Contribution: In this full research paper, we contribute to the gamification literature by exploring students' emotions when completing educational tasks in a gamified and non-gamified education system, qualitatively explaining some triggers for their emotions. Background: An emotion is a spontaneous, short-lived, and subjective response to whatever is happening. Despite the various existing theories, we draw on Ekman's theory, which identifies seven basic emotions: anger, fear, sadness, happiness, contempt, surprise, and disgust. In education, gamification has been widely used with one of its objectives being to provoke positive emotions in students. However, little is known about students' emotions when using gamified and non-gamified educational systems. Research Questions: Advancing the literature, we answered the following research question: what emotions are experienced by students when performing tasks using a gamified and non-gamified educational system? Methodology: For up to 30 minutes, nine males and seven females completed educational tasks in general knowledge, English, and reasoning. Some interacted with a gamified version of an educational platform, which included 10 gamification elements based on Self-determination theory, while others engaged with the same tasks in a non-gamified version of the same system. During system usage, we employed the Noldus Observer XT and FaceReader systems to map students' emotions over time. After system usage, semi-structured interview data provided a deeper understanding of why certain emotions were experienced. Finally, we conducted a Thematic Analysis to qualitatively interpret the data. Findings: Participants remained indifferent most of the time in both settings, indicating that the gamification elements had no extra role. The most frequent emotion was anger, an emotion experienced when a goal is prevented from being pursued. They also experienced some disgust. Those exposed to gamification elements were more likely to be happy and equally likely to be sad. In terms of gender, male participants felt more anger and disgust, while women felt more happiness and sadness.

Contribution: In this full research paper, we provide a qualitative view of how undergraduate students experienced flow state dimensions (i.e., challenge-skill balance, action-awareness merging, clear goals, unambiguous feedback, total concentration, sense of control, loss of self-consciousness, transformation of time and autotelic experience) in a gamified educational and a non-gamified educational system. Background: When in a flow state, individuals experience deep immersion and focus on tasks. Encouraging educational experiences toward flow is important because this construct is linked to cognition (academic performance) and behavior (engagement). Gamification has been implemented to bring students to a flow state. However, despite several studies (mostly quantitative) little is known about how the flow state is experienced in gamified educational systems. Research Questions: Advancing the literature, we answered the following research question: how do students experience flow state when performing tasks using a gamified and non-gamified educational system? Methodology: 68 students from 20 countries participated in an experiment. They were randomly assigned to an experimental group (a gamified system with 10 different gamification elements based on Self-determination theory) and a control group (the same system without gamification). Immediately after the experiment, we asked them to share some thoughts from their experience openly. We then conducted a qualitative study based on Thematic Analysis with 18 of them who volunteered for further interviews. Findings: We found that students who completed questions in the non-gamified version acknowledged time and social pressure as elements that sustained their engagement. However, we did not intentionally expose them to these game elements. A sense of competence emerged strongly as they completed the educational tasks through the system. We found different nuances of competition, including self-competition, associated with the sense of flow in both groups. The findings also suggest that the tasks play a role in the sense of flow they experienced.

Gamification is commonly employed to support the formation of positive psychological states and learning outcomes, with one of the primary psychological factors chiefly relevant to learning being the flow state. However, the effects of gamification on students’ flow experience are still little known. Filling this gap, we conducted a between-subjects controlled experiment (N = 65) to analyze the effects of gamification on students’ flow experience. Using descriptive and inferential statistical techniques, we compared the flow experience between participants who used a gamified version of an educational system (experimental group) and a group that used the same system without gamification (control group). The main results indicate that the employed gamification design did not affect students’ flow experience. Our study contributes especially to educational technologies and gamification fields, demonstrating that gamification may not affect students’ flow experience.

