scholarly journals Challenges in assessing the nature of effective collaboration in blended university courses

Author(s):  
Robert A Ellis ◽  
Ana-Maria Bliuc ◽  
Feifei Han

The ability to collaborate effectively face-to-face and online represents a critical skill for university graduates. However, there are still challenges regarding how to accurately assess this skill through traditional student learning measures. To better understand the nature of effective collaboration of university students in blended courses, the current study drew on the student approaches to learning framework and social network analysis techniques. We examined how student approaches to inquiry, approaches to online learning technologies, perceptions of the blended learning environment, different learning outcomes and configurations of collaboration are related. The methodologies commonly used in student approaches to learning research identified deep and surface approaches to inquiry and technologies, positive and negative perceptions of the integration of the learning environment, and of online workload, which also showed logical alignment with relatively better and poorer academic achievement in the course. Based on approaches, perceptions, and learning outcomes, students were divided into groups orientated towards understanding versus reproducing learning. The social network analysis techniques revealed features of different configurations of collaborations by different groups of students and their choices as to whether and with whom to collaborate during the learning process. Nuanced differences were found amongst different configurations of collaborations. Implications for practice or policy: When assessing student experience of collaboration, social network analysis techniques may be able to describe nuanced differences amongst different collaborative configurations. To encourage students’ collaboration, assessment tasks involving a large proportion of mandatory collaborative activities should be considered. To help student improve experience of collaboration, teachers may consider pairing students with a reproducing learning orientation with those having a deep disciplinary understanding.

2019 ◽  
Vol 8 (1) ◽  
pp. 009
Author(s):  
Carlos G. Figuerola ◽  
Tamar Groves ◽  
Francisco J. Rodríguez

The practice of historical research in recent years has been substantially affected by the emergence of the so-called digital humanities. New computer tools have been appearing, software systems capable of processing vast quantities of information in ways that until recently were inconceivable. Text mining and social network analysis techniques are sophisticated instruments that can help render a more enriching reading of the available data and draw useful conclusions. We reflect on this in the first part of this article, and then apply these tools to a practical case: quantifying and identifying the women who appear in university-related articles in the newspaper El País from its founding until 2011.


2008 ◽  
Vol 39 (1) ◽  
pp. 17-36 ◽  
Author(s):  
Demei Shen ◽  
Piyanan Nuankhieo ◽  
Xinxin Huang ◽  
Christopher Amelung ◽  
James Laffey

2020 ◽  
Vol 185 ◽  
pp. 02024
Author(s):  
Yuqing Liao ◽  
Jingliang Chen

Based on the green finance policies in China from 2017 to 2019, this paper extracts feature and high-frequency words from policy documents, uses word cloud diagram, co-occurrence matrix and social network analysis techniques to quantitatively analyse the information contained in the green finance policies over the past three years and highlights the hot issues in question, thus providing a multi-layered and wideranging pathway for facilitating the orderly development of green finance industries across China.


Author(s):  
PUSHPA PUSHPA ◽  
Dr. Shobha G

Social Network Analysis (SNA) is a set of research procedures for identifying group of people who share common structures in systems based on the relations among actors. Grounded in graph and system theories, this approach has proven to be powerful measures for studying networks in various industries like Telecommunication, banking, physics and social world, including on the web. Since Telecommunication industries deals with huge amount of data, manual analysis of data is very difficult. In this paper we explore the Social Network Analysis techniques for Churn Prediction in Telecom data. Typical work on social network analysis includes the construction of multi-relational telecom social network and centrality measures for prediction of churners in telecom social network.


Author(s):  
Eve D. Rosenzweig ◽  
Elliot Bendoly

Our study demonstrates the value of taking a more encompassing and explicit view of competition in manufacturing strategy research. In doing so, we go beyond a dyadic-based approach and investigate the ways in which the degree of competition among firms in a network influences performance. Using social network analysis techniques, we develop a novel measure—which we refer to as competitor infighting—that captures the extent to which a firm's rivals compete amongst themselves. Our results suggest that a firm has a greater, unimpeded opportunity to demonstrate market gains as the degree of competition among its rivals increases, all else equal. In fact, competitor infighting is a better predictor of market performance in our sample than is a simpler, though perhaps more traditional, count of competitors. It serves an important moderating role in the relationship between a firm's operational weaknesses and market performance. As predicted, we find that as competitor infighting increases, the relationship between operational weaknesses and market performance is diminished.


Author(s):  
Eve D. Rosenzweig ◽  
Elliot Bendoly

Our study demonstrates the value of taking a more encompassing and explicit view of competition in manufacturing strategy research. In doing so, we go beyond a dyadic-based approach and investigate the ways in which the degree of competition among firms in a network influences performance. Using social network analysis techniques, we develop a novel measure—which we refer to as competitor infighting—that captures the extent to which a firm's rivals compete amongst themselves. Our results suggest that a firm has a greater, unimpeded opportunity to demonstrate market gains as the degree of competition among its rivals increases, all else equal. In fact, competitor infighting is a better predictor of market performance in our sample than is a simpler, though perhaps more traditional, count of competitors. It serves an important moderating role in the relationship between a firm's operational weaknesses and market performance. As predicted, we find that as competitor infighting increases, the relationship between operational weaknesses and market performance is diminished.


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