For many types of learners one can compute the statistically 'optimal' way to select data. We review how these techniques have been used with feedforward neural networks. We then show how the same principles may be used to select data for two alternative, statistically-based learning architectures: mixtures of Gaussians and locally weighted regression. While the techniques for neural networks are expensive and approximate, the techniques for mixtures of Gaussians and locally weighted regression are both efficient and accurate.

Previous studies have reported high attrition rates in large-enrollment science courses where teacher-centered instruction was prevalent. The scientific literature provides strong evidence that student-centered teaching, which involves extensive active learning, leads to deepened learning as the result of effective student engagement. Consequently, professional development initiatives have continually focused on assisting academics with the implementation of active learning. Generally, higher education institutions engage faculty in professional development through in-service workshops that facilitate learning new teaching techniques in a specific context. These workshops usually do not include self-scrutiny concerning teaching or do they provide continuous support for the implementation of strategies learned in the workshop. The purpose of this study was to explore the influence of a professional development program that consisted of a workshop focused on the implementation of active learning in large science courses and extended to include post-workshop activities, on participants’ enactment of teaching practices introduced in the workshop. More specifically, through a qualitative methodology and employing transformative learning theory, this work evaluated the influence of science instructors’ engagement in dialogue and critical self-reflection on their teaching approaches and practices. Engagement in critical reflection was facilitated through watching of teaching videotapes followed by participants’ engagement in dialogue about teaching with the researcher. Findings suggest that providing continuous post-workshop support by fostering engagement in critical self-reflection and dialogue, can lead to transformative learning about teaching. More specifically, participation in the program led to the transformation of teaching practices, while teaching approaches remained unchanged. While some obstacles to the transformation of teaching approaches were identified, major outcomes indicate that meaningful professional development can go far beyond learning how to use new teaching strategies through faculty engagement in critical reflection and dialogue on teaching.

Thesis (Ph.D.)--Antioch University, 2006. / Title from PDF t.p. (viewed Mar. 27, 2007). Advisor: Alan Guskin, Ph.D. Keywords: late life learning, cognitive strategies, mental acuity, benefits of learning, lifespan learning, importance of learning. Includes bibliographical references (p. 217-227).

Active learning has proven to be a successful strategy in quick development of corpora to be used in statistical induction of natural language. A vast majority of studies in this field has concentrated on finding and testing various informativeness measures for samples / however, representativeness measures for samples have not been thoroughly studied. In this thesis, we introduce a novel representativeness measure which is, being based on Zipf&#039 / s law, model-independent and validated both theoretically and empirically. Experiments conducted on WSJ corpus with a wide-coverage parser show that our representativeness measure leads to better performance than previously introduced representativeness measures when used with most of the known informativeness measures.

Learning-by-doing learning environments support a wealth of physical engagement in activities. However, there is also a lot of variability in what participants learn in each enactment of these types of environments. Therefore, it is not always clear how participants are learning in these environments. In order to design technologies to support learning in these environments, we must have a greater understanding of how participants engage in learning activities, their goals for their engagement, and the types of help they need to cognitively engage in learning activities. To gain a greater understanding of participant engagement and factors and circumstances that promote and inhibit engagement, this dissertation explores and answers several questions: What are the types of interactions and experiences that promote and /or inhibit learning and engagement in learning-by-doing learning environments? What are the types of configurations that afford or inhibit these interactions and experiences in learning-by-doing learning environments? I explore answers to these questions through the context of two enactments of Kitchen Science Investigators (KSI), a learning-by-doing learning environment where middle-school aged children learn science through cooking from customizing recipes to their own taste and texture preferences. In small groups, they investigate effects of ingredients through the design of cooking and science experiments, through which they experience and learn about chemical, biological, and physical science phenomena and concepts (Clegg, Gardner, Williams,&Kolodner, 2006). The research reported in this dissertation sheds light on the different ways participant engagement promotes and/or inhibits cognitive engagement in by learning-by-doing learning environments through two case studies. It also provides detailed descriptions of the circumstances (social, material, and physical configurations) that promote and/or inhibit participant engagement in these learning environments through cross-case analyses of these cases. Finally, it offers suggestions about structuring activities, selecting materials and resources, and designing facilitation and software-realized scaffolding in the design of these types of learning environments. These design implications focus on affording participant engagement in science content and practices learning. Overall, the case studies, cross-case analyses, and empirically-based design implications begin to bridge the gap between theory and practice in the design and implementation of these learning environments. This is demonstrated by providing detailed and explanatory examples and factors that affect how participants take up the affordances of the learning opportunities designed into these learning environments.

Thesis (PhD) - Australian Graduate School of Entrepreneurship, Faculty of Business and Enterprise, Swinburne University of Technology, 2009. / A thesis is submitted in fulfilment of the requirements for the degree Doctor of Philosophy, Faculty of Business and Enterprise, Swinburne University of Technology - 2009. Typescript. Includes bibliographical references (p. 225-238)

