@article{Miguel-Alonso2025,

title = {Identifying users of immersive virtual-reality serious games through machine-learning techniques},

author = {Ines Miguel-Alonso and Juan J. Rodríguez and Ana Serrano-Mamolar and Andres Bustillo},

doi = {10.1007/s10055-025-01232-y},

issn = {1434-9957},

year  = {2025},

date = {2025-12-00},

urldate = {2025-12-00},

journal = {Virtual Reality},

volume = {29},

number = {4},

publisher = {Springer Science and Business Media LLC},

abstract = {<jats:title>Abstract</jats:title>

          <jats:p>User identification is currently an open issue in immersive Virtual Reality (iVR) environments. Three main goals are usually associated with the use of tracking-data and Machine-Learning (ML) techniques: safeguarding privacy, user authentication, and user-experience customization. However, research to date has only involved very limited recordings of user data (<jats:italic>e</jats:italic>.<jats:italic>g</jats:italic>., on a single session and for low-interactive situations), rare in real iVR environments. So, the research gap between real iVR data and ML techniques for user identification is addressed in this paper. To do so, a 3-session iVR experience of operating a bridge crane is considered. In this simple yet highly interactive learning action, the dataset records of user performance show rapid changes between one experience and another. Eye, head, and hand movements of 64 users of similar age and with comparable previous experience were all recorded while engaged with the experience. The final raw dataset had a size of approximately 50 M data points with 25 attributes that were mainly temporal series values. Secondly, different ML algorithms were used for user identification: Decision Tree, Random Forest, XGBoost, k-Nearest Neighbors, Support Vector Machines, and Multilayer Perceptron. The results showed that ML ensemble learning techniques, particularly Random Forest, were the most suitable solutions on the basis of different measures for the prediction of user identity. Additionally, the inclusion of stress and no-stress conditions significantly enhanced model performance, highlighting the importance of data diversity. Temporal segmentation revealed that user identification during later phases of the exercise was slightly more effective, due to increased individual variability. Finally, a minimum duration of the iVR experience was identified as a requirement to assure high identification rates.</jats:p>},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Garrido-Labrador2025,

title = {SSLearn: A Semi-Supervised Learning library for Python},

author = {José L. Garrido-Labrador and Jesús M. Maudes-Raedo and Juan J. Rodríguez and César I. García-Osorio},

doi = {10.1016/j.softx.2024.102024},

issn = {2352-7110},

year  = {2025},

date = {2025-02-00},

urldate = {2025-02-00},

journal = {SoftwareX},

volume = {29},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Guillen-Sanz2025,

title = {Can Immersive Virtual Reality Environments Improve Stress Reduction? Experimental Design With Progressive Muscle Relaxation Training},

author = {H. Guillen-Sanz and M. C. Escolar-Llamazares and I. Quevedo Bayona and M. A. Martínez-Martín and A. Bustillo},

doi = {10.1109/access.2025.3579493},

issn = {2169-3536},

year  = {2025},

date = {2025-01-13},

urldate = {2025-00-00},

journal = {IEEE Access},

volume = {13},

pages = {104312--104329},

publisher = {Institute of Electrical and Electronics Engineers (IEEE)},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Garrido-Labrador2024,

title = {Ensemble methods and semi-supervised learning for information fusion: A review and future research directions},

author = {José Luis Garrido-Labrador and Ana Serrano-Mamolar and Jesús Maudes-Raedo and Juan J. Rodríguez and César García-Osorio},

doi = {10.1016/j.inffus.2024.102310},

issn = {1566-2535},

year  = {2024},

date = {2024-07-00},

urldate = {2024-07-00},

journal = {Information Fusion},

volume = {107},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Guillen-Sanz2024c,

title = {A systematic review of wearable biosensor usage in immersive virtual reality experiences},

author = {Henar Guillen-Sanz and David Checa and Ines Miguel-Alonso and Andres Bustillo},

