Auflistung nach Autor:in "Mokbel, Bassam"
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- ZeitschriftenartikelAutonomous Learning of Representations(KI - Künstliche Intelligenz: Vol. 29, No. 4, 2015) Walter, Oliver; Haeb-Umbach, Reinhold; Mokbel, Bassam; Paassen, Benjamin; Hammer, BarbaraBesides the core learning algorithm itself, one major question in machine learning is how to best encode given training data such that the learning technology can efficiently learn based thereon and generalize to novel data. While classical approaches often rely on a hand coded data representation, the topic of autonomous representation or feature learning plays a major role in modern learning architectures. The goal of this contribution is to give an overview about different principles of autonomous feature learning, and to exemplify two principles based on two recent examples: autonomous metric learning for sequences, and autonomous learning of a deep representation for spoken language, respectively.
- KonferenzbeitragFeedback provision strategies in intelligent tutoring systems based on clustered solution spaces(DeLFI 2012: Die 10. e-Learning Fachtagung Informatik der Gesellschaft für Informatik e.V., 2012) Gross, Sebastian; Mokbel, Bassam; Hammer, Barbara; Pinkwart, NielsDesigning an Intelligent Tutoring System (ITS) usually requires precise models of the underlying domain, as well as of how a human tutor would respond to student mistakes. As such, the applicability of ITSs is typically restricted to welldefined domains where such a formalization is possible. The extension of ITSs to ill-defined domains constitutes a challenge. In this paper, we propose the provision of feedback based on solution spaces which are automatically clustered by machine learning techniques operating on sets of student solutions. We validated our approach in an expert evaluation with a data set from a programming course. The evaluation confirmed the feasibility of the proposed feedback provision strategies.
- ZeitschriftenartikelInterview with Helge Ritter(KI - Künstliche Intelligenz: Vol. 26, No. 4, 2012) Mokbel, Bassam
- ZeitschriftenartikelLearning Feedback in Intelligent Tutoring Systems(KI - Künstliche Intelligenz: Vol. 29, No. 4, 2015) Gross, Sebastian; Mokbel, Bassam; Hammer, Barbara; Pinkwart, NielsIntelligent Tutoring Systems (ITSs) are adaptive learning systems that aim to support learners by providing one-on-one individualized instruction. Typically, instructing learners in ITSs is build on formalized domain knowledge and, thus, the applicability is restricted to well-defined domains where knowledge about the domain being taught can be explicitly modeled. For ill-defined domains, human tutors still by far outperform the performance of ITSs, or the latter are not applicable at all. As part of the DFG priority programme “Autonomous Learning”, the FIT project has been conducted over a period of 3 years pursuing the goal to develop novel ITS methods, that are also applicable for ill-defined problems, based on implicit domain knowledge extracted from educational data sets. Here, machine learning techniques have been used to autonomously infer structures from given learning data (e.g., student solutions) and, based on these structures, to develop strategies for instructing learners.