Renata Burbaitė

Refactoring of Heterogeneous Meta-Program into k-stage Meta-Program

The paper presents: (1) a graph-based theoretical background to refactoring a correct heterogeneous meta-program into its k-stage representation; (2) the refactoring method; (3) refactoring experiments with tasks taken from different domains, including real world tasks, such as meta-programs to teach Computer Science (CS) topics using educational robots. Refactoring meta-programs by staging enables to flexibly adapt them to the different context of use. To do that (semi-)automatically, we use the contextual information as a priority relation (e.g. highest, lowest, etc.) introduced within the meta-program specification. We implement the refactoring method using the so-called activating/de-activating label (index) to change the role of meta-language constructs at different stages. The contribution of the paper is: (1) applying the known (in programming) staging concept to heterogeneous meta-programming; (2) a theoretical background, properties and the method to solve tasks of this kind of refactoring. DOI: http://dx.doi.org/10.5755/j01.itc.43.1.3715

Model-Driven Approach for Body Area Network Application Development

This paper introduces the sensor-networked IoT model as a prototype to support the design of Body Area Network (BAN) applications for healthcare. Using the model, we analyze the synergistic effect of the functional requirements (data collection from the human body and transferring it to the top level) and non-functional requirements (trade-offs between energy-security-environmental factors, treated as Quality-of-Service (QoS)). We use feature models to represent the requirements at the earliest stage for the analysis and describe a model-driven methodology to design the possible BAN applications. Firstly, we specify the requirements as the problem domain (PD) variability model for the BAN applications. Next, we introduce the generative technology (meta-programming as the solution domain (SD)) and the mapping procedure to map the PD feature-based variability model onto the SD feature model. Finally, we create an executable meta-specification that represents the BAN functionality to describe the variability of the problem domain though transformations. The meta-specification (along with the meta-language processor) is a software generator for multiple BAN-oriented applications. We validate the methodology with experiments and a case study to generate a family of programs for the BAN sensor controllers. This enables to obtain the adequate measure of QoS efficiently through the interactive adjustment of the meta-parameter values and re-generation process for the concrete BAN application.

Generative Learning Object (GLO) Specialization: Teacher's and Learner's View

The paper introduces the stage-based specialization of the initial reusable GLOs treated as meta-programs. The aim is to support pre-programmed user-guided adaptation of the Computer Science (CS) teaching content within the educational robot environment. Specialization of GLOs by staging enables to flexibly (automatically) prepare the content at a higher level for the different contexts of use. We describe the approach along with the case study from the user’s perspective taking into account the specializer tool we have developed. The contribution of the paper is the staged specialization for the pre-programmed adaptation of the learning content.

Personal Generative Library of Educational Resources: A Framework, Model and Implementation

We discuss the Personal Generative Library (PGL) concept that covers models to describe some structural, functional and managerial aspects. Since the concept, to some extent, was realized in our previous research, in this paper, we focus more on the managerial aspects. In this regard, we propose the feature model-driven approach to implement those aspects using meta-programming techniques. First, we present the basic idea and theoretical background of the approach. Then we discuss the PGL architecture, its functionality and management procedures that are supported by the developed meta-programs. We outline the process of designing meta-programs through the series transformations of feature models. The main contribution of the paper is the implementation of the concept itself that enables, to some extent, to resolve the well known problems: library scaling and excluding synonymy in search. Furthermore, we have extended the potential of generative reuse (meaning a higher extent of automation as compared to the component-based reuse) by applying it not only at the library entity level (a great deal of PGL items are generative learning objects (GLO)), but also at the whole library, i.e. its management level. Therefore, the approach enables the automatic formation of annotations for PGL entities and generation of queries to support managing procedures. We have approved the approach by presenting a case study and some experimental results. DOI: http://dx.doi.org/10.5755/j01.itc.45.4.14910

Personal Generative Library for STEM-Driven Educational Resources

This chapter, we introduce a novel concept, called personal generative library (PGL), and analyse the approach build on this concept. We firstly discuss the approach in a wider context, i.e. for personalized learning, and then show how it can be applied to STEM-driven education. The term “generative” indicates two aspects. The first is that a large body of the library entities are generative (smart) learning objects. The second means that procedures to maintain the library itself are implemented as meta-programs to generate the instances of concrete programs on demand. In the context of STEM, we categorize PGL as a teacher’s library for the general use and learner’s library for the personalized use. This chapter includes the theoretical part representing a background in designing the PGL and the experimental part presenting results of the developed maintaining procedures. In addition, we provide an evaluation of the approach.

