European Higher Education institutions have been implementing active learning strategies in different contexts. In order to learn and disseminate these approaches, it is important to understand how these successful active learning strategies can be implemented in new contexts. The EXTEND ERASMUS+ project aims to develop Engineering Education Centres in Russia and Tajikistan in order to make a contribution for the development of these countries' schools of Engineering. One of the first steps in pursue of this objective is the study of European teaching and learning best practices and the definition of a set of useful recommendations for the teachers of Engineering schools. A question raised by this approach was what can be recommended to engineering teachers from the analysis of teaching and learning best practices? The objective of this paper is to develop a method for the analysis and recommendations and to present the results of the application of this method in 16 European teaching and learning best practices. The method was qualitative and developed by brainstorming between experts of the projects from different areas of knowledge. This method included the definition of a glossary, selection of best practices, collection of the information, analysis in relation to the best practices, analysis of maturity levels regarding the current level of partner countries and development of collaborative recommendations. The main recommendations for the Russia and Tajik contexts are to develop Project Based Learning approaches in interaction with industry, and additionally for Tajik partners to develop entrepreneurial and management competences in engineering students.This work is based on outcomes of the project EXTEND –“Excellence in Engineering Education through Teacher Training and New Pedagogic Approaches in Russia and Tajikistan” that has been funded with support from the European Commission (Project Number: 586060-EPP-1-2017-1-RO-EPPKA2-CBHE-JP). This publication reflects the views only of the authors, and the Commission cannot be held responsible for any use which may be made of the information contained therein. The authors would like to thank to the members of the consortium and all that made possible to collect the data we used in this paper.This work has been supported by FCT –Fundação para aCiência e Tecnologia within the Project Scope UIDCEC003192019
