The influence of SRA programming on algorithmic thinking and self-efficacy using Lego robotics in two types of instruction

Abstract

This study investigates the development of algorithmic thinking as a part of computational thinking skills and self-efficacy of primary school pupils using programmable robots in different instruction variants. Computational thinking is defined in the context of twenty-first century skills and describes processes involved in (re)formulating a problem in a way that a computer can process it. Programming robots offers specific affordances as it can be used to develop programs following a Sense-Reason-Act (SRA) cycle. The literature provides evidence that programming robots has the potential to enhance algorithmic thinking as a component of computational thinking. Specifically there are indications that pupils who use SRA-programming learn algorithmic skills better and achieve a higher level of selfefficacy in an open, scaffold learning environment than through direct instruction. In order to determine the influence of the instruction variant used, an experimental research design was made in which pupils solved algorithm-based mathematical problems (grid diagrams) in a preliminary measurement and their self-efficacy determined via a questionnaire. As an intervention, pupils learn to solve programming issues in pairs using "Lego NXT" robots and "Mindstorms" software in two instruction variants. The post-measurement consists of a Lego challenge, solving mathematical problems (grid diagrams), and a repeated selfefficacy questionnaire. This research shows an increase of our measures on algorithmic thinking dependent on the amount of SRA usage (though not significant). Programming using the SRA-cycle can be considered as the cause of the measured effect. The instruction variant used during the robotic intervention seems to play only a marginal role.

The Impact of SRA-Programming on Computational Thinking in a Visual Oriented Programming Environment

“…From our research we know that the application of SRA when programming tangible robots affects the development of computational thinking skills (Fanchamps et al, 2019). We also know that a dynamic environment in which unforeseen situations occur, in which pupils can no longer rely on linear, sequential programming to successfully solve programming tasks, it requires the application of SRAthinking (Fanchamps et al submitted).…”

“…Despite the fact that a visual environment seems eminently suitable for learning how to program, it is questionable whether pupils can use such an environment to achieve programming skills of higher complexity (Kaučič & Asič, 2011). Our previous research indicates that pupils, when programming robots have difficulty applying the principles of SRA despite understanding these principles (Fanchamps et al, 2019). However, when pupils find themselves in a situation where the programming task only can be solved through an SRA-approach, they tend to use complex programming concepts in a way as operationalized in computational thinking.…”

“…Research conducted by Caci et al (2013), Krugman (2004) and Slangen (2016) shows that applying SRA-programming improves pupils' programming skills and abilities to solve tasks of higher complexity. It is also demonstrated when physically tangible robots are programmed through the application of SRA, it leads to a higher level of development of computational thinking skills among primary school pupils, then when physical robots are programmed without the application of SRA (Fanchamps et al, 2019).…”

“…Visual programming environments are, due to their high imaginative power, ideally suited to teach pupils the underlying concepts of programming (Carlisle, 2009). In previous research, several authors introduced the SRA-principle which has been identified as an instrumental way of thinking for learning to program robots (Fanchamps et al, 2019;Slangen, 2016;Wong, 2014). SRA can be seen as a didactic instrument to ease the process of programming.…”

“…Visual programming environments appear to offer excellent possibilities for SRAprogramming, making the connection between sensing (input), reasoning and acting (output) visible and comprehensible (Fanchamps et al, 2019;Slangen, 2016;Wong, 2014). Appling SRA is an advanced way of programming that manifests itself in the development of CT-skills (e.g.…”