A Multi-Agent Modeling Social Network Analysis of Cooperative Learning Groups Within a Simulated Adult Education Classroom Learning Environment

Illiteracy and a lack of a high school diploma are impediments to a fulfilled and meaningful life. Low or reduced literacy and non-attainment of a high school diploma are significant problems in the United States. Adult education can be a vehicle to address these ever-present issues. A disproportionate number of students in adult education are minorities, members of lower socioeconomic statuses and traditionally marginalized groups who lack effective literacy skills and/or a high school diploma. Adult education and its related entities can serve as a vehicle to address these pervasive issues, but adult education as a program type is a field that has not been thoroughly researched. Given the extreme variance in the constituency of many adult education classrooms and the volatile nature of many adult learners’ intrinsic and extrinsic situations, research is limited and effective classroom practices specific to adult education are not well understood. Understanding the nature of the adult education classroom and the student networks within them may provide a better understanding of the complexities of the adult education classroom which, in turn, should engender further research and a better understanding of what types of cooperative learning environments and paradigms work best for adult learners. Social network analysis can assist in learning about the composition and connectivity of student learning groups and the formation of cooperative learning practices which has been shown to promote positive student outcomes. In an ever-changing classroom setting, where open enrollment is the standard, the role of incumbents versus newcomers to the adult education class in creating and maintaining student groups sheds light on how student groups can evolve and affect positive student outcomes both in the classroom and in the outside world

A Risk Based Approach to Node Insertion within Social Networks

Social Network Analysis (SNA) is a primary tool for counter-terrorism operations, ranging from resiliency and influence to interdiction on threats stemming from illicit overt and clandestine network operations. In an ideal world, SNA would provide a perfect course of action to eliminate dangerous situations that terrorist organizations bring. Unfortunately, the covert nature of terrorist networks makes the effects of these techniques unknown and possibly detrimental. To avoid potentially harmful changes to enemy networks, tactical involvement must evolve, beginning with the intelligent use of network in filtration through the application of the node insertion problem. The framework for the node insertion problem includes a risk-benefit model to assess the utility of various node insertion scenarios. This model incorporates local, intermediate and global SNA measures, such as Laplacian centrality and assortative mixing, to account for the benefit and risk. Application of the model to the Zachary Karate Club produces a set of recommended insertion scenarios. A designed experiment validates the robustness of the methodology against network structure and characteristics. Ultimately, the research provides an SNA method to identify optimal and near-optimal node insertion strategies and extend past node utility models into a general form with the inclusion of benefit, risk, and bias functions

A NETWORK INTRUSION DETECTION SYSTEM USING DECISION TREE MACHINE LEARNING ON AN ISTN ARCHITECTURE

In recent years, the Navy has shown interest in an integrated satellite-terrestrial networking (ISTN) architecture for unmanned systems. With the development of satellite networks and growing numbers of unmanned system networks being connected, security and privacy are major concerns in an ISTN. In this thesis, we develop a network intrusion detection system (NIDS) specifically for an ISTN. We identify the critical location of the NIDS within the ISTN architecture and use the decision tree machine learning algorithm to perform cyber-attack detection against various threat vectors, including distributed denial of service. The decision tree algorithm is used to classify and segregate attack traffic from benign traffic. We use an open source ISTN data set available in the literature to train our algorithm. The decision tree is implemented using different split criteria, varying number of splits, and the use of principal component analysis (PCA). We manipulate the size of the training data and the number of data features to achieve reasonable false positive rates. We show that our NIDS framework based on decision tree learning can effectively detect and segregate different attack data classes.Civilian, DSO National Labs, SingaporeApproved for public release. Distribution is unlimited

A Quantitative Methodology for Vetting Dark Network Intelligence Sources for Social Network Analysis

