ABSTRACT 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.

Serious Games (SGs) have gained attention as powerful educational tools because of their potential to provide reliable assessments and evaluate hard-to-measure constructs and competencies that are difficult to capture using traditional forms of assessment. Specifically, this study presents a human-centered approach to model and detect persistence—a key component of successful learning outcomes—in the context of SGs. With this purpose in mind, we developed a comprehensive rubric to characterize persistence behaviors in SGs. To design the rubric, we identified a set of persistence profiles and characteristics from previous literature and elaborated a general rubric for identifying persistence behaviors at the level of individual attempts. These characteristics were then mapped onto measurable features within Shadowspect, the SG used for data collection. Following this rubric, two annotators manually labeled 1,374 level attempts from 64 students using two visualization methods: in-game and text replays. With a comprehensive dataset of 2,748 labeled attempts, we trained and evaluated Machine Learning (ML) models for each type of replay to classify persistence behaviors across four categories: Persistence , Non-persistence , Unproductive persistence , and No behavior . Our results indicate that while text-based replays enable efficient annotation with promising performance, in-game replays may provide finer detail for certain complex behaviors, highlighting the strengths and limitations of each visualization method. This work contributes the use of SGs for assessment, illustrating a transparent and adaptable AI-driven approach that enhances reliability and user-centered insights, highlighting the complementary role of human input in optimizing AI-based models to achieve meaningful, user-centered assessments in education.

Video games have assumed an important place in our daily lives. This has led to an increasing interest on the use of games for non-entertainment purposes, introducing the concept of Serious Games (SGs). In particular, SGs are being explored because of their potential to provide reliable assessments, but also because they can measure competences that would be difficult to measure using traditional forms of assessment. However, one of the key issues is that assessment machinery has to be designed specifically for each game, increasing the time and effort when designing and implementing Game-Based Assessments (GBAs). In this research, we introduce a novel approach to develop interoperable GBAs by: (1) designing and creating an ontology that can standardize the GBA area; (2) conducting a validation study on literature metrics to replicate them and designing novel metrics using data from different SGs; (3) conducting a case study that illustrates how our approach can be used in a real life scenario with real data. Our results confirm that the designed ontology can be used to effectively perform GBAs, along with the metrics replicated and designed in the system. We expect our work to solve the current limitations regarding GBA interoperability, thus allowing the deployment of Game-Based Assessments as a Service (GBAaaS).

Games are increasingly being recognized as valuable tools for learning. In addition, they are also being explored for their potential to provide valid and reliable assessments, as they allow to create authentic and engaging assessment contexts through interactive and immersive environments. However, there are challenges to enable Game-based Assessment (GBA) at scale, including the need for interoperability between assessment models and machinery, and the complexity of managing and processing large amounts of data generated by users' interaction with games. In this study, we propose a novel approach that combines the use of ontologies and Big Data technologies for developing interoperable GBAs. The architecture enables assessments to be performed using data from different games, and we also designed and implemented a service API that facilitates the Game-Based Assessment as a Service (GBAaaS) paradigm. GBAaaS simplifies the GBA development process and enables its adoption at scale, making it a promising approach for future developments in this field.

Abstract When detecting cyberattacks in Industrial settings, it is not sufficient to determine whether the system is suffering a cyberattack. It is also fundamental to explain why the system is under a cyberattack and which are the assets affected. In this context, the Anomaly Detection based on Machine Learning (ML) and Deep Learning (DL) techniques showed great performance when detecting cyberattacks in industrial scenarios. However, two main limitations hinder using them in a real environment. Firstly, most solutions are trained using a supervised approach, which is impractical in the real industrial world. Secondly, the use of black‐box ML and DL techniques makes it impossible to interpret the decision made by the model. This article proposes an interpretable and semi‐supervised system to detect cyberattacks in Industrial settings. Besides, our proposal was validated using data collected from the Tennessee Eastman Process. To the best of our knowledge, this system is the only one that offers interpretability together with a semi‐supervised approach in an industrial setting. Our system discriminates between causes and effects of anomalies and also achieved the best performance for 11 types of anomalies out of 20 with an overall recall of 0.9577, a precision of 0.9977, and a F1‐score of 0.9711.

