Refine
Document Type
- Conference Proceeding (33)
- Part of a Book (9)
- Article (reviewed) (5)
- Book (2)
- Article (unreviewed) (2)
- Report (1)
Is part of the Bibliography
- yes (52)
Keywords
- Gamification (12)
- Assistive Technology (8)
- Human Computer Interaction (7)
- Deafblindness (5)
- Roboter (5)
- Soziale Roboter (5)
- Wearables (5)
- Human Resources (4)
- Robotik (4)
- Social Robots (4)
Gamification, die spielerische Anreicherung von Tätigkeiten, erfreut sich zunehmender Beliebtheit. Insbesondere in den Bereichen Gesundheit (Exergames) oder Lernen (Serious Games, Edutainment) gibt es eine Vielzahl erfolgreicher Anwendungen. Weniger verbreitet ist Gamification dagegen bislang bei Arbeitsprozessen. Zwar gibt es erfolgreiche Ansätze im Bereich Dienstleistung und Service (z. B. bei Callcentern), der Bereich der industriellen Produktion wurde jedoch bis vor wenigen Jahren nicht adressiert.
Dieses Kapitel gibt einen Überblick der Entwicklung von Gamification und zeigt den Stand der Technik. Wir leiten allgemeine Anforderungen für Gamification im Produktionsumfeld ab und stellen zwei neue Ansätze aus der aktuellen Forschung vor. Diese werden in einer Studie mit Trainern aus der Automobilbranche auf Akzeptanz untersucht. Die Ergebnisse zeigen eine insgesamt positive Haltung zur Gamifizierung der Produktion und eine sehr hohe Akzeptanz insbesondere des Pyramiden-Designs.
Defining Recrutainment: A Model and a Survey on the Gamification of Recruiting and Human Resources
(2017)
Recrutainment, is a hybrid word combining recruiting and entertainment. It describes the combination of activities in human resources and gamification. Concepts and methods from game design are now used to assess and select future employees. Beyond this area, recrutainment is also applied for internal processes like professional development or even marketing campaigns. This paper’s contribution has four components: (1) we provide a conceptual background, leading to a more precise definition of recrutainment; (2) we develop a new model for analyzing solutions in recrutainment; (3) we present a corpus of 42 applications and use the new model to assess their strengths and potentials; (4) we provide a bird’s eye view on the state of the art in recrutainment and show the current weighting of gamification and recruiting aspects.
Applications helping us to maintain the focus on work are called “Zenware” (from concentration and Zen). While form factors, use cases and functionality vary, all these applications have a common goal: creating uninterrupted, focused attention on the task at hand. The rise of such tools exemplifies the users’ desire to control their attention within the context of omnipresent distraction. In expert interviews we investigate approaches in the context of attention-management at the workplace of knowledge workers. To gain a broad understanding, we use judgement sampling in interviews with experts from several disciplines. We especially explore how focus and flow can be stimulated. Our contribution has four components: a brief overview on the state of the art (1), a presentation of the results (2), strategies for coping with digital distractions and design guidelines for future Zenware (3) and an outlook on the overall potential in digital work environments (4).
Gamifying rehabilitation is an efficient way to improve motivation and exercise frequency. However, between flow theory, self-determination theory or Bartle's player types there is much room for speculation regarding the mechanics required for successful gamification, which in turn leads to increased motivation. For our study, we selected a gamified solution for motion training (an exergame) where the playful design elements are extremely simple. The contribution is three-fold: we show best practices from the state of the art, present a study analyzing the effects of simple gamification mechanics on a quantitative and on a qualitative level and discuss strategies for playful design in therapeutic movement games.
Designing Authentic Emotions for Non-Human Characters. A Study Evaluating Virtual Affective Behavior
(2017)
While human emotions have been researched for decades, designing authentic emotional behavior for non-human characters has received less attention. However, virtual behavior not only affects game design, but also allows creating authentic avatars or robotic companions. After a discussion of methods to model and recognize emotions, we present three characters with a decreasing level of human features and describe how established design techniques can be adapted for such characters. In a study, 220 participants assess these characters' emotional behavior, focusing on the emotion "anger". We want to determine how reliable users can recognize emotional behavior, if characters increasingly do not look and behave like humans. A secondary aim is determining if gender has an impact on the competence in emotion recognition. The findings indicate that there is an area of insecure attribution of virtual affective behavior not distant but close to human behavior. We also found that at least for anger, men and women assess emotional behavior equally well.
This work demonstrates the potentials of procedural content generation (PCG) for games, focusing on the generation of specific graphic props (reefs) in an explorer game. We briefly portray the state-of-the-art of PCG and compare various methods to create random patterns at runtime. Taking a step towards the game industry, we describe an actual game production and provide a detailed pseudocode implementation showing how Perlin or Simplex noise can be used efficiently. In a comparative study, we investigate two alternative implementations of a decisive game prop: once created traditionally by artists and once generated by procedural algorithms. 41 test subjects played both implementations. The analysis shows that PCG can create a user experience that is significantly more realistic and at the same time perceived as more aesthetically pleasing. In addition, the ever-changing nature of the procedurally generated environments is preferred with high significance, especially by players aged 45 and above.
This chapter portrays the historical and mathematical background of dynamic and procedural content generation (PCG). We portray and compare various PCG methods and analyze which mathematical approach is suited for typical applications in game design. In the next step, a structural overview of games applying PCG as well as types of PCG is presented. As abundant PCG content can be overwhelming, we discuss context-aware adaptation as a way to adapt the challenge to individual players’ requirements. Finally, we take a brief look at the future of PCG.
We present the design outline of a context-aware interactive system for smart learning in the STEM curriculum (science, technology, engineering, and mathematics). It is based on a gameful design approach and enables "playful coached learning" (PCL): a learning process enriched by gamification but also close to the learner's activities and emotional setting. After a brief introduction on related work, we describe the technological setup, the integration of projected visual feedback and the use of object and motion recognition to interpret the learner's actions. We explain how this combination enables rapid feedback and why this is particularly important for correct habit formation in practical skills training. In a second step, we discuss gamification methods and analyze which are best suited for the PCL system. Finally, emotion recognition, a major element of the final PCL design not yet implemented, is briefly outlined.
Gamification implies the application of methods and design patterns from gaming to non-gaming areas like learning or working. We applied an existing gamification design to production processes in an organization which provides sheltered employment for impaired persons. In contrast to existing work, we investigated not only a short period but a complete workday to measure the effects on the work performance. The study indicates that gamification has (1) a negative effect on workers with considerable cognitive impairments, (2) no significant effect on workers with medium cognitive impairments and (3) a positive effect on workers with mild cognitive impairments.