Social network analysis (SNA) is used by the DoD to describe and analyze social networks, leading to recommendations for operational decisions. However, social network models are constructed from various information sources of indeterminate reliability. Inclusion of unreliable information can lead to incorrect models resulting in flawed analysis and decisions. This research develops a methodology to assist the analyst by quantitatively identifying and categorizing information sources so that determinations on including or excluding provided data can be made. This research pursued three main thrusts. It consolidated binary similarity measures to determine social network information sources\u27 concordance and developed a methodology to select suitable measures dependent upon application considerations. A methodology was developed to assess the validity of individual sources of social network data. This methodology utilized source pairwise comparisons to measure information sources\u27 concordance and a weighting schema to account for sources\u27 unique perspectives of the underlying social network. Finally, the developed methodology was tested over a variety of generated networks with varying parameters in a design of experiments paradigm (DOE). Various factors relevant to conditions faced by SNA analysts potentially employing this methodology were examined. The DOE was comprised of a 24 full factorial design augmented with a nearly orthogonal Latin hypercube. A linear model was constructed using quantile regression to mitigate the non-normality of the error terms

Immersive simulations with extreme teams

Extreme teams (ETs) work in challenging, high pressured contexts, where poor performance can have severe consequences. These teams must coordinate their skill sets, align their goals, and develop shared awareness, all under stressful conditions. How best to research these teams poses unique challenges as researchers seek to provide applied recommendations while conducting rigorous research to test how teamwork models work in practice. In this article, we identify immersive simulations as one solution to this, outlining their advantages over existing methodologies and suggesting how researchers can best make use of recent advances in technology and analytical techniques when designing simulation studies. We conclude that immersive simulations are key to ensuring ecological validity and empirically reliable research with ETs

Identifying the Goal, User model and Conditions of Recommender Systems for Formal and Informal Learning

Drachsler, H., Hummel, H. G. K., & Koper, R. (2009). Identifying the Goal, User model and Conditions of Recommender Systems for Formal and Informal Learning. Journal of Digital Information, 10(2), 4-24.The following article addresses open questions of the discussions in the first SIRTEL workshop at the EC-TEL conference 2007. It argues why personal recommender systems have to be adjusted to the specific characteristics of learning to support lifelong learners. Personal recommender systems strongly depend on the context or domain they operate in, and it is often not possible to take one recommender system from one context and transfer it to another context or domain. The article describes a number of distinct differences for personalized recommendation to consumers in contrast to recommendations to learners. Similarities and differences are translated into specific demands for learning and specific requirements for personal recommendation systems. It further suggests an evaluation approach for recommender systems in technology-enhanced learning.The work on this publication has been sponsored by the TENCompetence Integrated Project that is funded by the European Commission's 6th Framework Programme, priority IST/Technology Enhanced Learning. Contract 027087 [http://www.tencompetence.org

Social dynamics, network structure, and information diffusion in fish shoals.

Animal populations are often highly structured, with individuals differing in terms of whom they interact with and how frequently they do so. The resulting pattern of relationships constitutes a population’s social network. In this dissertation, I examine how environmental variation can shape social networks and influence information flow within them. In Chapter I, I review the history of social network analysis in animal behavior research, and discuss recent insights generated by network approaches in behavioral ecology. I focus on the fields of: social learning, collective behavior, animal personalities, and cooperation. Animal network studies are often criticized for a lack of replication at the network level and an over-reliance on descriptive approaches in lieu of hypothesis testing. Small, shoaling fish may provide a means to address these concerns, as manipulative experiments can be conducted on replicate social groups under captive conditions. Chapters III–V examine the impacts of environmental variation on the social networks of Trinidadian guppy (Poecilia reticulata) shoals, the social dynamics from which they emerge, and information diffusion within them. In the experiments described in Chapter III, I manipulated shoal composition in terms of within-group familiarity. Mixed shoals of familiar and unfamiliar fish exhibited greater homogeneity in network structure relative to other groups, which likely contributed to the rapid diffusion of foraging information observed within them. In the experiments discussed in Chapter IV, I manipulated the within-shoal mixture of personality types. In addition to impacting frequencies of partner switching and patterns of phenotypic assortment, individual- and group-level personality variation had strong effects on the initial acquisition of novel foraging information and the speed of its transmission through a group. In the experiments in Chapter V, I manipulated the ambient predation risk perceived by groups. High-risk conditions were associated with shifts in network structure consistent with attempts to minimize predation risk. High ambient risk also impeded the acquisition and subsequent transmission of foraging information, likely due to heightened neophobia and/or an increase in the perceived costs of personal sampling. I conclude in Chapter VI by considering the broader implications of my work and highlighting promising avenues for future research