaluation conducted on 4 programming tasks performed by 720 students, our technique helped to fix incorrect submissions for 87% of the students. In the second domain, we use repetitive code edits applied by developers to the same project to synthesize a program transformation that applies these edits to other locations in the code. In our evaluation conducted on 56 scenarios of repetitive edits taken from three large C# open-source projects, REFAZER learns the intended program transformation in 84% of the cases using only 2.9 examples on average.

In large introductory programming classes, teacher feedback on individual incorrect student submissions is often infeasible. Program synthesis techniques are capable of fixing student bugs and generating hints automatically, but they lack the deep domain knowledge of a teacher and can generate functionally correct but stylistically poor fixes. We introduce a mixed-initiative approach which combines teacher expertise with data-driven program synthesis techniques. We demonstrate our novel approach in two systems that use different interaction mechanisms. Our systems use program synthesis to learn bug-fixing code transformations and then cluster incorrect submissions by the transformations that correct them. The MistakeBrowser system learns transformations from examples of students fixing bugs in their own submissions. The FixPropagator system learns transformations from teachers fixing bugs in incorrect student submissions. Teachers can write feedback about a single submission or a cluster of submissions and propagate the feedback to all other submissions that can be fixed by the same transformation. Two studies suggest this approach helps teachers better understand student bugs and write reusable feedback that scales to a massive introductory programming classroom.

The Maker movement has encouraged more people to start working with electronics and embedded processors. A key challenge in developing and debugging custom embedded systems is understanding their behavior, particularly at the boundary between hardware and software. Existing tools such as step debuggers and logic analyzers only focus on software or hardware, respectively. This paper presents a new development environment designed to illuminate the boundary between embedded code and circuits. Bifröst automatically instruments and captures the progress of the user's code, variable values, and the electrical and bus activity occurring at the interface between the processor and the circuit it operates in. This data is displayed in a linked visualization that allows navigation through time and program execution, enabling comparisons between variables in code and signals in circuits. Automatic checks can detect low-level hardware configuration and protocol issues, while user-authored checks can test particular application semantics. In an exploratory study with ten participants, we investigated how Bifröst influences debugging workflows.

Virtual reality filmmakers creating 360-degree video currently rely on cinematography techniques that were developed for traditional narrow field of view film. They typically edit together a sequence of shots so that they appear at a fixed-orientation irrespective of the viewer's field of view. But because viewers set their own camera orientation they may miss important story content while looking in the wrong direction. We present new interactive shot orientation techniques that are designed to help viewers see all of the important content in 360-degree video stories. Our viewpoint-oriented technique reorients the shot at each cut so that the most important content lies in the the viewer's current field of view. Our active reorientation technique, lets the viewer press a button to immediately reorient the shot so that important content lies in their field of view. We present a 360-degree video player which implements these techniques and conduct a user study which finds that users spend 5.2-9.5% more time viewing the important points (manually labelled) of the scene with our techniques compared to the traditional fixed-orientation cuts. In practice, 360-degree video creators may label important content, but we also provide an automatic method for determining important content in existing 360-degree videos.

Recent advances in program synthesis offer means to automatically debug student submissions and generate personalized feedback in massive programming classrooms. When automatically generating feedback for programming assignments, a key challenge is designing pedagogically useful hints that are as effective as the manual feedback given by teachers. Through an analysis of teachers' hint-giving practices in 132 online Q&A posts, we establish three design guidelines that an effective feedback design should follow. Based on these guidelines, we develop a feedback system that leverages both program synthesis and visualization techniques. Our system compares the dynamic code execution of both incorrect and fixed code and highlights how the error leads to a difference in behavior and where the incorrect code trace diverges from the expected solution. Results from our study suggest that our system enables students to detect and fix bugs that are not caught by students using another existing visual debugging tool.

This paper explores personal fabrication as a means of engaging new audiences in the creation of electronic devices, which play an ever-increasing role in our lives but which most people have little involvement in creating. We describe a six-session workshop in which eight participants made wifi-connected devices through the design and fabrication of custom printed circuit boards. We explore the implications of using components and processes analogous to those in commercial products, as opposed to the limited set of higher-level building blocks found in toolkits for hobbyist electronics. We highlight unique advantages and challenges of a personal fabrication approach. We explore the role of attitudes in identifying a suitable audience for these activities. We discuss insights into commercial electronic products offered by personal fabrication and explore its role in a world of mass-produced electronic devices. Finally, we suggest opportunities for future tools and technologies.

We fuse science and design thinking to create a novel, IoT interactive urban lights system focused on increasing positive affect among pedestrians. Our contributions are three-fold. First, the design, construction, and evaluation of an efficient interactive lighting system focused on well-being, as opposed to systems focused on utility or landscaping. Second, we used scientific methods to discover basic design parameters for affective outcomes. Third, we optimized user experiences for low energy profiles, positive affect, and interactivity. Tested interactions show positive and some unexpected negative responses. Optimal interactive designs cut energy consumption by 75% while maintaining positive affect. Furthermore, card sorting design exercises revealed an inverse relationship between perceived pleasant feelings and interactivity. We conclude by discussing the implications of our research for the design of coherent, attractive, and efficient urban lighting.

