https://hdl.handle.net/10259/6191 Fri, 17 Apr 2026 17:32:33 GMT 2026-04-17T17:32:33Z https://hdl.handle.net/10259/9223 A systematic review of virtual 3D reconstructions of Cultural Heritage in immersive Virtual Reality Rodríguez García, Bruno; Guillén Sanz, Henar; Checa Cruz, David; Bustillo Iglesias, Andrés Immersive Virtual Reality (iVR) devices are increasingly affordable and accessible to consumers. The widespread adoption of this technology for professional training is now finding its way into various other fields. One field that is gaining significant popularity is Cultural Heritage (CH), where iVR enables the reconstruction and exploration of lost heritage. However, an up-to-date systematic review of iVR within this field will be of great benefit. Hence, the present review of 94 papers published between 2013 and 2022 that follows PRISMA methodology on virtual reconstruction of CH for iVR. The aim is to identify the key factors behind the development of these applications and their standards. To do so, a statistical analysis on the following topics was performed: (1) nationality, publication date, and article type; (2) heritage type and its current state of preservation; (3) the area of final application and the features of the reconstructions; (4) the characteristics of the iVR experience; and (5) the assessment of the iVR applications. Finally, a roadmap of best practices is outlined for the virtual reconstruction of CH using iVR and some of the most promising future research lines are outlined. Tue, 02 Apr 2024 00:00:00 GMT https://hdl.handle.net/10259/9223 2024-04-02T00:00:00Z https://hdl.handle.net/10259/6355 Immersive virtual-reality computer-assembly serious game to enhance autonomous learning Checa Cruz, David; Miguel Alonso, Inés; Bustillo Iglesias, Andrés Immersive virtual reality (VR) environments create a very strong sense of presence and immersion. Nowadays, especially when student isolation and online autonomous learning is required, such sensations can provide higher satisfaction and learning rates than conventional teaching. However, up until the present, learning outcomes with VR tools have yet to prove their advantageous aspects over conventional teaching. The project presents a VR serious game for teaching concepts associated with computer hardware assembly. These concepts are often included in any undergraduate’s introduction to Computer Science. The learning outcomes are evaluated using a pre-test of previous knowledge, a satisfaction/usability test, and a post-test on knowledge acquisition, structured with questions on different knowledge areas. The results of the VR serious game are compared with another two learning methodologies adapted to online learning: (1) an online conventional lecture; and (2) playing the same serious game on a desktop PC. An extensive sample of students (n = 77) was formed for this purpose. The results showed the strong potential of VR serious games to improve student well-being during spells of confinement, due to higher learning satisfaction. Besides, ease of usability and the use of in-game tutorials are directly related with game-user satisfaction and performance. The main novelty of this research is related to academic performance. Although a very limited effect was noted for learning theoretical knowledge with the VR application in comparison with the other methodologies, this effect was significantly improved through visual knowledge, understanding and making connections between different concepts. It can therefore be concluded that the proposed VR serious game has the potential to increase student learning and therefore student satisfaction, by imparting a deeper understanding of the subject matter to students. Wed, 01 Dec 2021 00:00:00 GMT https://hdl.handle.net/10259/6355 2021-12-01T00:00:00Z https://hdl.handle.net/10259/6238 Analysis of the Learning Process through Eye Tracking Technology and Feature Selection Techniques Sáiz Manzanares, María Consuelo; Ramos Pérez, Ismael; Arnaiz Rodríguez, Adrián; Rodríguez Arribas, Sandra; Almeida, Leandro; Martin, Caroline Françoise In recent decades, the use of technological resources such as the eye tracking methodology is providing cognitive researchers with important tools to better understand the learning process. However, the interpretation of the metrics requires the use of supervised and unsupervised learning techniques. The main goal of this study was to analyse the results obtained with the eye tracking methodology by applying statistical tests and supervised and unsupervised machine learning techniques, and to contrast the effectiveness of each one. The parameters of fixations, saccades, blinks and scan path, and the results in a puzzle task were found. The statistical study concluded that no significant differences were found between participants in solving the crossword puzzle task; significant differences were only detected in the parameters saccade amplitude minimum and saccade velocity minimum. On the other hand, this study, with supervised machine learning techniques, provided possible features for analysis, some of them different from those used in the statistical study. Regarding the clustering techniques, a good fit was found between the algorithms used (k-means ++, fuzzy k-means and DBSCAN). These algorithms provided the learning profile of the participants in three types (students over 50 years old; and students and teachers under 50 years of age). Therefore, the use of both types of data analysis is considered complementary. Thu, 01 Jul 2021 00:00:00 GMT https://hdl.handle.net/10259/6238 2021-07-01T00:00:00Z https://hdl.handle.net/10259/6192 When is resampling beneficial for feature selection with imbalanced wide data? Ramos Pérez, Ismael; Arnaiz González, Álvar; Rodríguez Diez, Juan José; García Osorio, César This paper studies the effects that combinations of balancing and feature selection techniques have on wide data (many more attributes than instances) when different classifiers are used. For this, an extensive study is done using 14 datasets, 3 balancing strategies, and 7 feature selection algorithms. The evaluation is carried out using 5 classification algorithms, analyzing the results for different percentages of selected features, and establishing the statistical significance using Bayesian tests. Some general conclusions of the study are that it is better to use RUS before the feature selection, while ROS and SMOTE offer better results when applied afterwards. Additionally, specific results are also obtained depending on the classifier used, for example, for Gaussian SVM the best performance is obtained when the feature selection is done with SVM-RFE before balancing the data with RUS. Tue, 01 Feb 2022 00:00:00 GMT https://hdl.handle.net/10259/6192 2022-02-01T00:00:00Z