Figure 4 - uploaded by Lorenz Cuno Klopfenstein
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Larger smartphones have become increasingly commonplace, sometimes blurring the boundaries between phones and tablets. Most UI guidelines and usability studies are rarely updated and are still based on smaller screens or one-handed operations, which can be tiresome on large devices that may require different, or even two-handed postures. Past usabi...
Context in source publication
Context 1
... filtering step removed 929 data points (1.5% of total). The 1.587 game sessions are distributed among the 6 dif- ferent game modes as shown in Figure 4: the large majority of users did use the first mode (i.e., left hand grip and right index tap). Both right hand modes correspond to more than 57% of sessions together, while left hand only modes cover slightly less than 1 /5 of usage. ...
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Citations
... In [18], a large amount of data is collected from a heterogeneous population for the study of touchscreen operation in natural environments using the gamification technique. A gamified crowdsourcing system named as Quizz is developed, which is used to assess the knowledge of users and gain new insights from them. ...
Crowdsourcing is a model where individuals or organizations receive services from a large group of Internet users including ideas, finances, completing a complex task, etc. Several crowdsourcing websites have failed due to lack of user participation; hence, the success of crowdsourcing platforms is manifested by the mass of user participation. However, an issue of motivating users to participate in crowdsourcing platform stays challenging. We have proposed a new approach, i.e., reinforcement learning-based gamification method to motivate users. Gamification has been a practical approach to engaging users in many fields, but still, it needs an improvement in the Crowdsourcing platform. In this paper, the gamification approach is strengthened by a reinforcement learning algorithm. We have created an intelligent agent using the Reinforcement learning algorithm (Q-learning). This agent suggests an optimal action plan that yields maximum reward points to the users for their active participation in the Crowdsourcing application. Also, its performance is compared with the SARSA algorithm (On- policy learning), which is another Reinforcement learning algorithm.
