Data Visualization Literacy assessments are often administered via fixed sets of Data Visualization (DV) items, despite heterogeneity in how different people interpret the same DV. In this study, we predict Human Interpretation Correctness, i.e., whether a specific person will answer a DV item correctly, prior to their exposure to the target DV. We operationalize this as Predicting Human Interpretation Correctness (P-HIC), a binary classification task using 22 pre-exposure features spanning Human Profile, Human Performance, and Item difficulty (i.e., experts’ ratings and Rasch model). In an online survey, 1083 participants answered 32 DV items (eight DVs × four items), yielding 34,656 responses. Across 32 item-specific datasets, 10× 10-fold cross-validation shows that a Bagging ensemble of J48 decision trees, combined with feature selection, performs best, achieving a median AUC of 0.73 and a median kappa of 0.33. Feature analyses indicate that item difficulty estimated by the Rasch model dominates prediction, followed by experts’ ratings and prior correctness (increasing in importance across sessions), while profile features contribute little. These results suggest that pre-exposure misinterpretation risk can be estimated above chance and warrant future evaluation of assistive item-ranking strategies in simulated and real adaptive assessment settings.

We contribute an autoethnographic reflection on the complexity of defining and measuring visualization literacy (i.e., the ability to interpret and construct visualizations) to expose our tacit thoughts that often exist in-between polished works and remain unreported in individual research papers. Our work is inspired by the growing number of empirical studies in visualization research that rely on visualization literacy as a basis for developing effective data representations or educational interventions. Researchers have already made various efforts to assess this construct, yet it is often hard to pinpoint either what we want to measure or what we are effectively measuring. In this autoethnography, we gather insights from 14 internal interviews with researchers who are users or designers of visualization literacy tests. We aim to identify what makes visualization literacy assessment a "wicked" problem. We further reflect on the fluidity of visualization literacy and discuss how this property may lead to misalignment between what the construct is and how measurements of it are used or designed. We also examine potential threats to measurement validity from conceptual, operational, and methodological perspectives. Based on our experiences and reflections, we propose several calls to action aimed at tackling the wicked problem of visualization literacy measurement, such as by broadening test scopes and modalities, improving test ecological validity, making it easier to use tests, seeking interdisciplinary collaboration, and drawing from continued dialogue on visualization literacy to expect and be more comfortable with its fluidity.

This paper presents a study about the mapping of the EUDability of End-User Development (EUD) tools with the Computational Thinking (CT) skills of users. This mapping provides an approach to evaluate the suitability of a EUD environment in supporting people performing their daily work while managing and exploiting EUD tools. EUDability is a construct encompassing different dimensions that need to be assessed through a careful scrutiny by human-computer interaction experts, while CT skills should mirror those dimensions from the point of view of assessing the level of ability of users in managing problems with a computational thinking attitude. Moving from the healthcare domain, we present two cases: a tool for geriatric professionals supporting them in the preparation of cognitive exercises for elderly patients; and a tool for pharmacists, which empowers them to create robot programs related to the preparation of personalized medications. These cases have been exploited to show how to unify the EUDability assessment with the CT skills assessment. In particular, the application of the EUDability evaluation method for each tool, as well as the administration of the Computational Thinking Scale to domain experts are shown. The results of the two assessments are reported and discussed, together with the limitations of the present study. The results show the goodness of fit of the proposed EUD tools in the healthcare domain.

Nowadays, end users can create routines for Amazon Echo and Google Nest devices using a companion app (Amazon Alexa and Google Home, respectively) running on smartphones. Our work explores the possibility of transferring this End-User Development activity directly to the smart speakers, with and without a touchscreen. To this aim, we designed and developed two Amazon Skills (one for Amazon Echo Show and the other for Amazon Echo Dot) and two Google Actions (one for Google Nest Hub and the other for Google Home Speaker). Then, we carried out two controlled experiments, involving 40 participants, to compare routine creation through multi-modal interaction (based on vision, speech, and touch) with routine creation through speech-only interaction. Driven by our research questions, we found that for routine creation the multi-modal interaction is preferred to the speech-only one and the perceived quality of interaction seems to depend on the brand of the smart speaker.

Measuring software functional size via standard Function Points Analysis (FPA) requires the availability of fully specified requirements and specific competencies. Most of the time, the need to measure software functional size occurs well in advance with respect to these ideal conditions, under the lack of complete information or skilled experts. To work around the constraints of the official measurement process, several estimation methods for FPA have been proposed and are commonly used. Among these, the International Function Points User Group (IFPUG) has adopted the “High-level FPA” method (also known as the NESMA method). This method avoids weighting each data and transaction function by using fixed weights instead. Applying High-level FPA, or similar estimation methods, is faster and easier than carrying out the official measurement process but inevitably yields an approximation in the measures. In this article, we contribute to the problem of estimating software functional size measures by using machine learning. To the best of our knowledge, machine learning methods were never applied to the early estimation of software functional size. Our goal is to understand whether machine learning techniques yield estimates of FPA measures that are more accurate than those obtained with High-level FPA or similar methods. An empirical study on a large dataset of functional size predictors was carried out to train and test three of the most popular and robust machine learning methods, namely Random Forests, Support Vector Regression , and Neural Networks. A systematic experimental phase, with cycles of dataset filtering and splitting, parameter tuning, and model training and validation, is presented. The estimation accuracy of the obtained models was then evaluated and compared to that of fixed-weight models (e.g., High-level FPA) and linear regression models, also using a second dataset as the test set. We found that Support Vector Regression yields quite accurate estimation models. However, the obtained level of accuracy does not appear significantly better with respect to High-level FPA or to models built via ordinary least squares regression. Noticeably, fairly good accuracy levels were obtained by models that do not even require discerning among different types of transactions and data.

