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}
}
2015
Palero, Fernando; Gonzalez-Pardo, Antonio; Camacho, David
Simple gamer interaction analysis through tower defense games Conference
6th International Conference on Computational Collective Intelligence Technologies and Applications (ICCCI 2014), Lecture Notes in Artificial Intelligence of Springer-Verlag, 2015.
BibTeX | Tags: Data Analysis, Data Mining, Videogames
@conference{2015-PaleroEtAl,
title = {Simple gamer interaction analysis through tower defense games},
author = {Fernando Palero and Antonio Gonzalez-Pardo and David Camacho},
year = {2015},
date = {2015-01-01},
urldate = {2015-01-01},
booktitle = {6th International Conference on Computational Collective Intelligence Technologies and Applications (ICCCI 2014)},
pages = {185-194},
publisher = {Lecture Notes in Artificial Intelligence of Springer-Verlag},
keywords = {Data Analysis, Data Mining, Videogames},
pubstate = {published},
tppubtype = {conference}
}
2012
Menéndez, Héctor D; Camacho, David
A Genetic Graph-Based Clustering Algorithm Book Section
In: Intelligent Data Engineering and Automated Learning - IDEAL 2012, vol. 7435, pp. 216-225, Springer Berlin / Heidelberg, 2012, ISSN: 978-3-642-32638-7, (10.1007/978-3-642-32639-4_27).
Abstract | Links | BibTeX | Tags: Clustering, Data Analysis, Data Mining, Graph Theory
@incollection{springerlink:10.1007/978-3-642-32639-4_27,
title = {A Genetic Graph-Based Clustering Algorithm},
author = {Héctor D Menéndez and David Camacho},
url = {http://dx.doi.org/10.1007/978-3-642-32639-4_27},
issn = {978-3-642-32638-7},
year = {2012},
date = {2012-01-01},
urldate = {2012-01-01},
booktitle = {Intelligent Data Engineering and Automated Learning - IDEAL 2012},
volume = {7435},
pages = {216-225},
publisher = {Springer Berlin / Heidelberg},
series = {Lecture Notes in Computer Science},
abstract = {The interest in the analysis and study of clustering techniques have grown since the introduction of new algorithms based on the continuity of the data, where problems related to image segmentation and tracking, amongst others, makes difficult the correct classification of data into their appropriate groups, or clusters. Some new techniques, such as Spectral Clustering (SC), uses graph theory to generate the clusters through the spectrum of the graph created by a similarity function applied to the elements of the database. The approach taken by SC allows to handle the problem of data continuity though the graph representation. Based on this idea, this study uses genetic algorithms to select the groups using the same similarity graph built by the Spectral Clustering method. The main contribution is to create a new algorithm which improves the robustness of the Spectral Clustering algorithm reducing the dependency of the similarity metric parameters that currently affects to the performance of SC approaches. This algorithm, named Genetic Graph-based Clustering (GGC), has been tested with different synthetic and real-world datasets, the experimental results have been compared against classical clustering algorithms like K-Means, EM and SC.},
note = {10.1007/978-3-642-32639-4_27},
keywords = {Clustering, Data Analysis, Data Mining, Graph Theory},
pubstate = {published},
tppubtype = {incollection}
}
Menéndez, Héctor D; Bello-Orgaz, Gema; Camacho, David
Features selection from high-dimensional web data using clustering analysis Proceedings Article
In: Proceedings of the 2nd International Conference on Web Intelligence, Mining and Semantics, pp. 20:1–20:9, ACM, New York, NY, USA, 2012, ISSN: 978-1-4503-0915-8.
Links | BibTeX | Tags: Clustering, Data Analysis, Data Projection, Feature Selection, Sports, Web Mining
@inproceedings{Menendez:2012:FSH:2254129.2254155,
title = {Features selection from high-dimensional web data using clustering analysis},
author = {Héctor D Menéndez and Gema Bello-Orgaz and David Camacho},
url = {http://doi.acm.org/10.1145/2254129.2254155},
issn = {978-1-4503-0915-8},
year = {2012},
date = {2012-01-01},
urldate = {2012-01-01},
booktitle = {Proceedings of the 2nd International Conference on Web Intelligence, Mining and Semantics},
pages = {20:1--20:9},
publisher = {ACM},
address = {New York, NY, USA},
series = {WIMS '12},
keywords = {Clustering, Data Analysis, Data Projection, Feature Selection, Sports, Web Mining},
pubstate = {published},
tppubtype = {inproceedings}
}
Delgado, Maria Soledad; Gómez, David; Pulido-Valdeolivas, Irene; Lopez, Javier; Martín, J. A.; Morán, F.; Rausell, Estrella
Gait patterns in a reference dataset of healthy children Journal Article
In: Gait & Posture, vol. 36, pp. S97, 2012.
