Computational design has become a predominant element of various engineering tasks. However, the ever increasing complexity of numerical models creates the need for efficient methodologies. Specifically, computational design under uncertainties remains sparsely used in engineering settings due to its computational cost. This dissertation proposes a coherent framework for various branches of computational design under uncertainties, including model update, reliability assessment and reliability-based design optimization. Through the use of machine learning techniques, computationally inexpensive approximations of the constraints, limit states, and objective functions are constructed. Specifically, a novel adaptive sampling strategy allowing for the refinement of any approximation only in relevant regions has been developed, referred to as generalized max-min. This technique presents various computational advantages such as ease of parallelization and applicability to any metamodel. Three approaches tailored for computational design under uncertainties are derived from the previous approximation technique. An algorithm for reliability assessment is proposed and its efficiency is demonstrated for different probabilistic settings including dependent variables using copulas. Additionally, the notion of fidelity map is introduced for model update settings with large number of dependent responses to be matched. Finally, a new reliability-based design optimization method with local refinement has been developed. A derivation of sampling-based probability of failure derivatives is also provided along with a discussion on numerical estimates. This derivation brings additional flexibility to the field of computational design. The knowledge acquired and techniques developed during this Ph.D. have been synthesized in an object-oriented MATLAB toolbox. The help and ergonomics of the toolbox have been designed so as to be accessible by a large audience.

As universities transition from instructor-driven to student-centered learning environments, the institutional learning structure is being redesigned to emphasize active learning. Instructional technologies, employing active learning models, have been a critical component in the redesign. The active learning model suggests that the student engages in various activities, and uses various strategies, to gather information and achieve understanding. Technology-driven learning environments therefore often instill activities that direct the student's learning. Use of on-line technologies, such as the Internet, is one method for creating active learning activities that direct the student's learning. This experiment explores how active learning activities, specifically how a student engages in research by accessing on-line information, affects their understanding of the material. The experiment is a 2 (Task Complexity) x 2 (Data Resource) design testing a student's (N=194) ability to synthesize information as they traversed through a specified set of resources. The findings indicate that students who access topic-specific resources engage in more research activities than students who access broad-topic resources. Furthermore, the findings indicate that students who access topic-specific resources will synthesize the relevant material into a more clear and concise response than students who access broad-topic resources. Suggestions and further research are posited to further understand how instructors can engage use of on-line resources, specifically the Internet, and instructional technologies, such as Distance Learning, to facilitate student learning.

A journey in metaxis explores the facilitation of drama workshops using an adaptation of Theatre of the Oppressed, a participatory drama process used with high school students, teachers and others in the community. New possibilities of engagement open up as knowing emerges through a variety o f forms of dramatic action which are simultaneously the medium, subject and re-presentation of research. As a theatre pedagogue I explore how knowing and meaning emerge through theatre and in the interplay between my life and my work. Writing, then reading, narratives of my practice engages me in a conversation that helps me draw attention to my practice. Diverse roles and points of view of the drama facilitator begin to become apparent as these narratives speak through a spiralling process of shared experiences. Commentaries on these experiences lead to discussions of the implications of this inquiry for other forms of reflective leadership practice in drama and in education. Particular attention is placed on the role of the body and mind (bodymind) of facilitator and participants as they journey into an increasing awareness of senses, histories, the landscapes worked in, and the relationships that intertwine through the constant ebb and flow of the drama workshop. Using a framework that parallels the drama workshop I facilitate, I play with forms of texts, languages and styles to enter into the text(ure) of the worlds of facilitation so that we may come face to face with kinaesthetic and discursive experiences remembered and reconsidered. Writing my body into this exploration enables me to become mindfully aware of, and extends and transforms, my practice. I re-awaken the memory of my senses and re-connect with them in the moments of "performing" my teaching. Such poetic and expressive writing enables an evocation of the world of drama. Writing from and through a sensing body means that reflection on practice becomes not merely reporting experiences, but also celebrating and expressing the multi-vocal, multi-layered events that develop drama facilitation skills. Writing, then reading, about this process of coming to know my identity-in-process as a drama facilitator enables the interpretation, interrogation and transformation of how one becomes facilitator, "making the way as we go," (re)writing/performing our presence.