In the realm of industrial anomaly detection, machine and deep learning models face a critical vulnerability to adversarial attacks. In this context, existing attack methodologies primarily target continuous features, often in the context of images, making them unsuitable for the categorical or discrete features prevalent in industrial systems. To fortify the cybersecurity of industrial environments, this paper introduces a groundbreaking adversarial attack approach tailored to the unique demands of these settings. Our novel technique enables the creation of targeted adversarial samples that are valid within the framework of supervised cyberattack detection models in industrial scenarios, preserving the consistency of discrete values and correcting cases where an adversarial sample transitions into a normal one. Our approach leverages the SHAP interpretability method to identify the most salient features for each sample. Subsequently, the Projected Gradient Descent technique is employed to perturb continuous features, ensuring adversarial sample generation. To handle categorical features for a specific adversarial sample, our method scrutinizes the closest sample within the normal training dataset and replicates its categorical feature values. Additionally, Decision Trees trained within a Random Forest are utilized to ensure that the resulting adversarial samples maintain the essential abnormal behavior required for detection. The validation of our proposal was conducted using the WADI dataset obtained from a water distribution plant, providing a realistic industrial context. During validation, we assessed the mean error and the total number of adversarial samples generated by our approach, comparing it with the original Projected Gradient Descent method and the Carlini & Wagner attack across various parameter configurations. Remarkably, our proposal consistently achieved the best trade-off between mean error and the number of generated adversarial samples, showcasing its superiority in safeguarding industrial systems.

Abstract During the last few years, there has been increasing attention paid to serious games (SGs), which are games used for non‐entertainment purposes. SGs offer the potential for more valid and reliable assessments compared to traditional methods such as paper‐and‐pencil tests. However, the incorporation of assessment features into SGs is still in its early stages, requiring specific design efforts for each game and adding significant time to the design of Game‐based Assessments (GBAs). In this research, we present a completely novel framework that aims to perform interoperable GBAs by: (a) integrating a common GBA ontology model to process RDF data; (b) developing in‐game metrics to infer useful information and assess learners; (c) integrating a service API to provide an easy way to interact with the framework. We then validate our approach through performance evaluation and two use cases, demonstrating its effectiveness in real‐world scenarios with large‐scale datasets. Our results show that the developed framework achieves excellent performance, replicating metrics from previous literature. We anticipate that our work will help alleviate current limitations in the field and facilitate the deployment of GBAs as a Service.

The recent pandemic has changed the way we see education. During recent years, Massive Open Online Course (MOOC) providers, such as Coursera or edX, are reporting millions of new users signing up on their platforms. Though online review systems are standard among many verticals, no standardized or fully decentralized review systems exist in the MOOC ecosystem. In this vein, we believe that there is an opportunity to leverage available open MOOC reviews in order to build simpler and more transparent reviewing systems, allowing users to really identify the best courses out there. Specifically, in our research we analyze 2.4 million reviews (which is the largest MOOC reviews dataset used until now) from five different platforms in order to determine the following: (1) if the numeric ratings provide discriminant information to learners, (2) if NLP-driven sentiment analysis on textual reviews could provide valuable information to learners, (3) if we can leverage NLP-driven topic finding techniques to infer themes that could be important for learners, and (4) if we can use these models to effectively characterize MOOCs based on the open reviews. Results show that numeric ratings are clearly biased (63% of them are 5-star ratings), and the topic modeling reveals some interesting topics related with course advertisements, the real applicability, or the difficulty of the different courses.

