University of Nebraska Omaha

About: University of Nebraska Omaha is a education organization based out in Omaha, Nebraska, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 4526 authors who have published 8905 publications receiving 213914 citations. The organization is also known as: UNO & University of Omaha.

Cognitive learning efficiency through the use of design patterns in teaching

Abstract: Nowadays we need to teach students how to become flexible problem solvers in a dynamic world. The pace in which technology changes and complexity increases requires increased efficiency in learning and understanding. This requires the engineers of tomorrow to quickly gain knowledge and insight outside their prime area of expertise. To transfer practical, how-to knowledge, to re-use design solutions and to teach students design solutions in the context of a specific domain, design patterns can be used. Design patterns offer a way to transfer knowledge that is more practical and 'ready for use' than a generic theory-based transfer of knowledge. However, the advantage of design patterns might go beyond re-use, design efficiency and flexibility. This paper argues that in addition to the benefits described above, there is a specific added value for the use of design patterns by students to acquire design skills and domain knowledge. To analyze this proposition we will analyze the literature on cognitive load and cognitive learning processes, and relate this to experiences from three case studies in which novices and experts were offered design patterns to develop and implement systems and processes. We will reflect on implications of the use of design patterns in computer-based teaching as well as on a potential support tool to improve the accessibility of pattern languages.

Eddies in the Stream The Prevalence of Uncertain Plans for Retirement

Abstract: OBJECTIVE This study examined an assumption of retirement theory that typifies older workers as preretirees who are planfully engaged in paths toward retirement. METHODS Using survey responses among workers in the 1992 and 1994 waves of the Health and Retirement Study, we described the prevalence of nonsubstantive answers to questions about the expected form and timing of retirement (e.g., "don't know," "haven't thought about it"). We tested explanations for this uncertainty as an artifact of the survey process, but also as an outcome of the opportunity structure for retirement planning. RESULTS Survey procedure did generate some of these noncommittal responses. Depending on question type, approximately 10% to 40% of workers did not state when or how they would retire, and such responses were less prevalent across age and time. In addition, categorical uncertainty about form and timing was theoretically predictable in a framework that supposed that workers less subject to a socially attended life--at work or away--would be more undecided about the future. DISCUSSION Uncertainty is an authentic, meaningful stance toward retirement that theory and research design should not ignore. Just as actual transitions to retirement can be ambiguous or blurred, the expectation of retirement, as well, can be untidy.

Robust ensemble learning for mining noisy data streams

Abstract: In this paper, we study the problem of learning from concept drifting data streams with noise, where samples in a data stream may be mislabeled or contain erroneous values. Our essential goal is to build a robust prediction model from noisy stream data to accurately predict future samples. For noisy data sources, most existing works rely on data preprocessing techniques to cleanse noisy samples before the training of decision models. In data stream environments, these data preprocessing techniques are, unfortunately, hard to apply, mainly because the concept drifting in a data stream may make it very difficult to differentiate noise from samples of changing concepts. Accordingly, we propose an aggregate ensemble (AE) learning framework. The aim of AE is to build a robust ensemble model that can tolerate data errors. Theoretical and empirical studies on both synthetic and real-world data streams demonstrate that the proposed AE learning framework is capable of building accurate classification models from noisy data streams.

Toward Valid Measures of Public Sector Productivity: Performance Measures in Urban Transit

Abstract: In recent years, performance measurement has become the focus of attention in a variety of public sector fields. Unfortunately, too little has been done to develop valid operational definitions of performance, or to identify the weaknesses and biases inherent m certain types of performance measures. Thus, the potential exists for the inappropriate use of certain indicators in performance evaluations and decisions. One field in which there has been increasing effort to deal with performance problems is that of transit. Regardless, the nebulous nature of "performance" has been all too apparent in this industry. The terms "productivity," "efficiency," and "effectiveness" have been used synonymously in some instances, while in other cases "efficiency" and "effectiveness" have been considered to be different aspects of overall "productivity." This confusion is of major significance, because the use of performance measures in operations assessment, decision making, and resource allocation is increasing. Furthermore, since it increasingly is being urged that subsidy payments be linked to the performance of a transit system, and since subsidies now constitute over half of transit revenues, the performance measurement problem is particularly important. This paper examines weaknesses and biases inherent in commonly used measures of urban mass transit performance. It is shown that measures of efficiency, such as cost per passenger, are being incorrectly used as measures of effectiveness and that various traditional measures of efficiency, such as those which incorporate mileage, can be misleading when applied in decision making. Suggestions are made for developing valid performance indicators and for developing safeguards that will avoid present shortcomings.