A significant problem in many machine learning tasks is that it is time consuming and costly to gather the necessary labeled data for training the learning algorithm to a reasonable level of performance. In reality, it is often the case that a small amount of labeled data is available and that more unlabeled data could be labeled on demand at a cost. If the labeled data is obtained by a process outside of the control of the learner, then the learner is passive. If the learner picks the data to be labeled, then this becomes active learning. This has the advantage that the learner can pick data to gain specific information that will speed up the learning process. Support Vector Machines (SVMs) have many properties that make them attractive to use as a learning algorithm for many real world applications including classification tasks. Some researchers have proposed algorithms for active learning with SVMs, i.e. algorithms for choosing the next unlabeled instance to get label for. Their approach is supervised in nature since they do not consider all unlabeled instances while looking for the next instance. In this thesis, we propose three new algorithms for applying active learning for SVMs in a semi-supervised setting which takes advantage of the presence of all unlabeled points. The suggested approaches might, by reducing the number of experiments needed, yield considerable savings in costly classification problems in the cases when finding the training data for a classifier is expensive.

A significant problem in many machine learning tasks is that it is time consuming and costly to gather the necessary labeled data for training the learning algorithm to a reasonable level of performance. In reality, it is often the case that a small amount of labeled data is available and that more unlabeled data could be labeled on demand at a cost. If the labeled data is obtained by a process outside of the control of the learner, then the learner is passive. If the learner picks the data to be labeled, then this becomes active learning. This has the advantage that the learner can pick data to gain specific information that will speed up the learning process. Support Vector Machines (SVMs) have many properties that make them attractive to use as a learning algorithm for many real world applications including classification tasks. Some researchers have proposed algorithms for active learning with SVMs, i.e. algorithms for choosing the next unlabeled instance to get label for. Their approach is supervised in nature since they do not consider all unlabeled instances while looking for the next instance. In this thesis, we propose three new algorithms for applying active learning for SVMs in a semi-supervised setting which takes advantage of the presence of all unlabeled points. The suggested approaches might, by reducing the number of experiments needed, yield considerable savings in costly classification problems in the cases when finding the training data for a classifier is expensive.

With the increasing stress on teachers and students to meet and raise mathematics standards in schools, especially in the secondary level, the need for strong curricula and supporting materials for teachers has grown. A good curriculum, however, must do more than align with state standards and teach to the state exams; it must encourage students to enjoy mathematics. In an effort to help ease the plague of math anxiety, this thesis presents an eighth grade curriculum, called MathTAKStic, not only directly aligning with the Texas state standards, the Texas Essential Knowledge Skills (TEKS), but also encouraging students to pursue higher level thinking through active learning and logical thinking. To test the curriculum and find out its usefulness, several lessons were taught at a middle school. Although the scores of those learning with the curriculum were not always better than others, MathTAKStic led to a greater increase in students’ performance compared to those who were not exposed to the lessons, an increased interest in math and a plethora of ideas for the future. These results were concluded based on a comparison of students’ scores from the previous year to the current year on the Texas standardized test. Overall, the increase in passing scores of MathTAKStic students preceded other classes in the same school.

Thesis (Ed. D.)--University of Nevada, Reno, 2008. / "December 2008." Includes bibliographical references (leaves 146-157). Library also has microfilm. Ann Arbor, Mich. : ProQuest Information and Learning Company, [2009]. 1 microfilm reel ; 35 mm. Online version available on the World Wide Web.

Thesis (MA)--University of Montana, 2009. / Contents viewed on December 11, 2009. Title from author supplied metadata. Includes bibliographical references.

Thesis (Ph.D.)--Indiana University, 2010. / Title from screen (viewed on March 3, 2010). School of Nursing, Indiana University-Purdue University Indianapolis (IUPUI). Advisor(s): Judith Halstead, Anna McDaniel, Mary L. Fisher, Lillian Stokes. Includes vitae. Includes bibliographical references (leaves 98-107).

Background: Constructive alignment (CA) is a pedagogical approach that emphasizes the alignment between the intended learning outcomes (ILOs), teaching and learning activities (TLAs) and assessment tasks (ATs) as well as creation of a teaching/learning environment where students will be able to actively create their knowledge. Objectives: This paper aims at investigating the extent of constructively-aligned courses in Computer Engineering and Informatics department at Dalarna University, Sweden. This study is based on empirical observations of teacher’s perceptions of implementation of CA in their courses. Methods: Ten teachers (5 from each department) were asked to fill a paper-based questionnaire, which included a number of questions related to issues of implementing CA in courses. Results: Responses to the items of the questionnaire were mixed. Teachers clearly state the ILOs in their courses and try to align the TLAs and ATs to the ILOs. Computer Engineering teachers do not explicitly communicate the ILOs to the students as compared to Informatics teachers. In addition, Computer Engineering teachers stated that their students are less active in learning activities as compared to Informatics teachers. When asked about their subjective ratings of teaching methods all teachers stated that their current teaching is teacher-centered but they try to shift the focus of activity from them to the students. Conclusions: From teachers’ perspectives, the courses are partially constructively-aligned. Their courses are “aligned”, i.e. ILOs, TLAs and ATs are aligned to each other but they are not “constructive” since, according to them, there was a low student engagement in learning activities, especially in Computer Engineering department. / <p>Högskolepedagogik, högskolepedagogisk utbildning, BHU</p>

