This study set out to design and assess the feasibility of an active learning approach to a legal research module. The study was a case study of the second year undergraduate Legal Research Writing and Reasoning (LRWR) module on the Pietermaritzburg campus of the University of KwaZulu-Natal. This module forms part of the basic law degree curriculum. The author, a subject librarian at the University of KwaZulu-Natal, has been involved with this module for several years. The module is situated within the general lecture timetable and the lecture format is unsuitable for a module such as this one that requires practical work. Students of law need to have a sound knowledge of the published legal literature because of the particular nature of the role of legal literature in the study of law, the vast array of literature available and the complex presentation of information within the sources of law. Students of law also need to be able to read, understand and apply the law to given situations. Legal education in South Africa has undergone considerable changes since 1994 alongside those in higher education generally. Since 2001 the LLB degree has become a four year undergraduate degree replacing the old three year undergraduate plus two year post-graduate qualification. New national qualification requirements emphasise a range of skills such as problem-solving, numeracy, computer skills, writing, and finding and using information. This is partly as a means of redressing the differential preparedness of students for university, a legacy of schooling of variant quality that was a feature of Apartheid governance prior to 1994. Thus students are having to complete the law degree in a shortened time period; do not have the benefit of an undergraduate degree before embarking on the law degree, and need to develop competencies in a range of skills and knowledge adjacent to substantive law modules. Information literacy is a process, an active problem-solving process and an amalgam of skills and knowledge concerned with identifying an information need, finding, evaluating and using a range of information to answer that need in appropriate ways. The problem-solving nature of the study of law, the new national requirements for a legal education and the characteristics of information literacy suggest that these three elements could be usefully combined in an active learning and teaching process to enable students of law to develop a holistic approach to learning skills and knowledge of legal research, writing and reasoning in the South African context. The research questions that arose in response to the research problem required an investigation into current research and writing with regard to information literacy, legal education, learning, teaching and assessment and whether an active learning approach was feasible with a large class size of approximately 130 students. The situation in South African law faculties as regards legal research teaching and learning needed to be considered to situate the current study in the broader national context. The literature review enabled the development of a theoretical framework for the LRWR module that took cognisance of a range of national, institutional and classroom climates, aims, objectives, outcomes and content for modules, the study of law, characteristics of learners and factors affecting their performance, teaching strategies, instructional design, assessment and information literacy. The module itself was designed in terms of a problem-solving situation which encompassed a range of integrated skills in order to manage the problem. An active learning approach was adopted in the form of group and class discussion, with a range of scaffolded written, oral and practical exercises and assignments to help students investigate the problem scenario from a number of perspectives. The design of the module required data in the form of demographic characteristics and work habits of the students in the class inclusive of learning styles which were acquired through the application of a questionnaire and learning styles inventory. Knowledge and skills with respect to module content were measured in terms of a pre- and post-test. A reflection exercise and focus groups provided evidence about how the students responded to the overall design of the module and in particular the active learning approach. The data collected and analysed suggested that the integration of information literacy, problem-solving processes with respect to the study of law and active learning was feasible and successful in this large class situation to varying degrees. The students in the module had expanded their repertoire of skills and knowledge, had appreciated the relationship between research, writing, reasoning and discussion and enjoyed the active learning approach. The contribution this research makes is with regard to the character, design and implementation of information literacy programmes in academic libraries in South Africa in particular, given the dearth of published practitioner research in this country. The research has also provided a comprehensive theoretical and practical framework for developing an information literacy programme within the changing South African legal education context. The research in this specific context usefully provides a baseline from which to develop and promote information literacy as a critical approach within the study of law. / Thesis (Ph.D. (Information Studies)) - University of KwaZulu-Natal, Pietermaritzburg, 2008.

The purpose of this study is to investigate the teacher change process and extend our understanding of how variability in the ways that primary school teachers in Bangladesh implement innovative pedagogical practices, such as active learning, reflects variations in their understanding, attitude, experience, and skill in the use of those pedagogical approaches. Multiple forms of data gathering were employed based on the concerns-based adoption model (CBAM) including an open-ended statement of concern, interviews, and class observations from a purposive sample of ten teachers working in ten different schools. Additional interviews were also conducted with staff responsible for the teachers' professional development. Five main findings emerged from the research. First, there was a split between novice teachers who were committed to following the prescriptive lesson plans and more experienced teachers who adapted their lessons to accommodate differences in student readiness and performance. Second, the majority of teachers appeared to be satisfied with their use active learning methods and the mandated lessons with little projected variation in how they will implement the innovation in the future. Third, the class observation findings indicate that the majority of teachers were rated as ideal users of active learning methods in the classroom. Fourth, findings indicate that professional development and a commitment to building networks among teachers and support staff helped facilitate teachers' confidence and competency. Fifth, among the most influential factors shaping teachers' use of active learning methods were the availability of supplementary learning and teaching resources. Implications for professional development and support for teachers, the applicability of CBAM-based research in low-income country contexts like Bangladesh, as well as future areas of comparative, international, and development education research are discussed in light of those findings.

