Game designers frequently invest in aesthetic improvements such as music, sound effects, and animations. However, their exact value for attracting and retaining players remains unclear. Seeking to estimate this value in two popular Flash games, we conducted a series of large-scale A/B tests in which we selectively removed aesthetic improvements and examined the effect of each component on play time, progress, and return rate. We found that music and sound effects had little or no effect on player retention in either game, while animations caused users to play more. We also found, counterintuitively, that optional rewards caused players to play less in both games. In one game, this gameplay modification affected play time three times as much as the largest aesthetic variation. Our methodology provides a way to determine where resources may be best spent during the game design and development process.

Secondary game objectives, optional challenges that players can choose to pursue or ignore, are a fundamental element of game design. Still, little is known about how secondary objectives affect player behavior. It is commonly believed that secondary objectives such as coins or collectible items can increase a game's flexibility, replayability, and depth. In contrast, we present results from analysis of two popular online Flash games showing that secondary objectives can easily harm the retention of many players. We support our findings with data collected from over 27,000 players through large-scale A/B tests in which we measured play time, progress, and return rate. We show that while secondary objectives can encourage long-term players to extend their playtime, they can also cause many players to play for less time. By modifying secondary objectives so that they reinforce the primary goal of the game instead of distracting from it, we are able to avoid negative consequences and still maintain the retention of long-term players. Our results suggest that secondary objectives that support the primary goal of the game are consistently useful, while secondary objectives that do not support the main goal require extensive testing to avoid negative consequences.

Background: One area of biomedical research concerns is applying new treatments to cure human diseases, moving bench-side research to the bedside practice. While using animal models is crucial in the research process, researchers should strictly adhere to the moral 4R framework to protect animal welfare—replacement, reduction, refinement, and responsibility. Virtual reality (VR) applies computer technology to create a simulated environment, allowing players to immerse and interact with animated 3D contexts. We developed a virtual animal-holding simulator (ViSi) using immersive virtual reality technology for students studying in the undergraduate biomedical sciences programme. The specific objectives of the paper are to 1) describe the development of the VR courseware for animal training and 2) describe the learning experience among students.Method and Result: An evaluation of the courseware was conducted among Year one and two biomedical sciences students. Students who participated in ViSi responded positively about their involvement in the virtual environment experience and their concentration on the assigned task.Discussion: ViSi is a reliable simulation technology that can train animal handling skills, which replaces real animals, while learners’ multi-cognition could still be enhanced with simulation training. Thus, the impact of immersive VR technology integrated into skills training is promising, although few technical problems are to be resolved.

Training in handling laboratory animals is fundamentally imperative to the responsible use of animals in research. Animal welfare topic is underdeveloped in the tertiary education, where instruction is majorly delivered in the format of lecture and group discussion only. Students with limited exposure to the laboratory were inattentive to animal welfare and uncertain how ethics intertwine with science. This paper describes a multi-disciplinary experience in developing and implementing virtual reality (VR) simulation to enhance contextual learning of using animal models in research with digital technology in biomedical science teaching at higher education. The in-house developed courseware consists of student-centred stimulations designed with game elements implemented at the tutorial session. At the first game level, the setting situates at in the preparation room that requires learners to apply the laboratory safety knowledge to wear personal protective equipment. At the second game level, the environment situates at the restricted experimental room to perform hands-on injections on mice. If the learner fails to pick up appropriate safety equipment at the first level, the learner is prohibited from entering the next level. During the simulation, the learner’s interaction is also displayed to the monitor that supports parallel teaching to the larger class. At the debriefing, 3Rs principles were reinforced as a sample framework for performing humane animal research. We illustrate how the hybrid uses of VR technology with gamification, together with didactic pedagogy, offers promise in enforcing working knowledge into better task performance, specifically research skills training. Our experience and students’ feedback show using immersive VR for educational purposes to encourage the learner applying conceptual knowledge in the simulated laboratory setting. Further application of VR in science for vocational training or higher education is feasible to engage students or stakeholders from various disciplines.

To prevent harmful AI behavior, people need to specify constraints that forbid undesirable actions. Unfortunately, this is a complex task, since writing rules that distinguish harmful from non-harmful actions tends to be quite difficult in real-world situations. Therefore, such decisions have historically been made by a small group of powerful AI companies and developers, with limited community input. In this paper, we study how to enable a crowd of non-AI experts to work together to communicate high-quality, reliable constraints to AI systems. We first focus on understanding how humans reason about temporal dynamics in the context of AI behavior, finding through experiments on a novel game-based testbed that participants tend to adopt a long-term notion of harm, even in uncertain situations that do not affect them directly. Building off of this insight, we explore task design for long-term constraint specification, developing new filtering approaches and new methods of promoting user reflection. Next, we develop a novel rule-based interface which allows people to craft rules in an accessible fashion without programming knowledge. We test our approaches on a real-world AI problem in the domain of education, and find that our new filtering mechanisms and interfaces significantly improve constraint quality and human efficiency. We also demonstrate how these systems can be applied to other real-world AI problems (e.g. in social networks).