Mindmap-Supported Writing Model: The Effect of Mind map as a Prewriting Activities on Elementary Students’ Writing Performances through Private Social Network Learning Sites

2021 ◽  
Author(s):  
Melissa Mustika ◽  
Hercy N. H. Cheng ◽  
Tak-Wai Chan
Keyword(s):  
Social Network ◽  
Elementary Students ◽  
Network Learning

scholarly journals Individual Differences in Learning Social and Non-Social Network Structures

2017 ◽  
Author(s):  
Steven Tompson ◽  
Ari E Kahn ◽  
Emily B. Falk ◽  
Jean M Vettel ◽  
Danielle S Bassett
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Individual Differences ◽  
Social Contexts ◽  
Higher Order ◽  
Social Orientation ◽  
Order Structure ◽  
Network Learning ◽  
Social Traits ◽  
The Impact

Learning about complex associations between pieces of information enables individuals to quickly adjust their expectations and develop mental models. Yet, the degree to which humans can learn higher-order information about complex associations is not well understood; nor is it known whether the learning process differs for social and non-social information. Here, we employ a paradigm in which the order of stimulus presentation forms temporal associations between the stimuli, collectively constituting a complex network structure. We examined individual differences in the ability to learn network topology for which stimuli were social versus non-social. Although participants were able to learn both social and non-social networks, their performance in social network learning was uncorrelated with their performance in non-social network learning. Importantly, social traits, including social orientation and perspective-taking, uniquely predicted the learning of social networks but not the learning of non-social networks. Taken together, our results suggest that the process of learning higher-order structure in social networks is independent from the process of learning higher-order structure in non-social networks. Our study design provides a promising approach to identify neurophysiological drivers of social network versus non-social network learning, extending our knowledge about the impact of individual differences on these learning processes. Implications for how people learn and adapt to new social contexts that require integration into a new social network are discussed.

scholarly journals Discussion on Social Network Learning from the Long Tail

IERI Procedia ◽  
2012 ◽  
Vol 2 ◽  
pp. 492-497 ◽  
Author(s):  
Xiuping Du ◽  
Yiwen Wang ◽  
Wenrui Du ◽  
Aiwen Feng
Keyword(s):  
Social Network ◽  
Long Tail ◽  
Network Learning

scholarly journals Functional brain network architecture supporting the learning of social networks in humans

2019 ◽  
Author(s):  
Steven Tompson ◽  
Ari E Kahn ◽  
Emily B. Falk ◽  
Jean M Vettel ◽  
Danielle S Bassett
Keyword(s):  
Social Networks ◽  
Community Structure ◽  
Social Network ◽  
Brain Network ◽  
Temporoparietal Junction ◽  
Functional Brain ◽  
Network Learning ◽  
Functional Brain Network ◽  
The Social ◽  
The Impact

Most humans have the good fortune to live their lives embedded in richly structured social groups. Yet, it remains unclear how humans acquire knowledge about these social structures to successfully navigate social relationships. Here we address this knowledge gap with an interdisciplinary neuroimaging study drawing on recent advances in network science and statistical learning. Specifically, we collected BOLD MRI data while participants learned the community structure of both social and non-social networks, in order to examine whether the learning of these two types of networks was differentially associated with functional brain network topology. From the behavioral data in both tasks, we found that learners were sensitive to the community structure of the networks, as evidenced by a slower reaction time on trials transitioning between clusters than on trials transitioning within a cluster. From the neuroimaging data collected during the social network learning task, we observed that the functional connectivity of the hippocampus and temporoparietal junction was significantly greater when transitioning between clusters than when transitioning within a cluster. Furthermore, temporoparietal regions of the default mode were more strongly connected to hippocampus, somatomotor, and visual regions during the social task than during the non-social task. Collectively, our results identify neurophysiological underpinnings of social versus non-social network learning, extending our knowledge about the impact of social context on learning processes. More broadly, this work offers an empirical approach to study the learning of social network structures, which could be fruitfully extended to other participant populations, various graph architectures, and a diversity of social contexts in future studies.

scholarly journals Empirical study on the effects of social network–supported group concept mapping

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
Yen-An Shih ◽  
Ben Chang
Keyword(s):  
Social Network ◽  
Learning Environment ◽  
Social Networking ◽  
Concept Mapping ◽  
Concept Maps ◽  
Network Learning ◽  
Individual Concept ◽  
Group Concept Mapping ◽  
Environment Control ◽  
Group Concept

Abstract Social networks provide traditional concept mapping of new opportunities for concept construction with grouping, social interaction, and collaborative functions. However, little effort has been made to explore the effects of social network–supported concept mapping compared with traditional individual concept construction. This paper explores the effects of social network–supported group concept mapping (SCM) activity and compares them with the effects of individual concept mapping (ICM) activity. A platform named CoCoing.info (http://cocoing.info) is utilized to examine the SCM and ICM activities under three studies, which drove the following research questions: (1) Do map structure (i.e., propositions, hierarchies, examples, cross-links, and scores) and mapping activity (i.e., map modification period and frequency) differ between ICM and SCM in students on specialized courses? (2) Do map structure and mapping activity differ between ICM and SCM in students on general education courses? (3) What are the effects of group size on SCM? In study I, four classes are selected to ensure a strong social network learning environment control. On the basis of study I, study II extends the controlled environment within an open social networking environment with a total of 1106 SCM maps and 569 ICM maps to produce an improved overview of concept mapping. The findings of studies I and II are consistent, demonstrating that the students constructed more comprehensive concept maps and had a higher modification period and frequency with SCM than with ICM, which indicates that in a social network learning environment, SCM is favorable to ICM. Study III considers each participant’s contributions to identify an optimal group number. The results of study III indicate that groups with two to seven members perform better than larger groups. Overall, the findings demonstrate the benefits of integrating concept mapping with social networking for student learning outcomes.

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