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,...
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| Vydáno v: | BMC medical education Ročník 19; číslo 1; s. 160 - 11 |
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| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
London
BioMed Central
22.05.2019
BioMed Central Ltd Springer Nature B.V BMC |
| Témata: | |
| ISSN: | 1472-6920, 1472-6920 |
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| Abstract | 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. |
|---|---|
| AbstractList | Abstract 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 rs(133) = 0.27, p = 0.01), interaction with tutors (rs (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. 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. 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. 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 (133) = 0.27, p = 0.01), interaction with tutors (r (133) = 0.22, p = 0.02) are positively correlated with academic performance. 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. 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.BACKGROUNDSocial 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.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.METHODSThis 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.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 rs(133) = 0.27, p = 0.01), interaction with tutors (rs (133) = 0.22, p = 0.02) are positively correlated with academic performance.RESULTSThe 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 rs(133) = 0.27, p = 0.01), interaction with tutors (rs (133) = 0.22, p = 0.02) are positively correlated with academic performance.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.CONCLUSIONSSocial 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. 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. 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 rs(133) = 0.27, p = 0.01), interaction with tutors (rs (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. 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. 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. 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.sub.s(133) = 0.27, p = 0.01), interaction with tutors (r.sub.s (133) = 0.22, p = 0.02) are positively correlated with academic performance. 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. 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. 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.sub.s(133) = 0.27, p = 0.01), interaction with tutors (r.sub.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. Keywords: Social network analysis, problem-based learning, Blended learning, blended problem-based learning, Learning analytics |
| ArticleNumber | 160 |
| Audience | Academic |
| Author | Saqr, Mohammed Alamro, Ahmad |
| Author_xml | – sequence: 1 givenname: Mohammed orcidid: 0000-0001-5881-3109 surname: Saqr fullname: Saqr, Mohammed email: mohammed.saqr@uef.fi organization: School of Computing, University of Eastern Finland, Department of Computer and System Sciences (DSV), Stockholm University – sequence: 2 givenname: Ahmad surname: Alamro fullname: Alamro, Ahmad organization: College of Medicine, Qassim University |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/31113441$$D View this record in MEDLINE/PubMed https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-287258$$DView record from Swedish Publication Index (Kungliga Tekniska Högskolan) https://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-170024$$DView record from Swedish Publication Index (Stockholms universitet) |
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| Keywords | Blended learning, blended problem-based learning Learning analytics Social network analysis, problem-based learning |
| Language | English |
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Social network analysis (SNA) might have an unexplored value in the study of interactions in technology-enhanced learning at large and in online... 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... Background Social network analysis (SNA) might have an unexplored value in the study of interactions in technology-enhanced learning at large and in online... Background: Social network analysis (SNA) might have an unexplored value in the study of interactions in technology-enhanced learning at large and in online... Abstract Background Social network analysis (SNA) might have an unexplored value in the study of interactions in technology-enhanced learning at large and in... |
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| SubjectTerms | 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 |
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