Predicting teamwork results from social network analysis

Crespo, Pedro Terras; Antunes, Cláudia

doi:10.1111/exsy.12038

Cited by 22 publications

(9 citation statements)

References 26 publications

(33 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While adding to our understanding of the process, these studies focused on the individual collaborator not the group as a unit of analysis. Some examples come from other domains such as [33], who created a recommender system based on group interactivity. Researchers used data from eight engineering courses and compared centrality measures to average group grades to recommend a better team composition [33].…”

Section: Sna In Healthcare Educationmentioning

confidence: 99%

“…Some examples come from other domains such as [33], who created a recommender system based on group interactivity. Researchers used data from eight engineering courses and compared centrality measures to average group grades to recommend a better team composition [33]. Another example from Engineering used SNA to investigate the correlation between a team's project score and the team balance; their results pointed to a positive correlation, although, the sample size was small [34].…”

Section: Sna In Healthcare Educationmentioning

confidence: 99%

See 1 more Smart Citation

What makes an online problem-based group successful? A learning analytics study using social network analysis

et al. 2020

View full text Add to dashboard Cite

Background: Although there is a wealth of research focusing on PBL, most studies employ self-reports, surveys, and interviews as data collection methods and have an exclusive focus on students. There is little research that has studied interactivity in online PBL settings through the lens of Social Network Analysis (SNA) to explore both student and teacher factors that could help monitor and possibly proactively support PBL groups. This study adopts SNA to investigate how groups, tutors and individual student's interactivity variables correlate with group performance and whether the interactivity variables could be used to predict group performance. Methods: We do so by analyzing 60 groups' work in 12 courses in dental education (598 students). The interaction data were extracted from a Moodle-based online learning platform to construct the aggregate networks of each group. SNA variables were calculated at the group level, students' level and tutor's level. We then performed correlation tests and multiple regression analysis using SNA measures and performance data. Results: The findings demonstrate that certain interaction variables are indicative of a well-performing group; particularly the quantity of interactions, active and reciprocal interactions among students, and group cohesion measures (transitivity and reciprocity). A more dominating role for teachers may be a negative sign of group performance. Finally, a stepwise multiple regression test demonstrated that SNA centrality measures could be used to predict group performance. A significant equation was found, F (4, 55) = 49.1, p < 0.01, with an R2 of 0.76. Tutor Eigen centrality, user count, and centralization outdegree were all statistically significant and negative. However, reciprocity in the group was a positive predictor of group improvement. Conclusions: The findings of this study emphasized the importance of interactions, equal participation and inclusion of all group members, and reciprocity and group cohesion as predictors of a functioning group. Furthermore, SNA could be used to monitor online PBL groups, identify important quantitative data that helps predict and potentially support groups to function and co-regulate, which would improve the outcome of interacting groups in PBL. The information offered by SNA requires relatively little effort to analyze and could help educators get valuable insights about their groups and individual collaborators.

show abstract

Section: Sna In Healthcare Educationmentioning

confidence: 99%

Section: Sna In Healthcare Educationmentioning

confidence: 99%

What makes an online problem-based group successful? A learning analytics study using social network analysis

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Despite the challenges mentioned, SNA may be principally effective in studying the relational dimension of blended PBL by means of visual analytics and quantitative mathematical analysis [ 26 , 27 , 29 , 30 , 41 ]. With the support of visual analytics the PBL group structure, the learner-learner, and the learner-tutor interactions can be mapped in order to identify influential and isolated learners as well as group functioning [ 27 , 28 , 42 ].…”

Section: Introductionmentioning

confidence: 99%

Using social network analysis to understand online Problem-Based Learning and predict performance

2018

View full text Add to dashboard Cite

Social network analysis (SNA) may be of significant value in studying online collaborative learning. SNA can enhance our understanding of the collaborative process, predict the under-achievers by means of learning analytics, and uncover the role dynamics of learners and teachers alike. As such, it constitutes an obvious opportunity to improve learning, inform teachers and stakeholders. Besides, it can facilitate data-driven support services for students. This study included four courses at Qassim University. Online interaction data were collected and processed following a standard data mining technique. The SNA parameters relevant to knowledge sharing and construction were calculated on the individual and the group level. The analysis included quantitative network analysis and visualization, correlation tests as well as predictive and explanatory regression models. Our results showed a consistent moderate to strong positive correlation between performance, interaction parameters and students’ centrality measures across all the studied courses, regardless of the subject matter. In each of the studied courses, students with stronger ties to prominent peers (better social capital) in small interactive and cohesive groups tended to do better. The results of correlation tests were confirmed using regression tests, which were validated using a next year dataset. Using SNA indicators, we were able to classify students according to achievement with high accuracy (93.3%). This demonstrates the possibility of using interaction data to predict underachievers with reasonable reliability, which is an obvious opportunity for intervention and support.

show abstract

“…These systems form a specific type of information-filtering technique that attempts to suggest information (blogs, news, music, travel plans, web pages, images, tags, content communities, collaborative projects, social networking sites, virtual game worlds, virtual social worlds) that is likely to interest the users [68].…”

Section: Social Media Recommendationsmentioning

confidence: 99%

SMORE: Towards a semantic modeling for knowledge representation on social media

Villanueva

González‐Carrasco

López-Cuadrado

et al. 2016

Science of Computer Programming

View full text Add to dashboard Cite

This research presents SMORE, a semantic model for knowledge representation on social media. In order to provide recommendations, the model provides the elements for representing the content through the use of an ontological model and semantic techniques for the characterization and relationships between user profiles, products and social networks. In fact, with this model could be the basis of recommendation system based on social media data, and it could be exploited use by the recommendations on different products, which are stored in the Web and with similar characteristics between them. Moreover, SMORE represent the information related to user of the social networks in order to have a user characterization to be used for future recommendations in several domains. The semantic model has been evaluated with semantic data extracted from a trusted social network as, Twitter, obtaining the information specified by an expert in the field of marketing for recommendations in the automotive industry.

show abstract

Predicting teamwork results from social network analysis

Cited by 22 publications

References 26 publications

What makes an online problem-based group successful? A learning analytics study using social network analysis

What makes an online problem-based group successful? A learning analytics study using social network analysis

Using social network analysis to understand online Problem-Based Learning and predict performance

SMORE: Towards a semantic modeling for knowledge representation on social media

Contact Info

Product

Resources

About