Machine teaching [Elektronische Ressource] : a machine learning approach to technology enhanced learning / eingereicht von Markus Weimer
148 pages
Deutsch
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Machine teaching [Elektronische Ressource] : a machine learning approach to technology enhanced learning / eingereicht von Markus Weimer

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Obtenez un accès à la bibliothèque pour le consulter en ligne
En savoir plus
148 pages
Deutsch

Description

Machine TeachingA Learning Approach to Technology Enhanced LearningDissertationZur Erlangung des akademischen Gradeseines Doktor-Ingenieur (Dr.-Ing.)Eingereicht vonDiplom Wirtschaftsinformatiker Markus Weimergeb. in HadamarAngenommen vom Fachbereich Informatikder Technischen Universitat¨ DarmstadtGutachter: Prof. Dr. Max Muhlh¨ auser¨ (TU Darmstadt)Prof. Dr. Alexander J. SmolaAustralian National University, Canberra, AustralienYahoo! Research, Santa Clara, CA, USAProf. Dr. Petra Gehring (TU Darmstadt)Tag der Einreichung: 14.07.2009Tag der Disputation: 24.09.2009Darmstadter¨ Dissertationen, D17 Erschienen in 2010 in Darmstadt.AbstractMany applications of Technology Enhanced Learning are based on strong assump-tions: Knowledge needs to be standardized, structured and most of all externalizedinto learning material that preferably is annotated with meta-data for efficient re-use.A vast body of valuable knowledge does not meet these assumptions, including infor-mal knowledge such as experience and intuition that is key to many complex activities.We notice that knowledge, even if not standardized, structured and externalized, canstill be observed through its application. We refer to this observable knowledge as PRAC-TICED KNOWLEDGE. We propose a novel approach to Technology Enhanced Learningnamed MACHINE TEACHING to convey this knowledge: Machine Learning techniquesare used to extract machine models of Practiced Knowledge from observational data.

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Publié par
Publié le 01 janvier 2010
Nombre de lectures 30
Langue Deutsch
Poids de l'ouvrage 2 Mo

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