Adding energy-saving products to your house can benefit the economy, the environment and your living comfort. However, these products are very costly, and many people cannot afford them using their own savings. There exist several options for funding these projects, but people do not take advantage of such due to lack of information and the common negative view on using external funding. Psychological objections on taking loans include future time perspective, perception of short time rewards and connotation of loans itself. This paper presents a serious game aimed at changing people's mindset on taking loans to retrofit energy into their homes; Supreme Green Time Machine is a tycoon game in which you can acquire energy-saving products for your home. A main mechanic in the game is the opportunity to take loans to fund the purchase of these upgrades. Combined with other underlying mechanics, such as the time progress and social feedback, the game targets the different psychological objections to long term loans for home retrofitting. From a preliminary evaluation, we conclude that Supreme Green Time Machine effectively succeeds in making players more positive towards using loans to retrofit their homes.
In popular crowdsourcing marketplaces like Amazon Mechanical Turk, crowd workers complete tasks posted by requesters in return for monetary rewards. Task requesters are solely responsible for deciding whether to accept or reject submitted work. Rejecting work can directly affect the monetary reward of corresponding workers, and indirectly influence worker qualifications and their future work opportunities in the marketplace. Unexpected or unwarranted rejections therefore result in negative emotions and reactions among workers. Despite the high prevalence of rejections in crowdsourcing marketplaces, little research has explored ways to mitigate the negative emotional repercussions of rejections on crowd workers. Addressing this important research gap, we investigate whether introducing self-reflection at different stages after task execution can alleviate the emotional toll of rejection decisions on crowd workers. Our work is inspired by prior studies in psychology that have shown that self-reflection on negative personal experiences can positively affect one's emotion. To this end, we carried out an experimental study investigating the impact of explicit self-reflection on the emotions of rejected crowd workers. Results show that allowing workers to self-reflect on their delivered work, especially before receiving a rejection, has a significantly positive impact on their self-reported emotions in terms of valence and dominance. Our findings reveal that introducing a self-reflection stage before workers receive acceptance or rejection decisions on submitted work, can help in positively influencing the emotions of a worker. These findings have important design implications towards fostering a healthier requester-worker relationship and contributing towards the sustainability of the crowdsourcing marketplace.
It takes considerable time, experience, and direct assistance from teachers to become a skilled writer. Handwriting fluency is one of the predictors of writing quality among students. However, students do not receive enough teacher supervision as a beginner to develop handwriting fluency in a proper manner. The "Calligraphy tutor" presented in this paper, is an application developed to assist teachers to help students learn proper handwriting fluency skills. Calligraphy tutor is designed to support deliberate practice of handwriting, in which teachers play the central role. To reduce workload of teachers, Calligraphy tutor automates repetitive actions such as providing mundane real-time feedback, while also collecting performance data from students, allowing students to practice without the presence of a teacher. The collected performance data is used by teachers to further personalise students' training.
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