Schlüsselqualifikationen im Hochschulbereich

Ausgehend von dem von Friedrich Edding geprägten Begriff der Schlüsselfähigkeit entwickelte Dieter Mertens Anfang der 1970er Jahre sein Konzept der Schlüsselqualifikationen. Damit suchte er Wege, Menschen so zu qualifizieren, dass sie ihnen übertragene berufliche Aufgaben, auch in einem sich rasch verändernden Umfeld, bewältigen können. In der vorliegenden Arbeit wird gezeigt, dass die Rezeption des Begriffs in verschiedenen Bildungsbereichen mit unterschiedlichen Intensitäten und Resultaten verlief. Am folgenreichsten war die Rezeption in der Berufsbildung. Von wenigen skeptischen Stimmen abgesehen, stieß das Konzept der Schlüsselqualifikationen auf positive Zustimmung und mehrere Umsetzungsversuche wurden unternommen. Diese führten allerdings zu einer Verlängerung der Liste der Schlüsselqualifikationen (die ursprünglich von Mertens als abschließbar angedacht war). Ein Konsens, was in der Berufsbildung als Schlüsselqualifikationen zu gelten hat, ist immer noch nicht in Sicht. In den allgemeinbildenden Schulen hingegen fand das Konzept keine große Beachtung. Zwar wurde hin und wieder auf den Begriff zurückgegriffen, um beispielsweise allgemein verbindliche Standards in der Schule zu thematisieren oder neuen Inhalten eine Legitimation zu verschaffen, dennoch griff die Debatte in der Schulpädagogik nicht nachhaltig. Gründe dafür liegen zum einen in der dem Konzept inhärenten berufsvorbereitenden Funktion, die der Idealvorstellung einer zweckfreien Bildung widerspricht, zum anderen in der relativ straffen und zentralisierten Gestaltung der Lehrpläne. Die vorliegende Arbeit setzt sich mit dem Konzept der Schlüsselqualifikationen im Hochschulbereich auseinander. Eine Untersuchung von ca. 130 deutsch- und englischsprachigen Arbeiten, die Schlüsselqualifikationen im Hochschulbereich zum Thema haben, belegt eine sehr große Heterogenität der zugrunde liegenden Vorstellungen und konkreten Modelle. Es wird gezeigt, dass die zwei wichtigsten Ordnungsschemata, die gelegentlich zur Abgrenzung der Schlüsselqualifikationen gegenüber anderen Bildungskomponenten herangezogen werden (nämlich die Taxonomie der Lernziele von Bloom und das Handlungskompetenzmodell von Roth in der Weiterentwicklung von Reetz) mit keinem kohärenten Rahmenwerk, das der Fülle der Modelle von Schlüsselqualifikationen im Hochschulbereich gerecht wäre, aufwarten können. Eine Alternative bietet eine diskursanalytische Perspektive foucaultscher Prägung. Begriffen als eine diskursive Formation, haben Modelle der Schlüsselqualifikationen ihre Gemeinsamkeit nicht in dem vermeintlich gemeinsamen Gegenstand. Demnach sind Schlüsselqualifikationen in der heutigen Hochschuldebatte keine Qualifikationen suis generis, die eine eigene Kategorie bilden, sondern eine Antwort auf die Herausforderungen, die die verschiedenartigen Veränderungen in und um Hochschulen mit sich bringen. Es lassen sich drei Kontexte identifizieren, in denen die Modelle der Schlüsselqualifikationen der Herausforderung zu begegnen versuchen: in der Gesellschaft im Allgemeinen, in der vor-universitären Bildung sowie in der Hochschulbildung und in der Berufswelt. In diesen Kontexten artikulieren die Modelle der Schlüsselqualifikationen verschiedene Dimensionen, um Gesellschafts-, Studier und Beschäftigungsfähigkeit der Studierenden zu fördern. Eine vergleichende Analyse der Debatten und Modelle der Schlüsselqualifikationen in der BRD und in England zeigt, dass diese drei Kontexte in beiden Ländern vorfindbar sind, jedoch deren inhaltliche Vorstellung und konkrete Umsetzung aufgrund der Partikularitäten des jeweiligen Hochschulsystems unterschiedliche Akzentuierungen erfahren. Anders als in der BRD betonen die Modelle der Förderung der Studierfähigkeit in England die Brückenkurse als Hilfestellung bei der Vorbereitung auf das Studium. Das hängt mit den uneinheitlichen Zugangsregelungen zum Hochschulstudium und der hierarchischen Struktur im englischen Hochschulsystem zusammen. Bei der Förderung der Beschäftigungsfähigkeit setzen die Modelle der Schlüsselqualifikationen in Deutschland, wo traditionell ein Hochschulstudium als berufsvorbereitend angesehen wird, den Akzent auf Praxisbezug des Studiums und auf Flexibilität. In England hingegen, wo sich das Studium per se nicht als berufsqualifizierend versteht, rücken die Modelle der Schlüsselqualifikationen den Übergang in den Arbeitsmarkt und das Karrieremanagement - subsumiert unter dem Konzept der employability - in den Vordergrund.