doi = {10.1007/s10055-024-00970-9},

issn = {1434-9957},

year  = {2024},

date = {2024-06-00},

urldate = {2024-06-00},

journal = {Virtual Reality},

volume = {28},

number = {2},

publisher = {Springer Science and Business Media LLC},

abstract = {<jats:title>Abstract</jats:title><jats:p>Wearable biosensors are increasingly incorporated in immersive Virtual Reality (iVR) applications. A trend that is attributed to the availability of better quality, less costly, and easier-to-use devices. However, consensus is yet to emerge over the most optimal combinations. In this review, the aim is to clarify the best examples of biosensor usage in combination with iVR applications. The high number of papers in the review (560) were classified into the following seven fields of application: psychology, medicine, sports, education, ergonomics, military, and tourism and marketing. The use of each type of wearable biosensor and Head-Mounted Display was analyzed for each field of application. Then, the development of the iVR application is analyzed according to its goals, user interaction levels, and the possibility of adapting the iVR environment to biosensor feedback. Finally, the evaluation of the iVR experience was studied, considering such issues as sample size, the presence of a control group, and post-assessment routines. A working method through which the most common solutions, the best practices, and the most promising trends in biofeedback-based iVR applications were identified for each field of application. Besides, guidelines oriented towards good practice are proposed for the development of future iVR with biofeedback applications. The results of this review suggest that the use of biosensors within iVR environments need to be standardized in some fields of application, especially when considering the adaptation of the iVR experience to real-time biosignals to improve user performance.</jats:p>},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Miguel-Alonso2024e,

title = {Evaluation of the novelty effect in immersive Virtual Reality learning experiences},

author = {Ines Miguel-Alonso and David Checa and Henar Guillen-Sanz and Andres Bustillo},

doi = {10.1007/s10055-023-00926-5},

issn = {1434-9957},

year  = {2024},

date = {2024-03-00},

urldate = {2024-03-00},

journal = {Virtual Reality},

volume = {28},

number = {1},

publisher = {Springer Science and Business Media LLC},

abstract = {<jats:title>Abstract</jats:title><jats:p>In this study, the novelty effect or initial fascination with new technology is addressed in the context of an immersive Virtual Reality (iVR) experience. The novelty effect is a significant factor contributing to low learning outcomes during initial VR learning experiences. The aim of this research is to measure the effectiveness of a tutorial at mitigating the novelty effect of iVR learning environments among first-year undergraduate students. The iVR tutorial forms part of the iVR learning experience that involves the assembly of a personal computer, while learning the functions of the main components. 86 students participated in the study, divided into a Control group (without access to the tutorial) and a Treatment group (completing the tutorial). Both groups showed a clear bimodal distribution in previous knowledge, due to previous experience with learning topics, giving us an opportunity to compare tutorial effects with students of different backgrounds. Pre- and post-test questionnaires were used to evaluate the experience. The analysis included such factors as previous knowledge, usability, satisfaction, and learning outcomes categorized into remembering, understanding, and evaluation. The results demonstrated that the tutorial significantly increased overall satisfaction, reduced the learning time required for iVR mechanics, and improved levels of student understanding, and evaluation knowledge. Furthermore, the tutorial helped to homogenize group behavior, particularly benefiting students with less previous experience in the learning topic. However, it was noted that a small number of students still received low marks after the iVR experience, suggesting potential avenues for future research.</jats:p>},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Maestro-Prieto2024b,

title = {Semi-supervised diagnosis of wind-turbine gearbox misalignment and imbalance faults},

author = {Jose Alberto Maestro-Prieto and José Miguel Ramírez-Sanz and Andrés Bustillo and Juan José Rodriguez-Díez},

