Person:
Caballero Roldán, Rafael

Loading...
Profile Picture
First Name
Rafael
Last Name
Caballero Roldán
Affiliation
Universidad Complutense de Madrid
Faculty / Institute
Informática
Department
Sistemas Informáticos y Computación
Area
Lenguajes y Sistemas Informáticos
Identifiers
UCM identifierORCIDScopus Author IDDialnet IDGoogle Scholar ID

Search Results

Now showing 1 - 2 of 2
  • Publication
    Short term cloud nowcasting for a solar power plant based on irradiance historical Data
    (Universidad Nacional de La Plata, 2018-12) Caballero Roldán, Rafael; Zarzalejo Tirado, Luis Fernando; Otero Martín, Álvaro; Piñuel Moreno, Luis; Wilbert, Stefan
    This work considers the problem of forecasting the normal solar irradiance with high spatial and temporal resolution (5 minutes). The forecasting is based on a dataset registered during one year from the high resolution radiometric network at a operational solar power plan at Almeria, Spain. In particular, we show a technique for forecasting the irradiance in the next few minutes from the irradiance values obtained on the previous hour. Our proposal employs a type of recurrent neural network known as LSTM, which can learn complex patterns and that has proven its usability for forecasting temporal series. The results show a reasonable improvement with respect to other prediction methods typically employed in the studies of temporal series.
  • Publication
    Predicting students' knowledge after playing a serious game based on learning analytics data: A case study
    (Wiley, 2019-12) Alonso Fernández, Cristina; Martínez Ortiz, Iván; Caballero Roldán, Rafael; Freire Morán, Manuel; Fernández Manjón, Baltasar
    Serious games have proven to be a powerful tool in education to engage, motivate, and help students learn. However, the change in student knowledge after playing games is usually measured with traditional (paper) prequestionnaires–postquestionnaires. We propose a combination of game learning analytics and data mining techniques to predict knowledge change based on in-game student interactions. We have tested this approach in a case study for which we have conducted preexperiments–postexperiments with 227 students playing a previously validated serious game on first aid techniques. We collected student interaction data while students played, using a game learning analytics infrastructure and the standard data format Experience API for Serious Games. After data collection, we developed and tested prediction models to determine whether knowledge, given as posttest results, can be accurately predicted. Additionally, we compared models both with and without pretest information to determine the importance of previous knowledge when predicting postgame knowledge. The high accuracy of the obtained prediction models suggests that serious games can be used not only to teach but also to measure knowledge acquisition after playing. This will simplify serious games application for educational settings and especially in the classroom easing teachers' evaluation tasks.