3000 / futuristische Phantasmen und aktuelle Fantasien der Technokultur

Die Kernthese dieser Arbeit ist eine Leerstelle. Ihre Formulierung beruht auf einer Beobachtung, wie sie von William Gibson stammen könnte: kulturell avantgardistische Gruppen der Gesellschaft internalisieren für ihre technologisch gestützten Praktiken futuristische Phantasmen geschichtlicher Vorläufer, die in Literatur und Mediengeschichte detektivisch aufspürbar sind. Werden diese Verinnerlichungen in aktuelle Fantasien umgewandelt, entsteht eine hybride Mischung vielfältigster Beschäftigungen, Reflexionen und Entwürfe von Technokultur. Bringt man diese Kopplungen auf das Jahr 3000, die nächste epochale zukünftige Zäsur, wird die Absurdität des Projekts, Technokultur überhaupt zu thematisieren deutlich. Technokultur ist im dynamischen Wandel sozio-ikonografisch untersuchbar, wird aber durch Unschärferelation zum object trouvé und Triebmoment für eine in es selbst hinein assimilierbare Analyse. Der daraus folgenden Vermessenheit kann durch instrumentelle Serendipity begegnet werden, die hier nicht Effekt wäre, stattdessen als Methode Verwendung findet: Finden statt Suchen. Das verhältnismäßig neue Schreib/Lese-Medium Hypertext bietet sich dafür als geradezu prädestiniert an. Hypertext ist prinzipiell unabgeschlossen, er folgt hier Arbeitsprinzipien wie sie seit den frühen 1990ern in Online-Tagebüchern und seit den frühen 2000er Jahren in Weblogs (World Wide Web Logbooks) auszumachen sind: Notizen, Found Text (analog zu Found Footage), Zitate, Fragmente, die kurze Form, kurz: wissenschaftliche Alltagstextproduktion wird nach Ordnungskriterien a-systematisiert und verwoben - weniger archiviert denn gesammelt. Eine Art Second Hand Theorie entsteht.

Hierarchical Object Recognition Using Libraries of Parameterized Model Sub-Parts

This thesis describes the development of a model-based vision system that exploits hierarchies of both object structure and object scale. The focus of the research is to use these hierarchies to achieve robust recognition based on effective organization and indexing schemes for model libraries. The goal of the system is to recognize parameterized instances of non-rigid model objects contained in a large knowledge base despite the presence of noise and occlusion. Robustness is achieved by developing a system that can recognize viewed objects that are scaled or mirror-image instances of the known models or that contain components sub-parts with different relative scaling, rotation, or translation than in models. The approach taken in this thesis is to develop an object shape representation that incorporates a component sub-part hierarchy- to allow for efficient and correct indexing into an automatically generated model library as well as for relative parameterization among sub-parts, and a scale hierarchy- to allow for a general to specific recognition procedure. After analysis of the issues and inherent tradeoffs in the recognition process, a system is implemented using a representation based on significant contour curvature changes and a recognition engine based on geometric constraints of feature properties. Examples of the system's performance are given, followed by an analysis of the results. In conclusion, the system's benefits and limitations are presented.

