Abstract
Semantic measures evaluate and compare the strength of relations between entities. To assess their accuracy, semantic measures are compared against human-generated gold standards. Existing semantic gold standards are mainly focused on concepts. Nevertheless, semantic measures are frequently applied both to concepts and instances. Games with a purpose are used to offload to humans computational or data collection needs, improving results by using entertainment as motivation for higher engagement. We present Grettir, a system which allows the creation of crowdsourced semantic relations datasets for named entities through a game with a purpose where participants are asked to compare pairs of entities. We describe the system architecture, the algorithms and implementation decisions, the first implemented instance – dedicated to the comparison of music artists – and the results obtained. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
Web programming education is an important component of modern computer science curricula. Assessing students’ web programming skills can be time-consuming and challenging for educators. This paper introduces Webpal, an automated assessment tool for web programming exercises in entry-level courses. Webpal evaluates web applications coded in HTML, CSS, and Javascript, and provides feedback to students. This tool integrates with Virtual Learning Environments (VLEs) through an API, allowing the creation, storage, and access to exercises while assessing student attempts based on the created exercises. The evaluation process comprises various subcomponents: static assessment, interface matching, functional testing, and feedback management. This approach aims to provide feedback that helps students overcome their challenges in web programming assignments. This paper also presents a demo showcasing the tool’s features and functionality in a simulated VLE environment. © Luís Maia Costa, José Paulo Leal, and Ricardo Queirós; licensed under Creative Commons License CC-BY 4.0.

Abstract
This paper proposes an interactive approach for narrative extraction from semantic graphs. The proposed approach extracts events from RDF triples, maps them to their corresponding attributes, and assembles them into a chronological sequence to form narrative graphs. The approach is evaluated on the Wikidata graph and achieves promising results in terms of narrative quality and coherence. The paper also discusses several avenues for future work, including the integration of machine learning, graph embedding methods and the exploration of advanced techniques for attention-based narrative labeling and semantic role labeling. Overall, the proposed method offers a promising approach to narrative extraction from semantic graphs and has the potential to be useful in various applications, including chatbots, conversational agents, and content creation tools. © Daniil Lystopadskyi, André Santos, and José Paulo Leal;

Abstract
The paper discusses an ongoing project that aims to enhance the UX of teachers while using e-learning systems. Specifically, the project focuses on developing the teacher’s user interface (UI) for Agni, a web-based code playground for learning JavaScript. The goal is to design an intuitive UI with valuable features that will encourage more teachers to use the system. To achieve this goal, the paper explores the use of a headless Content Management System (CMS) called Strapi. The primary research question the paper seeks to answer is whether a headless CMS, specifically Strapi, can provide a good UX to teachers. A usability evaluation of the built-in Strapi UI for content creation and management reveals it to be generally consistent and user-friendly but challenging and unintuitive to create courses with programming exercises. As a result, the decision was made to develop a new teacher’s UI based on the existing Agni UI for students in an editable version. Once the development is complete, a new usability evaluation of the fully developed teacher’s UI will be conducted with the Strapi UI evaluation as a baseline for comparison. © Yannik Bauer, José Paulo Leal, and Ricardo Queirós; 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).