Effects of distributed learning patterns on elementary student learning of computational thinking

Casanova de Vilalta, Lydia

Effects of distributed learning patterns on elementary student learning of computational thinking

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Citation

Casanova de Vilalta, L. Effects of distributed learning patterns on elementary student learning of computational thinking. 2020. handle: http://hdl.handle.net/10230/46266

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Abstract

Applied cognitive psychology has been a center topic for a number of research works in last decades. In particular, there are studies conducted on memory, cognition and on the science of learning. The latter are meant to find new methods in order to improve the process of learning. The use of the Retrieval Practice (RP) and Distributed Learning (DL) has been proved to be improving the efficiency of learning when compared to Massive Learning (ML) practices (which is the most used method in formal education). Even though there are studies which proved the advantages of using DL, different patterns can be found and it is not clear which one is the best to apply to get the best students’ results. This study was conducted in the last 3 courses of primary education and the learning topic was Computational Thinking (CT). CT is one of the 21st century skills that gained more attention and it is progressively being incorporated in formal learning since the early stages of education. CT refers to understanding how to develop stepby-step solutions of problems, helping students to use and improve logical thinking, pattern recognition and decomposition skills. New generations are likely to live in a technologically integrated society, hence, CT might become an essential skill that will enable them to understand and manipulate the technology that surrounds them. Thus, the main goal of this project is to find out how the learning process is affected by different patterns of distributed practice and discover which is the distribution that leads to a better performance in the latter, as well as being suitable to apply in formal learning contexts. In order to do that, two different patterns of DL and a ML group (as a control group) will be compared with the aim to find which one of the two reaches the best performance using RP as a constant in all groups. As a result, I expect that DL groups will perform better than the ML groups, and I forsee to find out whether there is a significant difference in performance between the two DL patterns tested.