Cross-device interactions involve input and output on multiple computing devices. Implementing and reasoning about interactions that cover multiple devices with a diversity of form factors and capabilities can be complex. To assist developers in programming cross-device interactions, we created DemoScript, a technique that automatically analyzes a cross-device interaction program while it is being written. DemoScript visually illustrates the step-by-step execution of a selected portion or the entire program with a novel, automatically generated cross-device storyboard visualization. In addition to helping developers understand the behavior of the program, DemoScript also allows developers to revise their program by interactively manipulating the cross-device storyboard. We evaluated DemoScript with 8 professional programmers and found that DemoScript significantly improved development efficiency by helping developers interpret and manage cross-device interaction; it also encourages testing to think through the script in a development process.

Expert feedback is valuable but hard to obtain for many designers. Online crowds can provide fast and affordable feedback, but workers may lack relevant domain knowledge and experience. Can expert rubrics address this issue and help novices provide expert-level feedback? To evaluate this, we conducted an experiment with a 2x2 factorial design. Student designers received feedback on a visual design from both experts and novices, who produced feedback using either an expert rubric or no rubric. We found that rubrics helped novice workers provide feedback that was rated nearly as valuable as expert feedback. A follow-up analysis on writing style showed that student designers found feedback most helpful when it was emotionally positive and specific, and that a rubric increased the occurrence of these characteristics in feedback. The analysis also found that expertise correlated with longer critiques, but not the other favorable characteristics. An informal evaluation indicates that experts may instead have produced value by providing clearer justifications.

Digital image editing is usually an iterative process; users repetitively perform short sequences of operations, as well as undo and redo using history navigation tools. In our collected data, undo, redo and navigation constitute about 9 percent of the total commands and consume a significant amount of user time. Unfortunately, such activities also tend to be tedious and frustrating, especially for complex projects.

Illustrations of human movements are used to communicate ideas and convey instructions in many domains, but creating them is time-consuming and requires skill. We introduce DemoDraw, a multi-modal approach to generate these illustrations as the user physically demonstrates the movements. In a Demonstration Interface, DemoDraw segments speech and 3D joint motion into a sequence of motion segments, each characterized by a key pose and salient joint trajectories. Based on this sequence, a series of illustrations is automatically generated using a stylistically rendered 3D avatar annotated with arrows to convey movements. During demonstration, the user can navigate using speech and amend or re-perform motions if needed. Once a suitable sequence of steps has been created, a Refinement Interface enables fine control of visualization parameters. In a three-part evaluation, we validate the effectiveness of the generated illustrations and the usability of DemoDraw. Our results show 4 to 7-step illustrations can be created in 5 or 10 minutes on average.

The recent proliferation of easy to use electronic components and toolkits has introduced a large number of novices to designing and building electronic projects. Nevertheless, debugging circuits remains a difficult and time-consuming task. This paper presents a novel debugging tool for electronic design projects, the Toastboard, that aims to reduce debugging time by improving upon the standard paradigm of point-wise circuit measurements. Ubiquitous instrumentation allows for immediate visualization of an entire breadboard's state, meaning users can diagnose problems based on a wealth of data instead of having to form a single hypothesis and plan before taking a measurement. Basic connectivity information is displayed visually on the circuit itself and quantitative data is displayed on the accompanying web interface. Software-based testing functions further lower the expertise threshold for efficient debugging by diagnosing classes of circuit errors automatically. In an informal study, participants found the detailed, pervasive, and context-rich data from our tool helpful and potentially time-saving.

Video production is a collaborative process in which stakeholders regularly review drafts of the edited video to indicate problems and offer suggestions for improvement. Although practitioners prefer in-person feedback, most reviews are conducted asynchronously via email due to scheduling and location constraints. The use of this impoverished medium is challenging for both providers and consumers of feedback. We introduce VidCrit, a system for providing asynchronous feedback on drafts of edited video that incorporates favorable qualities of an in-person review. This system consists of two separate interfaces: (1) A feedback recording interface captures reviewers' spoken comments, mouse interactions, hand gestures and other physical reactions. (2) A feedback viewing interface transcribes and segments the recorded review into topical comments so that the video author can browse the review by either text or timelines. Our system features novel methods to automatically segment a long review session into topical text comments, and to label such comments with additional contextual information. We interviewed practitioners to inform a set of design guidelines for giving and receiving feedback, and based our system's design on these guidelines. Video reviewers using our system preferred our feedback recording interface over email for providing feedback due to the reduction in time and effort. In a fixed amount of time, reviewers provided 10.9 (σ=5.09) more local comments than when using text. All video authors rated our feedback viewing interface preferable to receiving feedback via e-mail.

