Two frameworks for integrating knowledge in induction

The use of knowledge in inductive learning is critical for improving the quality of the concept definitions generated, reducing the number of examples required in order to learn effective concept definitions, and reducing the computation needed to find good concept definitions. Relevant knowledge may come in many forms (such as examples, descriptions, advice, and constraints) and from many sources (such as books, teachers, databases, and scientific instruments). How to extract the relevant knowledge from this plethora of possibilities, and then to integrate it together so as to appropriately affect the induction process is perhaps the key issue at this point in inductive learning. Here the focus is on the integration part of this problem; that is, how induction algorithms can, and do, utilize a range of extracted knowledge. Preliminary work on a transformational framework for defining knowledge-intensive inductive algorithms out of relatively knowledge-free algorithms is described, as is a more tentative problems-space framework that attempts to cover all induction algorithms within a single general approach. These frameworks help to organize what is known about current knowledge-intensive induction algorithms, and to point towards new algorithms

Quantum Information Dynamics and Open World Science

One of the fundamental insights of quantum mechanics is that complete knowledge of the state of a quantum system is not possible. Such incomplete knowledge of a physical system is the norm rather than the exception. This is becoming increasingly apparent as we apply scientific methods to increasingly complex situations. Empirically intensive disciplines in the biological, human, and geosciences all operate in situations where valid conclusions must be drawn, but deductive completeness is impossible. This paper argues that such situations are emerging examples of {it Open World} Science. In this paradigm, scientific models are known to be acting with incomplete information. Open World models acknowledge their incompleteness, and respond positively when new information becomes available. Many methods for creating Open World models have been explored analytically in quantitative disciplines such as statistics, and the increasingly mature area of machine learning. This paper examines the role of quantum theory and quantum logic in the underpinnings of Open World models, examining the importance of structural features of such as non-commutativity, degrees of similarity, induction, and the impact of observation. Quantum mechanics is not a problem around the edges of classical theory, but is rather a secure bridgehead in the world of science to come

The early stages of teaching: Teachers with good principles

The teachers in their learning process, go through different stages. Of all these phases which we are most interested in this article is the one that begins with the first contacts with the reality of the school, assuming the role reserved for vocational teachers. Teacher induction is the time period covering the early years, in which teachers have to make the transition from students to teachers. It is a period of tension and intensive learning contexts and generally unknown during which the novice teachers should acquire professional knowledge in addition to ensuring a certain balance. Induction programs for novice teachers are a real alternative to further that the first years as teachers are not frustrating year

Keeping New Teachers: A First Look at the Influences of Induction in the Chicago Public Schools

Examines whether participation in a formal induction program can improve teachers' experiences and job satisfaction, and demonstrates that strong levels of mentoring and support for new teachers greatly improve their desire to continue teaching

Inducing safer oblique trees without costs

Decision tree induction has been widely studied and applied. In safety applications, such as determining whether a chemical process is safe or whether a person has a medical condition, the cost of misclassification in one of the classes is significantly higher than in the other class. Several authors have tackled this problem by developing cost-sensitive decision tree learning algorithms or have suggested ways of changing the distribution of training examples to bias the decision tree learning process so as to take account of costs. A prerequisite for applying such algorithms is the availability of costs of misclassification. Although this may be possible for some applications, obtaining reasonable estimates of costs of misclassification is not easy in the area of safety. This paper presents a new algorithm for applications where the cost of misclassifications cannot be quantified, although the cost of misclassification in one class is known to be significantly higher than in another class. The algorithm utilizes linear discriminant analysis to identify oblique relationships between continuous attributes and then carries out an appropriate modification to ensure that the resulting tree errs on the side of safety. The algorithm is evaluated with respect to one of the best known cost-sensitive algorithms (ICET), a well-known oblique decision tree algorithm (OC1) and an algorithm that utilizes robust linear programming

ACCCN Workforce Standards for Intensive Care Nursing: Systematic and evidence review, development, and appraisal

Background: The intensive care nursing workforce plays an essential role in the achievement of positive healthcare outcomes. A growing body of evidence indicates that inadequate nurse staffing and poor skill mix are associated with negative outcomes for patients, and potentially compromises nurses’ ability to maintain the safety of those in their care. In Australia, the Australian College of Critical Care Nurses (ACCCN) has previously published a position statement on intensive care staffing. There was a need for a stronger more evidence based document to support the intensive nursing workforce. Objectives: To undertake a systematic and evidence review of the evidence related to intensive care nurse staffing and quality of care, and determine evidence-based professional standards for the intensive care nursing workforce in Australia. Methods: The National Health and Medical Research Council standard for clinical practice guidelines methodology was employed. The English language literature, for the years 2000-2015 was searched. Draft standards were developed and then peer- and consumer-reviewed. Results: A total of 553 articles was retrieved from the initial searches. Following evaluation, 231 articles met the inclusion criteria and were assessed for quality using established criteria. This evidence was used as the basis for the development of ten workforce standards, and to establish the overall level of evidence in support of each standard. All draft standards and their subsections were supported multi-professionally (median score >6) and by consumers (85–100% agreement). Following minor revisions, independent appraisal using the AGREE II tool indicated that the standards were developed with a high degree of rigour. Conclusion: The ACCCN intensive care nursing nurse workforce standards are the first to be developed using a robust, evidence-based process. The standards represent the optimal nurse workforce to achieve the best patient outcomes and to maintain a sustainable intensive care nursing workforce for Australia

Transition to Teaching: An Alternative Certification Program through Partnership Between a Public School District and a Public University

The reauthorization of the Elementary and Secondary Education Act has had a significant impact on licenses for teachers working in school programs. The act refers to highly qualified teachers in both Title I and Title ll. This paper defines alternative certification programs. This paper then describes a partnership, funded by the US. Department of Education, between a school district and a university to establish an alternative licensure program to train highly qualified secondary mathematics teachers.The goal of this partnership is to provide an infrastructure that supports the recruitment. preparation, placement, induction, and retention of highly qualified teachers through a new alternative route to teacher licensure. In addition, this paper discusses processes and procedures used in the project in light of the literature. lt discusses how candidates were selected for the project, strategies used to meet the competencies for licensure, and assessment of candidates

An information-based approach to integrating empirical and explanation-based learning

We describe a new approach to integrating explanation-based and empirical learning methods for learning relational concepts. The approach uses an information-based heuristic to evaluate components of a hypothesis that are proposed either by explanation-based or empirical methods. Providing domain knowledge to the integrated system can decrease the amount of search required during learning and increase the accuracy of learned concepts, even when the domain knowledge is incorrect and incomplete and there is noise in the training data

Investing In Results: How Business Roundtable Is Supporting Proven Education Reforms

The CEO effort to expand on what's working started in 2013 when Business Roundtable launched its Education Philanthropy Initiative. Two years later, this report examines how the five programs selected for their outstanding work in K-12 education reform have reached more students and improved educational outcomes as a result of the more than $15 million contributed to the Initiative by Roundtable CEOs