2017
Olivares-Rodriguez, Cristian; Guenaga, Mariluz; Garaizar, Pablo
Automatic Assessment of Creativity in Heuristic Problem-solving Based on Query Diversity Journal Article
In: DYNA, vol. 92, no. 4, pp. 449-455, 2017.
Abstract | Links | BibTeX | Tags: aprendizaje automático, Búsqueda de información, information search, machine learning, patrón de consultas, problem solving, query pattern, resolución de problemas
@article{Olivares-Rodriguez2017,
title = {Automatic Assessment of Creativity in Heuristic Problem-solving Based on Query Diversity},
author = {Cristian Olivares-Rodriguez and Mariluz Guenaga and Pablo Garaizar},
url = {http://home/learninglabdeust/public_html.revistadyna.com/Articulos/Ficha.aspx?IdMenu=a5c9d895-28e0-4f92-b0c2-c0f86f2a940b&Cod=8243&Codigo=b25ba3bd-bef9-4391-8317-6a8209d9309e},
doi = {10.6036/8243},
year = {2017},
date = {2017-07-03},
journal = {DYNA},
volume = {92},
number = {4},
pages = {449-455},
abstract = {Creative problem-solving emerges as one of the most relevant skill of the 21st century knowledge society. Fortunately, there are many creativity training programmes that have proven effective. However, most of these programmes require a previous measurement of creativity, which involves time-consuming tasks conducted by experienced reviewers, i.e. far from primary school classroom dynamics. In this study, we propose a model to predict the creative quality of students’ solutions based on the analysis of query patterns and the use of Wikipedia. This model has been able to predict the creative quality of solutions produced by 226 school students, aged 10 to 12 years old, reaching a sensitivity of 78.43%. The agreement among reviewers regarding students’ creative characteristics has also been evaluated using two rubrics. We hope this model can be used to foster prompt detection of non-creative solutions in order to enable intervention and improve the final result in terms of creativity.},
keywords = {aprendizaje automático, Búsqueda de información, information search, machine learning, patrón de consultas, problem solving, query pattern, resolución de problemas},
pubstate = {published},
tppubtype = {article}
}
Creative problem-solving emerges as one of the most relevant skill of the 21st century knowledge society. Fortunately, there are many creativity training programmes that have proven effective. However, most of these programmes require a previous measurement of creativity, which involves time-consuming tasks conducted by experienced reviewers, i.e. far from primary school classroom dynamics. In this study, we propose a model to predict the creative quality of students’ solutions based on the analysis of query patterns and the use of Wikipedia. This model has been able to predict the creative quality of solutions produced by 226 school students, aged 10 to 12 years old, reaching a sensitivity of 78.43%. The agreement among reviewers regarding students’ creative characteristics has also been evaluated using two rubrics. We hope this model can be used to foster prompt detection of non-creative solutions in order to enable intervention and improve the final result in terms of creativity.
2016
Casado-Mansilla, Diego; de Armentia, Juan López; Ventura, Daniela; Garaizar, Pablo; Lopez-de-Ipiña, Diego
Embedding Intelligent Eco-aware Systems within Everyday Things to Increase People’s Energy Awareness Journal Article
In: Soft Computing, vol. 20, no. 5, pp. 1695-1711, 2016.
Abstract | Links | BibTeX | Tags: ARIMA models, Eco-aware everyday things, Energy awareness, machine learning, Persuasive eco-feedback, Time series
@article{Casado-Mansilla2016,
title = {Embedding Intelligent Eco-aware Systems within Everyday Things to Increase People’s Energy Awareness},
author = {Diego Casado-Mansilla and Juan López de Armentia and Daniela Ventura and Pablo Garaizar and Diego Lopez-de-Ipiña },
url = {https://morelab.deusto.es/media/publications/2015/journalarticle/embedding-intelligent-eco-aware-systems-within-everyday-things-to-increase-peoples-energy-awareness.pdf},
doi = {10.1007/s00500-015-1751-0},
year = {2016},
date = {2016-05-01},
journal = {Soft Computing},
volume = {20},
number = {5},
pages = {1695-1711},
abstract = {There is a lack of energy consumption awareness in working spaces. People in their workplaces do not receive energy consumption feedback nor do they pay a monthly invoice to electricity providers. In order to enhance workers’ energy awareness, we have transformed everyday shared electrical appliances which are placed in common spaces (e.g. beamer projectors, coffee-makers, printers, screens, portable fans, kettles, and so on) into persuasive eco-aware everyday things. The proposed approach lets these appliances report their usage patterns to a Cloud-server where the data are transformed into time-series and then processed to obtain the appliances’ next-week usage forecast. Autoregressive integrated moving average model has been selected as the potentially most accurate method for processing such usage predictions when compared with the performance exhibited by three different configurations of Artificial neural networks. Our major contribution is the application of soft computing techniques to the field of sustainable persuasive technologies. Thus, consumption predictions are used to trigger timely persuasive interactions to help device users to operate the appliances as efficiently, energy-wise, as possible. Qualitative and quantitative results were gathered in a between-three-groups study related with the use of shared electrical coffee-makers at workplace. The goal of these studies was to assess the effectiveness of the proposed eco-aware design in a workplace environment in terms of energy saving and the degree of affiliation between people and the smart appliances to create a green-team relationship.