We describe Lamello, an approach for creating tangible input components that recognize user interaction via passive acoustic sensing. Lamello employs comb-like structures with varying-length tines at interaction points (e.g., along slider paths). Moving a component generates tine strikes; a real-time audio processing pipeline analyzes the resultant sounds and emits high-level interaction events. Our main contributions are in the co-design of the tine structures, information encoding schemes, and audio analysis. We demonstrate 3D printed Lamello-powered buttons, sliders, and dials.

This research investigates how to introduce synchronous interactive peer learning into an online setting appropriate both for crowdworkers (learning new tasks) and students in massive online courses (learning course material). We present an interaction framework in which groups of learners are formed on demand and then proceed through a sequence of activities that include synchronous group discussion about learner-generated responses. Via controlled experiments with crowdworkers, we show that discussing challenging problems leads to better outcomes than working individually, and incentivizing people to help one another yields still better results. We then show that providing a mini-lesson in which workers consider the principles underlying the tested concept and justify their answers leads to further improvements. Combining the mini-lesson with the discussion of the multiple-choice question leads to significant improvements on that question. We also find positive subjective responses to the peer interactions, suggesting that discussions can improve morale in remote work or learning settings.

Feedback is an important component of the design process, but gaining access to high-quality critique outside a classroom or firm is challenging. We present CrowdCrit, a web-based system that allows designers to receive design critiques from non-expert crowd workers. We evaluated CrowdCrit in three studies focusing on the designer's experience and benefits of the critiques. In the first study, we compared crowd and expert critiques and found evidence that aggregated crowd critique approaches expert critique. In a second study, we found that designers who got crowd feedback perceived that it improved their design process. The third study showed that designers were enthusiastic about crowd critiques and used them to change their designs. We conclude with implications for the design of crowd feedback services.

The results of a study of online peer learning suggests that it may be advantageous to automatically assign students to small peer learning groups based on how many students initially get answers to questions correct.

Searching for scenes in movies is a time-consuming but crucial task for film studies scholars, film professionals, and new media artists. In pilot interviews we have found that such users search for a wide variety of clips---e.g., actions, props, dialogue phrases, character performances, locations---and they return to particular scenes they have seen in the past. Today, these users find relevant clips by watching the entire movie, scrubbing the video timeline, or navigating via DVD chapter menus. Increasingly, users can also index films through transcripts---however, dialogue often lacks visual context, character names, and high level event descriptions. We introduce SceneSkim, a tool for searching and browsing movies using synchronized captions, scripts and plot summaries. Our interface integrates information from such sources to allow expressive search at several levels of granularity: Captions provide access to accurate dialogue, scripts describe shot-by-shot actions and settings, and plot summaries contain high-level event descriptions. We propose new algorithms for finding word-level caption to script alignments, parsing text scripts, and aligning plot summaries to scripts. Film studies graduate students evaluating SceneSkim expressed enthusiasm about the usability of the proposed system for their research and teaching.

To fabricate functional objects, designers create assemblies combining existing parts (e.g., mechanical hinges, electronic components) with custom-designed geometry (e.g., enclosures). Modeling complex assemblies is outside the reach of the growing number of novice ``makers' with access to digital fabrication tools. We aim to allow makers to design and 3D print functional mechanical and electronic assemblies. Based on a formative exploration, we created Makers' Marks, a system based on physically authoring assemblies with sculpting materials and annotation stickers. Makers physically sculpt the shape of an object and attach stickers to place existing parts or high-level features (such as parting lines). Our tool extracts the 3D pose of these annotations from a scan of the design, then synthesizes the geometry needed to support integrating desired parts using a library of clearance and mounting constraints. The resulting designs can then be easily 3D printed and assembled. Our approach enables easy creation of complex objects such as TUIs, and leverages physical materials for tangible manipulation and understanding scale. We validate our tool through several design examples: a custom game controller, an animated toy figure, a friendly baby monitor, and a hinged box with integrated alarm.

Programmers frequently turn to the web to solve problems and find example code. For the sake of brevity, the snippets in online instructions often gloss over the syntax of languages like CSS selectors and Unix commands. Programmers must compensate by consulting external documentation. In this paper, we propose language-specific routines called Tutorons that automatically generate context-relevant, on-demand micro-explanations of code. A Tutoron detects explainable code in a web page, parses it, and generates in-situ natural language explanations and demonstrations of code. We build Tutorons for CSS selectors, regular expressions, and the Unix command “wget”. We demonstrate techniques for generating natural language explanations through template instantiation, synthesizing code demonstrations by parse tree traversal, and building compound explanations of co-occurring options. Through a qualitative study, we show that Tutoron-generated explanations can reduce the need for reference documentation in code modification tasks.

In computer-aided education, the goal of automatic feedback is to provide a meaningful explanation of students' mistakes. We focus on providing feedback for constructing a deterministic finite automaton that accepts strings that match a described pattern. Natural choices for feedback are binary feedback (correct/wrong) and a counterexample of a string that is processed incorrectly. Such feedback is easy to compute but might not provide the student enough help. Our first contribution is a novel way to automatically compute alternative conceptual hints. Our second contribution is a rigorous evaluation of feedback with 377 students. We find that providing either counterexamples or hints is judged as helpful, increases student perseverance, and can improve problem completion time. However, both strategies have particular strengths and weaknesses. Since our feedback is completely automatic, it can be deployed at scale and integrated into existing massive open online courses.

