Use of wikis as a collaborative ICT tool for extending the frontiers of knowledge in tertiary institutions

The human brain works much like a network of computers connected by nodes. These nodes allow computers on the same network to communicate effectively. Educators have discovered that today’s learning environment functions much the same way, with learners connecting to the internet, to other learners and to their teachers to increase their knowledge. This discovery has led to a paradigm shift in education which has transformed the learning environment from teacher-centered to learner-centered. The learner-centered environment allows for interactivity, communication and collaboration. When Web 2.0 technologies are used in the classroom, learners and teachers are given the opportunity to extend the frontiers of knowledge by collaborating and contributing to knowledge. This paper explores the possibility of using Wikis – a Web 2.0 technology – to extend the frontiers of knowledge. It also discusses how Wikis are presently being used in education; how to create a Wiki site using three different Wiki host platforms; and how to contribute content to Wikipedia – which is the world’s largest Wiki site. Finally, recommendations are given on what management of institutions can do to encourage the use of Wikis in the classroom.KEYWORDS: Collaboration, Web 2.0 technology, Wikis, Wikipedia, 21st century skills, Frontiers of knowledg

An Attention-based Collaboration Framework for Multi-View Network Representation Learning

Learning distributed node representations in networks has been attracting increasing attention recently due to its effectiveness in a variety of applications. Existing approaches usually study networks with a single type of proximity between nodes, which defines a single view of a network. However, in reality there usually exists multiple types of proximities between nodes, yielding networks with multiple views. This paper studies learning node representations for networks with multiple views, which aims to infer robust node representations across different views. We propose a multi-view representation learning approach, which promotes the collaboration of different views and lets them vote for the robust representations. During the voting process, an attention mechanism is introduced, which enables each node to focus on the most informative views. Experimental results on real-world networks show that the proposed approach outperforms existing state-of-the-art approaches for network representation learning with a single view and other competitive approaches with multiple views.Comment: CIKM 201

Analyzing covert social network foundation behind terrorism disaster

This paper addresses a method to analyze the covert social network foundation hidden behind the terrorism disaster. It is to solve a node discovery problem, which means to discover a node, which functions relevantly in a social network, but escaped from monitoring on the presence and mutual relationship of nodes. The method aims at integrating the expert investigator's prior understanding, insight on the terrorists' social network nature derived from the complex graph theory, and computational data processing. The social network responsible for the 9/11 attack in 2001 is used to execute simulation experiment to evaluate the performance of the method.Comment: 17pages, 10 figures, submitted to Int. J. Services Science

Collaboration in an Open Data eScience: A Case Study of Sloan Digital Sky Survey

Current science and technology has produced more and more publically accessible scientific data. However, little is known about how the open data trend impacts a scientific community, specifically in terms of its collaboration behaviors. This paper aims to enhance our understanding of the dynamics of scientific collaboration in the open data eScience environment via a case study of co-author networks of an active and highly cited open data project, called Sloan Digital Sky Survey. We visualized the co-authoring networks and measured their properties over time at three levels: author, institution, and country levels. We compared these measurements to a random network model and also compared results across the three levels. The study found that 1) the collaboration networks of the SDSS community transformed from random networks to small-world networks; 2) the number of author-level collaboration instances has not changed much over time, while the number of collaboration instances at the other two levels has increased over time; 3) pairwise institutional collaboration become common in recent years. The open data trend may have both positive and negative impacts on scientific collaboration.Comment: iConference 201