Computer Science

Gamification has been widely employed in the educational domain over the past eight years when the term became a trend. However, the literature states that gamification still lacks formal definitions to support the design and analysis of gamified strategies. This paper analysed the game elements employed in gamified learning environments through a previously proposed and evaluated taxonomy while detailing and expanding this taxonomy. In the current paper, we describe our taxonomy in-depth as well as expand it. Our new structured results demonstrate an extension of the proposed taxonomy which results from this process, is divided into five dimensions, related to the learner and the learning environment. Our main contribution is the detailed taxonomy that can be used to design and evaluate gamification design in learning environments.

This paper proposes a set of techniques to investigate eye gaze and fixation patterns while users interact with electronic user interfaces. In particular, two case studies are presented - one on analysing eye gaze while interacting with deceptive materials in web pages and another on analysing graphs in standard computer monitor and virtual reality displays. We analysed spatial and temporal distributions of eye gaze fixations and sequence of eye gaze movements. We used this information to propose new design guidelines to avoid deceptive materials in web and user-friendly representation of data in 2D graphs. In 2D graph study we identified that area graph has lowest number of clusters for user's gaze fixations and lowest average response time. The results of 2D graph study were implemented in virtual and mixed reality environment. Along with this, it was ob-served that the duration while interacting with deceptive materials in web pages is independent of the number of fixations. Furthermore, web-based data visualization tool for analysing eye tracking data from single and multiple users was developed.

We propose "aquanims" as new design metaphors for animated transitions that preserve displayed areas during the transformation. Animated transitions are used to facilitate understanding of graphical transformations between different visualizations. Area is key information to preserve during filtering or ordering transitions of area-based charts like bar charts, histograms, treemaps, or mosaic plots. As liquids are incompressible fluids, we use a hydraulic metaphor to convey the sense of area preservation during animated transitions: in aquanims, graphical objects can change shape, position, color, and even connectedness but not displayed area, as for a liquid contained in a transparent vessel or transferred between such vessels communicating through hidden pipes. We present various aquanims for product plots like bar charts and histograms to accommodate changes in data, in the ordering of bars or in a number of bins, and to provide animated tips. We also consider confusion matrices visualized as fluctuation diagrams and mosaic plots, and show how aquanims can be used to ease the understanding of different classification errors of real data.

Graph data have become increasingly common. Visualizing them helps people better understand relations among entities. Unfortunately, existing graph visualization tools are primarily designed for single-person desktop use, offering limited support for interactive web-based exploration and online collaborative analysis. To address these issues, we have developed Argo Lite, a new in-browser interactive graph exploration and visualization tool. Argo Lite enables users to publish and share interactive graph visualizations as URLs and embedded web widgets. Users can explore graphs incrementally by adding more related nodes, such as highly cited papers cited by or citing a paper of interest in a citation network. Argo Lite works across devices and platforms, leveraging WebGL for high-performance rendering. Argo Lite has been used by over 1,000 students at Georgia Tech's Data and Visual Analytics class. Argo Lite may serve as a valuable open-source tool for advancing multiple CIKM research areas, from data presentation, to interfaces for information systems and more.

The Art and Science Interaction Lab (ASIL) is a unique, highly flexible and modular interaction science research facility to effectively bring, analyse and test experiences and interactions in mixed virtual/augmented contexts as well as to conduct research on next-gen immersive technologies. It brings together the expertise and creativity of engineers, performers, designers and scientists creating solutions and experiences shaping the lives of people. The lab is equipped with state-of-the-art visual, auditory and user-tracking equipment, fully synchronized and connected to a central backend. This synchronization allows for highly accurate multi-sensor measurements and analysis.

Artificial intelligence (AI) is now widely used to facilitate social interaction, but its impact on social relationships and communication is not well understood. We study the social consequences of one of the most pervasive AI applications: algorithmic response suggestions ("smart replies"). Two randomized experiments (n = 1036) provide evidence that a commercially-deployed AI changes how people interact with and perceive one another in pro-social and anti-social ways. We find that using algorithmic responses increases communication efficiency, use of positive emotional language, and positive evaluations by communication partners. However, consistent with common assumptions about the negative implications of AI, people are evaluated more negatively if they are suspected to be using algorithmic responses. Thus, even though AI can increase communication efficiency and improve interpersonal perceptions, it risks changing users' language production and continues to be viewed negatively.

