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Proper statistical modeling incorporates domain theory about how concepts relate and details of how data were measured. However, data analysts currently lack tool support for recording and reasoning about domain assumptions, data collection, and modeling choices in an integrated manner, leading to mistakes that can compromise scientific validity. For instance, generalized linear mixed-effects models (GLMMs) help answer complex research questions, but omitting random effects impairs the generalizability of results. To address this need, we present Tisane, a mixed-initiative system for authoring generalized linear models with and without mixed-effects. Tisane introduces a study design specification language for expressing and asking questions about relationships between variables. Tisane contributes an interactive compilation process that represents relationships in a graph, infers candidate statistical models, and asks follow-up questions to disambiguate user queries to construct a valid model. In case studies with three researchers, we find that Tisane helps them focus on their goals and assumptions while avoiding past mistakes.
With the increasing growth and impact of machine learning and other math-intensive fields, it is more important than ever to broaden access to mathematical notation. Can new visual and interactive displays help a wider readership successfully engage with notation? This paper provides the first detailed qualitative analysis of math augmentation—the practice of embellishing notation with novel visual design patterns to improve its readability. We present two qualitative studies of the practice of math augmentation. First is an analysis of 1.1k augmentations to 281 formulas in 47 blogs, textbooks, and other documents containing mathematical expressions. Second is an interview study with 12 authors who had previously designed custom math augmentations ("maugs"). This paper contributes a comprehensive inventory of the kinds of maugs that appear in math documents, and a detailed account of how authors’ tools ought to be redesigned to support efficient creation of math augmentations. These studies open a critical new design space for HCI researchers and interface designers.
Most modern applications are immutable and turn-key despite the acknowledged benefits of empowering users to modify their software. Writing extensible software remains challenging, even for expert programmers. Reprogramming or extending existing software is often laborious or wholly blocked, requiring sophisticated knowledge of application architecture or setting up a development environment. We present Varv, a programming model representing reprogrammable interactive software as a declarative data structure. Varv defines interactive applications as a set of concepts that consist of a schema and actions. Applications in Varv support incremental modification, allowing users to reprogram through addition and selectively suppress, modify, or add behavior. Users can define high-level concepts, creating an abstraction layer and effectively a domain-specific language for their application domain, emphasizing reuse and modification. We demonstrate the reprogramming and collaboration capabilities of Varv in two case studies and illustrate how the event engine allows for extensive tooling support.
Emoticons are indispensable in online communications. With users' growing needs for more customized and expressive emoticons, recent messaging applications begin to support (limited) multi-modal emoticons: e.g., enhancing emoticons with animations or vibrotactile feedback. However, little empirical knowledge has been accumulated concerning how people create, share and experience multi-modal emoticons in everyday communication, and how to better support them through design. To tackle this, we developed VibEmoji, a user-authoring multi-modal emoticon interface for mobile messaging. Extending existing designs, VibEmoji grants users greater flexibility to combine various emoticons, vibrations, and animations on-the-fly, and offers non-aggressive recommendations based on these components' emotional relevance. Using VibEmoji as a probe, we conducted a four-week field study with 20 participants, to gain new understandings from in-the-wild usage and experience, and extract implications for design. We thereby contribute both a novel system and various insights for supporting users' creation and communication of multi-modal emoticons.