Group Formation is a complex and important step to design effective collaborative learning activities. Through the adequate selection of individuals to a group, it is possible to create environments that foster the occurrence of meaningful interactions, and thereby, increasing robust learning and intellectual growth. Many researchers indicate that the inadequate formation of groups can demotivate students and hinder the learning process. Thus, in the field of Com-puter-Supported Collaborative Learning (CSCL), there are several studies focusing on developing and testing group formation in collaborative learning contexts using best practices and other pedagogical approaches. Nevertheless, the CSCL community lacks a comprehensive understanding on which computa-tional techniques (i.e. algorithms) has supported group formation. To the best of our knowledge, there is no study aimed at gathering and analyzing the research findings on this topic using a systematic method. To fill this gap, this research conducted a systematic mapping with the objective of summarizing the studies on algorithms for group formation in CSCL contexts. Initially, by searching on six digital libraries, we collected 256 studies. Then, after a careful analysis of each study, we verified that only 48 were related to group formation applied to collaborative learning contexts. Finally, we categorized the contributions of these studies to present an overview of the findings produced by the communi-ty. This overview shows that: (i) there is a gradual increase on research pub-lished in this topic; (ii) 41% of the algorithms for group formation area based on probabilistic models; (iii) most studies presented the evaluation of tools that implement these algorithms; but (iv) only 2% of the studies provide their source code; and finally, (v) there is no tool or guideline to compare the benefits, dif-ferences and specificities of group formation algorithms available to date. As a result of this work an infographic is also available at: http://infografico.caed-lab.com/mapping/gf.