},
keywords = {ARIMA models, Eco-aware everyday things, Energy awareness, machine learning, Persuasive eco-feedback, Time series},
pubstate = {published},
tppubtype = {article}
}
There is a lack of energy consumption awareness in working spaces. People in their workplaces do not receive energy consumption feedback nor do they pay a monthly invoice to electricity providers. In order to enhance workers’ energy awareness, we have transformed everyday shared electrical appliances which are placed in common spaces (e.g. beamer projectors, coffee-makers, printers, screens, portable fans, kettles, and so on) into persuasive eco-aware everyday things. The proposed approach lets these appliances report their usage patterns to a Cloud-server where the data are transformed into time-series and then processed to obtain the appliances’ next-week usage forecast. Autoregressive integrated moving average model has been selected as the potentially most accurate method for processing such usage predictions when compared with the performance exhibited by three different configurations of Artificial neural networks. Our major contribution is the application of soft computing techniques to the field of sustainable persuasive technologies. Thus, consumption predictions are used to trigger timely persuasive interactions to help device users to operate the appliances as efficiently, energy-wise, as possible. Qualitative and quantitative results were gathered in a between-three-groups study related with the use of shared electrical coffee-makers at workplace. The goal of these studies was to assess the effectiveness of the proposed eco-aware design in a workplace environment in terms of energy saving and the degree of affiliation between people and the smart appliances to create a green-team relationship.
2015
Casado-Mansilla, Pablo; de Armentia, Juan López; Ventura, Daniela; Garaizar, Pablo; Lopez-de-Ipiña, Diego
Embedding Intelligent Eco-aware Systems within Everyday Things to Increase People’s Energy Awareness Journal Article
In: Soft Computing Journal, vol. 20, no. 5, pp. 1695-1711, 2015, ISSN: 1433-7479.
Abstract | Links | BibTeX | Tags: ARIMA models, Eco-aware everyday things, Energy awareness, machine learning, Persuasive eco-feedback, Time series
@article{Casado-Mansilla2015,
title = {Embedding Intelligent Eco-aware Systems within Everyday Things to Increase People’s Energy Awareness},
author = {Pablo Casado-Mansilla and Juan López de Armentia and Daniela Ventura and Pablo Garaizar and Diego Lopez-de-Ipiña},
url = {https://link.springer.com/content/pdf/10.1007%2Fs00500-015-1751-0.pdf},
doi = {10.1007/s00500-015-1751-0},
issn = {1433-7479},
year = {2015},
date = {2015-06-26},
journal = {Soft Computing Journal},
volume = {20},
number = {5},
pages = {1695-1711},
abstract = {There is a lack of energy consumption awareness in working spaces. People in their workplaces do not receive energy consumption feedback nor do they pay a monthly invoice to electricity providers. In order to enhance workers’ energy awareness, we have transformed everyday shared electrical appliances which are placed in common spaces (e.g. beamer projectors, coffee-makers, printers, screens, portable fans, kettles, and so on) into persuasive eco-aware everyday things. The proposed approach lets these appliances report their usage patterns to a Cloud-server where the data are transformed into time-series and then processed to obtain the appliances’ next-week usage forecast. Autoregressive integrated moving average model has been selected as the potentially most accurate method for processing such usage predictions when compared with the performance exhibited by three different configurations of Artificial neural networks. Our major contribution is the application of soft computing techniques to the field of sustainable persuasive technologies. Thus, consumption predictions are used to trigger timely persuasive interactions to help device users to operate the appliances as efficiently, energy-wise, as possible. Qualitative and quantitative results were gathered in a between-three-groups study related with the use of shared electrical coffee-makers at workplace. The goal of these studies was to assess the effectiveness of the proposed eco-aware design in a workplace environment in terms of energy saving and the degree of affiliation between people and the smart appliances to create a green-team relationship.},