The purpose of this research was to describe how two fifth-grade teachers help students understand social studies and language arts concepts through simulations. I observed two fifth-grade teachers, Lindsey and Paula, as they conducted a simulation focused on the Lewis and Clark expedition. I spent 100 hours over a period of eight weeks in the teachers classrooms. The following research questions guided my inquiry: 1. Why do the two teachers use simulations? 2. How do the two teachers implement simulations? 3. How do the ten students respond to simulations? 4. What do the ten students think about simulations? To answer these questions, I interviewed each study participant three times, analyzed teacher resource materials and student work samples, videotaped and audiotaped the students and teachers behaviors, and observed the teachers and students interactions. I followed a phenomenological theoretical orientation and reported my findings through a descriptive case study. A detailed account of the early, middle, and late stages of a simulation depicted the participants actions. I discovered that the two teachers used simulations because they believed simulations targeted students learning styles and enabled students to retain the material over time. Lindsey felt simulations allowed her to integrate content and create an active learning environment, and Paula believed simulations involved the students with authentic learning. To implement the simulation, the teachers increased students background knowledge on Westward Expansion, prepared them for their roles throughout the action phase, and evaluated student learning through written and oral assessments. I observed how two groups of five students interacted throughout the simulation. I learned how they formulated an identity for their team, discussed dilemmas, resolved conflicts, and completed their tasks. The students shared positive and negative opinions about their roles as captains, journal writers, interpreters, and privates. They explained how they had learned about the content, teamwork, and historical figures associated with the Lewis and Clark expedition. All of the students gained on their posttests. Four of the students made connections with the simulation content to their lives and experienced positive attitudinal and academic transformations.

A primary goal in systems neuroscience is to understand how neural spike responses encode information about the external world. A popular approach to this problem is to build an explicit probabilistic model that characterizes the encoding relationship in terms of a cascade of stages: (1) linear dimensionality reduction of a high-dimensional stimulus space using a bank of filters or receptive fields (RFs); (2) a nonlinear function from filter outputs to spike rate; and (3) a stochastic spiking process with recurrent feedback. These models have described single- and multi-neuron spike responses in a wide variety of brain areas. This dissertation addresses Bayesian methods to efficiently estimate the linear and non-linear stages of the cascade encoding model. In the first part, the dissertation describes a novel Bayesian receptive field estimator based on a hierarchical prior that flexibly incorporates knowledge about the shapes of neural receptive fields. This estimator achieves error rates several times lower than existing methods, and can be applied to a variety of other neural inference problems such as extracting structure in fMRI data. The dissertation also presents active learning frameworks developed for receptive field estimation incorporating a hierarchical prior in real-time neurophysiology experiments. In addition, the dissertation describes a novel low-rank model for the high dimensional receptive field, combined with a hierarchical prior for more efficient receptive field estimation. In the second part, the dissertation describes new models for neural nonlinearities using Gaussian processes (GPs) and Bayesian active learning algorithms in closed-loop neurophysiology experiments to rapidly estimate neural nonlinearities. The dissertation also presents several stimulus selection criteria and compare their performance in neural nonlinearity estimation. Furthermore, the dissertation presents a variation of the new models by including an additional latent Gaussian noise source, to infer the degree of over-dispersion in neural spike responses. The proposed model successfully captures various mean-variance relationships in neural spike responses and achieves higher prediction accuracy than previous models. / text

The phrase "teachable moment" has a taken-for-granted connotation of readiness to learn, but has been rarely defined and researched in the literature of higher education. This study described faculty members' experiences of teachable moments in their undergraduate classrooms. This included the conditions in which they emerge, and the decision-making processes used by faculty members to determine if and how to pursue such moments. If professors have opportunities to clarify their understandings of such moments, the ability to capitalize on otherwise unplanned teaching opportunities may be enhanced. Seventeen experienced social science and humanities faculty members teaching undergraduate classes at a large research university participated in two semi-structured active interviews (Gubrium and Holstein, 2003). The interviews addressed their understandings of, experiences with, and decisions about teachable moments in the classroom. These interviews yielded descriptions of teachable moments as creating a heightened sense of engagement and interaction about a topic of shared interest. Teachable moments fall along a continuum of predictability, with some moments being intentionally designed by the professor and others emerging spontaneously during a class as a result of current events or student comments. When confronted with surprise moments professors consider a complex set of interacting elements to decide whether to pursue or postpone the exploration of the moment. They ask themselves several questions Is there time? How does this fit with goals for the class, course or program? Are the students and I ready to examine this? What impact will this have on classroom dynamics? Does this warrant in-class exploration, or should it be pursued outside of class? The set of considerations can be examined as manifestations of Schon's (1987) theory of reflection-in-action which describes how professionals make decisions in surprise situations when previously effective responses do not work, and more specifically of Steier and Ostrenko's (2000) adaptation of Schon's model, .reflection-in-interaction. Implications for theories and practices of teaching of college teaching as well as for opportunities for faculty development were described.

Traditional supervised machine learning algorithms are expected to have access to a large corpus of labeled examples, but the massive amount of data available in the modern world has made unlabeled data much easier to acquire than accompanying labels. Active learning is an extension of the classical paradigm intended to lessen the expense of the labeling process by allowing the learning algorithm to intelligently choose which examples should be labeled. In this dissertation, we demonstrate that the power to make adaptive label queries has benefits beyond reducing labeling effort over passive learning. We develop and explore several novel methods for active learning that exemplify these new capabilities. Some of these methods use active learning for a non-standard purpose, such as computational speedup, structure discovery, and domain adaptation. Others successfully apply active learning in situations where prior results have given evidence of its ineffectiveness. Specifically, we first give an active algorithm for learning disjunctions that is able to overcome a computational intractability present in the semi-supervised version of the same problem. This is the first known example of the computational advantages of active learning. Next, we investigate using active learning to determine structural properties (margins) of the data-generating distribution that can further improve learning rates. This is in contrast to most active learning algorithms which either assume or ignore structure rather than seeking to identify and exploit it. We then give an active nearest neighbors algorithm for domain adaptation, the task of learning a predictor for some target domain using mostly examples from a different source domain. This is the first formal analysis of the generalization and query behavior of an active domain adaptation algorithm. Finally, we show a situation where active learning can outperform passive learning on very noisy data, circumventing prior results that active learning cannot have a significant advantage over passive learning in high-noise regimes.