Gleichwertigkeit von beruflicher und hochschulischer Bildung? Ergebnisse aus Äquivalenzvergleichen nach dem "Oldenburger Anrechnungsmodell"

Die bildungspolitische Forderung nach verstärkter Durchlässigkeit zwischen beruflicher und akademischer Bildung hat sich in Beschlüssen und Aktivitäten der Kultusministerkonferenz (KMK), der Hochschulrektorenkonferenz (HRK) und des Bundesministeriums für Bildung und Forschung (BMBF) niedergeschlagen, die u.a. eine pauschale Anrechnung beruflicher Fortbildungsqualifikationen auf ein Hochschulstudium vorsehen. Lernergebnisse sollen angerechnet werden, wenn sie "nach Inhalt und Niveau dem Teil eines Studiums gleichwertig sind, der ersetzt werden soll" (KMK 2002). In einem Modellprojekt wurde daher ein quantitatives Instrument zur Bestimmung des Niveaus von Lerneinheiten entwickelt, das bei Äquivalenzvergleichen beruflicher Fortbildungsqualifikationen mit Hochschulstudiengängen Verwendung findet. Aus den bisherigen Ergebnissen dieser Äquivalenzvergleiche kann ein vorläufiges Fazit zur Gleichwertigkeit von Lerneinheiten aus kaufmännischen beruflichen Fortbildungen und wirtschaftswissenschaftlichen Bachelor-Studiengängen gegeben werden

Die pluralen Strukturen der Erwachsenenbildung Zur Geschichte der Erwachsenenbildung in Niedersachsen 1947-1960. Bd. 1-2

In dieser umfassenden historischen Untersuchung wird die gesamte oeffentliche Erwachsenenbildung Niedersachsens einschliesslich der Zusammenarbeit mit den Hochschulen ins Blickfeld gerueckt. Der zweite Band enthaelt zahlreiche Dokumente und ZeitzeugeninterviewsAvailable from http://docserver.bis.uni-oldenburg.de/publikationen/bisverlag/2001/gieplu00/giep lu00.html / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekSIGLEDEGerman

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset