Evaluate learning communities in the online social media: Facebook groups

被引：4

作者：

Adraoui, Meriem ^{[1
]}

Retbi, Asmaa ^{[1
]}

Idrissi, Mohammed Khalidi ^{[1
]}

Bennani, Samir ^{[1
]}

机构：

[1] MOHAMMED Vth UNIV RABAT, Mohammadia Sch Engineers EMI, Dept Comp Engn, Ave Ibn Sina BP 765 Agdal, Rabat, Morocco

来源：

PROCEEDINGS OF THE 12TH INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS: THEORIES AND APPLICATIONS (SITA'18) | 2018年

关键词：

Interaction social; social learning analytics (SLA); Facebook; Group; Degree centrality; learning communities; safe; at-risk;

D O I：

10.1145/3289402.3289505

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Nowadays, Facebook is considered like an online learning tool. It is an integral part of users' daily routine, whether teachers or learners. The idea of this article is to evaluate learners by communities in Facebook groups based on their interactions via comments and like. In order to achieve our goal, we will proceed in two steps, the first one consists to implement two community detection algorithms (ForceAtlas2 and Louvian Blondel), and in the second step we will calculate three measures of centrality (degree, closeness, betweenness) of the Social Learning Analytics process (SLA) to identify communities at-risk.

引用

页数：6

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