arXiv subjects

Félix J. García Clemente

Publications and source records attributed to Félix J. García Clemente.

6 recordsLinked to original sources

arXiv2026

Dynamic Management of a Deep Learning-Based Anomaly Detection System for 5G Networks

Fog and mobile edge computing (MEC) will play a key role in the upcoming fifth generation (5G) mobile networks to support decentralized applications, data analytics and management into the network itself by using a highly distributed compute model. Furthermore, increasing attention is paid to providing user-centric cybersecurity solutions, which particularly require collecting, processing and analyzing significantly large amount of data traffic and huge number of network connections in 5G networks. In this regard, this paper proposes a MEC-oriented solution in 5G mobile networks to detect network anomalies in real-time and in autonomic way. Our proposal uses deep learning techniques to analyze network flows and to detect network anomalies. Moreover, it uses policies in order to provide an efficient and dynamic management system of the computing resources used in the anomaly detection process. The paper presents relevant aspects of the deployment of the proposal and experimental results to show its performance.

cs.CR↗

arXiv2022

A Systematic Literature Review of Game-based Assessment Studies: Trends and Challenges

Technology has become an essential part of our everyday life, and its use in educational environments keeps growing. In addition, games are one of the most popular activities across cultures and ages, and there is ample evidence that supports the benefits of using games for assessment. This field is commonly known as game-based assessment (GBA), which refers to the use of games to assess learners' competencies, skills, or knowledge. This paper analyzes the current status of the GBA field by performing the first systematic literature review on empirical GBA studies. It is based on 65 research papers that used digital GBAs to determine: (1) the context where the study has been applied; (2) the primary purpose; (3) the domain of the game used; (4) game/tool availability; (5) the size of the data sample; (6) the computational methods and algorithms applied; (7) the targeted stakeholders of the study; and (8) what limitations and challenges are reported by authors. Based on the categories established and our analysis, the findings suggest that GBAs are mainly used in K-16 education and for assessment purposes, and that most GBAs focus on assessing STEM content, and cognitive and soft skills. Furthermore, the current limitations indicate that future GBA research would benefit from the use of bigger data samples and more specialized algorithms. Based on our results, we discuss current trends in the field and open challenges (including replication and validation problems), providing recommendations for the future research agenda of the GBA field.

cs.CY↗

arXiv2022

Modern Physics demonstrations with DIY Smartphone Spectrometers

Smartphones are widely available and used extensively by students worldwide. These phones often come equipped with high-quality cameras that can be combined with basic optical elements to build a cost-effective DIY spectrometer. Here, we discuss a series of demonstrations and pedagogical exercises, accompanied by our DIY diffractive spectrometer that uses a free web platform for instant spectral analysis. Specifically, these demonstrations can be used to encourage hands-on and inquiry-based learning of wave optics, broadband vs discrete light emission, quantization, Heisenberg's energy-time uncertainty relation, and the use of spectroscopy in day-to-day life. Hence, these simple tools can be readily deployed in high school classrooms to communicate the practices of science.

physics.ed-ph↗

arXiv2022

Large Scale Analysis of Open MOOC Reviews to Support Learners' Course Selection

The recent pandemic has changed the way we see education. It is not surprising that children and college students are not the only ones using online education. Millions of adults have signed up for online classes and courses during last years, and MOOC providers, such as Coursera or edX, are reporting millions of new users signing up in their platforms. However, students do face some challenges when choosing courses. 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. We expect our study to shed some light on the area and promote a more transparent approach in online education reviews, which are becoming more and more popular as we enter the post-pandemic era.

cs.CY↗

arXiv2019

Easy Java/JavaScript Simulations as a tool for Learning Analytics

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 students 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 online learning platforms.

physics.ed-ph↗

arXiv2017

Deployment of physics simulation apps using Easy JavaScript Simulations

Physics simulations are widely used to improve the learning process in science and engineering education. Deployment of a computational physics simulation/model is extremely complex given the fact that both knowledge and skills for the science equations and the computational and programming aspects are required for a fully functional simulation, typically requiring a science educator and computer scientists/developer to work together. However, when using Easy JavaScript Simulation (EjsS) modeling toolkit, the instructor can be both the science educator and computer programmer, only needing to define the simulation variables, model and view, and the modeling toolkit can generate the computer codes for the physics simulation. Moreover, the programming aspects can become even more complex if simulations require being optimized for both Android and iOS mobile devices. The current version of EjsS provides instructors with an authoring tool that includes facilities for the creation of such JavaScript simulations as mobile apps, thus simplifying the purely programming aspects. This paper presents a new and novel EjsS functionality to generate physics simulation apps for iOS and Android. The generation process is based on the integration of the Ionic/Cordova and AngularJS technologies into EjsS. Finally, we present several working examples based on the works of Open Source Physics at Singapore.

physics.ed-ph↗