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Abstract Gamification possesses a great potential to shape human behaviors and performance. However, the mixed results in gamification research suggest the need to develop a thorough understanding of the mechanistic underpinnings of gamification. Although self-determination theory (SDT) provides a solid theoretical framework to achieve such purposes, it has been mostly applied superficially, limiting its potential to significantly impact gamification research and practice. This paper suggests that SDT, particularly three ideas in SDT – a continuum of motivation, the mutually supportive nature of basic psychological needs, and the functional significance of an event – have not been fully explored in gamification research and practice. Attention to the three ideas and relevant SDT literature will advance gamification research and practice in the following ways. First, the idea of continuum of motivation can serve as a powerful framework to conceptualize gamification. Second, the ideas of mutually supportive nature of basic psychological needs and the functional significance of an event can help us develop a nuanced instead of simplistic approach to gamification design. Finally, the three ideas suggest important research directions that have yet to be fully explored in current gamification research. 

Though many studies suggest the positive effects of leaderboard on participants’ learning and motivation, research also shows that not all students benefit from the use of leaderboard. Based on self-determination theory (SDT), this study examined how students’ actual competence, perceived competence, perceived autonomy, and perceived relatedness affected students’ enjoyment and intention for future participation in a leaderboard-based math practice game. The results showed that both perceived competence and perceived autonomy were closely related to students’ enjoyment and intention for future participation, while students’ actual competence and perceived relatedness were not related to their enjoyment or intention for future participation in such leaderboard-based math practice games. The findings of the study offer valuable insights and recommendations for both gamification research and practice. 

Neuromorphic vision sensors or event cameras have made the visual perception of extremely low reaction time possible, opening new avenues for high-dynamic robotics applications. These event cameras’ output is dependent on both motion and texture. However, the event camera fails to capture object edges that are parallel to the camera motion. This is a problem intrinsic to the sensor and therefore challenging to solve algorithmically. Human vision deals with perceptual fading using the active mechanism of small involuntary eye movements, the most prominent ones called microsaccades. By moving the eyes constantly and slightly during fixation, microsaccades can substantially maintain texture stability and persistence. Inspired by microsaccades, we designed an event-based perception system capable of simultaneously maintaining low reaction time and stable texture. In this design, a rotating wedge prism was mounted in front of the aperture of an event camera to redirect light and trigger events. The geometrical optics of the rotating wedge prism allows for algorithmic compensation of the additional rotational motion, resulting in a stable texture appearance and high informational output independent of external motion. The hardware device and software solution are integrated into a system, which we call artificial microsaccade–enhanced event camera (AMI-EV). Benchmark comparisons validated the superior data quality of AMI-EV recordings in scenarios where both standard cameras and event cameras fail to deliver. Various real-world experiments demonstrated the potential of the system to facilitate robotics perception both for low-level and high-level vision tasks. 

One of the most used game design elements in gamification design is leaderboard, a scoreboard showing participants’ current scores and rankings. Though many studies suggest the positive effects of leaderboard on participants’ learning and motivation (Kalogiannakis, Papadakis, & Zourmpakis, 2021), research also shows that not all students benefit from the use of leaderboard (Andrade, Mizoguchi, & Isotani, 2016; Nicholson, 2013). Based on self-determination theory (SDT) (Ryan & Deci, 2017), we designed a study where undergraduate math students completed a leaderboard-based review, and addressed two questions: (RQ1) How are students’ perceived autonomy and competence associated with their enjoyment and intention of continued participation? (RQ2) How is student actual competence associated with their enjoyment and intention of continued participation? 

One of the most used game design elements in gamification design is leaderboard, a scoreboard showing participants’ current scores and rankings. Though many studies suggest the positive effects of leaderboard on participants’ learning and motivation (Kalogiannakis, Papadakis, & Zourmpakis, 2021), research also shows that not all students benefit from the use of leaderboard (Andrade, Mizoguchi, & Isotani, 2016; Nicholson, 2013). Based on self-determination theory (SDT) (Ryan & Deci, 2017), we designed a study where undergraduate math students completed a leaderboard-based review, and addressed two questions: (RQ1) How are students’ perceived autonomy and competence associated with their enjoyment and intention of continued participation? (RQ2) How is student actual competence associated with their enjoyment and intention of continued participation? 

Abstract Disease registries, surveillance data, and other datasets with extremely large sample sizes become increasingly available in providing population‐based information on disease incidence, survival probability, or other important public health characteristics. Such information can be leveraged in studies that collect detailed measurements but with smaller sample sizes. In contrast to recent proposals that formulate additional information as constraints in optimization problems, we develop a general framework to construct simple estimators that update the usual regression estimators with some functionals of data that incorporate the additional information. We consider general settings that incorporate nuisance parameters in the auxiliary information, non‐i.i.d. data such as those from case‐control studies, and semiparametric models with infinite‐dimensional parameters common in survival analysis. Details of several important data and sampling settings are provided with numerical examples.