A gamified approach for improving the learning performance of K-6 students using Easter eggs.

Gamification is mainly used to increase user engagement and motivation, hence increasing the user base and user activity. Defined by applying game elements to non-gaming contexts, gamification is mostly integrated with software applications in order to provide a gameful experience for users. Education has been one of the areas where gamification studies have focused a lot during the last decade. Young students with the age range of 7-12 years old (K-6) require different teaching methods to use their full potential. However, the methods and principles presented in studies on gamification and its application in education are not dedicated to K-6 students. Furthermore, the evolution of video games has brought new opportunities to develop new gamification elements and principles. In this research, the easter egg element has been implemented as a gamification element. Easter eggs can trigger children's curiosity by encouraging them to find all the Easter eggs, promising special rewards and perks. Additionally, a gamified approach is proposed for implementing a gamified software application for K-6 students. Based on the proposed approach, Science Island is implemented as an online gamified web application for K-6 students. In order to assess the proposed approach, a group of 47 sixth-grade students was selected to use the application for an observation period of 2 months. Feedbacks from students showed that more than 82% of the students agreed with the effectiveness of gamification in their educational performance. Additionally, the results from the data analysis revealed that students' learning performance was improved significantly after applying gamification elements; showing an increase of 0.63 in average quiz score from the second month compared to the first month. Furthermore, the user activity rate at the end of the observation period showed increased motivation among students for using the software application.

A fast density-based clustering algorithm for real-time Internet of Things stream.

Data streams are continuously generated over time from Internet of Things (IoT) devices. The faster all of this data is analyzed, its hidden trends and patterns discovered, and new strategies created, the faster action can be taken, creating greater value for organizations. Density-based method is a prominent class in clustering data streams. It has the ability to detect arbitrary shape clusters, to handle outlier, and it does not need the number of clusters in advance. Therefore, density-based clustering algorithm is a proper choice for clustering IoT streams. Recently, several density-based algorithms have been proposed for clustering data streams. However, density-based clustering in limited time is still a challenging issue. In this paper, we propose a density-based clustering algorithm for IoT streams. The method has fast processing time to be applicable in real-time application of IoT devices. Experimental results show that the proposed approach obtains high quality results with low computation time on real and synthetic datasets.