Analysis of Procrastination State of E-learning Based on Clustering and Social Network

2021 ◽  
Author(s):  
Tianjiao Zhai ◽  
Jianhua Qu ◽  
Yuting Ling
Keyword(s):  
Social Network ◽  
E Learning

scholarly journals Profile reliability to improve recommendation in social-learning context

2018 ◽  
Vol 44 (2) ◽  
pp. 433-454 ◽  
Author(s):  
Corinne Amel Zayani ◽  
Leila Ghorbel ◽  
Ikram Amous ◽  
Manel Mezghanni ◽  
André Péninou ◽  
...  
Keyword(s):  
Social Network ◽  
Social Learning ◽  
Learning System ◽  
Learning Context ◽  
Content Type ◽  
Semantic Relationships ◽  
The Hierarchical Structure ◽  
E Learning ◽  
Many Sources

Purpose Generally, the user requires customized information reflecting his/her current needs and interests that are stored in his/her profile. There are many sources which may provide beneficial information to enrich the user’s interests such as his/her social network for recommendation purposes. The proposed approach rests basically on predicting the reliability of the users’ profiles which may contain conflictual interests. The paper aims to discuss this issue. Design/methodology/approach This approach handles conflicts by detecting the reliability of neighbors’ profiles of a user. The authors consider that these profiles are dependent on one another as they may contain interests that are enriched from non-reliable profiles. The dependency relationship is determined between profiles, each of which contains interests that are structured based on k-means algorithm. This structure takes into consideration not only the evolutionary aspect of interests but also their semantic relationships. Findings The proposed approach was validated in a social-learning context as evaluations were conducted on learners who are members of Moodle e-learning system and Delicious social network. The quality of the created interest structure is assessed. Then, the result of the profile reliability is evaluated. The obtained results are satisfactory. These results could promote recommendation systems as the selection of interests that are considered of enrichment depends on the reliability of the profiles where they are stored. Research limitations/implications Some specific limitations are recorded. As the quality of the created interest structure would evolve in order to improve the profile reliability result. In addition, as Delicious is used as a main data source for the learner’s interest enrichment, it was necessary to obtain interests from other sources, such as e-recruitement systems. Originality/value This research is among the pioneer papers to combine the semantic as well as the hierarchical structure of interests and conflict resolution based on a profile reliability approach.

Visualizing Co-Authorship Social Networks and Collaboration Recommendations With CNARe

2018 ◽  
pp. 173-188
Author(s):  
Michele A. Brandão ◽  
Matheus A. Diniz ◽  
Guilherme A. de Sousa ◽  
Mirella M. Moro
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Large Scale ◽  
Online Tool ◽  
Strength Of Ties ◽  
E Learning

Studies have analyzed social networks considering a plethora of metrics for different goals, from improving e-learning to recommend people and things. Here, we focus on large-scale social networks defined by researchers and their common published articles, which form co-authorship social networks. Then, we introduce CNARe, an online tool that analyzes the networks and present recommendations of collaborations based on three different algorithms (Affin, CORALS and MVCWalker). Through visualizations and social networks metrics, CNARe also allows to investigate how the recommendations affect the co-authorship social networks, how researchers' networks are in a central and eagle-eye context, and how the strength of ties behaves in large co-authorship social networks. Furthermore, users can upload their own network in CNARe and make their own recommendation and social network analysis.

Social network analysis of a gamified e-learning course: Small-world phenomenon and network metrics as predictors of academic performance

2016 ◽  
Vol 60 ◽  
pp. 312-321 ◽  
Author(s):  
Luis de-Marcos ◽  
Eva García-López ◽  
Antonio García-Cabot ◽  
José-Amelio Medina-Merodio ◽  
Adrián Domínguez ◽  
...  
Keyword(s):  
Academic Performance ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Small World ◽  
E Learning ◽  
Network Metrics ◽  
Predictors Of Academic Performance

scholarly journals Implementing M-Learning System for Learning Mathematics Through Computer Games and Applying Neural Networks for Content Similarity Analysis of an Integrated Social Network

2021 ◽  
Vol 15 (13) ◽  
pp. 145
Author(s):  
Petar Juric ◽  
Marija Brkic Bakaric ◽  
Maja Matetic
Keyword(s):  
Neural Networks ◽  
Social Network ◽  
Computer Games ◽  
Primary Schools ◽  
Learning System ◽  
Learning Mathematics ◽  
Educational Computer ◽  
E Learning ◽  
Content Similarity ◽  
Educational Computer Games

In order to make e-learning systems more readily available for use, the majority of new systems are being developed in a form suitable for mobile learning, i.e. m-learning. The paper puts focus on the parts of the implementation of an e-learning system which is not restricted to desktop platforms, but works equally well on smartphones and tablets in the form of m-learning. The implemented system uses educational computer games for learning Mathematics in primary schools and has an integrated social network, which is used for communication and publishing of the content related to the game. Besides analysing the platforms used for accessing the system (desktop/mobile), since students are given a choice, the paper also questions how to interpret messages when they contain concepts in student jargon or generally unknown to teachers, and shows that these messages can be interpreted by applying neural networks.

The use of Tin Can API for web usage mining in E-learning applications on the social network

2015 ◽  
Author(s):  
W. M. Amir Fazamin W. Hamzah ◽  
Noraida Haji Ali ◽  
Md. Yazid Mohd Saman ◽  
Mohd Hafiz Yusoff ◽  
Azliza Yacob
Keyword(s):  
Social Network ◽  
Web Usage Mining ◽  
Web Usage ◽  
The Social ◽  
E Learning

The Sense of e-Learning Community Index (SeLCI) for Computer Supported Collaborative e-Learning (CSCeL)

2011 ◽  
pp. 672-698
Author(s):  
Niki Lambropoulos
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Learning Community ◽  
Social Engagement ◽  
Sense Of Belonging ◽  
Skills Acquisition ◽  
Online Teachers ◽  
Community Identification ◽  
E Learning

The aim of this research is to shed light in collaborative e-learning communities in order to observe, analyse and support the e-learning participants. The research context is the Greek teachers’ e-learning community, started in 2003 as part of a project for online teachers’ training and aimed at enabling teachers to acquire new competencies. However, these aims were not met because of passive participation; therefore this study aimed to enhance the Greek teachers’ social engagement to achieve the new skills acquisition. Therefore, the initial sense of community identification was based on empathy; however, because it was inadequate to fully describe the context,, a Sense of E-Learning Community Index (SeLCI) was developed. The new SeLCI attributes were: community evolution; sense of belonging; empathy; trust; intensity characterised by e-learners’ levels of participation and persistence on posting; collaborative e-learning quality measured by the quality in Computer Supported Collaborative eLearning (CSCeL) dialogical sequences, participants’ reflections on own learning; and social network analysis based on: global cohesion anchored in density, reciprocity, cliques and structural equivalence, global centrality derived from in- and out-degree centrality and closeness; and local nodes and centrality in real time. Forty Greek teachers participated in the study for 30 days using Moodle and enhanced Moodle with to measure participation, local Social network Analysis and critical thinking levels in CSCeL. Quantitative, qualitative, Social Network Analysis and measurements produced by the tools were used for data analysis. The findings indicated that each of the SeLCI is essential to enhance participation, collaboration, internalisation and externalisation of knowledge to ensure the e-learning quality and new skills acquisition. Affective factors in CSCeL (sense of belonging, empathy and trust) were also essential to increase reciprocity and promote active participation. Community management, e-learning activities and lastly, the technology appear to affect CSCeL.

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