Enhancing the Dexterity of a Robot Hand Using Controlled Slip

Humans can effortlessly manipulate objects in their hands, dexterously sliding and twisting them within their grasp. Robots, however, have none of these capabilities, they simply grasp objects rigidly in their end effectors. To investigate this common form of human manipulation, an analysis of controlled slipping of a grasped object within a robot hand was performed. The Salisbury robot hand demonstrated many of these controlled slipping techniques, illustrating many results of this analysis. First, the possible slipping motions were found as a function of the location, orientation, and types of contact between the hand and object. Second, for a given grasp, the contact types were determined as a function of the grasping force and the external forces on the object. Finally, by changing the grasping force, the robot modified the constraints on the object and affect controlled slipping slipping motions.

The Use of Grouping in Visual Object Recognition.

The report describes a recognition system called GROPER, which performs grouping by using distance and relative orientation constraints that estimate the likelihood of different edges in an image coming from the same object. The thesis presents both a theoretical analysis of the grouping problem and a practical implementation of a grouping system. GROPER also uses an indexing module to allow it to make use of knowledge of different objects, any of which might appear in an image. We test GROPER by comparing it to a similar recognition system that does not use grouping.

Statistical Object Recognition

Two formulations of model-based object recognition are described. MAP Model Matching evaluates joint hypotheses of match and pose, while Posterior Marginal Pose Estimation evaluates the pose only. Local search in pose space is carried out with the Expectation--Maximization (EM) algorithm. Recognition experiments are described where the EM algorithm is used to refine and evaluate pose hypotheses in 2D and 3D. Initial hypotheses for the 2D experiments were generated by a simple indexing method: Angle Pair Indexing. The Linear Combination of Views method of Ullman and Basri is employed as the projection model in the 3D experiments.

An Analysis of the Effect of Gaussian Error in Object Recognition

Object recognition is complicated by clutter, occlusion, and sensor error. Since pose hypotheses are based on image feature locations, these effects can lead to false negatives and positives. In a typical recognition algorithm, pose hypotheses are tested against the image, and a score is assigned to each hypothesis. We use a statistical model to determine the score distribution associated with correct and incorrect pose hypotheses, and use binary hypothesis testing techniques to distinguish between them. Using this approach we can compare algorithms and noise models, and automatically choose values for internal system thresholds to minimize the probability of making a mistake.

Predicate Dispatching in the Common Lisp Object System

I have added support for predicate dispatching, a powerful generalization of other dispatching mechanisms, to the Common Lisp Object System (CLOS). To demonstrate its utility, I used predicate dispatching to enhance Weyl, a computer algebra system which doubles as a CLOS library. My result is Dispatching-Enhanced Weyl (DEW), a computer algebra system that I have demonstrated to be well suited for both users and programmers.

Object Recognition with Pictorial Structures

This thesis presents a statistical framework for object recognition. The framework is motivated by the pictorial structure models introduced by Fischler and Elschlager nearly 30 years ago. The basic idea is to model an object by a collection of parts arranged in a deformable configuration. The appearance of each part is modeled separately, and the deformable configuration is represented by spring-like connections between pairs of parts. These models allow for qualitative descriptions of visual appearance, and are suitable for generic recognition problems. The problem of detecting an object in an image and the problem of learning an object model using training examples are naturally formulated under a statistical approach. We present efficient algorithms to solve these problems in our framework. We demonstrate our techniques by training models to represent faces and human bodies. The models are then used to locate the corresponding objects in novel images.

Compact Representations for Fast Nonrigid Registration of Medical Images

We develop efficient techniques for the non-rigid registration of medical images by using representations that adapt to the anatomy found in such images. Images of anatomical structures typically have uniform intensity interiors and smooth boundaries. We create methods to represent such regions compactly using tetrahedra. Unlike voxel-based representations, tetrahedra can accurately describe the expected smooth surfaces of medical objects. Furthermore, the interior of such objects can be represented using a small number of tetrahedra. Rather than describing a medical object using tens of thousands of voxels, our representations generally contain only a few thousand elements. Tetrahedra facilitate the creation of efficient non-rigid registration algorithms based on finite element methods (FEM). We create a fast, FEM-based method to non-rigidly register segmented anatomical structures from two subjects. Using our compact tetrahedral representations, this method generally requires less than one minute of processing time on a desktop PC. We also create a novel method for the non-rigid registration of gray scale images. To facilitate a fast method, we create a tetrahedral representation of a displacement field that automatically adapts to both the anatomy in an image and to the displacement field. The resulting algorithm has a computational cost that is dominated by the number of nodes in the mesh (about 10,000), rather than the number of voxels in an image (nearly 10,000,000). For many non-rigid registration problems, we can find a transformation from one image to another in five minutes. This speed is important as it allows use of the algorithm during surgery. We apply our algorithms to find correlations between the shape of anatomical structures and the presence of schizophrenia. We show that a study based on our representations outperforms studies based on other representations. We also use the results of our non-rigid registration algorithm as the basis of a segmentation algorithm. That algorithm also outperforms other methods in our tests, producing smoother segmentations and more accurately reproducing manual segmentations.

