![]() ![]() ![]() 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. 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. 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%. Correlation among variables was performed using the non-parametric Spearman rank correlation test. 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. ![]() The course uses blended PBL as the teaching method. This study involved 135 students and 15 teachers in 15 PBL groups in the course of “growth and development” at Qassim University. 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. 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. 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. ![]()
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