doi = {10.1007/s10489-024-05373-6},

issn = {1573-7497},

year  = {2024},

date = {2024-03-00},

urldate = {2024-03-00},

journal = {Appl Intell},

volume = {54},

number = {6},

pages = {4525--4544},

publisher = {Springer Science and Business Media LLC},

abstract = {<jats:sec>

                <jats:title>Abstract</jats:title>

                <jats:p>Both wear-induced bearing failure and misalignment of the powertrain between the rotor and the electrical generator are common failure modes in wind-turbine motors. In this study, Semi-Supervised Learning (SSL) is applied to a fault detection and diagnosis solution. Firstly, a dataset is generated containing both normal operating patterns and seven different failure classes of the two aforementioned failure modes that vary in intensity. Several datasets are then generated, maintaining different numbers of labeled instances and unlabeling the others, in order to evaluate the number of labeled instances needed for the desired accuracy level. Subsequently, different types of SSL algorithms and combinations of algorithms are trained and then evaluated with the test data. The results showed that an SSL approach could improve the accuracy of trained classifiers when a small number of labeled instances were used together with many unlabeled instances to train a Co-Training algorithm or combinations of such algorithms. When a few labeled instances (fewer than 10% or 327 instances, in this case) were used together with unlabeled instances, the SSL algorithms outperformed the result obtained with the Supervised Learning (SL) techniques used as a benchmark. When the number of labeled instances was sufficient, the SL algorithm (using only labeled instances) performed better than the SSL algorithms (accuracy levels of 87.04% vs. 86.45%, when labeling 10% of instances). A competitive accuracy of 97.73% was achieved with the SL algorithm processing a subset of 40% of the labeled instances.</jats:p>

              </jats:sec><jats:sec>

                <jats:title>Graphical abstract</jats:title>

                <jats:p>Steps and processes for approaching semi-supervised FDD of wind-turbine gearbox misalignment and imbalance faults</jats:p>

              </jats:sec>},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Ramírez-Sanz2023e,

title = {Semi-supervised learning for industrial fault detection and diagnosis: A systemic review},

author = {José Miguel Ramírez-Sanz and Jose-Alberto Maestro-Prieto and Álvar Arnaiz-González and Andrés Bustillo},

doi = {10.1016/j.isatra.2023.09.027},

issn = {0019-0578},

year  = {2023},

date = {2023-12-00},

urldate = {2023-12-00},

journal = {ISA Transactions},

volume = {143},

pages = {255--270},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Miguel-Alonso2023e,

title = {Design and development of a gamified tutorial for iVR serious games},

author = {Ines Miguel-Alonso and Henar Guillen-Sanz and Bruno Rodriguez-Garcia and Andres Bustillo},

doi = {10.34190/ecgbl.17.1.1563},

issn = {2049-100X},

year  = {2023},

date = {2023-09-29},

urldate = {2023-09-29},

journal = {ECGBL},

volume = {17},

number = {1},

pages = {411--417},

publisher = {Academic Conferences International Ltd},

abstract = {<jats:p>Serious games, including immersive Virtual Reality (iVR) experiences, can be challenging for players due to their unfamiliar control systems and mechanics. This study focuses on designing a gamified tutorial for iVR serious games that not only teaches iVR interactions but also enhances user enjoyment and engagement. The tutorial consists of progressively challenging mini-games that adapt to the user's performance. Tips and recommendations are provided through a robot avatar if users struggle or make mistakes. An optional narrative is included to enhance user engagement, but it is not mandatory for the iVR experience. Gamification elements, such as point collection and progress updates, are incorporated into the tutorial. It can be played independently or as an introduction to iVR serious games. The goal is to use gamification principles to maintain user engagement and flow while enhancing the learning experience in the virtual world.</jats:p>},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Serrano-Mamolar2023,

title = {Towards learner performance evaluation in iVR learning environments using eye-tracking and Machine-learning},

author = {Ana Serrano-Mamolar and Ines Miguel-Alonso and David Checa and Carlos Pardo-Aguilar},

doi = {10.3916/c76-2023-01},

issn = {1988-3293},

year  = {2023},

date = {2023-07-01},

urldate = {2023-07-01},

journal = {Comunicar: Media Education Research Journal},

volume = {31},

number = {76},

publisher = {Oxbridgepublishinghouse},

abstract = {<jats:p>At present, the use of eye-tracking data in immersive Virtual Reality (iVR) learning environments is set to become a powerful tool for maximizing learning outcomes, due to the low-intrusiveness of eye-tracking technology and its integration in commercial iVR Head Mounted Displays. However, the most suitable technologies for data processing should first be identified before their use in learning environments can be generalized. In this research, the use of machine-learning techniques is proposed for that purpose, evaluating their capabilities to classify the quality of the learning environment and to predict user learning performance. To do so, an iVR learning experience simulating the operation of a bridge crane was developed. Through this experience, the performance of 63 students was evaluated, both under optimum learning conditions and under stressful conditions. The final dataset included 25 features, mostly temporal series, with a dataset size of up to 50M data points. The results showed that different classifiers (KNN, SVM and Random Forest) provided the highest accuracy when predicting learning performance variations, while the accuracy of user learning performance was still far from optimized, opening a new line of future research. This study has the objective of serving as a baseline for future improvements to model accuracy using complex machine-learning techniques.</jats:p>