3D modelers often wish to showcase their models for sharing or review purposes. This may consist of generating static viewpoints of the model or authoring animated fly-throughs. Manually creating such views is often tedious and few automatic methods are designed to interactively assist the modelers with the view authoring process. We present a view authoring assistance system that supports the creation of informative view points, view paths, and view surfaces, allowing modelers to author the interactive navigation experience of a model. The key concept of our implementation is to analyze the model's workflow history, to infer important regions of the model and representative viewpoints of those areas. An evaluation indicated that the viewpoints generated by our algorithm are comparable to those manually selected by the modeler. In addition, participants of a user study found our system easy to use and effective for authoring viewpoint summaries.

Massive open online courses (MOOCs) rely primarily on discussion forums for interaction among students. We investigate how forum design affects student activity and learning outcomes through a field experiment with 1101 participants on the edX platform. We introduce a reputation system, which gives students points for making useful posts. We show that, as in other settings, use of forums in MOOCs is correlated with better grades and higher retention. Reputation systems additionally produce faster response times and larger numbers of responses per post, as well as differences in how students ask questions. However, reputation systems have no significant impact on grades, retention, or the students' subjective sense of community. This suggests that forums are essential for MOOCs, and reputation systems can improve the forum experience, but other techniques are needed to improve student outcomes and community formation. We also contribute a set of guidelines for running field experiments on MOOCs.

There are many tools that help programmers find code fragments, but most are inexpressive and rely on static information. We present a new technique for synthesizing code that is dynamic (giving accurate results and allowing programmers to reason about concrete executions), easy-to-use (supporting a wide range of correctness specifications), and interactive (allowing users to refine the candidate code snippets). Our implementation, which we call CodeHint, generates and evaluates code at runtime and hence can synthesize real-world Java code that involves I/O, reflection, native calls, and other advanced language features. We have evaluated CodeHint in two user studies and show that its algorithms are efficient and that it improves programmer productivity by more than a factor of two.

We study effects of introducing a real-time chatroom into a massive open online course with several thousand students, supplementing an existing forum. The chatroom was supported by teaching assistants, and generated thousands of lines of discussion by 28\% of 681 consenting chat condition participants, mostly on-topic. Despite this, chat activity remained low ($\mu=8.2$ messages per hour) and we could find no significant effect of chat use on objective or subjective dependent variables such as grades, retention, forum participation, or students' sense of community. Further investigation reveals that only 12\% of chat participants have substantive interactions, while the remainder are either passive or have trivial interactions that are unlikely to result in learning.

Peer learning, in which students discuss questions in small groups, has been widely reported to improve learning outcomes in traditional classroom settings. Classroom-based peer learning relies on students being in the same place at the same time to form peer discussion groups, but this is rarely true for online students in MOOCs. We built a software tool that facilitates chat-based peer learning in MOOCs by 1) automatically forming ad-hoc discussion groups and 2) scaffolding the interactions between students in these groups. We report on a pilot deployment of this tool; post-use surveys administered to participants show that the tool was positively received and support the feasibility of synchronous online collaborative learning in MOOCs.

Increasingly, authors are publishing long informational talks, lectures, and distance-learning videos online. However, it is difficult to browse and skim the content of such videos using current timeline-based video players. Video digests are a new format for informational videos that afford browsing and skimming by segmenting videos into a chapter/section structure and providing short text summaries and thumbnails for each section. Viewers can navigate by reading the summaries and clicking on sections to access the corresponding point in the video. We present a set of tools to help authors create such digests using transcript-based interactions. With our tools, authors can manually create a video digest from scratch, or they can automatically generate a digest by applying a combination of algorithmic and crowdsourcing techniques and then manually refine it as needed. Feedback from first-time users suggests that our transcript-based authoring tools and automated techniques greatly facilitate video digest creation. In an evaluative crowdsourced study we find that given a short viewing time, video digests support browsing and skimming better than timeline-based or transcript-based video players.

3D printers offer extraordinary flexibility for prototyping the shape and mechanical function of objects. We investigate how 3D models can be modified to facilitate the creation of interactive objects that offer dynamic input and output. We introduce a general technique for supporting the rapid prototyping of interactivity by removing interior material from 3D models to form internal pipes. We describe this new design space of pipes for interaction design, where variables include openings, path constraints, topologies, and inserted media. We then present PipeDream, a tool for routing such pipes through the interior of 3D models, integrated within a 3D modeling program. We use two distinct routing algorithms. The first has users define pipes' terminals, and uses path routing and physics-based simulation to minimize pipe bending energy, allowing easy insertion of media post-print. The second allows users to supply a desired internal shape to which we fit a pipe route: for this we describe a graph-routing algorithm. We present several prototypes created using our tool to show its flexibility and potential.

