Distributed learning is becoming an increasingly common method of further education in post-secondary institutions and programs across Canada and internationally. Academic libraries are not immune to this trend, and many are reviewing and revising their teaching methodology. All learners require information literacy instruction that is relevant, engaging, and embedded in curriculum; in a distributed learning environment, however, the design and delivery of that instruction may need to be adapted to respond to the challenges of instruction to distributed learners. Through a literature review of distributed learning models in academic libraries and consultation with faculty and librarians at the University of Saskatchewan, this research will assist in determining distributed learning models and instructional design best suited for the provision of information literacy instruction within this environment, with a specific focus on reaching out to rural communities with emerging technological infrastructure. / This is a preprint of an article submitted for consideration in the Journal of Library and Information Services in Distance Learning, 2013, Tasha Maddison; Journal of Library of Information Services in Distance Learning is available online at http://www.tandfonline.com/loi/wlis20#.VJRmTwIYE.

Telecare and especially Mobile Care Systems are getting more and more popular. They have two major benefits: first, they drastically improve the living standards and even health outcomes for patients. In addition, they allow significant cost savings for adult care by reducing the needs for medical staff. A common drawback of current Mobile Care Systems is that they are rather stationary in most cases and firmly installed in patients’ houses or flats, which makes them stay very near to or even in their homes. There is also an upcoming second category of Mobile Care Systems which are portable without restricting the moving space of the patients, but with the major drawback that they have either very limited computational abilities and only a rather low classification quality or, which is most frequently, they only have a very short runtime on battery and therefore indirectly restrict the freedom of moving of the patients once again. These drawbacks are inherently caused by the restricted computational resources and mainly the limitations of battery based power supply of mobile computer systems. This research investigates the application of novel Artificial Intelligence (AI) and Machine Learning (ML) techniques to improve the operation of 2 Mobile Care Systems. As a result, based on the Evolving Connectionist Systems (ECoS) paradigm, an innovative approach for a highly efficient and self-optimising distributed online machine learning algorithm called MECoS - Moving ECoS - is presented. It balances the conflicting needs of providing a highly responsive complex and distributed online learning classification algorithm by requiring only limited resources in the form of computational power and energy. This approach overcomes the drawbacks of current mobile systems and combines them with the advantages of powerful stationary approaches. The research concludes that the practical application of the presented MECoS algorithm offers substantial improvements to the problems as highlighted within this thesis.

This doctoral dissertation describes a research study that examined the effectiveness of an experimental Supplemental Instruction (SI) program that utilized computer-mediated communication (CMC) rather than traditional SI review sessions. During the Spring 1999 semester, six sections of an introductory computer course were offered via the Internet by a suburban community college district in Texas. Using Campbell and Stanley's Nonequivalent Control Group model, the online SI program was randomly assigned to four of the course sections with the two remaining sections serving as the control group. The students hired to lead the online review sessions participated in the traditional SI training programs at their colleges, and received training conducted by the researcher related to their roles as online discussion moderators. Following recommendations from Congos and Schoeps, the internal validity of the groups was confirmed by conducting independent t-tests comparing the students' cumulative credit hours, grade point averages, college entrance test scores, and first exam scores. The study's four null hypotheses were tested using multiple linear regression equations with alpha levels set at .01. Results indicated that the SI participants earned better course grades even though they had acquired fewer academic credits and had, on average, scored lower on their first course exams. Both the control group and the non-SI participants had average course grades of 2.0 on a 4.0 scale. The students who participated in at least one SI session had an average final course grade of 2.5, exceeding their previous grade point average of 2.15. Participation in one SI session using CMC was linked to a one-fourth letter grade improvement in students' final course grades. Although not statistically significant, on the average, SI participants had slightly better course retention, marginally increased course satisfaction, and fewer student-initiated contacts with their instructors.

