This project aims at putting into practice the “benchlearning” concept as an innovative concept to define evidence-based learning by combining quantitative and qualitative benchmarking, mutual learning and support to implementation of good PES practices. To this end, we will a) collect data from PES with time series starting in 2010, b) combine PES data with relevant data available elsewhere (e.g. LFS data), c) develop a methodology for data validation, data consolidation and data quality assessment and d) develop a methodology for the production of the overall PES benchmarking comparative statistics including assessment, comparison and analysis of data per indicator, PES, topic and other suitable criteria, including combinations of indicators and contextual variables.
In general, these tasks refer to the collection of potential performance outcomes. A prerequisite for this collection is a clear definition of performance dimensions of European PES and a translation of these dimensions into indicators based on rigorous theoretical considerations, in close cooperation with the PES working group Benchlearning and the Commission. Furthermore, this also involves the collection of indicators reflecting the context in which PES operate.
Crucial to this is the identification of valid performance outcomes using sound empirical work. In this endeavour, it will be extremely important to define criteria which have to be met by potential performance indicators in order to qualify as valid. The provision of meaningful and robust indicators for valid performance outcomes of PES – according to our conceptual framework – will be the central input for the combined analysis of performance outcomes and performance enablers conducted in the second part of the study. In this part we start by defining potential enablers and then – by an analytical process – aim at identifying true enablers that support improved benchmarked PES performance. By implementing this we will then be able to provide context-free valid performance outcomes linked to true enablers that will be the core elements of the benchlearning process.