High-level Interaction Design for Automated GUI Generation and Customization using Conceptual Models kaindl Hermann

Interaction design is considered important for achieving usable user interfaces. In this tutorial, we present our discourse-based approach for the specification of (classes of) dialogues through conceptual models. We also explain a previously identified and published duality of discourse- and task-based interaction design according to these approaches to high-level modeling for GUI generation. In addition, this tutorial demonstrates how graphical user interfaces (GUIs) can be automatically generated from such discourse-based models specifying a high-level interaction design. This generation approach is especially useful when user interfaces tailored for different devices are needed. It is based on model-transformation rules according to the model-driven architecture. This tutorial also shows how customization can be included into the generation itself. It presents our approach for managing model transformation rules for customization of GUIs in the context of their automated generation. Finally, this tutorial presents our approach for including custom widgets already during the automated generation, in order to make the result persistent also in case of re-generation.

Know-How Conceptualization & Analysis Azzam Maraee, Arnon Sturm, and Eric Yu

Know-how, which refers to the practical knowledge that connects desired objectives to actions, is a crucial foundation for today’s advanced technological society. As more new know-how is constantly being created, methods and techniques are needed for organizing, visualizing, understanding, analyzing, and applying know-how. In this tutorial, we provide an overview of approaches for addressing these needs, and elaborate on one specific approach – ME-map. We illustrate the approach and our experiences with examples from a number of areas in information system engineering. We further provide a formalization of the approach and analysis challenges.

Multilevel Modelling with the FMMLx and the Xmodeler Tony Clark and Ulrich Frank

Models are a key part of information system engineering and must be supported by technologies and approaches that foster reuse and standardisation whilst achieving a high degree of flexibility that meet the needs to a range of stakeholders. Abstraction and language engineering are both key to achieving these goals and there is an increasing interest in developing meta-modelling approaches that support patterns of abstraction and can be used to define domain specific modelling languages. The integration of these two approaches naturally leads to a requirement for meta-models to be integrated with conventional modelling to achieve a highly flexible framework where concepts and meta-concepts can be freely mixed. Such integration is termed Multilevel Modelling that raises new possibilities and opportunities for modelling in terms of its foundational principles, methodologies, and supporting technologies.

The goals of this tutorial are:

1. To motivate the need for Multilevel modelling as an emerging field and to provide definitions and foundational concepts.
2. To introduce a language that can be used to express Multilevel models and to use the language to introduce methodological concepts.
3. To introduce a technology for Multilevel modelling based on the XModeler toolkit.
4. To demonstrate the utility of Multilevel modelling through examples.

The Xmodeler is an advanced multilevel modelling and programming tool. Like other multilevel modelling tools it allows for creating models with an arbitrary number of classification levels. It also supports deep instantiation. Unlike most other tools, the Xmodeler features a common representation of models and code, which enables the execution of models without prior code generation. It also supports the implementation of multilevel DSMLs. Hence, the participants will learn to use a language engineering facility that enables a new generation of application systems, which are integrated with the models they are based on at run-time. Users of these systems are empowered to navigate the conceptual foundation of the software they use, and if needed, adapt it to changing requirements.

On paradoxes, robots and autonomous systems - conceptual model or losing control? Opher Etzion

Autonomous systems become an increasing part of the state of the practice. Driverless cars, autonomous systems within the human body, agriculture artificial workers and many others are emerging and changing life, as we have known it.
The planning and development of such systems is a blend between more traditional planning of intelligent systems, and self-adaptation of the system in a manner that its planner can neither predict nor understand.
In the tutorial we’ll discuss the conceptual components of autonomous systems, will look at examples in different areas of life, discuss the major dilemmas and see how the dilemma start with  the notion of paradox. 
The tutorial will also look at risks and dilemmas.

Teaching Conceptual Modelling: How can I improve? Monique Snoeck, Estefanía Serral Asensio, and Daria Bogdanova

Learning conceptual modeling (CM) is very hard for the simple reason that CM is a complex learning task, i.e., multiple solutions exist for a single problem and multiple paths exist to arrive at a single solution. Students are therefore often unsure, asking their teachers for feedback: “Am I doing it right?”. But also teachers are often wondering about the best approach to teach CM, wondering if they are doing it right.

This is not surprising, as teaching CM requires teaching a wide variety of skills ranging from “simple” skills, such as the ability to remember a modelling notation, to “complex” skills, such as the ability to translate a text description into a representative conceptual model. Determining the most adequate teaching method requires proper scaffolding and integration of learning goals and the use of timely feedback, and is therefore far from evident.

The aim of this tutorial is to provide the attendants with:
– an understanding of how conceptual modelling learning outcomes can be scaffolded based on Bloom’s taxonomy;
– a number of examples of assessments (exercises, quizzes and questions) to test CM knowledge at the different levels of this taxonomy;
– an overview of different types of feedback, and how these can be used to sustain the learning process.

We target a broad audience (faculty staff, professional industry educators, practitioners, researchers, students) that want to obtain/improve their skills in the domain of teaching conceptual modeling. Basic knowledge of conceptual modeling with ER or UML is required to attend the tutorial.

The tutorial will make use of a mix of lecturing (using powerpoint and beamer) and interactions with the participants using their smartphone, tablet or PC. The material used for the tutorial will be available to the participants as a free download.