Who Students Interact With? A Social Network Analysis Perspective on the Use of Twitter in Language Learning

Ullrich, Carsten; Borau, Kerstin; Stepanyan, Karen

doi:10.1007/978-3-642-16020-2_33

Cited by 14 publications

(9 citation statements)

References 8 publications

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“…To this end, we analysed records of 80, 000 interactions by 290 students - approximately 16 times more interactions with almost 3 times more students than previous studies on educational networks in natura 5678101215. Even so the data can still be considered to be sparse ( 4.6 interactions per person per day).…”

Section: Resultsmentioning

confidence: 99%

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The rich club phenomenon in the classroom

Vaquero

Cebrián

2013

Sci Rep

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We analyse the evolution of the online interactions held by college students and report on novel relationships between social structure and performance. Our results indicate that more frequent and intense social interactions generally imply better score for students engaging in them. We find that these interactions are hosted within a “rich-club”, mediated by persistent interactions among high performing students, which is created during the first weeks of the course. Low performing students try to engage in the club after it has been initially formed, and fail to produce reciprocity in their interactions, displaying more transient interactions and higher social diversity. Furthermore, high performance students exchange information by means of complex information cascades, from which low performing students are selectively excluded. Failure to engage in the rich club eventually decreases these students' communication activity towards the end of the course.

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Section: Resultsmentioning

confidence: 99%

“…social acceptance or willingness to communicate11. In general, it is not just about knowing “who” the students interact with, but “how” and “when” they do it and, importantly, what is the result of these interactions with regards to the educational outcome12.…”

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confidence: 99%

The rich club phenomenon in the classroom

Vaquero

Cebrián

2013

Sci Rep

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“…Furthermore, their work has been extended by integrating gender as additional variable to the network. It is found that, students tend to interact with the same gender and no significant evidence of popularity effect in relation to gender [14].…”

Section: Mohd Shahizan Othman Department Of Information Systemsmentioning

confidence: 80%

Ontology of moodle e-learning system for social network analysis

Yusof

Mansur

Othman

2011

2011 IEEE Conference on Open Systems

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The analysis using Social Network Analysis (SNA) is focusing more on the number or the frequency of social interactions based on adjacency matrix. This method cannot be used to observe the semantic relationship among e-Learning components. Therefore, this paper aims to propose a new paradigm of Social Network Analysis (SNA) using ontology structure of Moodle e-Learning. By considering the relationship of ontology, we can understand the meaning of semantic relationship more deeply. The ontology of Moodle e-learning system for social network analysis will become great advantage to make measurement not only based on the matrix or number of interactions but also based on the semantic meaning of the relationships among users.

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“…However, many studies of animal networks focus on species and social processes for which fewer observations are available, meaning that a multilevel network analysis (such as the multi-group analysis we used here) may not always be possible. However, past uses of RSiena have yielded interesting and valid results, even when repeated experiment cannot be performed ( Ullrich et al, 2010 ; van Zalk et al, 2011 ). It thus seems that this tool could be used to study a wide range of animal species, varying in group size, social complexity, and access by observers, as recently shown in Ilany et al (2015) .…”

Section: Discussionmentioning

confidence: 99%

Understanding Dynamics of Information Transmission in Drosophila melanogaster Using a Statistical Modeling Framework for Longitudinal Network Data (the RSiena Package)

et al. 2016

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Social learning – the transmission of behaviors through observation or interaction with conspecifics – can be viewed as a decision-making process driven by interactions among individuals. Animal group structures change over time and interactions among individuals occur in particular orders that may be repeated following specific patterns, change in their nature, or disappear completely. Here we used a stochastic actor-oriented model built using the RSiena package in R to estimate individual behaviors and their changes through time, by analyzing the dynamic of the interaction network of the fruit fly Drosophila melanogaster during social learning experiments. In particular, we re-analyzed an experimental dataset where uninformed flies, left free to interact with informed ones, acquired and later used information about oviposition site choice obtained by social interactions. We estimated the degree to which the uninformed flies had successfully acquired the information carried by informed individuals using the proportion of eggs laid by uninformed flies on the medium their conspecifics had been trained to favor. Regardless of the degree of information acquisition measured in uninformed individuals, they always received and started interactions more frequently than informed ones did. However, information was efficiently transmitted (i.e., uninformed flies predominantly laid eggs on the same medium informed ones had learn to prefer) only when the difference in contacts sent between the two fly types was small. Interestingly, we found that the degree of reciprocation, the tendency of individuals to form mutual connections between each other, strongly affected oviposition site choice in uninformed flies. This work highlights the great potential of RSiena and its utility in the studies of interaction networks among non-human animals.

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Who Students Interact With? A Social Network Analysis Perspective on the Use of Twitter in Language Learning

Cited by 14 publications

References 8 publications

The rich club phenomenon in the classroom

The rich club phenomenon in the classroom

Ontology of moodle e-learning system for social network analysis

Understanding Dynamics of Information Transmission in Drosophila melanogaster Using a Statistical Modeling Framework for Longitudinal Network Data (the RSiena Package)

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