A knowledge server including tools for professional know-how transfer

This paper presents a research in progress on the use of knowledge engineering and knowledge management techniques for the development of a strategic approach for the transfer of professional know-how. This transfer is based on the design of devices for sharing and learning clearly identified knowledge in the oil industry domains. This work is based on a pilot study which was carried out in the PED department (Petroleum Engineering & Development) and it deals with upstream activity of the oil group Sonatrach. After the different phases of knowledge mapping, critical knowledge assessment, and strategic alignment, the KM process focus on knowledge elicitation, sharing, transfer and learning, based on design and implementation of specific tools called Knowledge Server, including Knowledge Books and e-Learning.E-learning, Knowledge management, Knowledge transfer, Knowledge engineering, Knowledge servers, Computer assisted human learning, Case study

Proposed Principles for Promoting Pre-service Teacher Transfer of Group-based Learning to the Classroom: A Discussion Paper

The effective ‘transfer’ of knowledge and skills from university to the workplace is of global interest, yet this area of inquiry lacks research. Teacher educators, for example, require information on how to advance pre-service teachers’ transfer of group-based learning to the primary school classroom (Scott & Baker, 2003). Group-based learning (GBL) is a valued means of developing learners’ group work, personal attributes and interpersonal skills, and in the case pre-service teachers their professional skills.. Graduate teachers do not necessarily generalise GBL pedagogy to the classroom. This discussion paper draws from a qualitative case study that examined this pedagogy in a pre-service teacher education program at a University. The case study revealed three core GBL issues: ‘consistency and coherence’; ‘equity and fairness’; ‘pragmatism and adding value’. This paper proposes four principles of effective transfer and examines how, in relation to these three issues, these principles can promote effective transfer

Learning Features by Watching Objects Move

This paper presents a novel yet intuitive approach to unsupervised feature learning. Inspired by the human visual system, we explore whether low-level motion-based grouping cues can be used to learn an effective visual representation. Specifically, we use unsupervised motion-based segmentation on videos to obtain segments, which we use as 'pseudo ground truth' to train a convolutional network to segment objects from a single frame. Given the extensive evidence that motion plays a key role in the development of the human visual system, we hope that this straightforward approach to unsupervised learning will be more effective than cleverly designed 'pretext' tasks studied in the literature. Indeed, our extensive experiments show that this is the case. When used for transfer learning on object detection, our representation significantly outperforms previous unsupervised approaches across multiple settings, especially when training data for the target task is scarce.Comment: CVPR 201