Crowdsourced design feedback systems are emerging resources for getting large amounts of feedback in a short period of time. Traditionally, the feedback comes in the form of a declarative statement, which often contains positive or negative sentiment. Prior research has shown that overly negative or positive sentiment can strongly influence the perceived usefulness and acceptance of feedback and, subsequently, lead to ineffective design revisions. To enhance the effectiveness of crowdsourced design feedback, we investigate a new approach for mitigating the effects of negative or positive feedback by combining open-ended and thought-provoking questions with declarative feedback statements. We conducted two user studies to assess the effects of question-based feedback on the sentiment and quality of design revisions in the context of graphic design. We found that crowdsourced question-based feedback contains more neutral sentiment than statement-based feedback. Moreover, we provide evidence that presenting feedback as questions followed by statements leads to better design revisions than question- or statement-based feedback alone.

It is undeniable that people life patterns and technological developments are interrelated within a supply and demand cycle. In the education world, the emergence of the internet and mobile technologies has opened the learning boundaries through the use of mobile learning (m-learning). In Indonesia, the learning service industry has been begun to enliven the outside school education sector for almost five years ago. Even though the learning has been discussed around a decade ago, however, it is still rare studies that discuss the performance of the m-learning system based on the end-user perceptions in particular. Therefore, the study may still indispensable, especially from the perspectives of a developing nation. This paper elucidates the preliminary stage results of the above-mentioned study, including the results of the model development and its instrument testing. The DeLone and Mclean information system (IS) success model was adopted, combined with the individual motivation and organizational culture theories, and then adapted into the processional and causal logic of the success model. Around 50 respondent data were collected online and processed and analyzed based on the outer model assessments of the PLS-SEM method using SmartPLS 3.0 to know the reliability and validity of each indicator. The result shows that two of 31 are rejected indicators. The rejections may be the revision considerations for the next study stages. Although this may be trivial for experts, the clarity of its methodological explanations may guide the novice researchers, how to develop a research model and its instrument testing.

Advances in language modeling have led to the development of deep attention-based models that are performant across a wide variety of natural language processing (NLP) problems. These language models are typified by a pre-training process on large unlabeled text corpora and subsequently fine-tuned for specific tasks. Although considerable work has been devoted to understanding the attention mechanisms of pre-trained models, it is less understood how a model's attention mechanisms change when trained for a target NLP task. In this paper, we propose a visual analytics approach to understanding fine-tuning in attention-based language models. Our visualization, Attention Flows, is designed to support users in querying, tracing, and comparing attention within layers, across layers, and amongst attention heads in Transformer-based language models. To help users gain insight on how a classification decision is made, our design is centered on depicting classification-based attention at the deepest layer and how attention from prior layers flows throughout words in the input. Attention Flows supports the analysis of a single model, as well as the visual comparison between pre-trained and fine-tuned models via their similarities and differences. We use Attention Flows to study attention mechanisms in various sentence understanding tasks and highlight how attention evolves to address the nuances of solving these tasks.

There is a growing concern that e-commerce platforms are amplifying vaccine-misinformation. To investigate, we conduct two-sets of algorithmic audits for vaccine misinformation on the search and recommendation algorithms of Amazon -- world's leading e-retailer. First, we systematically audit search-results belonging to vaccine-related search-queries without logging into the platform -- unpersonalized audits. We find 10.47% of search-results promote misinformative health products. We also observe ranking-bias, with Amazon ranking misinformative search-results higher than debunking search-results. Next, we analyze the effects of personalization due to account-history, where history is built progressively by performing various real-world user-actions, such as clicking a product. We find evidence of filter-bubble effect in Amazon's recommendations; accounts performing actions on misinformative products are presented with more misinformation compared to accounts performing actions on neutral and debunking products. Interestingly, once user clicks on a misinformative product, homepage recommendations become more contaminated compared to when user shows an intention to buy that product.