Amateur instructional videos often show a single uninterrupted take of a recorded demonstration without any edits. While easy to produce, such videos are often too long as they include unnecessary or repetitive actions as well as mistakes. We introduce DemoCut, a semi-automatic video editing system that improves the quality of amateur instructional videos for physical tasks. DemoCut asks users to mark key moments in a recorded demonstration using a set of marker types derived from our formative study. Based on these markers, the system uses audio and video analysis to automatically organize the video into meaningful segments and apply appropriate video editing effects. To understand the effectiveness of DemoCut, we report a technical evaluation of seven video tutorials created with DemoCut. In a separate user evaluation, all eight participants successfully created a complete tutorial with a variety of video editing effects using our system.

Multi-stage code examples present multiple versions of a program where each stage increases the overall complexity of the code. In order to acquire strategies of program construction using a new language or API, programmers consult multi-stage code examples in books, tutorials and online videos. Authoring multi-stage code examples is currently a tedious process, as it involves keeping several stages of code synchronized in the face of edits and error corrections. We document these difficulties with a formative study examining how programmers author multi-stage code examples. We then present an IDE extension that helps authors create multi-stage code examples by propagating changes (insertions, deletions and modifications) to multiple saved versions of their code. Our system adapts revision control algorithms to the specific task of evolving example code. An informal evaluation finds that taking snapshots of a program as it is being developed and editing these snapshots in hindsight help users in creating multi-stage code examples.

3D printers enable designers and makers to rapidly produce physical models of future products. Today these physical prototypes are mostly passive. Our research goal is to enable users to turn models produced on commodity 3D printers into interactive objects with a minimum of required assembly or instrumentation. We present Sauron, an embedded machine vision-based system for sensing human input on physical controls like buttons, sliders, and joysticks. With Sauron, designers attach a single camera with integrated ring light to a printed prototype. This camera observes the interior portions of input components to determine their state. In many prototypes, input components may be occluded or outside the viewing frustum of a single camera. We introduce algorithms that generate internal geometry and calculate mirror placements to redirect input motion into the visible camera area. To investigate the space of designs that can be built with Sauron along with its limitations, we built prototype devices, evaluated the suitability of existing models for vision sensing, and performed an informal study with three CAD users. While our approach imposes some constraints on device design, results suggest that it is expressive and accessible enough to enable constructing a useful variety of devices.

We present a system that lets analysts use paid crowd workers to explore data sets and helps analysts interactively examine and build upon workers' insights. We take advantage of the fact that, for many types of data, independent crowd workers can readily perform basic analysis tasks like examining views and generating explanations for trends and patterns. However, workers operating in parallel can often generate redundant explanations. Moreover, because workers have different competencies and domain knowledge, some responses are likely to be more plausible than others. To efficiently utilize the crowd's work, analysts must be able to quickly identify and consolidate redundant responses and determine which explanations are the most plausible. In this paper, we demonstrate several crowd-assisted techniques to help analysts make better use of crowdsourced explanations: (1) We explore crowd-assisted strategies that utilize multiple workers to detect redundant explanations. We introduce color clustering with representative selection--a strategy in which multiple workers cluster explanations and we automatically select the most-representative result--and show that it generates clusterings that are as good as those produced by experts. (2) We capture explanation provenance by introducing highlighting tasks and capturing workers' browsing behavior via an embedded web browser, and refine that provenance information via source-review tasks. We expose this information in an explanation-management interface that allows analysts to interactively filter and sort responses, select the most plausible explanations, and decide which to explore further.

Human-centered design teams must integrate diverse individual perspectives into a shared understanding during conceptual design. The team's shared knowledge of their users becomes the basis for later design decisions. We conducted a formative study that shows how generic groupware is insufficient to support the transition from individual to collaborative creative work. We developed a set of design guidelines and implemented them in Dazzle, a collaborative shared display system for co-located design team meetings. Dazzle associates the action of showing information on the shared display with granting the rest of the team access to that information: showing is sharing. Dazzle also records a history of shown files. Team members can annotate this log using cross-platform synchronized clients. Teams of novice designers tested Dazzle over two consecutive sessions: the first focused on synthesizing user research, and the second focused on brainstorming. Dazzle was very effective at grounding team conversations about user research, but was used less for sharing information during brainstorming tasks. Items from the shared activity log were used as sources of inspiration and decision criteria during the brainstorming task. Future work includes additional support for active decision-making, and ambient feedback on design activity.

