This work presents a model for the selection of Collaborative Learning (CL) techniques considering specific characteristics and needs of the activity that teachers want to perform within their educational practice. This model considers the representation of the activity in terms of the required competencies defined from Bloom’s taxonomy. Then, using the characterization of a set of techniques conducted by experts, an algorithm is used for providing an affinity measure, doing a recommendation of the technique to use. A validation of the model from three case studies is also described, carried out by comparing experimental and control groups. The results show that CL allows for achieving better academic performance, but also that those techniques proposed by the recommendation model exhibited higher performance. Keywords Algorithm Group learning Taxonomy Recommendation Model