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

2019 ◽  
Author(s):  
Nardie L. J. A. Fanchamps ◽  
Lou Slangen ◽  
Paul Hennissen ◽  
Marcus Specht
Keyword(s):  
Self Efficacy ◽  
Computational Thinking ◽  
Thinking Skills ◽  
Preliminary Measurement ◽  
Grid Diagrams ◽  
Mathematical Problems ◽  
Measured Effect ◽  
Primary School Pupils ◽  
Types Of Instruction ◽  
Algorithmic Thinking

AbstractThis 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 self-efficacy 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 self-efficacy 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.

scholarly journals The Effect of Algorithm Education on Students’ Computer Programming Self-Efficacy Perceptions and Computational Thinking Skills

2020 ◽  
Vol 3 (3) ◽  
pp. 19-32
Author(s):  
PINAR MIHCI Türker ◽  
Ferhat Kadir Pala
Keyword(s):  
Self Efficacy ◽  
Teacher Candidates ◽  
Computer Programming ◽  
Computational Thinking ◽  
Thinking Skills ◽  
Rank Test ◽  
Signed Rank ◽  
Algorithmic Problems ◽  
Signed Rank Test ◽  
Algorithmic Thinking

In this study, the effect of algorithm education on teacher candidates’ computational thinking skills and computer programming self-efficacy perceptions were examined. In the study, one group pretest posttest experimental design was employed. The participants consisted of 24 (14 males and 10 females) teacher candidates, majoring in Computer Education and Instructional Technology (CEIT). In order to determine the teacher candidates’ computer programming self-efficacy perceptions, the Computer Programming Self-Efficacy Scale was used, whereas Computational Thinking Skills Scale was used to determine their computational thinking skills. The Wilcoxon Signed-Rank Test was used to analyze the differences between pretest and posttest scores of students' computer programming self-efficacy perceptions and computational thinking skills. Throughout the practices, 10 different algorithmic problems were presented to the students each week, and they were asked to solve these problems using flow chart. For 13 weeks, 130 different algorithmic problems were solved. Algorithm education positively and significantly increased students' simple programming tasks, complex programming tasks and programming self-efficacy perceptions. On the other hand, algorithm education had a positive and significant effect only on students’ algorithmic thinking sub-dimension but did not have any effect on other sub-dimensions and computational thinking skills in general.  

Assessing Algorithmic Thinking Skills in Early Childhood Education

2020 ◽  
pp. 104-139
Author(s):  
Kalliopi Kanaki ◽  
Michail Kalogiannakis ◽  
Dimitrios Stamovlasis
Keyword(s):  
Latent Class ◽  
Computational Thinking ◽  
Thinking Skills ◽  
Theory And Practice ◽  
Second Grade ◽  
Environmental Study ◽  
Assessment Instruments ◽  
School Students ◽  
Skill Levels ◽  
Algorithmic Thinking

This chapter presents part of a wider project aimed at developing computational thinking assessment instruments for first and second grade primary school students. The applicability of the specific proposed tool, which concerns merely the algorithmic thinking (AT), was tested within the Environmental Study course (ESc). The main pillar of the work is the computational environment PhysGramming. The assessment of AT was based on mental tasks involving puzzles which require AT abilities. The AT test comprised of four puzzles with 4, 6, 9, and 12 pieces respectively, and the puzzle-solving performance was measured at the nominal level (success/failure). Latent class analysis (LCA), a robust multivariate method for categorical data, was implemented, which distinguished two clusters/latent classes corresponding to two distinct levels of AT. Moreover, LCA with covariates, such as gender, grade, achievement in ESc, and the use of plan revealed the association of the above variables with the AT skill-levels. Finally, the results and their implications for theory and practice are discussed.

scholarly journals An Analysis of Students’ Computational Thinking Skills on The Number Patterns Lesson during The Covid-19 Pandemic

2021 ◽  
Vol 11 (2) ◽  
Author(s):  
Dwi Fitriani Rosali ◽  
Didi Suryadi
Keyword(s):  
Computational Thinking ◽  
Thinking Skills ◽  
Problem Decomposition ◽  
Education Curriculum ◽  
Descriptive Research ◽  
Educational Framework ◽  
Type Test ◽  
8Th Grade ◽  
Qualitative Descriptive ◽  
Algorithmic Thinking

