Der Rückblick zum TEWI-Kolloquium von Mordechai Ben-Ari am 01.10.2012 beinhaltet die Videoaufzeichnung:

Video

[iframe height=“350″ src=“https://video.aau.at/video.php?video=ftf_ben-ari.mp4″]

Abstract: Learning programming is a difficult task and many students fail to complete introductory computer science courses. The talk will describe our research into two approaches to improve learning of programming. Scratch is a visual programming environment intended for young people. They construct programs by dragging-and-dropping blocks labeled with commands and operations; the programs control the animation of sprites which provides a motivating context. We found that even middle-school students (age group 12-14) are capable of developing non-trivial software and, furthermore, they find it easier to learn professional programming languages when they reach secondary school. However, Scratch can cause students to develop bad programming habits that may be difficult to overcome and teachers must ensure that this doesn’t happen.

The other approach is to visualize the execution of programs written as text in professional programming languages. The Jeliot program animation system automatically generates detailed animations of programs written in the Java. Jeliot significantly facilitates learning because it provides a graphic display of the dynamic aspects of program execution that are hidden within the computer. An investigation into the use of Jeliot by secondary-school teachers showed a wide range of engagement, from full integration into the teaching practice to rejection caused by psychological factors.

Short CV: Mordechai (Moti) Ben-Ari is a full professor in the Department of Science Teaching of the Weizmann Institute of Science, where he heads the computer science education group. He is the author of numerous textbooks, including Principles of Concurrent and Distributed Computation, and Mathematical Logic for Computer Science. His group, in collaboration with the University of Eastern Finland, developed the Jeliot program animation system. In 2004, he received the ACM/SIGCSE Award for Outstanding Contributions to Computer Science Education, and in 2009 he was elected as a Distinguished Educator of the ACM.

Der Rückblick zum TEWI-Kolloquium von Aadu Ott am 16.10.2012 beinhaltet die Videoaufzeichnung sowie die Folien:

Video

[iframe height=“350″ src=“https://video.aau.at/video.php?video=ftf_ott.mp4″]

Pictures

Abstract: This presentation will discuss questions like: “Why introduce neurodidactics?” “Why now?” “What will the consequences be?” A synthesis between pedagogy, psychology, powerful computers, molecular biology and neuroscience has resulted in development of different scanning technologies, which are based on modern physics. This has led to a paradigmatic change from external observations of human behaviour to internal studies of information processing, in vivo, in the human brain. “Will next generation of curricula for education have an underpinning in neurodidactics?” “Will neurodidactics change pedagogy from art to science?”

Short-Bio: Aadu Ott is professor emeritus in Science and Technology Education at the University of Göteborg in Sweden. He is dozent in physics, but has mainly been active in the field of education, including compulsory schools and teacher training. He has during 30 years cooperated with Deutsches Museum in München with in service training of teachers in the History of Technology. He has also done research on neurodidactics using fMRI equipment and has had contact with ZNL, TransferCenter für Neurowissenschaft und lernen in Ulm. The last two years he has worked with the National Agency for Higher Education within a reform program for teacher education. He is now active in a program at Chalmers University of Technology which aims at creating a new kind of courses for students in technology. These students will, after five years of studies, receive a combination exam, including civil engineering and a teacher diploma.

Electromechanical sensors and actuators are important components appearing in all disciplines of engineering. Transducer is a commonly used generic term for those components. In this respect, two important classes of materials are discussed: firstly, ferroelectric and piezoelectric materials, and secondly, magnetic and magnetostrictive materials. In order to predict a transducer’s behavior, the utilized materials have to be characterized precisely. The two most decisive aspects in characterization of those materials are the physical models used to describe the material behavior as well as the measurement methods for the determination of the model parameters. Both aspects are closely linked and, therefore, have to be taken into account simultaneously.

This talk starts with an introduction of the material classes and their properties. The use of these materials for well known applications like piezoelectric stack actuators, ultrasonic transducers and electrical power transformers is presented. Furthermore, the talk highlights the most modern fields of applications like magnetostrictive thin film micro-actuators for micro electromechanical systems (MEMS) and piezoelectric smart materials. After this overview, the talk focuses on magnetic materials, especially on magnetic large signal hysteresis effects. The scalar Preisach model is discussed and compared to other approaches. The motivation for its vectorial extension is given. The methods and measurement setups for parameter identification are explained, with focus on the Vector Vibrating Sample Magnetometer. The talk closes with an outlook on future applications of large signal models in numerical simulation and control engineering.

Sensoren stellen die Schnittstellen zwischen der ”realen physikalischen” und der elektrischen/elektronischen (und digitalen) Welt dar. Nicht zuletzt aufgrund der stets steigenden Komplexität technischer Systeme nehmen nicht nur die Anzahl und die Aufgaben von Sensoren zu, sondern auch die Anforderungen an diese hinsichtlich Genauigkeit, Zuverlässigkeit und Sicherheit. Daneben sind auch zahlreiche weitere Anforderungen wie geringer Platz- und Energiebedarf oder geringe Kosten zu erfüllen

Um diesen hohen Anforderungen gerecht zu werden, werden oftmals „intelligente Sensoren“ eingesetzt, die neben einem (oder mehreren) Primärsensor(en) auch analoge und digitale Signalaufbereitung, Hard- und Software für Signalverarbeitung und Eigendiagnose sowie drahtgebundene oder drahtlose Kommunikationseinrichtungen aufweisen. Mitunter beinhalten derartige Sensoren eigene „Kraftwerke“, um sich aus der Umgebung mit Energie zu versorgen („Energy Harvesting“) und somit völlig autark arbeiten zu können. Für den Entwurf derartiger Systeme werden Methoden benötigt, die es den Designern ermöglichen, die Komplexität zu beherrschen.

In dem Vortrag werden Ansätze für den modellbasierten Design- und Optimierungsprozess von intelligenten Sensoren vorgestellt. Dabei spielen insbesondere die Themen Messunsicherheit, Zuverlässigkeit und Sicherheit eine große Rolle. Ein wesentlicher Aspekt ist die Berücksichtigung möglicher Störeinflüsse (Herstellungstoleranzen, Rauschen, Elektromagnetische Störer, Defekte, …) und deren Auswirkung während der gesamten Entwurfphase. Diese Vorgehensweise führt einerseits zu robusten Designs und erlaubt andererseits eine frühzeitige Erkennung von kritischen Faktoren, wodurch Fehlentwicklungen vermieden werden können