The Effects of the Social Network Data Presentation on Interaction Processes and Products in Computer Supported Collaborative Learning Environment

2004 ◽  
Vol 20 (1) ◽  
pp. 89-115 ◽  
Author(s):  
Dong-sik Kim ◽  
In-Gu Kang
Keyword(s):  
Social Network ◽  
Collaborative Learning ◽  
Learning Environment ◽  
Network Data ◽  
Computer Supported Collaborative Learning ◽  
Data Presentation ◽  
Social Network Data ◽  
Interaction Processes ◽  
The Social ◽  
Collaborative Learning Environment

The Multilevel p2 Model

Methodology ◽  
2006 ◽  
Vol 2 (1) ◽  
pp. 42-47 ◽  
Author(s):  
Bonne J. H. Zijlstra ◽  
Marijtje A. J. van Duijn ◽  
Tom A. B. Snijders
Keyword(s):  
Social Network ◽  
Network Data ◽  
Mcmc Algorithm ◽  
Independent Observation ◽  
Single Observation ◽  
Multiple Networks ◽  
Social Network Data ◽  
Multiple Observations ◽  
Practical Support ◽  
The Social

The p 2 model is a random effects model with covariates for the analysis of binary directed social network data coming from a single observation of a social network. Here, a multilevel variant of the p 2 model is proposed for the case of multiple observations of social networks, for example, in a sample of schools. The multilevel p 2 model defines an identical p 2 model for each independent observation of the social network, where parameters are allowed to vary across the multiple networks. The multilevel p 2 model is estimated with a Bayesian Markov Chain Monte Carlo (MCMC) algorithm that was implemented in free software for the statistical analysis of complete social network data, called StOCNET. The new model is illustrated with a study on the received practical support by Dutch high school pupils of different ethnic backgrounds.

scholarly journals Designing Educational Analytics Tools Using Visualization Based on Behavioral, Motivational, and Social Network Data

2019 ◽  
pp. 81-93
Author(s):  
Iliya L. Musabirov ◽  
Keyword(s):  
Social Network ◽  
Social Values ◽  
Network Data ◽  
Network Approach ◽  
Social Network Data ◽  
Use Of Data ◽  
University Courses ◽  
The Social ◽  
Social Network Approach ◽  
Visualization Tools

The article presents a description of the approach to the use of data visualization in various educational Analytics tools when building University courses. In addition to the analysis of educational behavior, socio-psychological approaches, including the theory of expectations and social values, and the social network approach, are separately considered as prospects for analysis. An example of designing training Analytics using modern data analysis and visualization tools is analyzed.

Social Network Analysis in Marketing

E-Marketing ◽  
2012 ◽  
pp. 185-197
Author(s):  
Przemyslaw Kazienko ◽  
Piotr Doskocz ◽  
Tomasz Kajdanowicz
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Relationships ◽  
Network Data ◽  
Social Network Data ◽  
The Social ◽  
Targeted Marketing ◽  
Potential Customers ◽  
Demographic Features

The chapter describes a method how to perform a classification task without any demographic features and based only on the social network data. The concept of such collective classification facilitates to identify potential customers by means of services used or products purchased by the current customers, i.e. classes they belong to as well as using social relationships between the known and potential customers. As a result, a personalized offer can be prepared for the new clients. This innovative marketing method can boost targeted marketing campaigns.

Social Network Analysis in Marketing

2011 ◽  
pp. 271-283
Author(s):  
Przemyslaw Kazienko ◽  
Piotr Doskocz ◽  
Tomasz Kajdanowicz
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Relationships ◽  
Network Data ◽  
Social Network Data ◽  
The Social ◽  
Targeted Marketing ◽  
Potential Customers ◽  
Demographic Features

The chapter describes a method how to perform a classification task without any demographic features and based only on the social network data. The concept of such collective classification facilitates to identify potential customers by means of services used or products purchased by the current customers, i.e. classes they belong to as well as using social relationships between the known and potential customers. As a result, a personalized offer can be prepared for the new clients. This innovative marketing method can boost targeted marketing campaigns.

LEARNING ENGLISH VOCABULARY USING WEB-BASED LEITNER BOX WITH SOCIAL NETWORK

2015 ◽  
Vol 77 (33) ◽  
Author(s):  
Yunxia Gao ◽  
Zaidatun Tasir ◽  
Jamalludin Harun ◽  
Nurul Farhana Jumaat
Keyword(s):  
Social Network ◽  
Collaborative Learning ◽  
Learning Environment ◽  
Positive Impact ◽  
Vocabulary Learning ◽  
Structured Interviews ◽  
Web Based ◽  
Collaborative Learning Environment ◽  
The Impact ◽  
The Web

The aim of the research is to explore the impact of the web-based Leitner Box which is enhanced with social network, particularly Facebook on English vocabulary learning. This research used mixed research design and the data were collected both in qualitative and quantitative ways. The instruments include questionnaire, semi-structured interviews, and performance tests. 35 university’s students were chosen randomly as the respondents for the questionnaire and 30 students from English class were chosen purposively to do the pre-test and post-test. From the findings, it is discovered that students agreed they have problems in learning vocabulary (mean = 3.98).   The web-based Leitner Box has a significant positive impact on English vocabulary learning (p<0.05). Findings from the questionnaires also revealed that students gave positive opinions toward web-based Leitner box (mean = 4.28).  In term of whether the element of social network can be beneficial to students, the findings showed that social network helps students to learn English vocabulary in this collaborative learning environment (mean = 4.28). The students claimed that web-based Leitner Box and social network make the vocabulary learning process much easier and more interesting by sharing information and actively participating in the collaborative learning environment.

Exploring Virtual Communities with the Internet Community Text Analyzer (ICTA)

2011 ◽  
pp. 205-223 ◽  
Author(s):  
Anatoliy Gruzd
Keyword(s):  
Social Network ◽  
Virtual Communities ◽  
Virtual Community ◽  
Automated Analysis ◽  
Network Data ◽  
Web Based ◽  
Social Network Data ◽  
Network Discovery ◽  
The Social ◽  
Web Based System

The chapter presents a new web-based system called ICTA (http://netlytic.org) for automated analysis and visualization of online conversations in virtual communities. ICTA is designed to help researchers and other interested parties derive wisdom from large datasets. The system does this by offering a set of text mining techniques coupled with useful visualizations. The first part of the chapter describes ICTA’s infrastructure and user interface. The second part discusses two social network discovery procedures used by ICTA with a particular focus on a novel content-based method called name networks. The main advantage of this method is that it can be used to transform even unstructured Internet data into social network data. With the social network data available it is much easier to analyze, and make judgments about, social connections in a virtual community.

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