737 resultados para personalized educational paths


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This file includes the ontology of patterns of educational settings adapted to a given organization as part of a formal framework for specifying, reusing and implementing educational settings. It includes a description of such an ontology.

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Annual directory of PreK-12, area education agencies, universities, colleges, and the Iowa Department of Education

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In a context in which organizations can no longer promise life-time employment and individuals increasingly experience inter-organizational mobility, this study tackles the question of whether organizational commitment is no longer related to new career orientations. To this end, it analyzes the relation between the underlying dimensions of protean (self direction and values driven) and boundaryless (boundaryless mindset and organizational mobility preference) career attitudes (Briscoe et al., 2006) and organizational commitment, within today's unstable and uncertain business scenario. Research results suggest that protean career attitudes contribute significantly to individuals emotional attachment to their employing organization. Furthermore, organizational mobility preference was found to be significant in predicting both affective and continuance commitment. Finally, future research suggestions and practical implications associated with the current study are provided.

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State Agency Audit Report

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State Agency Audit Report

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A Review of the Educational Excellence Program for Fiscal Years 1999 - 2002 and the Student Achievement and Teacher Quality Program for Fiscal Year 2002 within the Department of Education

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This study assesses the decline in second birth rates for men and women across different skill levels in transitional Russia. Changes within educational groups and occupational classes are observed over three distinct time periods: the Soviet era, economic crisis, and economic recovery. The most remarkable finding is the similarity in the extent second birth rates declined within educational groups and occupational classes during the economic crisis. Although further decline occurred in the recovery period, more variation emerged across groups.

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This paper describes a failure alert system and a methodology for content reuse in a new instructional design system called InterMediActor (IMA). IMA provides an environment for instructional content design, production and reuse, and for students’ evaluation based in content specification through a hierarchical structure of competences. The student assessment process and information extraction process for content reuse are explained.

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In this paper we present the theoretical and methodologicalfoundations for the development of a multi-agentSelective Dissemination of Information (SDI) servicemodel that applies Semantic Web technologies for specializeddigital libraries. These technologies make possibleachieving more efficient information management,improving agent–user communication processes, andfacilitating accurate access to relevant resources. Othertools used are fuzzy linguistic modelling techniques(which make possible easing the interaction betweenusers and system) and natural language processing(NLP) techniques for semiautomatic thesaurus generation.Also, RSS feeds are used as “current awareness bulletins”to generate personalized bibliographic alerts.

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Computer based training or distance education are facing dramatic changes withthe advent of standardization efforts, some of them concentrating in maximal reuse.This is of paramount importance for a sustainable -cost affordable- production ofeducational materials. Reuse in itself should not be a goal, though, since manymethodological aspects might be lost. In this paper we propose two contentproduction approaches for the InterMediActor platform under a competence-basedmethodology: either a bottom-up approach where content is designed from scratchor a top-down methodology where existing material can be gradually adapted tofulfil requisites to be used with maximal flexibility into InterMediActor.

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Student guidance is an always desired characteristic in any educational system, butit represents special difficulty if it has to be deployed in an automated way to fulfilsuch needs in a computer supported educational tool. In this paper we explorepossible avenues relying on machine learning techniques, to be included in a nearfuture -in the form of a tutoring navigational tool- in a teleeducation platform -InterMediActor- currently under development. Since no data from that platform isavailable yet, the preliminary experiments presented in this paper are builtinterpreting every subject in the Telecommunications Degree at Universidad CarlosIII de Madrid as an aggregated macro-competence (following the methodologicalconsiderations in InterMediActor), such that marks achieved by students can beused as data for the models, to be replaced in a near future by real data directlymeasured inside InterMediActor. We evaluate the predictability of students qualifications, and we deploy a preventive early detection system -failure alert-, toidentify those students more prone to fail a certain subject such that correctivemeans can be deployed with sufficient anticipation.

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Office of Treasurer of State, Iowa Educational Savings Plan Trust (Trust) for the year ended June 30, 2005. Independent Auditor's Report, Financial Statements and Supplementary Information.

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Building a personalized model to describe the drug concentration inside the human body for each patient is highly important to the clinical practice and demanding to the modeling tools. Instead of using traditional explicit methods, in this paper we propose a machine learning approach to describe the relation between the drug concentration and patients' features. Machine learning has been largely applied to analyze data in various domains, but it is still new to personalized medicine, especially dose individualization. We focus mainly on the prediction of the drug concentrations as well as the analysis of different features' influence. Models are built based on Support Vector Machine and the prediction results are compared with the traditional analytical models.