This dissertation analyzes trading behavior in financial markets from multiple perspectives. In chapter 1, "Exploratory Trading," I investigate the mechanisms underlying high-frequency traders' capacity to profitably anticipate price movements. I develop a model of how a trader could gather valuable private information by using her own orders in an exploratory manner to learn about market conditions. The model's predictions are borne out empirically, and I find that this "exploratory trading" model helps to resolve several central open questions about high-frequency trading. Chapters 2 and 3 focus on the trading behavior of individuals. Chapter 2, "Foundations of the Disposition Effect: Experimental Evidence," (co-authored with Johanna Mollerstrom), presents and analyzes results from a laboratory experiment intended to examine if and how "regret aversion"--aversion to admitting mistakes--affects people's trading decisions. Although the experimental results resolve little about regret aversion specifically, they reveal some novel and unexpected effects, most importantly that subjects radically changed their trading decisions when they were compelled to devote a minimal amount of extra attention. In chapter 3, "Price Targets," I analyze how rational investors who privately observe information of indeterminate quality use prices to learn about whether or not their private information is valuable. I derive implications about trading behavior that not only help to explain a variety of empirical puzzles, but also generate several new testable predictions. Although these three essays differ considerably in methodology and focus, they all address the same basic issue of understanding the foundations of trading behavior. / Economics

This thesis is a record of two research strands that have been intertwined during the development over a four year period of a classroom curriculum for an elective Geology 12 course in a new school. It discusses traditional belief systems identified as common to the practice of senior science and how one teacher wanted to challenge those beliefs to produce a working curriculum that would focus on long term learning within the framework of an externally prescribed curriculum and a provincially mandated external final exam that counted for 40% of the students mark. The teacher, working on her own in a portable for the first two years was in the unenviable position of being supplied with textbooks with a foreign focus and with supplies that as the title suggests were of little use over the long term. By Christmas of the first year a number of major problems had been identified, these problems falling into two major categories - developing strategies for long term learning that, within the operational constraints of grade 12, would enable the students to take far more responsibility for their own learning, and second, developing a science research programme for acquiring the resources, principally through field work, that were identified as being necessary for the programme. The major concerns within these two problem areas were identified and a four year timeline was developed for implementation. On the pedagogical side, after examining some of the literature on learning, particularly that around the area of cooperative learning that has had a substantial focus in recent years in a number of local school districts, reflecting on what worked for me in terms of my practice over 27 years of science teaching, I chose to focus on the Project for Enhancing Effective Learning (PEEL), out of Monash University, Australia as my working framework for learning. The process of developing this classroom curriculum was framed as a qualitative individual action research project over time as, within my professional life, there were no other teachers involved with the geology programme within the school, and at the same time being in a portable isolated me from my peers-l had no choice but to be self contained and self reliant. The pedagogical side of the process saw the evolvement of a programme that differed significantly in many ways from traditional senior science teaching. This is not to say that many teachers are not already reflecting on and trying to improve practice but for most of them it is through quiet reflection, discourse and evolution much as it had been for me until this time. For me this was the first time in my career that I was able to develop a programme from the very beginning. The thesis details the development of a multi-level learning strategy with an underlying theme being the development of more metacognitive students. The programme entails the identification of prior learning, reflective and collaborative practice, multiple processings of content and skills, peer assessment, and semi-formal reflective assessment. For many students, particularly during my first two years, most of these strategies were completely foreign to their cultural expectations of the teacher's role as dispenser of information to be regurgitated back through formal assessment. During the last two years these challenges to student thinking have been far less dramatic as I am now a known quantity in the school and the students taking my course expect to be working at becoming more independent long term learners. The programme is also built on the premise that for geology, relevant hands-on activities are an integral part of the learning process, and this other research strand is also explored and described. This is the story of the two research strands by which a semi-independent multi-level learning environment has been developed and implemented with a high degree of hands-on activities. Although a formal assessment of the programme is almost impossible to do within the constraints of my working environment, the personal feedback that I receive from the students, parents and colleagues indicates that it has been successful.

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.