Summary Cyber attacks are becoming a very common trend in existing networks, expecting to be even more acute in future 5G networks due to greater number of connected devices, higher mobile data volume, low latency, etc. Security mechanisms to tackle cyber threats should be updated when users, possibly carrying devices with some kind of malware, move in highly dynamic mobile networks in order to continue providing the same detection and mitigation capabilities along user's path. This paper presents BotsOnFIRE, an experiment of the EU H2020 SoftFIRE project for the detection and mitigation of botnets in the SoftFIRE federated testbed for 5G within FIRE+, by combining Software‐Defined Networking (SDN) and Network Functions Virtualization (NFV) technologies. Bots' mobility is considered to trigger reconfiguration of security‐related SDN/NFV applications, when needed, so as to update security capabilities of the SoftFIRE infrastructure. The BotsOnFIRE experiment contributes to the wider 5G objective of more secure and resilient networks and services, where botnets are actually one of the most powerful cyber threats capable of orchestrating the remote execution of cyber‐attacks. Experiments confirm that BotsOnFIRE is feasible to conduct the expected detection and mitigation procedures in the SoftFIRE federated environment, being evaluated through some Key Performance Indicators.

Summary Industrial control systems (ICS) are considered cyber‐physical systems that join both cyber and physical worlds. Due to their tight interaction, where humans and robots co‐work and co‐inhabit in the same workspaces and production lines, cyber‐attacks targeting ICS can alter production processes and even bypass safety procedures. As an example, these cyber‐attacks could interrupt physical industrial processes and cause potential injuries to workers. In this article, we present SafeMan, a unified management framework based on the Edge Computing paradigm that provides high‐performance applications for the detection and mitigation of both cyber‐attacks and safety threats in industrial scenarios. Three use cases show specific threats in manufacturing as well as the SafeMan actions carried out to detect and mitigate them. In order to validate our proposal, a pool of experiments was performed with Electra, an industrial dataset with normal network traffic and different cyber‐attacks by using a given number of Modbus TCP and S7Comm devices. The experiments measured the runtime performance of anomaly detection techniques based on machine learning and deep learning to detect cyber‐attacks in control networks. The experimental results show that Neural Networks report the best performance, being able to examine 217 feature vectors per second over Electra, and therefore demonstrating that it can be used as detection model for SafeMan in real scenarios.

This article presents a general way to enable automatic generation of digitized laboratories (a sort of digital twin for laboratory experimental setups) from remote laboratories and their easy deployment and publication. To demonstrate its effectiveness, we use two existing tools to generate and publish two digitized laboratories online from two implementations of a Snell's law remote laboratory, although they could be applied to many other remote laboratories. The first of these tools is a communication protocol that was designed to manipulate laboratory equipment through the Internet. This protocol can be used to automatically loop through different possible laboratory states and store them. The second one is a web platform that allows uploading files, that contain data sets of the laboratory states, to publish the digitized laboratory as a web application that is generated automatically.

In this paper we introduce the new and planned features of Easy Java/JavaScript Simulations (EJS) to support Learning Analytics (LA) and Educational Data Mining (EDM) research and practice in the use of simulations for the teaching and self-learning of natural sciences and engineering. Simulations created with EJS can now be easily embedded in a popular Learning Management System using a new plug-in that allows creation of full-fledged instructional units that also collect and record fine-grained, instructional-savvy data of the student’s interaction with the simulation. The resulting data can then be mined to obtain information about students’ performance, behaviors, or learning procedures with the intention to support student learning, provide instructors with timely information about student performance, and also help optimize the pedagogic design of the simulations themselves. We describe the current development and architecture, as well as future directions for testing and extending the current capabilities of EJS as a modelling and authoring tool to support LA and EDM research and practice in the use of simulations for teaching science, in particular in the context of the increasingly popular on-line learning platforms.

