Manuel Jesús Gómez Moratilla

Scheduling algorithms are an important part of the Computer Science curriculum as these algorithms control how resources are handled and shared within the operating system. Outside the realm of Computer Science, scheduling algorithms are a critical part of project management, enabling the translation of project plans into realistic and efficient operating time tables. Scheduling algorithms are often hard to understand by undergraduate students who struggle to visualise them and understand the practicalities of their implementation. Immersive Serious Games (iSG) have been shown to be an effective approach to visualise abstract concepts and immerse and engage students in practical tasks that require hands-on intervention. This paper presents the case study of “Sortica” a prototype iSG aimed at introducing students unfamiliar with scheduling algorithms to the foundational algorithms of the subject, such as last-in-first-out (LIFO) and first-in-first-out (FIFO). Within “Sortica” students took on the role of a robot worker tasked to rebuild town street buildings using the LIFO and FIFO algorithms. “Sortica” was developed following a human centred iterative approach and was tested through four data collection sessions for a total of 26 undergraduate participants from three modules in programmes that do not teach scheduling algorithms. A pre-test post-test method was used to test knowledge acquisition, the post-test also included questions regarding engagement, perceived learning and the NASA-TLX scale. Additionally, in-game analytics were collected to measure student performance within the iSG. Students were observed during the gameplay and interviewed to collect feedback regarding different aspects of the game. After data cleaning a preliminary data analysis shows that despite a high starting baseline (39% achieved perfect pre-test scores) participants felt that the iSG was highly engaging and helped solidify their understanding of the subject. Preliminary analysis of the NASA-TLX scale shows that the cognitive load was moderately high for the tasks, which could help explain why 4.35% of the valid responses showed a score decrease in the post-test. This work shows that the translation of abstract concepts of scheduling algorithms into mundane tasks makes “Sortica” an engaging tool for introduction and consolidation of basic scheduling algorithms knowledge and understanding. Furthermore, analysis of user friction reveals critical design considerations regarding diegetic interfaces, false affordances, and cognitive load in educational VR

Massive Open Online Courses (MOOCs) have established a new paradigm in education, enabling asynchronous, remote learning. Although MOOCs offer diverse educational content to students, comprehensive and realistic education requires hands‐on training. Therefore, we present our integration of remote laboratories within a MOOC focused on Industry 4.0, together with a mixed‐methods analysis combining demographic data, questionnaire responses, platform logs, and remote laboratory server records. The course incorporated seven distinct laboratories in two categories: remote Arduino‐based laboratories, which connected physical devices to the MOOC, and a virtual infrastructure based on JupyterHub to support practical activities. Our findings indicate that the majority of participants in our MOOC were actively employed, with a mean age of 46.9 years, and males representing 88%. The students were mainly motivated by personal and professional growth, and 66% of these learners had no prior remote laboratory experience. Learners reported generally positive perceptions of the laboratory experience, including high levels of interest, usefulness, and self‐efficacy, although engagement varied substantially across participants. Behavioral analyses revealed that students who ultimately pursued certification showed markedly higher participation and involvement in the remote laboratories than those who did not. At the same time, the course exhibited the substantial dropout patterns commonly reported in MOOCs, indicating that the inclusion of remote laboratories does not by itself eliminate persistence challenges. Our work provides an illustrative example of integrating remote laboratories into a MOOC and offers an analysis of enrolled students, delivering valuable findings for future researchers.

Creativity is often characterized by the capacity to generate novel ideas, explore unconventional approaches, and solve problems through intuition, curiosity, and innovative thinking. Assessing this multifaceted skill is both essential and challenging, especially in educational and game-based environments where creativity drives engagement and learning. In this study, we expanded upon existing research on creativity measurements in digital games to propose a comprehensive model for identifying creative solutions in serious digital puzzle games. This model was adapted to a geometry-focused puzzle game, with solutions manually evaluated across dimensions such as fluency, flexibility, and surprise. Using the resulting labelled dataset, we trained machine learning models to classify creative solutions, achieving promising results. These findings highlight the potential for integrated methods to advance creativity assessment and enhance educational game design, contributing to the development of tools that inspire and nurture creative thinking in learners.

In recent years, serious games (SGs) have emerged as a powerful tool in education by combining pedagogy and entertainment, facilitating the acquisition of knowledge and skills in engaging environments. SGs enable the collection of valuable interaction data from students, allowing for the analysis of student performance, with artificial intelligence (AI) playing a key role in processing this data to make informed inferences about their knowledge and skills. However, the lack of explainability in AI models represents a significant challenge. This research aims to develop an interpretable model for predicting students' performance in real‐time while playing an SG by: (1) calculating the performance of an interpretable prediction model of task completion in an SG and (2) demonstrating the application of the interpretable model for just‐in‐time (JIT) classroom interventions. Our results show that we are able to predict students' task completion in real‐time with a balanced accuracy result of 77.21% after a short playtime has elapsed. In addition, an explainable artificial intelligence (XAI) approach has been applied to ensure the interpretability of the developed models. This approach supports personalised learning experiences, unlocks AI benefits for non‐technical users, and maintains transparency in education.