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.

Striving to reduce the environmental impact of our growing energy demand creates tough new challenges in how we generate and use electricity. We need to develop Smart Grid systems in which distributed sustainable energy resources are fully integrated and energy consumption is efficient. Customers, i.e., consumers and distributed producers, require agent technology that automates much of their decision-making to become active participants in the Smart Grid. This thesis develops models and learning algorithms for such autonomous agents in an environment where customers operate in modern retail power markets and thus have a choice of intermediary brokers with whom they can contract to buy or sell power. In this setting, customers face a learning and multiscale decision-making problem – they must manage contracts with one or more brokers and simultaneously, on a finer timescale, manage their consumption or production levels under existing contracts. On a contextual scale, they can optimize their isolated selfinterest or consider their shared goals with other agents. We advance the idea that a Learning Utility Management Agent (LUMA), or a network of such agents, deployed on behalf of a Smart Grid customer can autonomously address that customer’s multiscale decision-making responsibilities. We study several relationships between a given LUMA and other agents in the environment. These relationships are semi-cooperative and the degree of expected cooperation can change dynamically with the evolving state of the world. We exploit the multiagent structure of the problem to control the degree of partial observability. Since a large portion of relevant hidden information is visible to the other agents in the environment, we develop methods for Negotiated Learning, whereby a LUMA can offer incentives to the other agents to obtain information that sufficiently reduces its own uncertainty while trading off the cost of offering those incentives. The thesis first introduces pricing algorithms for autonomous broker agents, time series forecasting models for long range simulation, and capacity optimization algorithms for multi-dwelling customers. We then introduce Negotiable Entity Selection Processes (NESP) as a formal representation where partial observability is negotiable amongst certain classes of agents. We then develop our ATTRACTIONBOUNDED- LEARNING algorithm, which leverages the variability of hidden information for efficient multiagent learning. We apply the algorithm to address the variable-rate tariff selection and capacity aggregate management problems faced by Smart Grid customers. We evaluate the work on real data using Power TAC, an agent-based Smart Grid simulation platform and substantiate the value of autonomous Learning Utility Management Agents in the Smart Grid.

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.

No description available.

This is a participative case study of a self-facilitating, collaborative, women’s learning group. The group’s longevity afforded a unique opportunity to investigate, in depth, both what encouraged its members to join at its outset, and what has sustained the participation of its current members for thirteen years. Its longevity also provided an opportunity to explore the impact of sustained membership on the women in the group. These two components of the study are its most significant original contributions to the existing literature which does not appear to cover anything similar. The initial raison d’étre of the group was its members’ professional development and this forms a central strand of the investigation, along with identity and self-facilitation. A pragmatic research paradigm, the collaborative nature of the group and the writer’s dual role as both participant and researcher were all influential in the decision to use a participative approach. A range of methods, chosen by the participants, was utilised during the investigation which, whilst participatory, is not emancipatory research. This experimental divergence from how a participative approach is traditionally employed is offered for consideration by researchers who wish to work in a new way that minimises power in other, non-emancipatory situations. The findings support, contradict and add to the literature. The mutuality of longevity and the depth of discourse and learning experienced by group members is a particularly striking aspect of this study. As members of the group have aged, its focus has segued from professional development to encompass a much broader agenda: it has shifted from contributing to members’ professional identity to sustaining their perceptions of self as women who remain capable of complex, critical thinking as they move out of full-time work. The longevity of the group has also fostered deep attachments between group members, despite the differences between them: sustained membership of the group, in turn, provides sustenance for its members. The significance of grounding, ground rules and group composition are highlighted, as is the need to contemplate how members will leave a group during its formation. Alignment between participants in a group is identified as important for its continuation but not always possible. This research makes no claim to offer a definitive model for collaborative learning groups but, instead poses a series of questions for consideration by others who are interested in collaborative learning.

Proceedings of International Summerworkshop Computer Science

The purpose of this qualitative thesis research was to explore the state of distance education in Saudi universities. The research focused on teaching and learning from the perspectives of the universities’ instructors as well as expert designers from the Ministry of Education working in distance education. By using a multiple case studies approach, this study aimed to understand the opportunities and challenges faced in the development of online learning environments at Saudi universities from an ethical and cultural perspectives. Data collection methods consisted of 28 in-depth, one-on-one interviews as well a thematic analysis of 152 supporting documents related to the universities’ strategies to deliver online learning. The advanced findings revealed how the recent integration of a blended learning model has helped to contribute to a shift in the Saudi distance education system, as it moves from a teacher-centered approach to a learner-centered approach. Furthermore, drawing on Hofstede’s Cultural Dimensions and Social Construction of Technology (SCOT), the study uncovers complex interactions between the Saudi learning culture, technology integration, and ethical issues. This research contributes unique knowledge about the state of online learning development in Saudi higher education to help enhance distance education development in Saudi Arabia, as well as in other areas of the world where similar distance education development initiatives are underway.

