Enhancing Just-in-Time E-Learning through Machine Learning on Desktop Context Sensors
Key: LFBG07-1
Author: Robert Lokaiczyk, Andreas Faatz, Arne Beckhaus, Manuel Görtz
Date: August 2007
Kind: In proceedings
Publisher: Springer
Book title: Modeling and Using Context, 6th International and Interdisciplinary Conference, CONTEXT 2007, Roskilde, Denmark, August 20-24, 2007, Proceedings
Keywords: APOSDLE context machine learning context e-learning 2007 lokaiczyk myOwn SAP
Abstract: The objective of novel e-learning strategies is to educate the learner during his actual work process. We focus on this new approach of in-place and in-time e-learning, which offers learning resources right in time the user is in need for it. A crucial factor for those modern taskoriented e-learning software is the userÂ’s context. To deliver learning resources to the user, which are both suitable and helpful with regards to the user’s current work situation and his competencies, the application always has to consider the learner’s actual work task, his environment, and history. In this paper, we present an architecture for the work task prediction, evaluate different machine learning algorithms in depth by their accuracy for that purpose and discuss the integration in our elearning environment. This validates the possible usage in real-world business scenarios.

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