@inbook{VanVaerenbergh2022,
author = {Van Vaerenbergh, Steven and P{\'e}rez-Suay, Adri{\'a}n},
editor = {Richard, Philippe R. and V{\'e}lez, M. Pilar and Van Vaerenbergh, Steven},
title = {A Classification of Artificial Intelligence Systems for Mathematics Education},
booktitle = {Mathematics Education in the Age of Artificial Intelligence: How Artificial Intelligence can Serve Mathematical Human Learning},
year = {2022},
publisher = {Springer International Publishing},
address = {Cham},
pages = {89--106},
abstract = {This chapter provides an overview of the different Artificial Intelligence (AI) systems that are being used in contemporary digital tools for Mathematics Education (ME). It is aimed at researchers in AI and Machine Learning (ML), for whom we shed some light on the specific technologies that are being used in educational applications; and at researchers in ME, for whom we clarify: (i) what the possibilities of the current AI technologies are, (ii) what is still out of reach and (iii) what is to be expected in the near future. We start our analysis by establishing a high-level taxonomy of AI tools that are found as components in digital ME applications. Then, we describe in detail how these AI tools, and in particular ML, are being used in two key applications, specifically AI-based calculators and intelligent tutoring systems. We finish the chapter with a discussion about student modeling systems and their relationship to artificial general intelligence.},
isbn = {978-3-030-86909-0},
doi = {10.1007/978-3-030-86909-0_5},
url = {https://doi.org/10.1007/978-3-030-86909-0_5}
}
@inbook{Moreno-Martínez2020,
author = {Moreno-Mart{\'i}nez, {\'A}lvaro and Piles, Mar{\'i}a and Mu{\~{n}}oz-Mar{\'i}, Jordi and Campos-Taberner, Manuel and Adsuara, Jose E. and Mateo, Anna and Perez-Suay, Adri{\'a}n and Javier Garc{\'i}a-Haro, Francisco and Camps-Valls, Gustau},
editor = {Prasad, Saurabh and Chanussot, Jocelyn},
title = {Machine Learning Methods for Spatial and Temporal Parameter Estimation},
booktitle = {Hyperspectral Image Analysis: Advances in Machine Learning and Signal Processing},
year = {2020},
publisher = {Springer International Publishing},
address = {Cham},
pages = {5--35},
abstract = {Monitoring vegetation with satellite remote sensingRemote sensing is of paramount relevance to understand the status and health of our planet. Accurate and constant monitoring of the biosphere has large societal, economical, and environmental implications, given the increasing demand of biofuels and food by the world population. The current democratization of machine learning, big data, and high processing capabilities allow us to take such endeavor in a decisive manner. This chapter proposes three novel machine learning approaches to exploit spatial, temporal, multi-sensor, and large-scale data characteristics. We show (1) the application of multi-output Gaussian processesGaussian processes for gap-fillingGap filling time series of soil moistureSoil moisture retrievals from three spaceborne sensors; (2) a new kernelDistribution regression distribution regressionKernel distribution regression model that exploits multiple observations and higher order relations to estimate county-level crop yield from time series of vegetation optical depth; and finally (3) we show the combination of radiative transfer modelsRadiative transfer models with random forests to estimate leaf area index, fraction of absorbed photosynthetically active radiation, fraction vegetation coverFraction vegetation cover, and canopy water contentCanopy water content at global scale from long-term time series of multispectral data exploiting the Google Earth Engine cloud processing capabilities. The approaches demonstrate that machine learning algorithms can ingest and process multi-sensor data and provide accurate estimates of key parameters for vegetation monitoring.},
isbn = {978-3-030-38617-7},
doi = {10.1007/978-3-030-38617-7_2},
url = {https://doi.org/10.1007/978-3-030-38617-7_2}
}
@incollection{Steven2022,
title = {Intelligent Learning Management Systems: Overview and Application in Mathematics Education},
author = {Van Vaerenbergh, Steven and Pérez-Suay, Adrián},
year = 2022,
booktitle = {Strategy, Policy, Practice, and Governance for AI in Higher Education Institutions},
publisher = {IGI Global},
address = {Hershey, PA},
pages = {206--232},
editor = {Almaraz-Menéndez, Fernando and Maz-Machado, Alexander and López-Esteban, Carmen and Almaraz-López, Cristina},
doi = {https://doi.org/10.4018/978-1-7998-9247-2.ch009}
}
@incollection{WileyBook,
title = {Clustering and Anomaly Detection with Kernels},
author = {Pérez-Suay, Adrián},
year = 2018,
booktitle = {Digital Signal Processing with Kernel Methods},
publisher = {Wiley-IEEE Press},
address = {},
pages = {},
editor = {Rojo-Alvarez, Jose Luis and Martinez-Ramon, Manel and Muñoz-Marí, Jordi and Camps-Valls, Gustau},
doi = {https://doi.org/10.1002/9781118705810.ch11},
url = {https://www.wiley.com/en-ie/Digital+Signal+Processing+with+Kernel+Methods-p-9781118611791}
}
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