keywords = {ARIMA models, Eco-aware everyday things, Energy awareness, machine learning, Persuasive eco-feedback, Time series},
pubstate = {published},
tppubtype = {article}
}
There is a lack of energy consumption awareness in working spaces. People in their workplaces do not receive energy consumption feedback nor do they pay a monthly invoice to electricity providers. In order to enhance workers’ energy awareness, we have transformed everyday shared electrical appliances which are placed in common spaces (e.g. beamer projectors, coffee-makers, printers, screens, portable fans, kettles, and so on) into persuasive eco-aware everyday things. The proposed approach lets these appliances report their usage patterns to a Cloud-server where the data are transformed into time-series and then processed to obtain the appliances’ next-week usage forecast. Autoregressive integrated moving average model has been selected as the potentially most accurate method for processing such usage predictions when compared with the performance exhibited by three different configurations of Artificial neural networks. Our major contribution is the application of soft computing techniques to the field of sustainable persuasive technologies. Thus, consumption predictions are used to trigger timely persuasive interactions to help device users to operate the appliances as efficiently, energy-wise, as possible. Qualitative and quantitative results were gathered in a between-three-groups study related with the use of shared electrical coffee-makers at workplace. The goal of these studies was to assess the effectiveness of the proposed eco-aware design in a workplace environment in terms of energy saving and the degree of affiliation between people and the smart appliances to create a green-team relationship.
Guenaga, Mariluz; Menchaca, Iratxe; Solabarrieta, J.
Project-Based Learning: Methodology and Assessment Learning Technologies and Assessment Criteria Conference
Using Educational Analytics to Improve Test Performance, 2015, ISBN: 978-3-319-24257-6.
Abstract | Links | BibTeX | Tags: aprendizaje automático, learning analytics, machine learning, mining educational data
@conference{Guenaga2015b,
title = {Project-Based Learning: Methodology and Assessment Learning Technologies and Assessment Criteria},
author = {Mariluz Guenaga and Iratxe Menchaca and J. Solabarrieta},
url = {https://home/learninglabdeust/public_html.researchgate.net/publication/283535054_Project-Based_Learning_Methodology_and_Assessment_Learning_Technologies_and_Assessment_Criteria},
doi = {10.1007/978-3-319-24258-3_68},
isbn = {978-3-319-24257-6},
year = {2015},
date = {2015-01-01},
booktitle = {Using Educational Analytics to Improve Test Performance},
pages = {601-604},
abstract = {This paper uses a project-based learning methodology in higher education to analyse its relation to a theoretical framework of competency. Based on this analysis, we propose a set of technological tools to support the development of competency at the university level as well as a set of indicators to systematize the assessment process. Finally, indicators are related to data that can be obtained from these technological tools. This is the basis for additional work on learning analytics that is used to support the assessment of a project-based learning approach. },
keywords = {aprendizaje automático, learning analytics, machine learning, mining educational data},
pubstate = {published},
tppubtype = {conference}
}
This paper uses a project-based learning methodology in higher education to analyse its relation to a theoretical framework of competency. Based on this analysis, we propose a set of technological tools to support the development of competency at the university level as well as a set of indicators to systematize the assessment process. Finally, indicators are related to data that can be obtained from these technological tools. This is the basis for additional work on learning analytics that is used to support the assessment of a project-based learning approach.
2014
Ventura, Daniela; Casado-Mansilla, Diego; de Armentia, Juan López; Garaizar, Pablo; Lopez-de-Ipiña, Diego; Catania, Vincenzco
ARIIMA: A Real IoT Implementation of a Machine-Learning Architecture for Reducing Energy Consumption Conference
vol. 8867, Springer, Cham, 2014, ISBN: 978-3-319-13102-3.
