The Experts below are selected from a list of 7050 Experts worldwide ranked by ideXlab platform
Ryan S Baker - One of the best experts on this subject based on the ideXlab platform.
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Educational Data Mining and learning analytics
The cambridge handbook of the learning sciences 2014 ISBN 978-1-107-62657-7 págs. 253-274, 2014Co-Authors: Ryan S Baker, Paul Salvador InventadoAbstract:In recent years, two communities have grown around a joint interest on how big Data can be exploited to benefit education and the science of learning: Educational Data Mining and Learning Analytics. This article discusses the relationship between these two communities, and the key methods and approaches of Educational Data Mining. The article discusses how these methods emerged in the early days of research in this area, which methods have seen particular interest in the EDM and learning analytics communities, and how this has changed as the field matures and has moved to making significant contributions to both Educational research and practice.
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The Wiley Handbook of Cognition and Assessment - Educational Data Mining and Learning Analytics
Learning Analytics, 2014Co-Authors: Ryan S Baker, Paul Salvador InventadoAbstract:In recent years, two communities have grown around a joint interest on how big Data can be exploited to benefit education and the science of learning: Educational Data Mining and Learning Analytics. This article discusses the relationship between these two communities, and the key methods and approaches of Educational Data Mining. The article discusses how these methods emerged in the early days of research in this area, which methods have seen particular interest in the EDM and learning analytics communities, and how this has changed as the field matures and has moved to making significant contributions to both Educational research and practice.
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Educational Data Mining: An Advance for Intelligent Systems in Education
IEEE Intelligent Systems, 2014Co-Authors: Ryan S BakerAbstract:Educational Data Mining methods have been successful at modeling a range of phenomena relevant to student learning in online intelligent systems. Here, the author considers the current state of the field, outlining recent strides and remaining challenges.
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the potentials of Educational Data Mining for researching metacognition motivation and self regulated learning
Educational Data Mining, 2013Co-Authors: Philip H Winne, Ryan S BakerAbstract:Our article introduces the Journal of Educational Data Mining's Special Issue on Educational Data Mining on Motivation, Metacognition, and Self-Regulated Learning. We outline general research challenges for Data Mining researchers who conduct investigations in these areas, the potential of EDM to advance research in this area, and issues in validating findings generated by EDM.
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EDM - Development of a Workbench to Address the Educational Data Mining Bottleneck
2012Co-Authors: Ma. Mercedes T. Rodrigo, Ryan S Baker, Bruce M. Mclaren, Alejandra JaymeAbstract:In recent years, machine-learning software packages have made it easier for Educational Data Mining researchers to create real-time detectors of cognitive skill as well as of metacognitive and motivational behavior that can be used to improve student learning. However, there remain challenges to overcome for these methods to become available to the wider Educational research and practice communities, including developing the labels that support supervised learning, distilling relevant and appropriate Data features, and setting up appropriate cross-validation and configuration algorithms. We discuss the development of an Educational Data Mining (EDM) Workbench designed to address these challenges.
Neil T Heffernan - One of the best experts on this subject based on the ideXlab platform.
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spectral clustering in Educational Data Mining
Educational Data Mining, 2011Co-Authors: Shubhendu Trivedi, Zachary A Pardos, Gabor N Sarkozy, Neil T HeffernanAbstract:Spectral Clustering is a graph theoretic technique to represent Data in such a way that clustering on this new representation is reduced to a trivial task. It is especially useful in complex Datasets where traditional clustering methods would fail to find groupings. In previous work we have shown the utility of using K-means clustering for exploiting structure in the Data to affect a significant improvement in prediction accuracy on Educational Datasets. In this work we show that by using Spectral Clustering we are able to further improve the student performance prediction. We evaluate an Educational Data Mining prediction task: predicting student state test scores from student features derived from a tutor and also explore some other EDM tasks using spectral clustering.
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EDM - Spectral Clustering in Educational Data Mining.
2011Co-Authors: Shubhendu Trivedi, Zachary A Pardos, Gabor N Sarkozy, Neil T HeffernanAbstract:Spectral Clustering is a graph theoretic technique to represent Data in such a way that clustering on this new representation is reduced to a trivial task. It is especially useful in complex Datasets where traditional clustering methods would fail to find groupings. In previous work we have shown the utility of using K-means clustering for exploiting structure in the Data to affect a significant improvement in prediction accuracy on Educational Datasets. In this work we show that by using Spectral Clustering we are able to further improve the student performance prediction. We evaluate an Educational Data Mining prediction task: predicting student state test scores from student features derived from a tutor and also explore some other EDM tasks using spectral clustering.
Shubhendu Trivedi - One of the best experts on this subject based on the ideXlab platform.
