Introduction: Computational thinking (Wing, 2006) has been identified as a 21th century skills that should be developed by everyone. The rationale of this study was to create an intervention in 6th grades classroom in the objective to teach a strategy to solve algorithm-based problems. In this particular experiment, we aimed to compare different teaching conditions and assess pupils’ performance of problem-solving at the end of the intervention. Our two experimental teaching scenarios implying the study of worked-examples. The worked-example effect is one of the most famous effects ensuing from the Cognitive Load findings (Sweller et al. 2019), allowing novices to improve significantly the acquisition of problem-solving schemes. The fundamental interrogation lies in understanding which pedagogical method and material can best facilitate the transfer of knowledge (Barnett & Ceci, 2002). How students will invest the strategy they learned. Analogical comparison is a method that is seen as a powerful tool to abstract knowledge and so improve the transfer (Hofstadter & Sander, 2013). Our objective is then to understand teaching worked-examples and analogical comparisons of worked examples in the objective to increase transfer. Method: The experiment consists to evaluate the effectiveness of three teaching methods in order to solve algorithmic-based problems. A between design was used and the conditions were split up within each classroom. Participants are 6th grade pupils and 21 classes were recruited. Half of the classes were recruited in Montpellier and the other half was recruited in Geneva. The experiment was exactly the same but the location was different. Allowing to test the intervention in two countries (France and Switzerland). The condition 1 is a classical problem-solving condition, were students have to solve problems without help. The condition 2 was focused on worked-examples. In this condition pupils had just to study worked-examples and answer some questions about the procedure. In the last condition, students were explicitly encouraged to compare analogically two worked-examples with a design that put the two worked-examples side-by-side and questions emphasizing the analogy, were asked. All problems throughout conditions were identical, just differed the way of teaching them. Analysis and Results: Data are still collected and will be available for the congress. Statistical analysis is going to be made comparing the different conditions on post-test problems. Discussion and Implications Understand which pedagogy could lead to a better transfer is a central question that should be permanently nurtured. In this project we aimed to understand if analogical comparison could be useful to push students to better transfer their knowledge. The accent is more precisely put on 6th graders. We investigate this perspective in the light of computational thinking, seen has a general discipline that should be developed by everyone, aiming to clarify if we could significantly help learners to transfer their knowledge and how can we develop computational skills with common-like problems in early stages. References: Barnett, S. M., & Ceci, S. J. (2002). When and where do we apply what we learn? : A taxonomy for far transfer. Psychological bulletin, 128(4), 612. Barr, D., Harrison, J., & Conery, L. (2011). Computational thinking : A digital age skill for everyone. Learning & Leading with Technology, 38(6), 20 23. Bocconi, S., Chioccariello, A., Dettori, G., Ferrari, A., Engelhardt, K., Kampylis, P., & Punie, Y. (2016). EXPLORING THE FIELD OF COMPUTATIONAL THINKING AS A 21ST CENTURY SKILL. 4725 4733. https://doi.org/10.21125/edulearn.2016.2136 Gick, M. L., & Holyoak, K. J. (1983). Schema induction and analogical transfer. Cognitive Psychology, 15(1), 1 38. https://doi.org/10.1016/0010-0285(83)90002-6 Hofstadter, D. R., & Sander, E. (2013). Surfaces and essences : Analogy as the fuel and fire of thinking. Basic books. Perkins, D., & Salomon, G. (1992). Transfer Of Learning. 11. Tricot, A., & Sweller, J. (2014). Domain-Specific Knowledge and Why Teaching Generic Skills Does Not Work. Educational Psychology Review, 26(2), 265 283. Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33 35. https://doi.org/10.1145/1118178.1118215