Social Network Analysis and Data Mining: An Application to the E-Learning Context

Abstract

Abstract. With the increasing popularity of social networking services like Facebook or Twitter, social network analysis has emerged again. Discovering the underlying relationships between people-as well as the reasons why they arise or the type of those interactions-and measuring their influence are examples of tasks that are becoming to be paramount in business. However, this is not the only field of application in which the use of social network analysis techniques might be appropriate. In this paper, we expose how social network analysis can be a tool of considerable utility in the educational context for addressing difficult problems, e.g., uncovering the students' level of cohesion, their degree of participation in forums, or the identification of the most influential ones. Furthermore, we show that the correct management of social behavior data, along with the use of the student activity, helps us build more accurate performance and dropout predictors. Our conclusions are drawn from the analysis of an e-learning course taught at the University of Cantabria for three consecutive academic years.

Three interaction patterns on asynchronous online discussion behaviours: A methodological comparison

“…SNA is a well‐known method to analyse phenomena with quantifiable data, and it enables researchers to examine the relationships among actors from a macro perspective (Cho, Lee, Stefanone, & Gay, ; Enriquez, ; Jun, ). In the teaching and learning context, it has been recognized as a useful method to assess students' knowledge construction (Dawson et al, ; Enriquez, ) and to observe their interaction patterns on the discussion boards (de Laat et al, ; Palazuelos, García‐Saiz, & Zorrilla, ), which have turned out to be one of the most influential factors affecting learning outcomes on computer‐supported collaborative learning (Liccardi et al, ; Vrasidas & McIsaac, ).…”

CREeLS: Crowdsourcing based Requirements Elicitation for eLearning Systems

“…Interactivity between the eLearning participants (Instructor, learner, course, and management) is important. Interactivity leads to better course results for learners [47], and to know the participants" opinions in the different modules of the eLS, e.g. course material, quizzes, assignments, scheduling, eLearning process, and participation between the different eLS users.…”

Towards Smart Asynchronous Discussion Activity: Using Social Network Analysis to Investigate Students’ Discussion Patterns

“…SNA is a powerful method to observe students' interactions on discussion board [9,11]. As peer influence where active social interaction is pointed out to be one of the most influential factor affecting online collaborative learning [12,13], and analyzing such an interaction to achieve academic success of students participating in online interaction is a meaningful step.…”

“…Furthermore, the SNA can be considered as a tool of social awareness [14,15]. Predictions of at-risk or advanced students based on the relationship between academic achievement records and social network factors results from the SNA [11,16]. It mainly involves educational data mining which is to discover meaningful knowledge by analyzing raw materials such as log data accumulated from every online activity of the target students.…”