Abstract
This paper is poised to inform educators, policy makers and software developers about the untapped potential of PWAs in creating engaging, effective, and personalized learning experiences in the field of programming education. We aim to address a significant gap in the current understanding of the potential advantages and underutilisation of Progressive Web Applications (PWAs) within the education sector, specifically for programming education. Despite the evident lack of recognition of PWAs in this arena, we present an innovative approach through the Framework for Gamification in Programming Education (FGPE). This framework takes advantage of the ubiquity and ease of use of PWAs, integrating it with a Pareto optimized gamified programming exercise selection model ensuring personalized adaptive learning experiences by dynamically adjusting the complexity, content, and feedback of gamified exercises in response to the learners' ongoing progress and performance. This study examines the mobile user experience of the FGPE PLE in different countries, namely Poland and Lithuania, providing novel insights into its applicability and efficiency. Our results demonstrate that combining advanced adaptive algorithms with the convenience of mobile technology has the potential to revolutionize programming education. The FGPE+ course group outperformed the Moodle group in terms of the average perceived knowledge (M = 4.11, SD = 0.51).

Abstract
As gamification spreads to new areas, new applications are being developed and the interest in evaluating gamified systems continues to grow. To date, however, no one has comprehensively approached this topic: multiple evaluation dimensions and measures have been proposed and applied without any effort to organize them into a full gamut of tools for the multi-dimensional evaluation of gamified systems. This paper addresses this gap by proposing GATUGU, a set of six perspectives of evaluation of gamified systems: General effects of gamification, Area-specific effects of gamification, Technical quality of gamified systems, Use of gamified systems, Gamefulness of gamified systems, and User experience of gamified systems. For each perspective, GATUGU indicates the relevant dimensions of evaluation, and, for each dimension, one measure is suggested. GATUGU does not introduce any new measurement tools but merely recommends one of the available tools for each dimension, considering their popularity and ease of use. GATUGU can guide researchers in selecting gamification system evaluation perspectives and dimensions and in finding adequate measurement tools. Thanks to conforming to GATUGU, the published gamification system evaluation results will become easier to compare and to perform various kinds of meta-analyses on them.

Abstract

Abstract
Malicious actors can cause severe damage by exploiting software vulnerabilities. In industrial settings, where critical infrastructures rely on software, handling these vulnerabilities with utmost care is crucial to prevent catastrophic consequences. For this purpose, a cybersecurity awareness platform called Sifu was created. This platform automatically assesses challenges to verify its compliance to secure coding guidelines. Using an artificial intelligence method, an interactive component provides players with solution-guiding hints. This paper presents an improved version of the Sifu platform, which evolves the tool in the following aspects: architecture, data model and user interface. The new platform separates the server and client-side using a REST API architecture. It also accommodates an intrinsic and richer layer of gamification, which explores the concept of game rooms at an organizational and gamification level. Finally, it offers an improved interactive training experience for individuals and organizations through a responsive and intuitive single-page web application. © José Carlos Paiva, Ricardo Queirós, and Tiago Gasiba; licensed under Creative Commons License CC-BY 4.0.

Abstract
Static source code analysis techniques are gaining relevance in automated assessment of programming assignments as they can provide less rigorous evaluation and more comprehensive and formative feedback. These techniques focus on source code aspects rather than requiring effective code execution. To this end, syntactic and semantic information encoded in textual data is typically represented internally as graphs, after parsing and other preprocessing stages. Static automated assessment techniques, therefore, draw inferences from intermediate representations to determine the correctness of a solution and derive feedback. Consequently, achieving the most effective semantic graph representation of source code for the specific task is critical, impacting both techniques' accuracy, outcome, and execution time. This paper aims to provide a thorough comparison of the most widespread semantic graph representations for the automated assessment of programming assignments, including usage examples, facets, and costs for each of these representations. A benchmark has been conducted to assess their cost using the Abstract Syntax Tree (AST) as a baseline. The results demonstrate that the Code Property Graph (CPG) is the most feature -rich representation, but also the largest and most space -consuming (about 33% more than AST).