A Note on Object Class Representation and Categorical Perception

We present a novel scheme ("Categorical Basis Functions", CBF) for object class representation in the brain and contrast it to the "Chorus of Prototypes" scheme recently proposed by Edelman. The power and flexibility of CBF is demonstrated in two examples. CBF is then applied to investigate the phenomenon of Categorical Perception, in particular the finding by Bulthoff et al. (1998) of categorization of faces by gender without corresponding Categorical Perception. Here, CBF makes predictions that can be tested in a psychophysical experiment. Finally, experiments are suggested to further test CBF.

A Trainable Object Detection System: Car Detection in Static Images

This paper describes a general, trainable architecture for object detection that has previously been applied to face and peoplesdetection with a new application to car detection in static images. Our technique is a learning based approach that uses a set of labeled training data from which an implicit model of an object class -- here, cars -- is learned. Instead of pixel representations that may be noisy and therefore not provide a compact representation for learning, our training images are transformed from pixel space to that of Haar wavelets that respond to local, oriented, multiscale intensity differences. These feature vectors are then used to train a support vector machine classifier. The detection of cars in images is an important step in applications such as traffic monitoring, driver assistance systems, and surveillance, among others. We show several examples of car detection on out-of-sample images and show an ROC curve that highlights the performance of our system.

View-Based Strategies for 3D Object Recognition

A persistent issue of debate in the area of 3D object recognition concerns the nature of the experientially acquired object models in the primate visual system. One prominent proposal in this regard has expounded the use of object centered models, such as representations of the objects' 3D structures in a coordinate frame independent of the viewing parameters [Marr and Nishihara, 1978]. In contrast to this is another proposal which suggests that the viewing parameters encountered during the learning phase might be inextricably linked to subsequent performance on a recognition task [Tarr and Pinker, 1989; Poggio and Edelman, 1990]. The 'object model', according to this idea, is simply a collection of the sample views encountered during training. Given that object centered recognition strategies have the attractive feature of leading to viewpoint independence, they have garnered much of the research effort in the field of computational vision. Furthermore, since human recognition performance seems remarkably robust in the face of imaging variations [Ellis et al., 1989], it has often been implicitly assumed that the visual system employs an object centered strategy. In the present study we examine this assumption more closely. Our experimental results with a class of novel 3D structures strongly suggest the use of a view-based strategy by the human visual system even when it has the opportunity of constructing and using object-centered models. In fact, for our chosen class of objects, the results seem to support a stronger claim: 3D object recognition is 2D view-based.

Fast Object Recognition in Noisy Images Using Simulated Annealing

A fast simulated annealing algorithm is developed for automatic object recognition. The normalized correlation coefficient is used as a measure of the match between a hypothesized object and an image. Templates are generated on-line during the search by transforming model images. Simulated annealing reduces the search time by orders of magnitude with respect to an exhaustive search. The algorithm is applied to the problem of how landmarks, for example, traffic signs, can be recognized by an autonomous vehicle or a navigating robot. The algorithm works well in noisy, real-world images of complicated scenes for model images with high information content.

A Formulation for Active Learning with Applications to Object Detection

We discuss a formulation for active example selection for function learning problems. This formulation is obtained by adapting Fedorov's optimal experiment design to the learning problem. We specifically show how to analytically derive example selection algorithms for certain well defined function classes. We then explore the behavior and sample complexity of such active learning algorithms. Finally, we view object detection as a special case of function learning and show how our formulation reduces to a useful heuristic to choose examples to reduce the generalization error.