The purpose was to develop a valid and reliable survey to determine if physical educators provide students with opportunities for self-determination. It was based on the self-determination model of Wehmeyer et al. (1998) and interviews with four university professors and five physical educators. The survey was sent to 153 physical educators from Montreal and Halifax; a 63.4% return rate resulted. The most frequent aspect of self-determination included in their teaching was the opportunity to "develop a sense of belonging in the class", while "choice-making" was provided least frequently. Class size and inadequate resource material were the most detrimental barriers to self-determination. The temporal stability of the survey was .49 for the elements of self-determination and .84 for barriers to self-determination. Finally four physical education teachers were observed in gymnasium settings, two had scored high on self-determination and two had a low score. Systematic observation techniques revealed that their teaching was consistent with their survey score. It was concluded that the survey had sufficient reliability and validity to warrant further development.

Hierarchical task decompositions play an essential role in the design of complex simulation and decision systems, such as the ones that arise in video games. Game designers find it very natural to adopt a divide-and-conquer philosophy of specifying hierarchical policies, where decision modules can be constructed somewhat independently. The process of choosing the parameters of these modules manually is typically lengthy and tedious. The hierarchical reinforcement learning (HRL) field has produced elegant ways of decomposing policies and value functions using semi-Markov decision processes. However, there is still a lack of demonstrations in larger nonlinear systems with discrete and continuous variables. To narrow this gap between industrial practices and academic ideas, we address the problem of designing efficient algorithms to facilitate the deployment of HRL ideas in more realistic settings. In particular, we propose Bayesian active learning methods to learn the relevant aspects of either policies or value functions by focusing on the most relevant parts of the parameter and state spaces respectively. To demonstrate the scalability of our solution, we have applied it to The Open Racing Car Simulator (TORCS), a 3D game engine that implements complex vehicle dynamics. The environment is a large topological map roughly based on downtown Vancouver, British Columbia. Higher level abstract tasks are also learned in this process using a model-based extension of the MAXQ algorithm. Our solution demonstrates how HRL can be scaled to large applications with complex, discrete and continuous non-linear dynamics.

Learning is one such innate general cognitive ability which has empowered the living animate entities and especially humans with intelligence. It is obtained by acquiring new knowledge and skills that enable them to adapt and survive. With the advancement of technology, a large amount of information gets amassed. Due to the sheer volume of increasing information, its analysis is humanly unfeasible and impractical. Therefore, for the analysis of massive data we need machines (such as computers) with the ability to learn and evolve in order to discover new knowledge from the analysed data. The majority of the traditional machine learning algorithms function optimally on a parametric (static) data. However, the datasets acquired in real practices are often vast, inaccurate, inconsistent, non-parametric and highly volatile. Therefore, the learning algorithms’ optimized performance can only be transitory, thus requiring a learning algorithm that can constantly evolve and adapt according to the data it processes. In light of a need for such machine learning algorithm, we look for the inspiration in humans’ innate cognitive learning ability. Active learning is one such biologically inspired model, designed to mimic humans’ dynamic, evolving, adaptive and intelligent cognitive learning ability. Active learning is a class of learning algorithms that aim to create an accurate classifier by iteratively selecting essentially important unlabeled data points by the means of adaptive querying and training the classifier on those data points which are potentially useful for the targeted learning task (Tong & Koller, 2002). The traditional active learning techniques are implemented under supervised or semi-supervised learning settings (Pang et al., 2009). Our proposed model performs the active learning in an unsupervised setting by introducing a discriminative selective sampling criterion, which reduces the computational cost by substantially decreasing the number of irrelevant instances to be learned by the classifier. The methods based on passive learning (which assumes the entire dataset for training is truly informative and is presented in advance) prove to be inadequate in a real world application (Pang et al., 2009). To overcome this limitation, we have developed Active Mode Incremental Nonparametric Discriminant Analysis (aIncNDA) which undertakes adaptive discriminant selection of the instances for an incremental NDA learning. NDA is a discriminant analysis method that has been incorporated in our selective sampling technique in order to reduce the effects of the outliers (which are anomalous observations/data points in a dataset). It works with significant efficiency on the anomalous datasets, thereby minimizing the computational cost (Raducanu & Vitri´a, 2008). NDA is one of the methods used in the proposed active learning model. This thesis presents the research on a discrimination-based active learning where NDA is extended for fast discrimination analysis and data sampling. In addition to NDA, a base classifier (such as Support Vector Machine (SVM) and k-Nearest Neighbor (k-NN)) is applied to discover and merge the knowledge from the newly acquired data. The performance of our proposed method is evaluated against benchmark University of California, Irvine (UCI) datasets, face image, and object image category datasets. The assessment that was carried out on the UCI datasets showed that Active Mode Incremental NDA (aIncNDA) performs at par and in many cases better than the incremental NDA with a lower number of instances. Additionally, aIncNDA also performs efficiently under the different levels of redundancy, but has an improved discrimination performance more often than a passive incremental NDA. In an application that undertakes the face image and object image recognition and retrieval task, it can be seen that the proposed multi-example active learning system dynamically and incrementally learns from the newly obtained images, thereby gradually reducing its retrieval (classification) error rate by the means of iterative refinement. The results of the empirical investigation show that our proposed active learning model can be used for classification with increased efficiency. Furthermore, given the nature of network data which is large, streaming, and constantly changing, we believe that our method can find practical application in the field of Internet security.