Links | BibTeX | Tags: Data Analysis, Growing cell structures, Health, Self-organizing map
@article{article,
title = {Gait patterns in a reference dataset of healthy children},
author = {Maria Soledad Delgado and David Gómez and Irene Pulido-Valdeolivas and Javier Lopez and J. A. Martín and F. Morán and Estrella Rausell},
doi = {10.1016/j.gaitpost.2011.10.345},
year = {2012},
date = {2012-01-01},
urldate = {2012-01-01},
journal = {Gait & Posture},
volume = {36},
pages = {S97},
keywords = {Data Analysis, Growing cell structures, Health, Self-organizing map},
pubstate = {published},
tppubtype = {article}
}
2010
Granados, Ana; Martínez, Rafael; Camacho, David; Rodríguez, Francisco Borja
Relevance of Contextual Information in Compression-Based Text Clustering Proceedings Article
In: IDEAL, pp. 259-266, 2010.
BibTeX | Tags: Clustering, Data Analysis
@inproceedings{DBLP:conf/ideal/GranadosMCR10,
title = {Relevance of Contextual Information in Compression-Based Text Clustering},
author = {Ana Granados and Rafael Martínez and David Camacho and Francisco Borja Rodríguez},
year = {2010},
date = {2010-01-01},
urldate = {2010-01-01},
booktitle = {IDEAL},
pages = {259-266},
crossref = {DBLP:conf/ideal/2010},
keywords = {Clustering, Data Analysis},
pubstate = {published},
tppubtype = {inproceedings}
}
2007
Camacho, David; R-Moreno, Maria D
DynJAQ: An adaptive and flexible dynamic FAQ system Journal Article
In: Int. J. Intell. Syst., vol. 22, no. 3, pp. 303-318, 2007.
BibTeX | Tags: Data Analysis
@article{DBLP:journals/ijis/CamachoR07,
title = {DynJAQ: An adaptive and flexible dynamic FAQ system},
author = {David Camacho and Maria D R-Moreno},
year = {2007},
date = {2007-01-01},
urldate = {2007-01-01},
journal = {Int. J. Intell. Syst.},
volume = {22},
number = {3},
pages = {303-318},
keywords = {Data Analysis},
pubstate = {published},
tppubtype = {article}
}
Granados, Ana; Cebrián, Manuel; Camacho, David; Rodríguez, Francisco Borja
Evaluating the Impact of Information Distortion on Normalized Compression Distance-driven Text Clustering Journal Article
In: CoRR, vol. abs/0711.4075, 2007.
BibTeX | Tags: Clustering, Data Analysis
@article{DBLP:journals/corr/abs-0711-4075,
title = {Evaluating the Impact of Information Distortion on Normalized Compression Distance-driven Text Clustering},
author = {Ana Granados and Manuel Cebrián and David Camacho and Francisco Borja Rodríguez},
year = {2007},
date = {2007-01-01},
urldate = {2007-01-01},
journal = {CoRR},
volume = {abs/0711.4075},
keywords = {Clustering, Data Analysis},
pubstate = {published},
tppubtype = {article}
}
2004
Camacho, David; R-Moreno, Maria D; López, Alberto; Castro, César
Using Hierarchical Knowledge Structures to Implement Dynamic FAQ Systems Proceedings Article
In: PAKM, pp. 496-507, 2004.
BibTeX | Tags: Computer-based Education, Data Analysis
@inproceedings{DBLP:conf/pakm/CamachoRLC04,
title = {Using Hierarchical Knowledge Structures to Implement Dynamic FAQ Systems},
author = {David Camacho and Maria D R-Moreno and Alberto López and César Castro},
year = {2004},
date = {2004-01-01},
urldate = {2004-01-01},
booktitle = {PAKM},
pages = {496-507},
crossref = {DBLP:conf/pakm/2004},
keywords = {Computer-based Education, Data Analysis},
pubstate = {published},
tppubtype = {inproceedings}
}