Online labor markets, such as Amazon's Mechanical Turk, have been used to crowdsource simple, short tasks like image labeling and transcription. However, expert knowledge is often lacking in such markets, making it impossible to complete certain classes of tasks. In this work we introduce an alternative mechanism for crowdsourcing tasks that require specialized knowledge or skill: communitysourcing --- the use of physical kiosks to elicit work from specific populations. We investigate the potential of communitysourcing by designing, implementing and evaluating Umati: the communitysourcing vending machine. Umati allows users to earn credits by performing tasks using a touchscreen attached to the machine. Physical rewards (in this case, snacks) are dispensed through traditional vending mechanics. We evaluated whether communitysourcing can accomplish expert work by using Umati to grade Computer Science exams. We placed Umati in a university Computer Science building, targeting students with grading tasks for snacks. Over one week, 328 unique users (302 of whom were students) completed 7771 tasks (7240 by students). 80% of users had never participated in a crowdsourcing market before. We found that Umati was able to grade exams with 2% higher accuracy (at the same price) or at 33% lower cost (at equivalent accuracy) than traditional single-expert grading. Mechanical Turk workers had no success grading the same exams. These results indicate that communitysourcing can successfully elicit high-quality expert work from specific communities.

Current multitouch frameworks require application developers to write recognition code for custom gestures; this code is split across multiple event-handling callbacks. As the number of custom gestures grows it becomes increasingly difficult to 1) know if new gestures will conflict with existing gestures, and 2) know how to extend existing code to reliably recognize the complete gesture set. Proton is a novel framework that addresses both of these problems. Using Proton, the application developer declaratively specifies each gesture as a regular expression over a stream of touch events. Proton statically analyzes the set of gestures to report conflicts, and it automatically creates gesture recognizers for the entire set. To simplify the creation of complex multitouch gestures, Proton introduces gesture tablature, a graphical notation that concisely describes the sequencing of multiple interleaved touch actions over time. Proton contributes a graphical editor for authoring tablatures and automatically compiles tablatures into regular expressions. We present the architecture and implementation of Proton, along with three proof-of-concept applications. These applications demonstrate the expressiveness of the framework and show how Proton simplifies gesture definition and conflict resolution.

Tutorials and sample workflows for complicated, feature-rich software packages are widely available online. As a result users must differentiate between workflows to choose the most suitable one for their task. We present Delta, an interactive workflow visualization and comparison tool that helps users identify the tradeoffs between workflows. We conducted an initial study to identify the set of attributes users attend to when comparing workflows, finding that they consider result quality, their knowledge of commands, and the efficiency of the workflow. We then designed Delta to surface these attributes at three granularities: a high-level, clustered view; an intermediate-level list view that contains workflow summaries; and a low-level detail view that allows users to compare two individual workflows. Finally, we conducted an evaluation of Delta on a small corpus of 30 workflows and found that the intermediate list view provided the best information density. We conclude with thoughts on how such a workflow comparison system could be scaled up to larger corpora in the future.

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

Preparing complex jobs for crowdsourcing marketplaces requires careful attention to workflow design, the process of decomposing jobs into multiple tasks, which are solved by multiple workers. Can the crowd help design such workflows? This paper presents Turkomatic, a tool that recruits crowd workers to aid requesters in planning and solving complex jobs. While workers decompose and solve tasks, requesters can view the status of worker-designed workflows in real time; intervene to change tasks and solutions; and request new solutions to subtasks from the crowd. These features lower the threshold for crowd employers to request complex work. During two evaluations, we found that allowing the crowd to plan without requester supervision is partially successful, but that requester intervention during workflow planning and execution improves quality substantially. We argue that Turkomatic's collaborative approach can be more successful than the conventional workflow design process and discuss implications for the design of collaborative crowd planning systems.

Users of complex software applications often learn concepts and skills through step-by-step tutorials. Today, these tutorials are published in two dominant forms: static tutorials composed of images and text that are easy to scan, but cannot effectively describe dynamic interactions; and video tutorials that show all manipulations in detail, but are hard to navigate. We hypothesize that a mixed tutorial with static instructions and per-step videos can combine the benefits of both formats. We describe a comparative study of static, video, and mixed image manipulation tutorials with 12 participants and distill design guidelines for mixed tutorials. We present MixT, a system that automatically generates step-by-step mixed media tutorials from user demonstrations. MixT segments screencapture video into steps using logs of application commands and input events, applies video compositing techniques to focus on salient infor-mation, and highlights interactions through mouse trails. Informal evaluation suggests that automatically generated mixed media tutorials were as effective in helping users complete tasks as tutorials that were created manually.

Proton++ is a declarative multitouch framework that allows developers to describe multitouch gestures as regular expressions of touch event symbols. It builds on the Proton framework by allowing developers to incorporate custom touch attributes directly into the gesture description. These custom attributes increase the expressivity of the gestures, while preserving the benefits of Proton: automatic gesture matching, static analysis of conflict detection, and graphical gesture creation. We demonstrate Proton++'s flexibility with several examples: a direction attribute for describing trajectory, a pinch attribute for detecting when touches move towards one another, a touch area attribute for simulating pressure, an orientation attribute for selecting menu items, and a screen location attribute for simulating hand ID. We also use screen location to simulate user ID and enable simultaneous recognition of gestures by multiple users. In addition, we show how to incorporate timing into Proton++ gestures by reporting touch events at a regular time interval. Finally, we present a user study that suggests that users are roughly four times faster at interpreting gestures written using Proton++ than those written in procedural event-handling code commonly used today.

