Abstract

A framework of indicators and a media competence self-assessment test for public administrations is proposed, in a field where few evaluation methods have been implemented up until now. The study is based on media literacy (ML) indicators divided in five general criteria defined by Pérez-Tornero and Celot (2009). These are: availability of media, ML context, use, critical understanding, and communication. The initial frameworks were compared with those used in in highly-regarded international media assessment systems. The article presents a test based on the qualitative comparison of international evaluation methods, as well as through in-depth interviews. The proposed tool is applied and refined through a research process that combines quantitative and qualitative methods. The study concludes that competences can be assessed by means of a questionnaire, but also finds that there are others, especially those related to the critical reading of information, that present greater complexity and that demand various types of tests. The application of the survey allows us to obtain useful recommendations for the development and improvement of ML indicators within public administrations.

Abstract

utomated driving in all its different forms is now underway and large European projects have been launched to investigate the effects in real life. Field Operational Tests are typically used to gather evidence to assess the impacts. The FESTA methodology was developed to design and conduct FOTs and analyse the outcomes. Currently the methodology is being updated to address the challenges of the upcoming pilots on road automation. The ultimate goal of performing automated driving studies is to gain insight into the impacts automation will have on transport and on society in general. In this paper we will explain how the series of FOT-Net (2008-2016) and the CARTRE (2016-2018) coordination and support actions address the development of a common methodology. Questions around the formulation of research questions, data analysis, user acceptance and impact assessment are addressed. One of the most important ways forward is the sharing of data, knowledge and experiences. Only by combining the knowledge sources will we be able to get a clearer picture of what the future impacts of road automation will be. FOT-Net has developed a data sharing framework and recommendations from this framework will be described.