The disruptive vision of Medical Cyber-Physical Systems (MCPS) enables the promising next-generation of eHealth systems that are intended to interoperate efficiently, safely, and securely. Safety-critical interconnected systems that analyze patients' vital signs gathered from medical devices, infer the state of the patient's health, and start treatments issuing information to doctors and medical actuators should improve the patients' safety in a cost-efficient fashion. Despite the benefits provided by the MCPS vision, it also opens the door to critical challenges like the security and privacy, Quality of Service (QoS), and high availability of the devices composed to support the MCPS scenario. The Integrated Clinical Environment (ICE) standard is a significant step toward promoting open coordination of heterogeneous medical devices by considering the previous challenges. However, a lot of effort is still required in order to cover the whole aspects of these challenges and enable the future eHealth. In this context, we identify critical shortcomings of ICE using challenge scenarios regarding security, QoS, and high availability. According to these concerns and following the ICE standard, we propose the novel ICE++ architecture, which is oriented to the Mobile Edge Computing paradigm and combines SDN and NFV techniques to manage efficiently and automatically the MCPS elements taking into account its security, QoS, and high availability. Finally, we perform experiments that demonstrate the potential usefulness of our solution regarding the efficient and automatic management of the ICE components.

In this paper we present the results obtained from a large experimental environment that makes use of Bluetooth Low Energy (BLE) as the core technology for a location estimation system. BLE is a common technology for this kind of geopositioning systems, but most of the existing proposals are based on the RSS (Received Signal Strength) value obtained by mobile smart devices from static emitters such as iBeacons or other similar tags. This is not our case, since we adopt a passive approach where monitors obtain advertising frames emitted by mobile BLE beacons with no computing capabilities. In our particular scenario, based on a commercial application of our system, we perform fast but exhaustive training procedures to produce an initial dataset that is then analyzed paying attention to important design parameters. A thorough analysis of the data by means of different data visualization techniques reveals valuable information about the behavior of the emitters, signal characterization, radio coverage and, mainly, possible features that can be employed lately by machine learning methods in order to provide accurate location estimations. This is useful to define a quick and continuous training life-cycle which enables the detection of inconsistent data or failures. Our analysis also suggests that a representation of the observations using alternative features provides similar or even better results than the RSS values in terms of efficiency and support for heterogeneous devices.

Users' privacy is a critical challenge for any information management system. The proliferation of mobile devices has promoted the use of context-aware solutions, making the protection of the users' information an even greater challenge. Addressing this requires a given mechanism that allows users to manage and control their personal information. In this sense, this paper proposes a privacy-preserving and context-aware system named MASTERY (Multicontext-Aware System That prEserves the useRs' privacY) that manages the privacy of the users' information in intra- and inter-context scenarios. MASTERY is a trusted third party that suggests to users a pool of privacy policies (profiles) aware to the context in which they are located, who can modify them according to their interests. These policies protect the privacy of the users' information being accessed from others without their consent. The information about users and contexts is managed by using semantic web techniques. This provides a common infrastructure that makes possible to represent, process, and share information between independent systems easily.

Mobile devices equipped with indoor positioning capabilities can access a broad range of di erent Location-Based Services (LBS). There are advanced LBS applications that use the user's location and preferences in order to give the most precise answer to location-dependent queries. To protect pr

Overlay networks represent a flexible approach for distributed services deployed across different administrative domains to group according to a given criteria without modification of the underlying network. Two key features for such overlays to be effective and useful are security and dynamicity. This paper introduces a proposal for the elements (i.e., architecture, protocols, and formal information models) needed to dynamically deploy secure overlay networks in certain multi-domain scenarios.

There are several reasons to offer PKI (public key infrastructure) services in IPv6 multidomain scenarios. The first reason is to provide IPv6-only or dual-stack connectivity to those Internet users and entities who want to use certification services, but there are other important motivations. If we want to enable and promote security services in IPv6 networks, like end-to-end security, AAA (authentication, authorization and accounting) services, HTTP or DNSsec services, or VPN networks, it is needed to offer the public key services required by the involved protocols. Other relevant reason is to allow services or devices to use X.509 public key certificates containing IPv6 information, such as IPv6 addresses used, for example, by any IPsec-based VPN end point. This is the main motivation of the research work presented in this paper where the most relevant design and implementation issues related with the deployment of PKI services in a multidomain IPv6 network are presented.