In recent years, numerous studies have investigated the effects of gamification on different students’ experiences (e.g., motivation, engagement, and accomplishment). However, the effects of gamification on students’ academic performance are still a mystery. To address this gap, we conducted a between-subjects controlled experiment (N = 71) to analyze the effects of gamification on students’ academic performance. Using descriptive and inferential statistical techniques, we compared the academic performance of participants who used a gamified version of an educational system (experimental group) and a group that used the same system without gamification (control group). The main results indicate that the student’s academic performance was significantly higher in the experimental group (i.e., students who used the gamified version of the system). We contribute to the fields of educational technology and gamification by providing new evidence about the effects of gamification on the actual students’ academic performance.

Comparing time surfaces at different integration time points, or from different seeding areas, can provide valuable insight into transport phenomena of fluid flows. Such a comparative study is challenging due to the often convoluted shapes of these surfaces. We propose a new approach for comparative flow visualization based on time surfaces, which exploits the idea of embedding the surfaces in a carefully designed, reformed 2D visualization space. Such an embedding enables new opportunities for comparative flow visualization. We present three different strategies for comparative flow visualization that take advantage of the reformation. By reforming the time surfaces, we not only mitigate occlusion issues, but we can devote also the third dimension of the visualization space to the comparative aspects of the visualization. Our approach is effective in a variety of flow study cases. The direct comparison of individual time surfaces reveals small scale differences and fine details about the fluid's motion. The concurrent study of multiple surface families enables the identification and the comparison of the most prominent motion patterns. This work was developed in close collaboration with an expert in fluid dynamics, who assessed the potential usefulness of this approach in his field.

Abstract Flow maps are widely used to provide an overview of geospatial transportation data. Existing solutions lack the support for the interactive exploration of multiple flow components at once. Flow components are given by different materials being transported, different flow directions, or by the need for comparing alternative scenarios. In this paper, we combine flows as individual ribbons in one composite flow map. The presented approach can handle an arbitrary number of sources and sinks. To avoid visual clutter, we simplify our flow maps based on a force‐driven algorithm, accounting for restrictions with respect to application semantics. The goal is to preserve important characteristics of the geospatial context. This feature also enables us to highlight relevant spatial information on top of the flow map such as traffic conditions or accessibility. The flow map is computed on the basis of flows between zones. We describe a method for auto‐deriving zones from geospatial data according to application requirements. We demonstrate the method in real‐world applications, including transportation logistics, evacuation procedures, and water simulation. Our results are evaluated with experts from corresponding fields.

Representation of molecular surfaces is a well established way to study the interaction of molecules. The state-of-the-art molecular representation is the SES model, which provides a detailed surface visualization. Nevertheless, it is computationally expensive, so the less accurate Gaussian model is traditionally preferred. We introduce a novel surface representation that resembles the SES and approaches the rendering performance of the Gaussian model. Our technique is based on the iterative blending of implicit functions and avoids any pre-computation. Additionally, we propose a GPU-based ray-casting algorithm that efficiently visualize our molecular representation. A qualitative and quantitative comparison of our model with respect to the Gaussian and SES models is presented. As showcased in the paper, our technique is a valid and appealing alternative to the Gaussian representation. This is especially relevant in all the applications where the cost of the SES is prohibitive.

Abstract Clipping is a fast, common technique for resolving occlusions. It only requires simple interaction, is easily understandable, and thus has been very popular for volume exploration. However, a drawback of clipping is that the technique indiscriminately cuts through features. Illustrators, for example, consider the structures in the vicinity of the cut when visualizing complex spatial data and make sure that smaller structures near the clipping plane are kept in the image and not cut into fragments. In this paper we present a new technique, which combines the simple clipping interaction with automated selective feature preservation using an elastic membrane. In order to prevent cutting objects near the clipping plane, the deformable membrane uses underlying data properties to adjust itself to salient structures. To achieve this behaviour, we translate data attributes into a potential field which acts on the membrane, thus moving the problem of deformation into the soft‐body dynamics domain. This allows us to exploit existing GPU‐based physics libraries which achieve interactive frame rates. For manual adjustment, the user can insert additional potential fields, as well as pinning the membrane to interesting areas. We demonstrate that our method can act as a flexible and non‐invasive replacement of traditional clipping planes.