By 2007, one half of higher education students are expected to enroll in distributed learning courses. Higher education institutions need to attract students searching the Internet for courses and need to provide students with enough information to select courses. Internet resource discovery tools are readily available, however, users have difficulty selecting relevant resources. In part this is due to the lack of a standard for representation of Internet resources. An emerging solution is metadata. In the educational domain, the IEEE Learning Technology Standards Committee (LTSC) has specified a Learning Object Metadata (LOM) standard. This exploratory study (a) determined criteria students think are important for selecting higher education courses, (b) discovered relationships between these criteria and students' demographic characteristics, educational status, and Internet experience, and (c) evaluated these criteria vis-à-vis the IEEE LTSC LOM standard. Web-based questionnaires (N=209) measured (a) the criteria students think are important in the selection of higher education courses and (b) three factors that might influence students' selections. Respondents were principally female (66%), employed full time (57%), and located in the U.S. (89%). The chi square goodness-of-fit test determined 40 criteria students think are important and exploratory factor analysis determined five common factors among the top 21 criteria, three evaluative factors and two descriptive. Results indicated evaluation criteria are very important in course selection. Spearman correlation coefficients and chi-square tests of independence determined the relationships between the importance of selection criteria and demographic characteristics, educational status, and Internet experience. Four profiles emerged representing groups of students with unique concerns. Side by side analysis determined if the IEEE LTSC LOM standard included the criteria of importance to students. The IEEE LOM by itself is not enough to meet students course selection needs. Recommendations include development of a metadata standard for course evaluation and accommodation of group differences in information retrieval systems.

When a family in British Columbia (B.C.) chooses to educate their child at home, they have two legal options: enrollment in a Distributed Learning (DL) program or registration under Section 12 (S12) of the School Act as a homeschooler. To date, there has been very little published on trends in participation rates and growth rates with regards to home educating options in B.C. The current study employs a quantitative archival design to document trends in DL and S12 across age, gender and location. Home educating is on the rise in B.C. over the past twenty years, largely due to an increase in enrollment in DL programs while registration under S12 has declined. Distinct patterns in age, gender and location between S12 and DL are apparent in the data. Growth rates among age categories in DL mirror declines in S12. While there are slightly more males than females in the total school-aged population in B.C., within DL programs there are more females than males at the secondary level. In 1993/1994 rural children were more likely to be educated at home than urban children in B.C.; today the opposite is true. Further research is needed to ascertain why these trends persist. / Graduate / 0525 / 0529 / ngardner@uvic.ca

"In this thesis, we study distributed statistical learning, in which multiple terminals, connected by links with limited capacity, cooperate to perform a learning task. As the links connecting the terminals have limited capacity, the messages exchanged between the terminals have to be compressed. The goal of this thesis is to investigate how to compress the data observations at multiple terminals and how to use the compressed data for inference. We first focus on the distributed parameter estimation problem, in which terminals send messages related to their local observations using limited rates to a fusion center that will obtain an estimate of a parameter related to the observations of all terminals. It is well known that if the transmission rates are in the Slepian-Wolf region, the fusion center can fully recover all observations and hence can construct an estimator having the same performance as that of the centralized case. One natural question is whether Slepian-Wolf rates are necessary to achieve the same estimation performance as that of the centralized case. In this thesis, we show that the answer to this question is negative. We then examine the optimality of data dimensionality reduction via sufficient statistics compression in distributed parameter estimation problems. The data dimensionality reduction step is often needed especially if the data has a very high dimension and the communication rate is not as high as the one characterized above. We show that reducing the dimensionality by extracting sufficient statistics of the parameter to be estimated does not degrade the overall estimation performance in the presence of communication constraints. We further analyze the optimal estimation performance in the presence of communication constraints and we verify the derived bound using simulations. Finally, we study distributed optimization problems, for which we examine the randomized distributed coordinate descent algorithm with quantized updates. In the literature, the iteration complexity of the randomized distributed coordinate descent algorithm has been characterized under the assumption that machines can exchange updates with an infinite precision. We consider a practical scenario in which the messages exchange occurs over channels with finite capacity, and hence the updates have to be quantized. We derive sufficient conditions on the quantization error such that the algorithm with quantized update still converge."

Committees of classifiers, also called mixtures or ensembles of classifiers, have become popular because they have the potential to improve on the performance of a single classifier constructed from the same set of training data. Bagging and boosting are some of the better known methods of constructing a committee of classifiers. Committees of classifiers are also important because they have the potential to provide a computationally scalable approach to handling massive datasets. When the emphasis is on computationally scalable approaches to handling massive datasets, the individual classifiers are often constructed from a small faction of the total data. In this context, the ability to improve on the accuracy of a hypothetical single classifier created from all of the training data may be sacrificed. The design of a committee of classifiers typically assumes that all of the training data is equally available to be assigned to subsets as desired, and that each subset is used to train a classifier in the committee. However, there are some important application contexts in which this assumption is not valid. In many real life situations, massive data sets are created on a distributed computer, recording the simulation of important physical processes. Currently, experts visually browse such datasets to search for interesting events in the simulation. This sort of manual search for interesting events in massive datasets is time consuming. Therefore, one would like to construct a classifier that could automatically label the "interesting" events. The problem is that the dataset is distributed across a large number of processors in chunks that are spatially homogenous with respect to the underlying physical context in the simulation. Here, a potential solution to this problem using ensembles is explored.