No description available.

Thesis (PhD)--University of South Australia, 2000

Learning is one such innate general cognitive ability which has empowered the living animate entities and especially humans with intelligence. It is obtained by acquiring new knowledge and skills that enable them to adapt and survive. With the advancement of technology, a large amount of information gets amassed. Due to the sheer volume of increasing information, its analysis is humanly unfeasible and impractical. Therefore, for the analysis of massive data we need machines (such as computers) with the ability to learn and evolve in order to discover new knowledge from the analysed data. The majority of the traditional machine learning algorithms function optimally on a parametric (static) data. However, the datasets acquired in real practices are often vast, inaccurate, inconsistent, non-parametric and highly volatile. Therefore, the learning algorithms’ optimized performance can only be transitory, thus requiring a learning algorithm that can constantly evolve and adapt according to the data it processes. In light of a need for such machine learning algorithm, we look for the inspiration in humans’ innate cognitive learning ability. Active learning is one such biologically inspired model, designed to mimic humans’ dynamic, evolving, adaptive and intelligent cognitive learning ability. Active learning is a class of learning algorithms that aim to create an accurate classifier by iteratively selecting essentially important unlabeled data points by the means of adaptive querying and training the classifier on those data points which are potentially useful for the targeted learning task (Tong & Koller, 2002). The traditional active learning techniques are implemented under supervised or semi-supervised learning settings (Pang et al., 2009). Our proposed model performs the active learning in an unsupervised setting by introducing a discriminative selective sampling criterion, which reduces the computational cost by substantially decreasing the number of irrelevant instances to be learned by the classifier. The methods based on passive learning (which assumes the entire dataset for training is truly informative and is presented in advance) prove to be inadequate in a real world application (Pang et al., 2009). To overcome this limitation, we have developed Active Mode Incremental Nonparametric Discriminant Analysis (aIncNDA) which undertakes adaptive discriminant selection of the instances for an incremental NDA learning. NDA is a discriminant analysis method that has been incorporated in our selective sampling technique in order to reduce the effects of the outliers (which are anomalous observations/data points in a dataset). It works with significant efficiency on the anomalous datasets, thereby minimizing the computational cost (Raducanu & Vitri´a, 2008). NDA is one of the methods used in the proposed active learning model. This thesis presents the research on a discrimination-based active learning where NDA is extended for fast discrimination analysis and data sampling. In addition to NDA, a base classifier (such as Support Vector Machine (SVM) and k-Nearest Neighbor (k-NN)) is applied to discover and merge the knowledge from the newly acquired data. The performance of our proposed method is evaluated against benchmark University of California, Irvine (UCI) datasets, face image, and object image category datasets. The assessment that was carried out on the UCI datasets showed that Active Mode Incremental NDA (aIncNDA) performs at par and in many cases better than the incremental NDA with a lower number of instances. Additionally, aIncNDA also performs efficiently under the different levels of redundancy, but has an improved discrimination performance more often than a passive incremental NDA. In an application that undertakes the face image and object image recognition and retrieval task, it can be seen that the proposed multi-example active learning system dynamically and incrementally learns from the newly obtained images, thereby gradually reducing its retrieval (classification) error rate by the means of iterative refinement. The results of the empirical investigation show that our proposed active learning model can be used for classification with increased efficiency. Furthermore, given the nature of network data which is large, streaming, and constantly changing, we believe that our method can find practical application in the field of Internet security.

Thesis (Ph. D.)--University of Toronto, 2005.

Thesis (D.Ed.) in Literacy Education--University of Maine, 2008. / Includes vita. Includes bibliographical references (leaves 127-132).