The Origins of Intelligence in Children

A key aspect of variability management in software product families is the explicit representation of the variability. Experiences at several industrial software development companies have shown that a software variability model should do four things: (1) uniformly represent variation points as first-class entities in all abstraction layers (ranging from features to code), (2) allow for the hierarchical organization of the variability, (3) allow for the first-class representation of simple (i.e., one-to-one) and complex (i.e., n-to-m) dependencies, and (4) allow for modeling the relations between dependencies. Existing variability modeling approaches support the first two requirements, but lack support for the latter two. The contribution of this paper is a framework for variability modeling-COVAMOF-that provides support for all four requirements.

COVAMOF: A framework for modeling variability in software product families

Nowadays, companies are demanding better‐prepared professionals to succeed in a digital society, and the acquisition of Science, Technology, Engineering, Arts, and Mathematics (STEAM)‐related competencies is a key issue. One of the main problems in this sense is how to integrate STEAM into the current educational curricula. This is not something related to a subject or educational trend but rather to new methodological approaches that can engage students. In this sense, such active methodologies that apply mechatronics and robotics could be an interesting path to pursue. Given this context, the first necessary task in evaluating the potential of this approach is to understand the landscape of the application of robotics and mechatronics in STEAM Education and how active methodologies are applied in this sense. To carry out this analysis in a systematic and replicable manner, it is necessary to follow a methodology. In this case, the researchers employ a systematic mapping review. This paper presents this process and its main findings. Fifty‐four studies have been selected out of 242 total studies analyzed. From these, beyond obtaining a clear vision of the STEAM landscape regarding project topics, we can also conclude that robotics and physical devices have been applied successfully with collaborative methodologies in STEAM Education. Regarding conclusions, this paper shows that robotics and mechatronics applied with active methodologies is to be a good means to engage students in STEAM disciplines and thus aid the acquisition of what is commonly known as “21st‐century skills.”

Fostering STEAM through challenge‐based learning, robotics, and physical devices: A systematic mapping literature review

Meta-Programming and Model-Driven Meta-Program Development: Principles, Processes and Techniques presents an overall analysis of meta-programming, focusing on insights of meta-programming techniques, heterogeneous meta-program development processes in the context of model-driven, feature-based and transformative approaches. The fundamental concepts of meta-programming are still not thoroughly understood, in this well organized book divided into three parts the authors help to address this. Chapters include: Taxonomy of fundamental concepts of meta-programming; Concept of structural heterogeneous meta-programming based on the original meta-language; Model-driven concept and feature-based modeling to the development process of meta-programs; Equivalent meta-program transformations and metrics to evaluate complexity of feature-based models and meta-programs; Variety of academic research case studies within different application domains to experimentally verify the soundness of the investigated approaches. Both authors are professors at Kaunas University of Technology with 15 years research and teaching experience in the field. Meta-Programming and Model-Driven Meta-Program Development: Principles, Processes and Techniques is aimed at post-graduates in computer science and software engineering and researchers and program system developers wishing to extend their knowledge in this rapidly evolving sector of science and technology.

Meta-Programming and Model-Driven Meta-Program Development: Principles, Processes and Techniques

Feature models have been used since the 90s to describe software product lines as a way of reusing common parts in a family of software systems. In 2010, a systematic literature review was published summarizing the advances and settling the basis of the area of automated analysis of feature models (AAFM). From then on, different studies have applied the AAFM in different domains. In this paper, we provide an overview of the evolution of this field since 2010 by performing a systematic mapping study considering 423 primary sources. We found six different variability facets where the AAFM is being applied that define the tendencies: product configuration and derivation; testing and evolution; reverse engineering; multi-model variability-analysis; variability modelling and variability-intensive systems. We also confirmed that there is a lack of industrial evidence in most of the cases. Finally, we present where and when the papers have been published and who are the authors and institutions that are contributing to the field. We observed that the maturity is proven by the increment in the number of journals published along the years as well as the diversity of conferences and workshops where papers are published. We also suggest some synergies with other areas such as cloud or mobile computing among others that can motivate further research in the future.

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Automated analysis of feature models: Quo vadis?

The paper’s contribution is a methodology that integrates two educational technologies (GLO and LEGO robot) to teach Computer Science (CS) topics at the school level. We present the methodology as a framework of 5 components (pedagogical activities, technology driven processes, tools, knowledge transfer actors, and pedagogical outcomes) and interactions among the components. GLOs are meta-programmed entities to generate LO instances on demand depending on the context of use and learning objectives. A GLO is a black-box entity, which is integrated in the framework through the generating process to source the teaching and learning process via robot-based visualization to demonstrate how programs and algorithms are transformed into realworld tasks and processes. The methodology is tested in the real e-learning setting. The pedagogical outcomes are evaluated by empirical data showing the increase of student engagement level, higher flexibility and reuse enhancement in learning.

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Teaching of Computer Science Topics Using Meta-Programming-Based GLOs and LEGO Robots

The paper introduces context-aware Generative Learning Objects (GLOs) to support wide-scale reusability and automaticadaptability in teaching Computer Science (CS) topics. We introduce the theoretical background of the approach to supportadaptation through transformations based on the principles of Design-for-Reuse and Design-with-Reuse, which are well known inSoftware Engineering (SWE), and validate the approach in the robotics-based CS course. We automatically adapt GLOs to thespecific context of teaching using transformation tools (refactoring tool and meta-language processor) to transform a set ofcontext-aware GLOs into usable LOs within the educational Arduino robotics environment.

Context-Aware Generative Learning Objects for Teaching Computer Science*

We present the constructionism-based approach towards using collaborating educational robots for teaching the abstract concepts of Computer Science (CS) such as task decomposition. We present the collaborative robot-based e-learning environment, which enables us to implement the principles of constructionism and collaborative learning for teaching students how to solve CS problems using LEGO multi-robots as tangible Collaborative Learning Objects (CLOs). We extended the existing approaches by a) providing a framework of robotic CLO based learning environment; and b) demonstrating the use of task decomposition and allocation principles for teaching CS algorithms and programming.

Educational robots as collaborative learning objects for teaching Computer Science

It is difficult to motivate learners to learn abstract Computer Science topics (e.g., data structures, algorithms and programming) with the adequate level of engagement. We present the process of constructing a LEGO robot, called the DRAWBOT (drawing robot), which enables to create the e-learning environment to demonstrate visually the solution of graph-based Computer Science tasks through teaching programming. Our research has confirmed the importance of using robot-based environments for teaching that was known so far in the literature on e-learning. We have extended the known approaches: a) by providing technical characteristics for the process to create the e-learning environment for the real setting; b) by smoothly integrating different phases of the process and considering it into entirety to support the constructivist learning model. DOI: http://dx.doi.org/10.5755/j01.eee.18.9.2825

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Renata Burbaite

Papers

Teaching of Computer Science Topics Using Meta-Programming-Based GLOs and LEGO Robots

Context-Aware Generative Learning Objects for Teaching Computer Science*

Educational robots as collaborative learning objects for teaching Computer Science

The LEGO NXT Robot-based e-Learning Environment to Teach Computer Science Topics

Model-driven processes and tools to design robot-based generative learning objects for computer science education