Practice of Smart STEM-Driven CS Education at High School

This chapter, we focus on a series of case studies that represent a sequenced combination of processes and outcomes of the discussed approach from the perspective of the practical use and value. The aim is to show the functionality, the capabilities and the progress of achievements gained by students in solving real-world tasks (or their prototypes) to support STEM-driven CS education. We have proposed the following methodology to represent the content of this chapter. Firstly, we introduce the CS (i.e. Programming Basics) curriculum in association with the real-world tasks to support STEM. This enables us to discover the most relevant case studies. We restrict and analyse three case studies and provide examples of learning paths related to each introduced case study. Next, we compare evolutional aspects of those case studies within the capabilities of evolutional models (M1 and M2 introduced in Chap. 6). Finally, we provide the assessment of the approach from the pedagogical viewpoint using known methodologies.

Agent-Based GLOs/SLOs for STEM

This chapter deals with the problem on how to enforce additionally the smart capabilities of the generative learning objects (GLOs) by connecting them with agent-based technology. We consider two aspects of this wide problem only. Firstly, we investigate similarities and differences among meta-programming-based GLOs and software agents. The result is that one can consider a GLO as a weak software agent without the autonomy in decision-making while selecting parameter values. Secondly, we introduce the technological agent enabling to replace the human’s actions in selecting technological parameter values by the agent. The provided experiment showed that using this agent it is possible to achieve a higher robot’s accuracy. The main contribution of this chapter is the agent-based architecture of the system and its partial implementation, enabling to solve the prescribed tasks more efficiently, i.e. with a less user’s intervention and a higher robot’s accuracy.

Model-Driven Design and Redesign of Smart STEM-Driven CS Content

This chapter deals with the design and redesign of the smart (generative) learning objects (GLOs/SLOs) as the smart content. We describe the model-driven framework to design CS-based SLOs with respect to their evolution curve and models M1 and M2 discussed in Chap. 6. In this regard, we outline two approaches, i.e. Approach 1 that focuses on the use of the model M1 and Approach 2 that focuses on the model M2. To represent the design framework for both approaches, we use the multi-level Y-charts to specify the design as a multi-level model-driven transformation process. Typically, the left branch of the Y-chart represents the problem domain, the right branch represents the solution domain and the vertical branch represents the solution itself at the given level of transformation. We introduce a horizontal transformation to map attributes of the problem domain onto the adequate attributes of the solution domain. To make the horizontal transformation feasible, we define a vertical transformation on each left and right branches resulting on the decreasing level of abstraction to represent the models and sub-models so that it would be possible to apply the adequate transformation rules. At the top level, we represent models (sub-models) by features models. They serve to represent either problem domain or solution domain, depending on the transformation level. At the implementation level, we represent the solution domain by meta-programming concepts resulting in the development of GLO/SLO as the executable meta-program specification. We illustrate that by providing examples of models and their executable specifications. We evaluate Approach 1 and Approach 2 by comparing their basic characteristics.

Smart Devices and Educational Robotics as Technology for STEM Knowledge

This chapter deals with integrative aspects of STEM in CS education from the technological perspective. Here, we focus on the available platforms of smart devices, mainly educational robots, and discuss the way on how educational robotics serves for delivery of the interdisciplinary knowledge. In addition, we discuss architectural aspects of educational robots and introduce a generic architecture to build a technological part of the smart educational environments to teach CS.

A Vision for Introducing STEM into CS Education at School

This chapter aims at showing on how it is possible to introduce and integrate the STEM paradigm into CS education by presenting a vision of the whole approach. We define this vision through the STEM-driven conceptual model and model-driven processes. The model includes the use of robot-based scenarios to deliver the interdisciplinary knowledge pieces defined as S-knowledge, T-knowledge, E-knowledge, M-knowledge and I-knowledge (meaning integrated knowledge). Model-driven processes define the vision on how to implement the conceptual model. We consider two kinds of processes, i.e. designing processes to create STEM resources in advance and usage processes to use the predesigned resources in the real STEM-driven educational setting.