Smart speakers can execute user-defined routines, namely, sequences of actions triggered by specific events or conditions. This paper presents a new approach to the creation of routines, which leverages the multi-modal features (vision, speech, and touch) offered by Amazon Alexa running on Echo Show devices. It then illustrates how end users found easier to create routines with the proposed approach than with the usual interaction with the Alexa app.

This paper makes a point of current perspectives on Data Visualization research that were essentially conceived to provide guidelines for finding the best mapping between data and visual representations. Going back to foundational concepts of HCI that rely on manipulation of visual symbols, we propose a new perspective, with the aim to focus on a different configuration, that considers visual signs, professional contexts and user practices. We argue that, so far, user practices have been neglected or left behind in design, evaluation and recommendation scenarios, reducing them to the pure relational focus among kind of data, kind of charts and in lab tasks. This may underestimate the potential of the pragmatic side of this relation, where humans manipulate and interpret signs on the basis of their "practical knowledge, a factor that should be considered to improve human interactions with Data Visualization tools. The perspective discussed here would bring into light and help frame open problems such as interactions in routine tasks and the interpretation of data through visual interactive tools in daily professional practices. By proposing a light but formal model of investigation of these pragmatic interactions, we would like to contribute to the current debate around data visualization as the new strategic tool for dealing with the growing complexity of big data streams, digitization of life, sensor and hardware-embedded intelligence.

In this work, we provide an overview of contemporary perspectives of design that may challenge the traditional design of IT and socio-technical systems. Our starting metaphor is that of ‘wicked problems’, where the singularity, incompleteness and intrinsic uncertainty of real world settings foregrounds how the worldview that designers offer to practitioners may be optimal in theory but useless in practice. To go beyond traditional notions of design and designer, we intercepted insights coming from minoritarian voices in both theoretic and practice-based design fields. ‘De-design’ is a term we coined to encompass this wide spectrum of approaches that make more resilient and sustainable information artifact, de-emphasize design as a theoretical construct, and reconsider practice as the leading principle of digital innovation. This paper is a narrative review of voices in an extensive array of fields: from Information Systems to Human-Computer Interaction, from End-User Development to Critical Design, from Software Design to Design Studies. Our contribution retraces the motivational roots of de-design and tries to characterise de-design by filling relational gaps between disparate approaches and by bringing them back to IT and socio-technical design, to make digital artifacts sustainable in all of the new environmental, organisational and cultural spaces near to come.

Big data analytics in healthcare would be almost useless, without suitable tools allowing users "see" them, and gain insight for their situated decisions. The VVH (Valuable Visualization in Healthcare) workshop focuses on the role of interactive data visualization tools by which people can make sense of healthcare data; these data include sensor data, the messages exchanged in social media, the emails between patients and their doctors, the content of patient records as well as the discussions among different specialists that led to such record content. All these data are used by different types of users, like doctors, nurses, policy makers and common citizens. The VVH workshop aims at contributing on: the assessment of the usability of advanced interactive tools of health-related data visualization; the assessment of the quality of the information and value for insight that these tools make available to their users; the collection of reports of either success stories or failures in the appropriation and use of complex and multidimensional healthcare datasets; the collection of methodological and design-oriented contributions that could share methods, techniques, and heuristics for the design of interactive tools and applications supporting data work, data telling and data interpretation in healthcare.

This paper focuses on the main question whether social media specifically conceived to enable local exchange trading schema can be adopted in different contexts than the western digitized society, where those systems have been considered a feasible alternative to money-based capitalism. We report a qualitative study employing focus groups to study the factors which may affect the adoption of these social media in Bangladesh, a developing country that exhibits characteristics such as strong young unemployment, gender-oriented underemployment, aging population, but also a reduced access to the service economy due to the lack of spare time. The benefits of local exchange seem to be particularly fitting the urban and social structure of Bangladesh.

Unambiguous definition of spatial position and orientation has crucial importance for robotics. In this paper we propose an ontology about positioning. It is part of a more extensive core ontology being developed by the IEEE RAS Working Group on ontologies for robotics and automation. The core ontology should provide a common ground for further ontology development in the field. We give a brief overview of concepts in the core ontology and then describe an integrated approach for representing quantitative and qualitative position information.

This paper presents the “Ontologica” system, a forefront project born from a joint effort between university and industry in the design of advanced information systems. The aim of the project is twofold: to adopt ontologies and new technologies to manage the Centralized Traffic Control logic in a railway and all the rules making the physical elements of a rail station acting as desired; to improve the user interface with a mechanism to interact with the system using natural language queries. The first results we obtained are very promising. In this paper we discuss them, with the “Ontologica” rationale and architecture.

“Ontology matching” is the process of finding correspondences between entities belonging to different ontologies. This paper describes a set of algorithms that exploit upper ontologies as semantic bridges in the ontology matching process and presents a systematic analysis of the relationships among features of matched ontologies (number of simple and composite concepts, stems, concepts at the top level, common English suffixes and prefixes, and ontology depth), matching algorithms, used upper ontologies, and experiment results. This analysis allowed us to state under which circumstances the exploitation of upper ontologies gives significant advantages with respect to traditional approaches that do no use them. We run experiments with SUMO-OWL (a restricted version of SUMO), OpenCyc, and DOLCE. The experiments demonstrate that when our “structural matching method via upper ontology” uses an upper ontology large enough (OpenCyc, SUMO-OWL), the recall is significantly improved while preserving the precision obtained without upper ontologies. Instead, our “nonstructural matching method” via OpenCyc and SUMO-OWL improves the precision and maintains the recall. The “mixed method” that combines the results of structural alignment without using upper ontologies and structural alignment via upper ontologies improves the recall and maintains the F-measure independently of the used upper ontology.