<jats:p>Actualmente, el uso de los datos del seguimiento de la mirada en entornos de aprendizaje de Realidad Virtual inmersiva (iVR) está destinado a ser una herramienta fundamental para maximizar los resultados de aprendizaje, dada la naturaleza poco intrusiva del eye-tracking y su integración en las gafas comerciales de Realidad Virtual. Pero, antes de que se pueda generalizar el uso del eye-tracking en entornos de aprendizaje, se deben identificar las tecnologías más adecuadas para el procesamiento de datos. Esta investigación propone el uso de técnicas de aprendizaje automático para este fin, evaluando sus capacidades para clasificar la calidad del entorno de aprendizaje y predecir el rendimiento de aprendizaje del usuario. Para ello, se ha desarrollado una experiencia docente en iVR para aprender el manejo de un puente-grúa. Con esta experiencia se ha evaluado el rendimiento de 63 estudiantes, tanto en condiciones óptimas de aprendizaje como en condiciones con factores estresores. El conjunto de datos final incluye 25 características, siendo la mayoría series temporales con un tamaño de conjunto de datos superior a 50 millones de puntos. Los resultados muestran que la aplicación de diferentes clasificadores como KNN, SVM o Random Forest tienen una alta precisión a la hora de predecir alteraciones en el aprendizaje, mientras que la predicción del rendimiento del aprendizaje del usuario aún está lejos de ser óptima, lo que abre una nueva línea de investigación futura. Este estudio tiene como objetivo servir como línea de base para futuras mejoras en la precisión de los modelos mediante el uso de técnicas de aprendizaje automático más complejas.</jats:p>},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Miguel-Alonso2023d,

title = {Countering the Novelty Effect: A Tutorial for Immersive Virtual Reality Learning Environments},

author = {Ines Miguel-Alonso and Bruno Rodriguez-Garcia and David Checa and Andres Bustillo},

doi = {10.3390/app13010593},

issn = {2076-3417},

year  = {2023},

date = {2023-01-00},

urldate = {2023-01-00},

journal = {Applied Sciences},

volume = {13},

number = {1},

publisher = {MDPI AG},

abstract = {<jats:p>Immersive Virtual Reality (iVR) is a new technology, the novelty effect of which can reduce the enjoyment of iVR experiences and, especially, learning achievements when presented in the classroom; an effect that the interactive tutorial proposed in this research can help overcome. Its increasingly complex levels are designed on the basis of Mayer’s Cognitive Theory of Multimedia Learning, so that users can quickly gain familiarity with the iVR environment. The tutorial was included in an iVR learning experience for its validation with 65 users. It was a success, according to the user satisfaction and tutorial usability survey. First, it gained very high ratings for satisfaction, engagement, and immersion. Second, high skill rates suggested that it helped users to gain familiarity with controllers. Finally, a medium-high value for flow pointed to major concerns related to skill and challenges with this sort of iVR experience. A few cases of cybersickness also arose. The survey showed that only intense cybersickness levels significantly limited performance and enjoyment; low levels had no influence on flow and immersion and little influence on skill, presence, and engagement, greatly reducing the benefits of the tutorial, despite which it remained useful.</jats:p>},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Arnau-González2023b,

title = {Toward Automatic Tutoring of Math Word Problems in Intelligent Tutoring Systems},

author = {Pablo Arnau-González and Ana Serrano-Mamolar and Stamos Katsigiannis and Turke Althobaiti and Miguel Arevalillo-Herráez},

doi = {10.1109/access.2023.3290478},

issn = {2169-3536},

year  = {2023},

date = {2023-00-00},

urldate = {2023-00-00},

journal = {IEEE Access},

volume = {11},

pages = {67030--67039},

publisher = {Institute of Electrical and Electronics Engineers (IEEE)},

keywords = {},

pubstate = {published},

tppubtype = {article}

}