The aim of this research is to investigate the role of social networks in computer science education. The Internet shows great potential for enhancing collaboration between people and the role of social software has become increasingly relevant in recent years. This research focuses on analyzing the role that social networks play in students' learning experiences. The construction of students' social networks, the evolution of these networks, and their effects on the students' learning experience in a university environment are examined.
The aim of this research is to investigate the role of social networks in computer science education. The Internet shows great potential for enhancing collaboration between people and the role of social software has become increasingly relevant in recent years. This research focuses on analyzing the role that social networks play in students' learning experiences. The construction of students' social networks, the evolution of these networks, and their effects on the students' learning experience in a university environment are examined.
MotivationFor decades, group formation has been a subject of study in many domains. In learning, teachers form groups of students for different types of collaborative activities. For the formation to be efficient, teachers need take into account any constraints that can influence the performance of the group as a whole and that of the individuals within the group, such as students' previous experience, gender, nationality, and interests. The formation of groups in this context involves the creation of balanced groups in terms of expected performance in addition to maximizing each individual's goal from the collaboration. As the number of formation's constraints grows, forming groups that satisfy these constraints increases in complexity. We know that the Semantic Web (SW) aims at providing a promising foundation for enriching resources with well defined meanings and making them understandable for programs and applications. The potential of the SW in this context has allowed the semantic formation of social networks to be successful [1]. From this point, we trust that the problem of constraint group formation can as well be solved using SW technologies. The question is how to apply the SW vision to the problem, and take the most of its potential to apply it in real life applications such as e-learning. In particular, the problem can be formulated as how can we generate optimal groups by reasoning over possibly incomplete data about the students. Research Overview and Essential QuestionsSince forming groups of students with attention to constraint satisfaction is not a simple task for the teacher to do manually, especially for a large number of students, the proposed research is intended to investigate the automation of constrained group formation. In order to cover different types of collaborative activities, we consider the formation of different types of groups including: Teams, Communities of Practice (CoPs), and Social Networks (SNs). We believe that by reasoning on learners' profiles and the teacher's constraints, we can achieve a powerful foundation for automated group formation. With respect to SW concepts, our present and future work intends to give appropriate answers to the following questions: What do we model for the formations of different types of groups? How do we enable the teacher to get the group formation they want? How do we achieve that formation? And how effectively we achieved it? Due to their self-organized nature, for formation of CoPs and SNs to be effective, the instructor has to provide a degree of dynamic self organization within these groups. In this research we address the question of how do we enable the dynamic formation of instructor-initiated CoPs and SNs? If we do not have all the required information about the users, how do we process the formation with incomplete
Many approaches to learning and teaching rely upon students working in groups. So far, many Computer-Supported Group Formation systems have been designed to facilitate the formation of optimal groups in learning. However, evaluating the quality of automated group formation is not always well reported. In this paper we propose a metrics framework for evaluating group formation based upon a model for constraint satisfaction-based group formation.
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