Caroline Bayart

Survey Mode Integration and Data Fusion: Methods and challenges

Development of new technologies (smart card, GPS, cell phones...) increases production of data sometimes in a continuous way. But for various reasons, including privacy concerns, these data generally not contain all needed information to match transportation needs especially for modelling purposes. It is also the case of most official data production such as travel surveys, time use surveys, housing surveys... which all contains precious information but none of them all the needed information. Data fusion methodologies allow to enrich the data in order to better meet data needs. In the same time data collection have to face increasing difficulties (cost, response rates...) in collecting data, representative of the whole population, through classical methods. Combining survey modes or methods are in some cases an opportunity to cope or at least to reduce this problem. Mixing these data impose to develop methods to identify modes or methods effects on collected data and to correct for them. This paper presents some of the methods available to treat data fusion and survey mode integration. Application are proposed on Lyon and Montreal areas.

Serious games in favour of knowledge management and double-loop learning?

How can universities develop a knowledge management dynamic in order to train knowledge workers who are effective in an organizational learning process? Can games, and more specifically serious games, contribute to reaching this goal? To answer this question, we hypothesize that play can serve as a lever for knowledge management and double-loop learning. The purpose of this article is to show that serious games contribute to training knowledge workers in an organizational learning process. From this perspective, we attempt to understand how serious games promote the acquisition of knowledge and we explain the research method used in the field (participant observation, investigation using questionnaires). The final part analyses the main results: a community of practice and organization learning, internalization through Learning by Doing and better understanding of the environment’s complexity, towards double-loop learning and student satisfaction with the serious game.