Remiantis nauju supratimu, mokymas(is) laikomas aktyviu procesu, kurio metu besimokantysis, remdamasis anksčiau įgytomis žiniomis ir savo unikalia patirtimi, formuoja naujas sąvokas, idėjas ar prasmes. Mokytojo vaidmuo suprantamas kaip pagalbininko, kuris turi rūpintis besimokančiojo žinių kūrimo procesu,o taip pat bendraudamas ir stebėdamas besimokančiuosius, lanksčiai ir kūrybingai įtraukti juos į mokymo(si) procesą. Šio darbo tikslas: pagrįsti aktyvaus mokymo įtaką mokinių mokymosi motyvacijai. / Nowadays theories say, that learning is an active method, where the teacher is a part of this process, like an assitent communicating with the pupils. Also nowadays theories say, that it is not enough give new information, it is important include practise. Some methods were used to evalue correlations these factors.

The White Paper on education states that students should be employable after graduation / Thesis

Community developers are often solicited to teach essential core concepts and strategies in the field but lack the consensus among their peers on which theories constitute the fundamentals. This study examines leading community development theories, concepts and approaches to establish the essential elements for a weeklong short course. In addition to content research, leading teaching theories were also explored to establish the core methods for teaching such a course. Active learning techniques were utilized to increase student participation in the learning process while building solidarity and capacity in the class. Finally, the short course was taught to a group in Banda Aceh, Indonesia and was evaluated for knowledge and attitude change through pretests, posttests, and journal entries.

The current study sought to extend knowledge on effectiveness of Electronic Response Systems (ERS) or “clickers” in a college classroom by comparing student assessment performance between two sections (n = 41 & 42) of a Biblical Studies course in a small evangelical university. Student characteristics were virtually identical in the classes, taught by the same instructor. In one section, the instructor used ERS two to four times a week to administer quizzes or start discussions. Results showed no statistically significant evidence of improved performance in the ERS class, measured on a wide variety of assignment, quiz, and exam scores, including pre-test/post-test improvement in knowledge. Gender, prior GPA, and other demographic differences did not interact with the manipulation. It was speculated that use of ERS may have failed to make a difference in the current study because the system was not used frequently enough or for engaging activities, or because the use of ERS in a small class may not have provided benefits beyond the usual class experience. Interestingly, however, a student survey given at the beginning and end of the semester showed that students in the ERS class significantly improved their opinion of the system, indicating that they felt they had performed better as a result of using the clickers. (Students’ opinions in the control class declined.) Thus, students believed that ERS had improved their performance, although objectively it had not. It was concluded that relying on student opinions on the benefits of ERS may be misleading. / Department of Educational Studies

Abstract When traditional lecture methods prove ineffective, some professors turn to alternative teaching styles. In particular, a flipped or inverted classroom, where students watch conceptual videos before coming to class and use class time for application and fine tuning of these concepts has become popular in recent years. However, little consensus exists on the efficacy of these strategies. The purpose of this study is to determine whether a flipped classroom structure implemented in a medical school course successfully improved student performance. To do so, I analyzed exam data from the University of Nebraska Medical Center before and after implementation of the alternate method in a course, and compared to another class taken in the same semester that did not undergo any change in teaching style. In addition, I investigated differences among particular student academic and demographic groups that may benefit from learning in an inverted classroom environment. My findings suggest that the flipped classroom strategy is advantageous to student learning and can significantly increase the performance of particular divisions of students such as those with lower-than-average MCAT scores and students who performed highly in their first year of medical school.