No / The rise of mainstream virtual learning environments has facilitated the operation of information spaces to support display, simulation, and interactive modelling. As well as disseminating information and knowledge, they can also be used to accumulate learning as each user is also a potential contributor. This paper examines the use of information environments to support mobile learning and dynamic decision making and the extent to which physical or social space can support the application. Such applications are not limited to the traditional data analysis and modelling functions but can be extended to include storytelling, theatre, and other aspects of the arts and the entertainment industry.

Phenotypic profiling systematically maps and analyzes observable traits (phenotypes) exhibited in cells, tissues, organisms or systems in response to various conditions, including chemical, genetic and disease perturbations. This approach seeks to comprehensively understand the functional consequences of perturbations on biological systems, thereby informing diverse research areas such as drug discovery, disease modeling, functional genomics and systems biology. Corresponding techniques should capture high-dimensional features to distinguish phenotypes affected by different conditions. Current methods mainly include fluorescence imaging, mass spectrometry and omics technologies, coupled with computational analysis, to quantify diverse features such as morphology, metabolism and gene expression in response to perturbations. Yet, they face challenges of high costs, complicated operations and strong batch effects. Vibrational imaging offers an alternative for phenotypic profiling, providing a sensitive, cost-effective and easily operated approach to capture the biochemical fingerprint of phenotypes. Among vibrational imaging techniques, infrared (IR) imaging has further advantages of high throughput, fast imaging speed and full spectrum coverage compared with Raman imaging. However, current biomedical applications of IR imaging mainly concentrate on "digital disease pathology", which uses label-free IR imaging with machine learning for tissue pathology classification and disease diagnosis. The thesis contributes as the first comprehensive study of using IR imaging for phenotypic profiling, focusing on three key areas. First, IR-active vibrational probes are systematically designed to enhance metabolic specificity, thereby enriching measured features and improving sensitivity and specificity for phenotype discrimination. Second, experimental workflows are established for phenotypic profiling using IR imaging across biological samples at various levels, including cellular, tissue and organ, in response to drug and disease perturbations. Lastly, complete data analysis pipelines are developed, including data preprocessing, statistical analysis and machine learning methods, with additional algorithmic developments for analyzing and mapping phenotypes. Chapter 1 lays the groundwork for IR imaging by delving into the theory of IR spectroscopy theory and the instrumentation of IR imaging, establishing a foundation for subsequent studies. Chapter 2 discusses the principles of popular machine learning methods applied in IR imaging, including supervised learning, unsupervised learning and deep learning, providing the algorithmic backbone for later chapters. Additionally, it provides an overview of existing biomedical applications using label-free IR imaging combined with machine learning, facilitating a deeper understanding of the current research landscape and the focal points of IR imaging for traditional biomedical studies. Chapter 3-5 focus on applying IR imaging coupled with machine learning for novel application of phenotypic profiling. Chapter 3 explores the design and development of IR-active vibrational probes for IR imaging. Three types of vibrational probes, including azide, 13C-based probes and deuterium-based probes are introduced to study dynamic metabolic activities of protein, lipids and carbohydrates in cells, small organisms and mice for the first time. The developed probes largely improve the metabolic specificity of IR imaging, enhancing the sensitivity of IR imaging towards different phenotypes. Chapter 4 studies the combination of IR imaging, heavy water labeling and unsupervised learning for tissue metabolic profiling, which provides a novel method to map metabolic tissue atlas in complex mammalian systems. In particular, cell type-, tissue- and organ-specific metabolic profiles are identified with spatial information in situ. In addition, this method further captures metabolic changes during brain development and characterized intratumor metabolic heterogeneity of glioblastoma, showing great promise for disease modeling. Chapter 5 developed Vibrational Painting (VIBRANT), a method using IR imaging, multiplexed vibrational probes and supervised learning for cellular phenotypic profiling of drug perturbations. Three IR-active vibrational probes were designed to measure distinct essential metabolic activities in human cancer cells. More than 20,000 single-cell drug responses were collected, corresponding to 23 drug treatments. Supervised learning is used to accurately predict drug mechanism of action at single-cell level with minimal batch effects. We further designed an algorithm to discover drug candidates with novel mechanisms of action and evaluate drug combinations. Overall, VIBRANT has demonstrated great potential across multiple areas of phenotypic drug screening.

The nature of learning and teaching in higher education has changed significantly in recent years. The emergence of social media and technologies has had a profound impact upon learner engagement and tutors have had to adapt their learning and teaching strategies accordingly. The thesis discusses the author’s published body of research and presents a pedagogical framework for engaging the networked learner. The framework is based upon three perspectives that have emerged from the author’s research. Firstly, different learning paradigms should be acknowledged when developing pedagogical approaches to using learning technologies. Secondly, the thesis discusses how the author’s research on learning technologies, including VLEs and iPod technologies, should embrace networked communities and learner empowerment. Thirdly, the research on learning approaches is discussed which acknowledges different learning behaviours and the adoption of differentiated methods in learning and teaching. Whilst discussing the evolving nature of the learning environment, the pedagogical framework draws together each of the aforementioned perspectives. The framework raises a number of factors for engaging the networked learner. A set of practical guidelines based around institutional, tutor and learner perspectives are discussed and underpin the application of the framework. The thesis concludes with theoretical observations on learning and learning theory and presents limitations and areas for further research.