Algorithms are more and more pervading our everyday life: from automatic checkouts in supermarkets and e-banking to booking a flight online. Understanding an algorithmic solution to a problem is a very relevant activity to improve end-users' involvement. To this end, adopting a meta-design approach may help to support end-users to appropriate the design skills necessary for contributing to system design, in new and engaging modalities. By acquiring Computational Thinking (CT) skills (e.g., algorithmic thinking, abstraction), end-users will be able to understand and trust algorithms, while at the same time participate in the design and development of systems evolving in accordance with their needs. In this work, we focus on two different ways of improving CT skills: playfulness and collaboration. We introduce a game-based system, TAPASPlay, to foster CT skills and we report the results of an exploratory study with 18 users; our hypothesis is that learning CT through gameplay is effective and we tested it by involving participants in game sessions providing playful experience and collaborative learning. Keywords Computational thinking • Gameplay • Tangible user interface • Constructionist video games This is an extended and revised version of a paper that was presented at the 2017 Workshop on Games-Human Interaction [19]. This paper significantly expands over the presentation and experimental validation of TAPASPlay.
This study presents a novel workflow to define how resilient communities can be analysed and improved through the optimisation of sustainable design principles through quantitative methods. Our model analyses successful sustainable communities extracting information about daily routines (commuting, working, use of buildings etc.). From these routines, we infer a set of key successful aspects based on location, density and proximity. We then model a resilient community and analyse it using a combination of clustering techniques to find patterns and correlations in the success of existing communities. The proposed workflow is applied to the city of Copenhagen as a case study. The aim of the proposed model is to suggest to designers and city-level policy makers improvements (with manipulation of variables like density, proximity and location of urban typologies) to help them to achieve different levels of sustainable goals as set out by the United Nations Global Challenges including integration inclusiveness and resilience. By using a clustering technique, patterns of proximity have been identified along with density and initial correlations in the observed urban typologies. Some of these correlations were used to illustrate the potential of this novel workflow.
Context: It is unclear that current approaches to evaluating or comparing competing software cost or effort models give a realistic picture of how they would perform in actual use. Specifically, we're concerned that the usual practice of using all data with some holdout strategy is at variance with the reality of a data set growing as projects complete.Objective: This study investigates the impact of using unrealistic, though possibly convenient to the researchers, ways to compare models on commercial data sets. Our questions are does this lead to different conclusions in terms of the comparisons and if so,are the results biased e.g., more optimistic than those that might realistically be achieved in practice. Method : We compare a traditional approach based on leave one out cross-validation with growing the data set chronologically using the Finnish and Desharnais data sets. Results: Our realistic, time-based approach to validation is significantly more conservative than leave-one-out cross-validation (LOOCV) for both data sets. Conclusion: If we want our research to lead to actionable findings it's incumbent upon the researchers to evaluate their models in realistic ways. This means a departure from LOOCV techniques, while further investigation is needed for other validation techniques, such as k-fold validation.
These days we are witnessing a spread of many new digital systems in public spaces featuring easy to use and engaging interaction modalities, such as multi-touch, gestures, tangible, and voice. This new user-centered paradigm-known as the Natural User Interface (NUI)-aims to provide a more natural and rich experience to end users; this supports its adoption in many ubiquitous domains, as it naturally holds for Pervasive Displays: these systems are composed of variously-sized displays and support many-to-many interactions with the same public screens at the same time. Due to their public and moderated nature, users need an easy way of adapting them to heterogeneous usage contexts in order to support their long-term adoption. In this paper, we propose an End-User Development approach to this problem introducing TAPAS, a system that combines a tangible interaction with $ This is an extended and revised version of a paper that was presented at the 2015 Symposium on Visual Languages and Human-Centric Computing [13]. This paper significantly expands over TAPAS' design rationale, presentation, and resulting discussion.
ant-colony based approach for real-time implicit collaborative information seeking. Information Processing and Management, Elsevier, 2017, 53 (3), pp. a b s t r a c tWe propose an approach based on Swarm Intelligence -more specifically on Ant Colony Optimization (ACO) -to improve search engines' performance and reduce information overload by exploiting collective users ' behavior . We designed and developed three dif-ferent algorithms that employ an ACO-inspired strategy to provide implicit collaborative-seeking features in real time to search engines. The three different algorithms -NaïveR -ank, RandomRank , and SessionRank -leverage on different principles of ACO in order to exploit users ' interactions and provide them with more relevant results . We designed an evaluation experiment employing two widely used standard datasets of query -click logs issued to two major Web search engines . The results demonstrated how each algorithm is suitable to be employed in ranking results of different types of queries depending on users' intent. 1 https://developers.google.com/+/web/+1button/ . 2 The inability to make a decision because of the huge quantity of information obtained by the users.
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