The phenotype inference from genotype in RNA viruses maps the viral genome/protein sequences to the molecular functions in order to understand the underlying molecular mechanisms that are responsible for the function changes. The inference is currently done through a laborious experimental process which is arguably inefficient, incomplete, and unreliable. The wealth of RNA virus sequence data in the presence of different phenotypes promotes the rise of computational approaches to aid the inference. Key residue identification and genotype-phenotype mapping function learning are two approaches to identify the critical positions out of hitchhikers and elucidate the relations among them. The existing computational approaches in this area focus on prediction accuracy, yet a number of fundamental problems have not been considered: the scalability of the data, the capability to suggest informative biological experiments, and the interpretability of the inferences. A common scenario of inference done by biologists with mutagenesis experiments usually involves a small number of available sequences, which is very likely to be inadequate for the inference in most setups. Accordingly biologists desire models that are capable of inferring from such limited data, and algorithms that are capable of suggesting new experiments when more data is needed. Another important but always been neglected property of the models is the interpretability of the mapping, since most existing models behave as ’black boxes’. To address these issues, in the thesis I design a supervised combinatorial filtering algorithm that systematically and efficiently infers the correct set of key residue positions from available labeled data. For cases where more data is needed to fully converge to an answer, I introduce an active learning algorithm to help choose the most informative experiment from a set of unlabeled candidate strains or mutagenesis experiments to minimize the expected total laboratory time or financial cost. I also propose Disjunctive Normal Form (DNF) as an appropriate assumption over the hypothesis space to learn interpretable genotype-phenotype functions. The challenges of these approaches are the computational efficiency due to the combinatorial nature of our algorithms. The solution is to explore biological plausible assumptions to constrain the solution space and efficiently find the optimal solutions under the assumptions. The algorithms were validated in two ways: 1) prediction quality in a cross-validation manner, and 2) consistency with the domain experts’ conclusions. The algorithms also suggested new discoveries that have not been discussed yet. I applied these approaches to a variety of RNA virus datasets covering the majority of interesting RNA phenotypes, including drug resistance, Antigenicity shift, Antibody neutralization and so on to demonstrate the prediction power, and suggest new discoveries of Influenza drug resistance and Antigenicity. I also prove the extension of the approaches in the area of severe acute community disease.

In this dissertation, the problem of learning from highly imbalanced data is studied. Imbalance data learning is of great importance and challenge in many real applications. Dealing with a minority class normally needs new concepts, observations and solutions in order to fully understand the underlying complicated models. We try to systematically review and solve this special learning task in this dissertation.We propose a new ensemble learning framework—Diversified Ensemble Classifiers for Imbal-anced Data Learning (DECIDL), based on the advantages of existing ensemble imbalanced learning strategies. Our framework combines three learning techniques: a) ensemble learning, b) artificial example generation, and c) diversity construction by reversely data re-labeling. As a meta-learner, DECIDL utilizes general supervised learning algorithms as base learners to build an ensemble committee. We create a standard benchmark data pool, which contains 30 highly skewed sets with diverse characteristics from different domains, in order to facilitate future research on imbalance data learning. We use this benchmark pool to evaluate and compare our DECIDL framework with several ensemble learning methods, namely under-bagging, over-bagging, SMOTE-bagging, and AdaBoost. Extensive experiments suggest that our DECIDL framework is comparable with other methods. The data sets, experiments and results provide a valuable knowledge base for future research on imbalance learning. We develop a simple but effective artificial example generation method for data balancing. Two new methods DBEG-ensemble and DECIDL-DBEG are then designed to improve the power of imbalance learning. Experiments show that these two methods are comparable to the state-of-the-art methods, e.g., GSVM-RU and SMOTE-bagging. Furthermore, we investigate learning on imbalanced data from a new angle—active learning. By combining active learning with the DECIDL framework, we show that the newly designed Active-DECIDL method is very effective for imbalance learning, suggesting the DECIDL framework is very robust and flexible.Lastly, we apply the proposed learning methods to a real-world bioinformatics problem—protein methylation prediction. Extensive computational results show that the DECIDL method does perform very well for the imbalanced data mining task. Importantly, the experimental results have confirmed our new contributions on this particular data learning problem.