Abstract | Links | BibTeX | Tags: ARIMA models, energy efficiency, IOT, machine learning
@conference{Ventura2014,
title = {ARIIMA: A Real IoT Implementation of a Machine-Learning Architecture for Reducing Energy Consumption},
author = {Daniela Ventura and Diego Casado-Mansilla and Juan López de Armentia and Pablo Garaizar and Diego Lopez-de-Ipiña and Vincenzco Catania},
url = {https://link.springer.com/chapter/10.1007/978-3-319-13102-3_72},
doi = {10.1007/978-3-319-13102-3_72},
isbn = {978-3-319-13102-3},
year = {2014},
date = {2014-12-01},
journal = {Lecture Notes in Computer Science },
volume = {8867},
publisher = {Springer, Cham},
abstract = {As the inclusion of more devices and appliances within the IoT ecosystem increases, methodologies for lowering their energy consumption impact are appearing. On this field, we contribute with the implementation of a RESTful infrastructure that gives support to Internet-connected appliances to reduce their energy waste in an intelligent fashion. Our work is focused on coffee machines located in common spaces where people usually do not care on saving energy, e.g. the workplace. The proposed approach lets these kind of appliances report their usage patterns and to process their data in the Cloud through ARIMA predictive models. The aim such prediction is that the appliances get back their next-week usage forecast in order to operate autonomously as efficient as possible. The underlying distributed architecture design and implementation rationale is discussed in this paper, together with the strategy followed to get an accurate prediction matching with the real data retrieved by four coffee machines.
},
keywords = {ARIMA models, energy efficiency, IOT, machine learning},
pubstate = {published},
tppubtype = {conference}
}
As the inclusion of more devices and appliances within the IoT ecosystem increases, methodologies for lowering their energy consumption impact are appearing. On this field, we contribute with the implementation of a RESTful infrastructure that gives support to Internet-connected appliances to reduce their energy waste in an intelligent fashion. Our work is focused on coffee machines located in common spaces where people usually do not care on saving energy, e.g. the workplace. The proposed approach lets these kind of appliances report their usage patterns and to process their data in the Cloud through ARIMA predictive models. The aim such prediction is that the appliances get back their next-week usage forecast in order to operate autonomously as efficient as possible. The underlying distributed architecture design and implementation rationale is discussed in this paper, together with the strategy followed to get an accurate prediction matching with the real data retrieved by four coffee machines.
2012
Matute, Helena; Vadillo, Miguel A.; Garaizar, Pablo
Springer, Boston, MA, 2012, ISBN: 978-1-4419-1428-6.
Abstract | Links | BibTeX | Tags: associative learning, casual learning, e-learning, machine learning
@book{Matute2012,
title = {Web-based experiment control software for research on human learning. Encyclopedia of the sciences of learning},
author = {Helena Matute and Miguel A. Vadillo and Pablo Garaizar},
editor = {Prof. Norbert M. Seel},
url = {https://helenamatute.files.wordpress.com/2014/02/matute-vadillo-garaizar-2012.pdf},
isbn = {978-1-4419-1428-6},
year = {2012},
date = {2012-02-04},
publisher = {Springer, Boston, MA},
abstract = {The Encyclopedia of the Sciences of Learning provides an up-to-date, broad and authoritative coverage of the specific terms mostly used in the sciences of learning and its related fields, including relevant areas of instruction, pedagogy, cognitive sciences, and especially machine learning and knowledge engineering. This modern compendium will be an indispensable source of information for scientists, educators, engineers, and technical staff active in all fields of learning. More specifically, the Encyclopedia provides fast access to the most relevant theoretical terms provides up-to-date, broad and authoritative coverage of the most important theories within the various fields of the learning sciences and adjacent sciences and communication technologies; supplies clear and precise explanations of the theoretical terms, cross-references to related entries and up-to-date references to important research and publications. The Encyclopedia also contains biographical entries of individuals who have substantially contributed to the sciences of learning; the entries are written by a distinguished panel of researchers in the various fields of the learning sciences.},
keywords = {associative learning, casual learning, e-learning, machine learning},
pubstate = {published},
tppubtype = {book}
}
The Encyclopedia of the Sciences of Learning provides an up-to-date, broad and authoritative coverage of the specific terms mostly used in the sciences of learning and its related fields, including relevant areas of instruction, pedagogy, cognitive sciences, and especially machine learning and knowledge engineering. This modern compendium will be an indispensable source of information for scientists, educators, engineers, and technical staff active in all fields of learning. More specifically, the Encyclopedia provides fast access to the most relevant theoretical terms provides up-to-date, broad and authoritative coverage of the most important theories within the various fields of the learning sciences and adjacent sciences and communication technologies; supplies clear and precise explanations of the theoretical terms, cross-references to related entries and up-to-date references to important research and publications. The Encyclopedia also contains biographical entries of individuals who have substantially contributed to the sciences of learning; the entries are written by a distinguished panel of researchers in the various fields of the learning sciences.