An increasing number of consumer products include user interfaces that rely on touch input. While digital fabrication techniques such as 3D printing make it easier to prototype the shape of custom devices, adding interactivity to such prototypes remains a challenge for many designers. We introduce Midas, a software and hardware toolkit to support the design, fabrication, and programming of flexible capacitive touch sensors for interactive objects. With Midas, designers first define the desired shape, layout, and type of touch sensitive areas, as well as routing obstacles, in a sensor editor. From this high-level specification, Midas automatically generates layout files with appropriate sensor pads and routed connections. These files are then used to fabricate sensors using digital fabrication processes, e.g., vinyl cutters and conductive ink printers. Using step-by-step assembly instructions generated by Midas, designers connect these sensors to the Midas microcontroller, which detects touch events. Once the prototype is assembled, designers can define interactivity for their sensors: Midas supports both record-and-replay actions for controlling existing local applications and WebSocket-based event output for controlling novel or remote applications. In a first-use study with three participants, users successfully prototyped media players. We also demonstrate how Midas can be used to create a number of touch-sensitive interfaces.

Programmers frequently use the Web while writing code: they search for libraries, code examples, tutorials, and documentation. This link between code and visited Web pages remains implicit today. Connecting source code and browsing histories might help programmers maintain con-text, reduce the cost of Web page re-retrieval, and enhance understanding when code is shared. This note introduces HyperSource, an IDE augmentation that associates browsing histories with source code edits. HyperSource comprises a browser extension that logs visited pages; an IDE that tracks user activity and maps pages to code edits; a source document model that tracks visited pages at a character level; and a user interface that enables interaction with these histories. We discuss relevance heuristics and privacy issues inherent in this approach. Informal log analyses and user feedback suggest that our annotation model is promising for code editing and might also apply to other document authoring tasks after refinement.

Set construction is the process of selecting and positioning virtual geometric objects to create a virtual environment used in a computer-animated film. Set construction artists often have a clear mental image of the set composition, but find it tedious to build their intended sets with current mouse and keyboard interfaces. We investigate whether multitouch input can ease the process of set construction. Working with a professional set construction artist at Pixar Animation Studios, we designed and developed Eden, a fully functional multitouch set construction application. In this paper, we describe our design process and how we balanced the advantages and disadvantages of multitouch input to develop usable gestures for set construction. Based on our design process and the user experiences of two set construction artists, we present a general set of lessons we learned regarding the design of a multitouch interface.

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

We present Stacksplorer, a new tool to support source code navigation and comprehension. Stacksplorer computes the call graph of a given piece of code, visualizes relevant parts of it, and allows developers to interactively traverse it. This augments the traditional code editor by offering an additional layer of navigation. Stacksplorer is particularly useful to understand and edit unknown source code because branches of the call graph can be explored and backtracked easily. Visualizing the callers of a method reduces the risk of introducing unintended side effects. In a quantitative study, programmers using Stacksplorer performed three of four software maintenance tasks significantly faster and with higher success rates, and Stacksplorer received a System Usability Scale rating of 85.4 from participants.

Many people learn how to use complex authoring applications through tutorials. However, user interfaces for authoring tools differ between versions, platforms, and competing products, limiting the utility of tutorials. Our goal is to make tutorials more useful by enabling users to repurpose tutorials between similar applications. We introduce UI translation interfaces which enable users to locate commands in one application using the interface language of another application. Our end-user tool, ShowMeHow, demonstrates two interaction techniques to accomplish translations: 1) direct manipulation of interface facades and 2) text search for commands using the vocabulary of another application. We discuss tools needed to construct the translation maps that enable these techniques. An initial study (n=12) shows that users can locate unfamiliar commands twice as fast with interface facades. A second study showed that users can work through tutorials written for one application in another application.

We investigate multistroke marking menus for multitouch devices and we show that using two hands can improve performance. We present two new two-handed multistroke marking menu variants in which users either draw strokes with both hands simultaneously or alternate strokes between hands. In a pair of studies we find that using two hands simultaneously is faster than using a single, dominant-handed marking menu by 10--15%. Alternating strokes between hands doubles the number of accessible menu items for the same number of strokes, and is similar in performance to using a one-handed marking menu. We also examine how stroke direction affects performance. When using thumbs on an iPod Touch, drawing strokes upwards and inwards is faster than other directions. For two-handed simultaneous menus, stroke pairs that are bilaterally symmetric or share the same direction are fastest. We conclude with design guidelines and sample applications to aid multitouch application developers interested in using one- and two-handed marking menus.

Interaction designers typically revise user interface prototypes by adding unstructured notes to storyboards and screen printouts. How might computational tools increase the efficacy of UI revision? This paper introduces d.note, a revision tool for user interfaces expressed as control flow diagrams. d.note introduces a command set for modifying and annotating both appearance and behavior of user interfaces; it also defines execution semantics so proposed changes can be tested immediately. The paper reports two studies that compare production and interpretation of revisions in d.note to freeform sketching on static images (the status quo). The revision production study showed that testing of ideas during the revision process led to more concrete revisions, but that the tool also affected the type and number of suggested changes. The revision interpretation study showed that d.note revisions required fewer clarifications, and that additional techniques for expressing revision intent could be beneficial.

