Möglichkeiten und Grenzen des Distance Learning am Beispiel des Alberta Distance Learning Centre in Kanada : Analyse und Transfer auf den Schulkontext Sachsen-Anhalts

Distance learning, Canadian educational system, institution, medial learning, school system Saxony-Anhalt, system theory, qualitative research

Proactive ontology-based content provision in the context of e-learning

Magdeburg, Univ., Fak. für Informatik, Diss., 2008

Document-oriented e-learning components

Magdeburg, Univ., Fak. für Informatik, Diss., 2009

Robust learning in safety-related domains : machine learning methods for solving safety-related application problems

Magdeburg, Univ., Fak. für Informatik, Diss., 2009

Ontology learning from semi-structured Web documents

Magdeburg, Univ., Fak. für Informatik, Diss., 2010

Machine learning with Lipschitz classifiers

Magdeburg, Univ., Fak. für Elektrotechnik und Informationstechnik, Diss., 2010

Implicit sequence learning in recurrent neural networks

Magdeburg, Univ., Fak. für Elektrotechnik, Diss., 2013

Auditory gating in the ventral striatum and auditory cortex : the role of stimulus-locking and the influence of discrimination learning

Magdeburg, Univ., Fak. für Naturwiss., Diss., 2014

Wissenstransfer durch Bewegtbild : Entwicklung einer Entscheidungsmatrix für den Einsatz verschiedener Formen des Bewegtbildes im E-Learning

[s.c.]

Sammlung von Ressourcen für ein E-Learning-Modul zur Terminologielehre und Terminologieverwaltung

Im Rahmen dieser vorliegenden Arbeit wurde im Internet nach Ressourcen für ein E-Learnind-Modul "Terminologielehre und Terminologieverwaltung" rechercheiert. In der Arbeit werden 19 Ressourcen nach Arten von z. B. elektronischen Büchern, online Präsentationen, Tutorial-Filmen, Online-Glossaren oder Software-Anwendungen für Terminologieverwaltung gesammelt. Das Ziel dieser Arbeit ist es, dass Menschen, die sich eine Grundkompetenz in der Terminologielehre und Terminologieverwaltung aneignen wollen, mit den Materialien lernen und üben können. Einige Socftwareanwendungen werden vorgestellt, mit denen man selbst Terminologiebestände bearbeiten oder Terminologiedatenbanken erstellen kann.

Blended learning - die neue Rolle der Ausbilder

Anja Schulz & Marcel Martsch

Associative learning in wild Anastrepha obliqua females (Diptera, Tephritidae) related to a protein source

The aim of the present study was to determine whether wild adult Anastrepha obliqua (Macquart, 1835) females are able to associate a compound (quinine sulphate - QS) not related to their habitual diet with a protein-enriched food. Females were first fed on diets based on brewer yeast and sucrose containing or not QS. The groups were then allowed to choose between their original diets and a diet with or without QS, depending on the previous treatment, and between a diet based on agar and a diet containing agar and QS. When the nutritional value of the diets was adequate, the females did not show any preference for the diet with or without QS. With respect to the agar diet and the agar + QS diet, females previously fed on a nutritive diet containing QS preferred the diet containing QS, indicating an association between the compound and the nutritional value of the diet. The importance of this behavioral strategy is discussed.

Consumers' behavior and the Bertrand paradox: an ACE approach

We analyze the classical Bertrand model when consumers exhibit some strategic behavior in deciding from which seller they will buy. We use two related but different tools. Both consider a probabilistic learning (or evolutionary) mechanism, and in the two of them consumers' behavior in uences the competition between the sellers. The results obtained show that, in general, developing some sort of loyalty is a good strategy for the buyers as it works in their best interest. First, we consider a learning procedure described by a deterministic dynamic system and, using strong simplifying assumptions, we can produce a description of the process behavior. Second, we use nite automata to represent the strategies played by the agents and an adaptive process based on genetic algorithms to simulate the stochastic process of learning. By doing so we can relax some of the strong assumptions used in the rst approach and still obtain the same basic results. It is suggested that the limitations of the rst approach (analytical) provide a good motivation for the second approach (Agent-Based). Indeed, although both approaches address the same problem, the use of Agent-Based computational techniques allows us to relax hypothesis and overcome the limitations of the analytical approach.

User-friendly parallel computations with econometric examples

This paper shows how a high level matrix programming language may be used to perform Monte Carlo simulation, bootstrapping, estimation by maximum likelihood and GMM, and kernel regression in parallel on symmetric multiprocessor computers or clusters of workstations. The implementation of parallelization is done in a way such that an investigator may use the programs without any knowledge of parallel programming. A bootable CD that allows rapid creation of a cluster for parallel computing is introduced. Examples show that parallelization can lead to important reductions in computational time. Detailed discussion of how the Monte Carlo problem was parallelized is included as an example for learning to write parallel programs for Octave.

Forward induction and the excess capacity puzzle: An experimental investigation.

While the theoretical industrial organization literature has long argued that excess capacity can be used to deter entry into markets, there is little empirical evidence that incumbent firms effectively behave in this way. Bagwell and Ramey (1996) propose a game with a specific sequence of moves and partially-recoverable capacity costs in which forward induction provides a theoretical rationalization for firm behavior in the field. We conduct an experiment with a game inspired by their work. In our data the incumbent tends to keep the market, in contrast to what the forward induction argument of Bagwell and Ramey would suggest. The results indicate that players perceive that the first mover has an advantage without having to pre-commit capacity. In our game, evolution and learning do not drive out this perception. We back these claims with data analysis, a theoretical framework for dynamics, and simulation results.