Open Access

„Grappa“ ist eine Middleware, die auf die Anbindung verschiedener Autobewerter an verschiedene E-Learning-Frontends respektive Lernmanagementsysteme (LMS) spezialisiert ist. Ein Prototyp befindet sich seit mehreren Semestern an der Hochschule Hannover mit dem LMS „moodle“ und dem Backend „aSQLg“ im Einsatz und wird regelmäßig evaluiert. Dieser Beitrag stellt den aktuellen Entwicklungsstand von Grappa nach diversen Neu- und Weiterentwicklungen vor. Nach einem Bericht über zuletzt gesammelte Erfahrungen mit der genannten Kombination von Systemen stellen wir wesentliche Neuerungen der moodle-Plugins, welche der Steuerung von Grappa aus moodle heraus dienen, vor. Anschließend stellen wir eine Erweiterung der bisherigen Architektur in Form eines neuentwickelten Grappa-php-Clients zur effizienteren Anbindung von LMS vor. Weiterhin berichten wir über die Anbindung eines weiteren Autobewerters „Graja“ für Programmieraufgaben in Java. Der Bericht zeigt, dass bereits wichtige Schritte für eine einheitliche Darstellung automatisierter Programmbewertung in LMS mit unterschiedlichen Autobewertern für die Studierenden absolviert sind. Die praktischen Erfahrungen zeigen aber auch, dass sowohl bei jeder der Systemkomponenten individuell, wie auch in deren Zusammenspiel via Grappa noch weitere Entwicklungsarbeiten erforderlich sind, um die Akzeptanz und Nutzung bei Studierenden sowie Lehrenden weiter zu steigern.

Hadoop is a Java-based open source programming framework, which supports the processing and storage of large volumes of data sets in a distributed computing environment. On the other hand, an overwhelming majority of organizations are moving their big data processing and storing to the cloud to take advantage of cost reduction – the cloud eliminates the need for investing heavily in infrastructures, which may or may not be used by organizations. This paper shows how organizations can alleviate some of the obstacles faced when trying to make Hadoop run in the cloud.

Several tools to support autograding of student provided SQL statements have already been introduced. The full potential of such tools can only be leveraged, if they extend beyond grading efficiency by also providing tutoring capabilities to the students. With that, tools become really useful by offering self-paced and individually timed learning experiences. In this paper we present an extension for an SQL autograder which improves the hints generated for students in cases where their solution is not entirely correct. Our approach is to compare the student’s solution with the model solution structurally to identify differences between the syntax trees describing the statements. This complements comparing the student’s query with a model solution based on query results. In addition to improving the quality of hints generated for the students, this concept can also be used easily for data manipulation language (DML) or data definition language (DDL) statements, thus extending the applicability of the autograder. Along with details about the concept we present some example hints generated to illustrate the usefulness of the approach. We also report anecdotally on experiences with the system in two different level database courses. Results from different instances of one of them show improvements of student learning as well as student involvement by using the newly generated hints.

Although machine learning (ML) for intrusion detection is attracting research, its deployment in practice has proven difficult. Major hindrances are that training a classifier requires training data with attack samples, and that trained models are bound to a specific network. To overcome these problems, we propose two new methods for anomaly-based intrusion detection. Both are trained on normal-only data, making deployment much easier. The first approach is based on One-class SVMs, while the second leverages our novel Cellwise Estimator algorithm, which is based on multidimensional OLAP cubes. The latter has the additional benefit of explainable output, in contrast to many ML methods like neural networks. The created models capture the normal behavior of a network and are used to find anomalies that point to attacks. We present a thorough evaluation using benchmark data and a comparison to related approaches showing that our approach is competitive.