As information technology advances, the demands for developing a reliable and highly accurate predictive model from many domains are increasing. Traditional classification algorithms can be limited in their performance on highly imbalanced data sets. In this dissertation, we study two common problems when training data is imbalanced, and propose effective algorithms to solve them. Firstly, we investigate the problem in building a multi-class classification model from imbalanced class distribution. We develop an effective technique to improve the performance of the model by formulating the problem as a multi-class SVM with an objective to maximize G-mean value. A ramp loss function is used to simplify and solve the problem. Experimental results on multiple real-world datasets confirm that our new method can effectively solve the multi-class classification problem when the datasets are highly imbalanced. Secondly, we explore the problem in learning a global classification model from distributed data sources with privacy constraints. In this problem, not only data sources have different class distributions but combining data into one central data is also prohibited. We propose a privacy-preserving framework for building a global SVM from distributed data sources. Our new framework avoid constructing a global kernel matrix by mapping non-linear inputs to a linear feature space and then solve a distributed linear SVM from these virtual points. Our method can solve both imbalance and privacy problems while achieving the same level of accuracy as regular SVM. Finally, we extend our framework to handle high-dimensional data by utilizing Generalized Multiple Kernel Learning to select a sparse combination of features and kernels. This new model produces a smaller set of features, but yields much higher accuracy.

To gain greater insight into the operation of online social networks, we applied Natural Language Processing (NLP) techniques to text-based communication to identify and describe underlying social structures in online communities. This paper presents our approach and preliminary evaluation for content-based, automated discovery of social networks. Our research question is: What syntactic and semantic features of postings in a threaded discussions help uncover explicit and implicit ties between network members, and which provide a reliable estimate of the strengths of interpersonal ties among the network members? To evaluate our automated procedures, we compare the results from the NLP processes with social networks built from basic who-to-whom data, and a sample of hand-coded data derived from a close reading of the text. For our test case, and as part of ongoing research on networked learning, we used the archive of threaded discussions collected over eight iterations of an online graduate class. We first associate personal names and nicknames mentioned in the postings with class participants. Next we analyze the context in which each name occurs in the postings to determine whether or not there is an interpersonal tie between a sender of the posting and a person mentioned in it. Because information exchange is a key factor in the operation and success of a learning community, we estimate and assign weights to the ties by measuring the amount of information exchanged between each pair of the nodes; information in this case is operationalized as counts of important concept terms in the postings as derived through the NLP analyses. Finally, we compare the resulting network(s) against those derived from other means, including basic who-to-whom data derived from posting sequences (e.g., whose postings follow whose). In this comparison we evaluate what is gained in understanding network processes by our more elaborate analyses.

In the paper the need to understand cultural differences is discussed. The authors show how cultural differences can be analyzed. They also describe how cultural information was exchanged and analyzed during the library and information studies course that was taught via the Internet simultanously in Finland and North Carolina. In addition, the authors discuss how libraries could use experiences of the common class when they act in a multicultural environment. In the paper, culture is defined to be a framework to our lives, something which affects our values, attitudes and behavior. In analyzing and understanding cultural differences it is important to pay attention to how members of various cultures see i) the nature of people, ii) a person's relationship to the external enviroment, iii) the person's relationship to other people, iv) the primary mode of the activity, v) people's orientation to space, and vi) the person's temporal orientation. These dimension will be explained in the paper. In addition, the authors pay attention to language and communication styles as a dimension of cultural differences. The paper describes differences in cultures of Finns, Sami People, North Carolians and Cherokee Indians. Sami People and Cherokee Indians were chosen to represent minor cultures in Finland and North Carolina. An interesting similarities can be found on the one hand between major cultures (Finland and North Carolina), and on the other hand between minor cultures (Sami and Cherokees). The authors propose that there are a few lessons learnt in the common class which can be useful also for libraries and librarians serving multicultural populations. They are i) to undertand people's behavior as a reflection of their cultural background, ii) to understand of differences in language and communication styles between cultures, iii) to understand that collaboration across cultural boundaries and sharing cultural informations occur together, and iv) to take advantage from the Internet in crossing cultural boundaries but not to forget that people have various attitudes toward the Internet and therefore some clients continue to prefer books and face-to-face interaction with library professionals. The authors emphasize that cross-cultural communication and collaboration does not occur effectively without understanding other cultures.