@article{9546766,

title = {Analysis of Students’ Behavior Through User Clustering in Online Learning Settings, Based on Self Organizing Maps Neural Networks},

author = {Maria Soledad Delgado and Federico Morán and José Carlos San José and Daniel Burgos},

doi = {10.1109/ACCESS.2021.3115024},

issn = {2169-3536 },

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {IEEE Access},

volume = {9},

pages = {132592-132608},

abstract = {An accurate analysis of user behaviour in online learning environments is a useful means of early follow up of students, so that they can be better supported to improve their performance and achieve the expected competences. However, that task becomes challenging due to the massive data that learning management systems store and categories. With the COVID-19 pandemic still on-going, face-to-face learning settings have migrate into online and blended ones, meaning an increase of online students and teachers in need for a tailored and effective support to their needs. A novel unsupervised clustering technique based on the Self-Organizing Map (SOM) artificial neural network model is used in this research to analyse 1,709,189 records of online students enrolled from 2015 to 2019 at Universidad Internacional de La Rioja (UNIR), a fully online Higher Education institution. SOM performs a precise and diverse user clustering based on those records. Results highlight that specific clusters are linked to the intake average profile at the university, with a clear relation between user interaction and a higher performance. Further, results show that, out of a targeted desk research compared to the analysis in this paper, face-to-face and online settings are connected through the methodological approach beyond the technology-based environment, which presents a similar behaviour in both contexts},

keywords = {Data Analysis, eLearning, Neural Networks, Self-organizing map, Unsupervised},

pubstate = {published},

tppubtype = {article}

}

@article{9380344,

title = {Classifying Model-View-Controller Software Applications Using Self-Organizing Maps},

author = {Daniel Guamán and Maria Soledad Delgado and Jennifer Pérez},

doi = {10.1109/ACCESS.2021.3066348},

isbn = {2169-3536},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {IEEE Access},

volume = {9},

pages = {45201-45229},

abstract = {The new era of information and the needs of our society require continuous change in software and technology. Changes are produced very quickly and software systems require evolving at the same velocity, which implies that the decision-making process of software architectures should be (semi-)automated to satisfy changing needs and to avoid wrong decisions. This issue is critical since suboptimal architecture design decisions may lead to high cost and poor software quality. Therefore, systematic and (semi-)automated mechanisms that help software architects during the decision-making process are required. Architectural patterns are one of the most important features of software applications, but the same pattern can be implemented in different ways, leaving to results of different quality. When an application requires to evolve, knowledge extracted from similar applications is useful for driving decisions, since quality pattern implementations can be reproduced in similar applications to improve specific quality attributes. Therefore, clustering methods are especially suitable for classifying similar pattern implementations. In this paper, we apply a novel unsupervised clustering technique, based on the well-known artificial neural network model Self-Organizing Maps, to classify Model-View-Controller (MVC) pattern from a quality point of view. Software quality is analyzed by 24 metrics organized into the categories of Count/Size, Maintainability, Duplications, Complexity, and Design Quality. The main goal of this work is twofold: to identify the quality features that establish the similarity of MVC applications without software architect bias, and to classify MVC applications by means of Self-Organizing Maps based on quality metrics. To that end, this work performs an exploratory study by conducting two analyses with a dataset of 87 Java MVC applications characterized by the 24 metrics and two attributes that describe the technology dimension of the application. The stated findings provide a knowledge base that can help in the decision-making process for the architecture of Java MVC applications.},

keywords = {Clustering, Neural Networks, Self-organizing map, Unsupervised},

pubstate = {published},

tppubtype = {article}

}

@article{12-Gonzalez-Pardo-CCPE,

title = {Communication by identity in bio-inspired multi-agent systems},

author = {Antonio Gonzalez-Pardo and Pablo Varona and David Camacho and Francisco Borja Rodrí­guez},

url = {https://aida.etsisi.upm.es/wp-content/uploads/2012/03/CCPE-GonzalezPardoEtAl.pdf},

issn = {1532-0626},

year  = {2012},

date = {2012-03-03},

urldate = {2012-03-03},

journal = {International Journal Concurrency and Computation: Practice & Experience.},

volume = {2012},

number = {24},

pages = {589-603},

keywords = {Graph Theory, Multi-agents systems, Neural Networks},

pubstate = {published},

tppubtype = {article}

}