Positioning of Learners in Learning Networks with Content, Metadata and Ontologies

Kalz, M, Van Bruggen, J., Rusmann, E., Giesbers, B., & Koper, R. (2007). Positioning of Learners in Learning Networks with Content-Analysis, Metadata and Ontologies. Interactive Learning Environments, 15, 191-200.Positioning in learning networks is a process that assists learners in finding a starting point and an efficient route through the network that will foster competence building. In the past we explored computational approaches to positioning that are based on the contents of the learning network and the behavior of those participating in it, more or less ignoring different efforts to stimulate positioning and competence development from a top-down-perspective. In this paper we introduce a research agenda for positioning in learning networks, discuss several cases and give an outlook on the development of a positioning service for learning networks.This work has been sponsored by the EU project TENCompetenc

Positioning for conceptual development using latent semantic analysis

With increasing opportunities to learn online, the problem of positioning learners in an educational network of content offers new possibilities for the utilisation of geometry-based natural language processing techniques. In this article, the adoption of latent semantic analysis (LSA) for guiding learners in their conceptual development is investigated. We propose five new algorithmic derivations of LSA and test their validity for positioning in an experiment in order to draw back conclusions on the suitability of machine learning from previously accredited evidence. Special attention is thereby directed towards the role of distractors and the calculation of thresholds when using similarities as a proxy for assessing conceptual closeness. Results indicate that learning improves positioning. Distractors are of low value and seem to be replaceable by generic noise to improve threshold calculation. Furthermore, new ways to flexibly calculate thresholds could be identified

Imitation Learning for Vision-based Lane Keeping Assistance

This paper aims to investigate direct imitation learning from human drivers for the task of lane keeping assistance in highway and country roads using grayscale images from a single front view camera. The employed method utilizes convolutional neural networks (CNN) to act as a policy that is driving a vehicle. The policy is successfully learned via imitation learning using real-world data collected from human drivers and is evaluated in closed-loop simulated environments, demonstrating good driving behaviour and a robustness for domain changes. Evaluation is based on two proposed performance metrics measuring how well the vehicle is positioned in a lane and the smoothness of the driven trajectory.Comment: International Conference on Intelligent Transportation Systems (ITSC

Towards a taxonomy of strategic research in the IMP tradition

The project reported in this article is part of a wider project, the aim of which is to investigate the contribution that interaction and networks (IMP) research has made to the field of strategy (Baraldi et al 2006). The specific aim of the sub-project described here is to develop, from the archive of IMP research, a better understanding of both the implicit and the explicit contributions that IMP research has made to the strategy field. The method employed is a systematic analysis of the IMP research database, using a content analysis approach, with the aim of developing a robust taxonomy of strategic research that has emerged from this body of knowledge. Specifically, this paper concerns itself with the development of the analytical method for this task, and with the presentation of the results of a pilot study conducted on 55 IMP research papers to test the analytical approachPeer reviewedSubmitted Versio