Lifelong and multirelational community detection to support social and collaborative e‐learning

Abstract

Learners' community choice has a crucial role in e‐learning effectiveness. Indeed, individual and structural factors (i.e., learners pre‐existing profiles and networks of interactions) significantly affect how learners develop a collaborative e‐learning environment. In this context, social network analysis, singularly community detection has been a good approach to improve collaborative environment through discovering pertinent learners' communities and new effective relations. However, with social media emergence, many real‐world networks, such as learners' networks, evolve the connections in multiple layers, where each layer represents a different type of relationship. This, combined with their continuous evolution over time, has brought new challenges to the field of community detection. Thus, this paper proposes a new configurable algorithm for detecting collaborative and lifelong communities within dynamic multirelational social learners' networks. To do so, this algorithm is based on a graph model to represent these different interactions as well as different learners' profiles and characteristics. Moreover, by using particle swarm optimization, it aims to optimize a configurable combined metric to detect the most relevant community appropriate to a given situation. Finally, it considers the temporal dimension to find the final lifelong community. By the end of this paper, experimental results using synthetic networks prove that the proposed algorithm achieves better results compared with other community detection algorithms. Therewith, experiments on a real e‐learning network show this algorithm's role in improving collaboration within learners' network.

The Value and Path of Realizing Lifelong Learning Education System under the "University + Community" Model in the New Era

“…In terms of knowledge transfer, on the one hand, the concept of lifelong education can be conveyed to students through teachers' teaching methods. On the other hand, college students should be actively encouraged to actively participate in community organizations and provide them with more opportunities to participate [11][12]. College students are the most important and basic link in the lifelong education system, and they are also the leaders in implementing the lifelong education model.…”

Analysis of the Functionalities of Open-Source Social Media for Academic Use

“…Social networks are defined as a variety of networked tools or technologies that emphasise social aspects of the internet, acting as a channel for communication, collaboration, and creative expression. Social media platforms support group interaction to establish communities and create and exchange content, such as Facebook, Instagram, and X (former Twitter), and the massification of these platforms has prompted a growing interest in research on how social networks are used by university students and professors (Rebhi et al, 2022;Arnal-Palacián, 2023;Carpenter et al, 2020;Acun, 2020).…”

A New Learning Resource Recommendation Method for Improving the Efficiency of Students' Online Independent Learning

“…At present, online learning is becoming a hot spot in the field of higher education, and is widely used in various sections and disciplines of college students' training for higher education. Under the online learning environment, college students have undergone great changes in learning methods and habits [1][2][3][4][5][6][7][8]. Based on the laws and characteristics of online learning, in order to obtain better learning efficiency, it is necessary to guide college students to study consciously and actively, and further realize student-centered high-quality education [9][10][11][12][13][14][15].…”