2021
Delgado, Maria Soledad; Morán, Federico; José, José Carlos San; Burgos, Daniel
Analysis of Students’ Behavior Through User Clustering in Online Learning Settings, Based on Self Organizing Maps Neural Networks Journal Article
In: IEEE Access, vol. 9, pp. 132592-132608, 2021, ISSN: 2169-3536 .
Abstract | Links | BibTeX | Tags: Data Analysis, eLearning, Neural Networks, Self-organizing map, Unsupervised
@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}
}
Guamán, Daniel; Delgado, Maria Soledad; Pérez, Jennifer
Classifying Model-View-Controller Software Applications Using Self-Organizing Maps Journal Article
In: IEEE Access, vol. 9, pp. 45201-45229, 2021, ISBN: 2169-3536.
Abstract | Links | BibTeX | Tags: Clustering, Neural Networks, Self-organizing map, Unsupervised
@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}
}
2017
Delgado, Maria Soledad; Higuera, Clara; Calle-Espinosa, Jorge; Morán, Federico; Montero, Francisco
A SOM prototype-based cluster analysis methodology Journal Article
In: Expert Systems with Applications, vol. 88, pp. 14-28, 2017, ISSN: 0957-4174.
Abstract | Links | BibTeX | Tags: Clustering, Metabolic network, Self-organizing map, Topology preserving, Unsupervised
@article{DELGADO201714,
title = {A SOM prototype-based cluster analysis methodology},
author = {Maria Soledad Delgado and Clara Higuera and Jorge Calle-Espinosa and Federico Morán and Francisco Montero},
url = {https://www.sciencedirect.com/science/article/pii/S0957417417304396},
doi = {https://doi.org/10.1016/j.eswa.2017.06.022},
issn = {0957-4174},
year = {2017},
date = {2017-01-01},
journal = {Expert Systems with Applications},
volume = {88},
pages = {14-28},
abstract = {Data clustering is aimed at finding groups of data that share common hidden properties. These kinds of techniques are especially critical at early stages of data analysis where no information about the dataset is available. One of the mayor shortcomings of the clustering algorithms is the difficulty for non-experts users to configure them and, in some cases, interpret the results. In this work a computational approach with a two-layer structure based on Self-Organizing Map (SOM) is presented for cluster analysis. In the first level, a quantization of the data samples using topology-preserving metrics to automatically determine the number of units in the SOM is proposed. In the second level the obtained SOM prototypes are clustered by means of a connectivity analysis to explore the quality of the partitioning with different number of clusters. The most important benefit of this two-layer procedure is that computational load decreases considerably in comparison with data based clustering methods, making it possible to cluster large data sets and to consider several different clustering alternatives in a limited time. This methodology produces a two-dimensional map representation of the, usually, high dimensional input space, along with quantitative information on viable clustering alternatives, which facilitates the exploration of the possible partitions in a dataset. The efficiency and interpretation of the methodology is illustrated by its application to artificial, benchmark and real complex biological datasets. The experimental results demonstrate the ability of the method to identify possible segmentations in a dataset, compared to algorithms that only yield a single clustering solution. The proposed algorithm tackles the intrinsic limitations of SOM and the parameter settings associated with the clustering methodology, without requiring the number of clusters or the SOM architecture as a prerequisite, among others. This way, it makes possible its application even by researchers with a limited expertise in machine learning.},
keywords = {Clustering, Metabolic network, Self-organizing map, Topology preserving, Unsupervised},
pubstate = {published},
tppubtype = {article}
}