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spectral clustering in Educational Data Mining
Educational Data Mining, 2011Co-Authors: Shubhendu Trivedi, Zachary A Pardos, Gabor N Sarkozy, Neil T HeffernanAbstract:Spectral Clustering is a graph theoretic technique to represent Data in such a way that clustering on this new representation is reduced to a trivial task. It is especially useful in complex Datasets where traditional clustering methods would fail to find groupings. In previous work we have shown the utility of using K-means clustering for exploiting structure in the Data to affect a significant improvement in prediction accuracy on Educational Datasets. In this work we show that by using Spectral Clustering we are able to further improve the student performance prediction. We evaluate an Educational Data Mining prediction task: predicting student state test scores from student features derived from a tutor and also explore some other EDM tasks using spectral clustering.
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EDM - Spectral Clustering in Educational Data Mining.
2011Co-Authors: Shubhendu Trivedi, Zachary A Pardos, Gabor N Sarkozy, Neil T HeffernanAbstract:Spectral Clustering is a graph theoretic technique to represent Data in such a way that clustering on this new representation is reduced to a trivial task. It is especially useful in complex Datasets where traditional clustering methods would fail to find groupings. In previous work we have shown the utility of using K-means clustering for exploiting structure in the Data to affect a significant improvement in prediction accuracy on Educational Datasets. In this work we show that by using Spectral Clustering we are able to further improve the student performance prediction. We evaluate an Educational Data Mining prediction task: predicting student state test scores from student features derived from a tutor and also explore some other EDM tasks using spectral clustering.
Gwm Matthias Rauterberg - One of the best experts on this subject based on the ideXlab platform.
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advances in learning analytics and Educational Data Mining
The European Symposium on Artificial Neural Networks, 2015Co-Authors: Mehrnoosh Vahdat, Alessandro Ghio, Luca Oneto, Davide Anguita, Mathias Funk, Gwm Matthias RauterbergAbstract:The growing interest in recent years towards Learning An- alytics (LA) and Educational Data Mining (EDM) has enabled novel ap- proaches and advancements in Educational settings. The wide variety of research and practice in this context has enforced important possibilities and applications from adaptation and personalization of Technology En- hanced Learning (TEL) systems to improvement of instructional design and pedagogy choices based on students needs. LA and EDM play an im- portant role in enhancing learning processes by oering innovative methods of development and integration of more personalized, adaptive, and inter- active Educational environments. This has motivated the organization of the ESANN 2015 Special Session in Advances in Learning Analytics and Educational Data Mining. Here, a review of research and practice in LA and EDM is presented accompanied by the most central methods, bene- ts, and challenges of the eld. Additionally, this paper covers a review of novel contributions into the Special Session.
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ESANN - Advances in learning analytics and Educational Data Mining
2015Co-Authors: Mehrnoosh Vahdat, Alessandro Ghio, Luca Oneto, Davide Anguita, Mathias Funk, Gwm Matthias RauterbergAbstract:The growing interest in recent years towards Learning An- alytics (LA) and Educational Data Mining (EDM) has enabled novel ap- proaches and advancements in Educational settings. The wide variety of research and practice in this context has enforced important possibilities and applications from adaptation and personalization of Technology En- hanced Learning (TEL) systems to improvement of instructional design and pedagogy choices based on students needs. LA and EDM play an im- portant role in enhancing learning processes by oering innovative methods of development and integration of more personalized, adaptive, and inter- active Educational environments. This has motivated the organization of the ESANN 2015 Special Session in Advances in Learning Analytics and Educational Data Mining. Here, a review of research and practice in LA and EDM is presented accompanied by the most central methods, bene- ts, and challenges of the eld. Additionally, this paper covers a review of novel contributions into the Special Session.
Zachary A Pardos - One of the best experts on this subject based on the ideXlab platform.
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spectral clustering in Educational Data Mining
Educational Data Mining, 2011Co-Authors: Shubhendu Trivedi, Zachary A Pardos, Gabor N Sarkozy, Neil T HeffernanAbstract:Spectral Clustering is a graph theoretic technique to represent Data in such a way that clustering on this new representation is reduced to a trivial task. It is especially useful in complex Datasets where traditional clustering methods would fail to find groupings. In previous work we have shown the utility of using K-means clustering for exploiting structure in the Data to affect a significant improvement in prediction accuracy on Educational Datasets. In this work we show that by using Spectral Clustering we are able to further improve the student performance prediction. We evaluate an Educational Data Mining prediction task: predicting student state test scores from student features derived from a tutor and also explore some other EDM tasks using spectral clustering.
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EDM - Spectral Clustering in Educational Data Mining.
2011Co-Authors: Shubhendu Trivedi, Zachary A Pardos, Gabor N Sarkozy, Neil T HeffernanAbstract:Spectral Clustering is a graph theoretic technique to represent Data in such a way that clustering on this new representation is reduced to a trivial task. It is especially useful in complex Datasets where traditional clustering methods would fail to find groupings. In previous work we have shown the utility of using K-means clustering for exploiting structure in the Data to affect a significant improvement in prediction accuracy on Educational Datasets. In this work we show that by using Spectral Clustering we are able to further improve the student performance prediction. We evaluate an Educational Data Mining prediction task: predicting student state test scores from student features derived from a tutor and also explore some other EDM tasks using spectral clustering.
