In the context of Computer Science (CS) education, one of the most important goals is the development of computational thinking (CT) and problem solving skills. Computational thinking skills are closely related to the CS concepts and approaches. These skills cover fundamental programming concepts such as task decomposition, abstractions and generalization, data structures and algorithms. On the other hand, CS education challenges, promote the search for new educational tools, methods, activities and resources. Consequently, CS education should be extended by adding the STEM (Science, Technology, Engineering and Mathematics) paradigm principles. The aim of this paper is to show how STEM-driven CS education supports and ensures the development of computational thinking at the high school. We present the already published STEM-driven CS education approach aiming at extending it by introducing a model to assess student achievements, according to the revised Bloom's taxonomy in the connection with CT skills. As a practical implementation, we describe a curriculum of Programming Basics at the high school with respect to STEM-driven aspects and CT skills. We introduce two case studies implemented in the real educational environment. They provide the evidence of availability of our approach.

The paper introduces a novel Generative Learning Object (GLO) model, the Stage-Based Model (SBM) to specify the learning content. New capabilities of the model are the content automatic generation and adaptation. Externally, our model has a similar structure as the known two-level generic models (i.e. metadata and content implementation). The internal structure, however, is quite different in both parts. The use of the external parameterization technology based on pre-programming predefines the internal structure. Furthermore, the structure is derived from the initial parameterized GLO model using the refactoring tool. The technology we use in both models is based on the parameter-function relationship so that to perform manipulations on parameters. Parameters represent metadata, while the relationship implements the content variability by pre-programming the possible changes in advance so that to create the space for adaptation. The SBM implements deep internal staging by allocating parameters and functions (further objects) into predefined stages according to the given context. For example, pedagogically related parameters (objectives, teaching model, etc.) have the highest priority and appear at the top stage while the others - at the remaining stages. Typically, objects in the initial GLO specification are active, i.e. are ready to perform the prescribed role when interpreted by adequate tools. In SBM, the top stage objects are active while the remaining are passive (not able to serve the prescribed role there). The essence of the approach is the stage-based de-activation and activation of the objects within the pre-programmed specification. That ensures the automatic stage-based generation and flexibility for adaptation. In this paper, we analyze the SBM capabilities, present a case study and extended results of using the approach to the robot-oriented teaching in computer science. We also provide the pedagogical evaluation of the approach.

Complexity of Internet of Things IoT applications and difficulty to predict their behaviour in wireless communications make modelling of IoT units an important research topic. The IoT unit is considered here as the two-node IoT model that supports bi-directional wireless communications. There are many approaches to model security and energy awareness; however, little is known about the synergistic effect of those factors at the application level under the influence of environmental factors such as noise. The paper introduces a modelling framework to model the security-energy-environment issues as main attributes to allow defining quality of service QoS for the IoT-based applications. Among others, the healthcare ones are regarded as predominant now. We model IoT units using the feature-based modelling methodology adopted from the software engineering domain. The result of modelling is a set of feature models with valid configurations that describe the energy-security-environment-performance relationships and possible constraints to support various IoT applications. Having feature models, we can select a configuration that is best suited for a given IoT application with respect to QoS requirements. As feature models represent abstract relationships of domain factors, we need additionally to provide experiments to determine concrete values of modelled features. We describe the experimental system to measure energy consumption under the influencing factors. Both models abstract and concrete allow reasoning about QoS of the IoT applications. Copyright © 2016 John Wiley & Sons, Ltd.

The success of learning objects (LO) is limited by the ability to integrate them into coherent teaching courses in order to enable the educational goal-oriented creation of competency. LO sequences must be defined that are adapted to different types of learners, their personal needs and knowledge states to allow for effective personalized learning. In this paper, we propose a method for specification of LO sequences using Sequence Feature Diagrams and describe the generation of LO sequences from Generative LOs using metaprogramming techniques.

The paper presents some research results of the ongoing EU project UNITE (FP6 IST-26964, 2006-2008). One of the distinguishing features of the project is the investigation of sustainability and degree of deployment of projectpsilas results through the creation of the UNITE network of schools (NoS). The UNITE NoS provides a social and environmental basis for the investigation and validation of the UNITE framework, i.e., technological platform, the pedagogical models, and the eLearning scenarios that are created, integrated, populated and used in the UNITE schools.

Design patterns, which encapsulate common solutions to the recurring design problems, have contributed to the increased reuse, quality and productivity in software design. We argue that hardware design patterns could be used for customizing and integrating the Intellectual Property (IP) components into System-on-Chip designs. We formulate the role of design patterns in HW design, and describe their implementation using metaprogramming. We propose a Wrapper design pattern for adapting the behavior of the soft IPs, and demonstrate its application to the communication interface synthesis.

We consider the one- and two-language approaches (1LA & 2LA) for developing generic components (GCs) for VHDL generators. By 1LA & 2LA we mean a generalization using "pure" VHDL, or using the VHDL abstractions mixed with Open PROMOL, the external scripting language we have developed for building GCs and generators, respectively. We present the evaluation of both approaches.