Interpreting compiler errors and exception messages is challenging for novice programmers. Presenting examples of how other programmers have corrected similar errors may help novices understand and correct such errors. This paper introduces HelpMeOut, a social recommender system that aids the debugging of error messages by suggesting solutions that peers have applied in the past. HelpMeOut comprises IDE instrumentation to collect examples of code changes that fix errors; a central database that stores fix reports from many users; and a suggestion interface that, given an error, queries the database for a list of relevant fixes and presents these to the programmer. We report on implementations of this architecture for two programming languages. An evaluation with novice programmers found that the technique can suggest useful fixes for 47% of errors after 39 person-hours of programming in an instrumented environment.

This paper introduces an interactive tabletop system that enhances creative collaboration across physical and digital artifacts. Pictionaire offers capture, retrieval, annotation, and collection of visual material. It enables multiple designers to fluidly move imagery from the physical to the digital realm; work with found, drawn and captured imagery; organize items into functional collections; and record meeting histories. These benefits are made possible by a large interactive table augmented with high-resolution overhead image capture. Summative evaluations with 16 professionals and four student pairs validated discoverability and utility of interactions, uncovered emergent functionality, and suggested opportunities for transitioning content to and from the table.

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

This note examines the role traditional input devices can play in surface computing. Mice and keyboards can enhance tabletop technologies since they support high fidelity input, facilitate interaction with distant objects, and serve as a proxy for user identity and position. Interactive tabletops, in turn, can enhance the functionality of traditional input devices: they provide spatial sensing, augment devices with co-located visual content, and support connections among a plurality of devices. We introduce eight interaction techniques for a table with mice and keyboards, and we discuss the design space of such interactions.

Creating multiple prototypes facilitates comparative reasoning, grounds team discussion, and enables situated exploration. However, current interface design tools focus on creating single artifacts. This paper introduces the Juxtapose code editor and runtime environment for designing multiple alternatives of both application logic and interface parameters. For rapidly comparing code alternatives, Juxtapose introduces selectively parallel source editing and execution. To explore parameter variations, Juxtapose automatically creates control interfaces for "tuning" application variables at runtime. This paper describes techniques to support design exploration for desktop, mobile, and physical interfaces, and situates this work in a larger design space of tools for explorative programming. A summative study of Juxtapose with 18 participants demonstrated that parallel editing and execution are accessible to interaction designers and that designers can leverage these techniques to survey more options, faster.

Sensors are becoming increasingly important in interaction design. Authoring a sensor-based interaction comprises three steps: choosing and connecting the appropriate hardware, creating application logic, and specifying the relationship between sensor values and application logic. Recent research has successfully addressed the first two issues. However, linking sensor input data to application logic remains an exercise in patience and trial-and-error testing for most designers. This paper introduces techniques for authoring sensor-based interactions by demonstration. A combination of direct manipulation and pattern recognition techniques enables designers to control how demonstrated examples are generalized to interaction rules. This approach emphasizes design exploration by enabling very rapid iterative demonstrate-edit-review cycles. This paper describes the manifestation of these techniques in a design tool, Exemplar, and presents evaluations through a first-use lab study and a theoretical analysis using the Cognitive Dimensions of Notation framework.

Source-code examples of APIs enable developers to quickly gain a gestalt understanding of a library's functionality, and they support organically creating applications by incrementally modifying a functional starting point. As an increasing number of web sites provide APIs, significantlatent value lies in connecting the complementary representations between site and service - in essence, enabling sites themselves to be the example corpus. We introduce d.mix, a tool for creating web mashups that leverages this site-to-service correspondence. With d.mix, users browse annotated web sites and select elements to sample. d.mix's sampling mechanism generates the underlying service calls that yield those elements. This code can be edited, executed, and shared in d.mix's wiki-based hosting environment. This sampling approach leverages pre-existing web sites as example sets and supports fluid composition and modification of examples. An initial study with eight participants found d.mix to enable rapid experimentation, and suggested avenues for improving its annotation mechanism.

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

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

Man-machine interaction (MMI) systems that utilize multimodal information about users' current emotional state are presently at the forefront of interest of the computer vision and artificial intelligence communities. A lifelike avatar can enhance interactive applications. In this paper, we present the implementation of GretaEngine and synthesized expressions, including intermediate ones, based on MPEG-4 standard and Whissel's emotion representation.

This paper introduces Gesture Engine, an animation system that synthesizes human gesturing behaviors from augmented conversation transcripts using a database of highlevel gesture definitions. An abstract scripting language to specify hand-arm gestures is introduced that incorporates knowledge from sign language research, psycholinguistics, and traditional keyframe animation. A new planning algorithm instantiates and adjusts gestures according to communicative context and temporal constraints obtained from a speech synthesizer The system animates an MPEG-4 compliant skeleton using Body Animation Parameters.