The role of social network analysis as a learning analytics tool in online problem based learning

Background Social network analysis (SNA) might have an unexplored value in the study of interactions in technology-enhanced learning at large and in online (Problem Based Learning) PBL in particular. Using SNA to study students’ positions in information exchange networks, communicational activities,...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:BMC medical education Ročník 19; číslo 1; s. 160 - 11
Hlavní autoři: Saqr, Mohammed, Alamro, Ahmad
Médium: Journal Article
Jazyk:angličtina
Vydáno: London BioMed Central 22.05.2019
BioMed Central Ltd
Springer Nature B.V
BMC
Témata:
Academic achievement
Academic Performance
Approaches to teaching and learning
Blended learning
Blended learning, blended problem-based learning
blended problem-based learning
Coding
Collaborative learning
Computer-Assisted Instruction - methods
Cooperative Learning
Data mining
Education
Education, Medical - methods
Embryology
Humans
International conferences
Interpersonal Relationship
Interviews
Learner Engagement
Learning
Learning Analytics
Learning management systems
Medical Education
Methods
Models, Theoretical
Network Analysis
Numerical analysis
Online education
Problem based learning
Problem-Based Learning - methods
Research Article
Social network analysis
Social network analysis, problem-based learning
Social Networking
Social networks
Social Structure
Students
Students, Medical
Studies
Study and teaching
Systematic review
Teachers
Teaching Methods
Technology
Theory of Medicine/Bioethics
Visualization
Saudi Arabia
Singapore
Blended learning, blended problem-based learning
Learning analytics
Social network analysis, problem-based learning
ISSN:1472-6920, 1472-6920
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Background Social network analysis (SNA) might have an unexplored value in the study of interactions in technology-enhanced learning at large and in online (Problem Based Learning) PBL in particular. Using SNA to study students’ positions in information exchange networks, communicational activities, and interactions, we can broaden our understanding of the process of PBL, evaluate the significance of each participant role and learn how interactions can affect academic performance. The aim of this study was to study how SNA visual and mathematical analysis can be sued to investigate online PBL, furthermore, to see if students’ position and interaction parameters are associated with better performance. Methods This study involved 135 students and 15 teachers in 15 PBL groups in the course of “growth and development” at Qassim University. The course uses blended PBL as the teaching method. All interaction data were extracted from the learning management system, analyzed with SNA visual and mathematical techniques on the individual student and group level, centrality measures were calculated, and participants’ roles were mapped. Correlation among variables was performed using the non-parametric Spearman rank correlation test. Results The course had 2620 online interactions, mostly from students to students (89%), students to teacher interactions were 4.9%, and teacher to student interactions were 6.15%. Results have shown that SNA visual analysis can precisely map each PBL group and the level of activity within the group as well as outline the interactions among group participants, identify the isolated and the active students (leaders and facilitators) and evaluate the role of the tutor. Statistical analysis has shown that students’ level of activity (outdegree r s (133) = 0.27, p  = 0.01), interaction with tutors (r s (133) = 0.22, p  = 0.02) are positively correlated with academic performance. Conclusions Social network analysis is a practical method that can reliably monitor the interactions in an online PBL environment. Using SNA could reveal important information about the course, the group, and individual students. The insights generated by SNA may be useful in the context of learning analytics to help monitor students’ activity.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:1472-6920
1472-6920
DOI:10.1186/s12909-019-1599-6