Interactions within virtual environments often require manipulating 3D virtual objects. To this end, researchers have endeavoured to find efficient solutions using either traditional input devices or focusing on different input modalities, such as touch and mid‐air gestures. Different virtual environments and diverse input modalities present specific issues to control object position, orientation and scaling: traditional mouse input, for example, presents non‐trivial challenges because of the need to map between 2D input and 3D actions. While interactive surfaces enable more natural approaches, they still require smart mappings. Mid‐air gestures can be exploited to offer natural manipulations mimicking interactions with physical objects. However, these approaches often lack precision and control. All these issues and many others have been addressed in a large body of work. In this article, we survey the state‐of‐the‐art in 3D object manipulation, ranging from traditional desktop approaches to touch and mid‐air interfaces, to interact in diverse virtual environments. We propose a new taxonomy to better classify manipulation properties. Using our taxonomy, we discuss the techniques presented in the surveyed literature, highlighting trends, guidelines and open challenges, that can be useful both to future research and to developers of 3D user interfaces.

Gamification has been explored recently as a way to promote content delivery in education, yielding promising results. However, little is known regarding how it helps different students experience learning and acquire knowledge. In this paper we study and analyze data from a gamified engineering course, to search for distinct behavior patterns. We examined data collected from two gamified years, between which game changes took place. By clustering students according to their performance, we identified three distinct student types, common to both years: Achievers, Disheartened, and Underachievers. Interestingly, in the second year a new type of student emerged: the Late Awakeners. In this paper we carefully describe each student type, and explain how gamification can provide for smarter learning by catering to students with different profiles. Furthermore, we discuss how our findings, both in gamification and cluster analysis can be used to develop adaptive and smart learning environments.

No abstract