A focus group study of fourteen University of Saskatchewan second to fourth year humanities and social science undergraduate students was conducted in the fall of 2011. The purpose of the research was to determine how students learn about library resources and services. Findings indicate that the participants often use a variety of informal, self-directed and information sharing strategies. Seeking help from professors, peers, friends, and family members is a common practice. Convenience, familiarity, and perceived knowledge are key factors that determine who and how these students learn about the library. Formal instruction and seeking assistance from librarians did not resonate for participants as a typical approach for learning about the library. The author suggests that undergraduate students engage in informal learning and information sharing as many ‘adult learners’ do, similar to an employment setting. The library, within the formal educational structure, lends itself to a more informal learning context. The study concludes that libraries must continue to develop resources, services, and innovative programs that support students’ informal learning styles, while also providing formal instruction as part of the undergraduate curriculum ensuring students are exposed early on to core foundational skills that contribute to their success as informal and self-directed learners.

In nature, many social species demonstrate collective behavior ranging from coordinated motion in flocks of birds and schools of fish to collective decision making in humans. Such distinct behavioral patterns at the group level are the consequence of local interactions among the individuals. We can learn from these biological systems, which have successfully evolved to operate in noisy and fault-prone environments, and understand how these complex interactions can be applied to engineered systems where robustness remains a major challenge. This dissertation addresses a two-scale approach to study these interactions- one in larger scale, where we are interested in the information exchange in a group and how it enables the group to reach a common decision, and the other in a smaller scale, where we are focused in the presence and directionality in the information exchange in a pair of individuals. To understand the interactions at large scale, we use a graph theoretic approach to study consensus or synchronization protocols over two types of biologically-inspired interaction networks. The first network captures both collaborative and antagonistic interactions and the second considers the impact of dynamic leaders in presence of purely collaborative interactions. To study the interactions at small scale, we use an information theoretic approach to understand the directionality of information transfer in a pair of individual using a real-world data-set of animal group motion. Finally, we choose the issue of same-sex marriage in the United States to demonstrate that collective opinion formation is not only a result of negotiations among the individuals, but also reflects inherent spatial and political similarities and temporal delays. / Ph. D. / Social animals exhibit coordination often referred to as ‘collective behavior’ that results from interactions among individuals in the group. This dissertation has demonstrated how interactions can be studied using mathematical modeling, at the same time reveals that real-world interactions are even more complex. Mathematical modeling provides capabilities to introduce biologically inspired phenomena, for example, the implementation of both friendly and hostile interactions that may coexist; and the presence of leader-follower interactions, which is another determinant of collective behavior. The results may find applications in real-world networks, where hostile and leader-follower interactions are prevalent, for example international relations, online social media sites, neural networks, and biologically inspired robotic interactions. We further extend our knowledge regarding interactions by choosing real world systems, the first to understand human decision making, for example in public policies; and the second in animal group motion. Public policy adoption is generally complex and depends on a variety of factors, and no exception is same-sex marriage in the United States which has been a volatile subject for decades until nationwide legalization on June 26, 2015. We target this timely issue and explore the opinion formation of senators and state-law as they evolve over two decades to identify factors that may have affected the dynamics. We unravel geographic proximity, and state-government ideology are significant contributors to the senators opinions and the state-law adoption. Moreover, we build a state-law adoption model which captures these driving factors, and demonstrates predictive power. This study will help to understand or model other public policies that propagate via social and political change. Next we choose the system of bats to investigate navigational leadership roles as they fly in pairs from direct observation of bat swarms in flight. Pairs of bats were continuously tracked in a mountain cave in Shandong Province, China, from which three-dimensional path points are extracted and converted to one-dimensional curvature time series. The study allows us to answer the question of whether individuals fly independently of each other or interact to plan flight paths.

In a large number of applications arising in various fields of study, time series are approximated using linear MMSE estimates. Such approximations include finite order moving average and autoregressive approximations as well as the causal Wiener filter. In this dissertation, we study two topics related to the estimation of wide sense stationary (WSS) time series using linear MMSE estimates. In the first part of this dissertation, we study the asymptotic behaviour of autoregressive (AR) and moving average (MA) approximations. Our objective is to investigate how faithfully such approximations replicate the original sequence, as the model order as well as the number of samples approach infinity. We consider two aspects: convergence of spectral density of MA and AR approximations when the covariances are known and when they are estimated. Under certain mild conditions on the spectral density and the covariance sequence, it is shown that the spectral densities of both approximations converge in L2 as the order of approximation increases. It is also shown that the spectral density of AR approximations converges at the origin under the same conditions. Under additional regularity assumptions, we show that similar results hold for approximations from empirical covariance estimates. In the second part of this dissertation, we address the problem of detecting interdependence relations within a group of time series. Ideally, in order to infer the complete interdependence structure of a complex system, dynamic behaviour of all the processes involved should be considered simultaneously. However, for large systems, use of such a method may be infeasible and computationally intensive, and pairwise estimation techniques may be used to obtain sub-optimal results. Here, we investigate the problem of determining Granger-causality in an interdependent group of jointly WSS time series by using pairwise causal Wiener filters. Analytical results are presented, along with simulations that compare the performance of a method based on finite impulse response Wiener filters to another using directed information, a tool widely used in literature. The problem is studied in the context of cyclostationary processes as well. Finally, a new technique is proposed that allows the determination of causal connections under certain sparsity conditions.