The development of the education curriculum in Indonesia makes students must have skills so that they can compete globally, especially in the 21st century. The development is closely related to technology and information. One of skills that support the development of technology and information is the <em>computational thinking</em> skills. This study aims to analyze students’ <em>computational thinking</em> skills on the number patterns lesson during the Covid-19 pandemic. This study was qualitative-descriptive research with the subjects of 4 students from 8th grade in Makassar. The instruments used in this study were a test of the <em>computational thinking</em> skills in the form of essay type test on the number patterns lesson and interview guidance. The results of this study indicated that all subjects met the first indicator of problem decomposition and one subject met the second indicator of problem decomposition, all subjects met the indicator of pattern recognition, three subjects met the indicator of abstraction and generalization, all subjects met the first indicator of algorithmic thinking and two subjects met the second indicator of algorithmic thinking on <em>computational thinking</em> skills. Thus, students’ <em>computational thinking</em> skills during the Covid-19 pandemic were still low, so an educational framework is needed to improve students’ <em>computational thinking</em> skills.

Assessing Algorithmic Thinking Skills in Early Childhood Education

2022 ◽  
pp. 488-523
Author(s):  
Kalliopi Kanaki ◽  
Michail Kalogiannakis ◽  
Dimitrios Stamovlasis
Keyword(s):  
Latent Class ◽  
Computational Thinking ◽  
Thinking Skills ◽  
Theory And Practice ◽  
Second Grade ◽  
Environmental Study ◽  
Assessment Instruments ◽  
School Students ◽  
Skill Levels ◽  
Algorithmic Thinking

This chapter presents part of a wider project aimed at developing computational thinking assessment instruments for first and second grade primary school students. The applicability of the specific proposed tool, which concerns merely the algorithmic thinking (AT), was tested within the Environmental Study course (ESc). The main pillar of the work is the computational environment PhysGramming. The assessment of AT was based on mental tasks involving puzzles which require AT abilities. The AT test comprised of four puzzles with 4, 6, 9, and 12 pieces respectively, and the puzzle-solving performance was measured at the nominal level (success/failure). Latent class analysis (LCA), a robust multivariate method for categorical data, was implemented, which distinguished two clusters/latent classes corresponding to two distinct levels of AT. Moreover, LCA with covariates, such as gender, grade, achievement in ESc, and the use of plan revealed the association of the above variables with the AT skill-levels. Finally, the results and their implications for theory and practice are discussed.

A Valid and Reliable Scale for Developing Programming-Oriented Computational Thinking

2020 ◽  
pp. 073563312096440
Author(s):  
Servet Kılıç ◽  
Seyfullah Gökoğlu ◽  
Mücahit Öztürk
Keyword(s):  
Conceptual Knowledge ◽  
Computational Thinking ◽  
Thinking Skills ◽  
Male Students ◽  
Significant Difference ◽  
Conceptual Semantic ◽  
One Year ◽  
High Level ◽  
Reliable Scale ◽  
Algorithmic Thinking

In this research, a scale was developed to determine the programming-oriented computational thinking skills of university students. The participants were 360 students studying in various departments at different universities in Turkey for computer programming. The scale consists of 33 items under conceptual knowledge, algorithmic thinking, and evaluation subscale. While there was no significant difference between the students’ conceptual knowledge and algorithmic thinking skills, the evaluation skills of male students differed significantly compared to females. Programming experience has a significant effect on conceptual knowledge, algorithmic thinking, and evaluation. The algorithmic thinking skills of the students who have low, middle, and high-level programming experience differed significantly. In terms of the development of conceptual knowledge and evaluation skills, it was observed that students should have at least one year of programming experience, but this experience will not make a significant difference if it is four years or more. It is thought that this scale, which is structured for different applications (e.g., web, game, robot) and learning environments (e.g., text, block) within the framework of its programming capabilities (conceptual, semantic, strategic knowledge), will contribute significantly to the evaluation of computational thinking as programming oriented.

scholarly journals Simulador de Robótica Educativa para la promoción del Pensamiento Computacional

2020 ◽  
Vol 20 (63) ◽  
Author(s):  
Cristian Manuel Ángel-Díaz ◽  
Eduardo Segredo ◽  
Rafael Arnay ◽  
Coromoto León
Keyword(s):  
Educational Institution ◽  
Computational Thinking ◽  
Software Tool ◽  
Thinking Skills ◽  
Visual Programming ◽  
Free Software ◽  
Different Types ◽  
Block Based ◽  
Visual Programming Language ◽  
Algorithmic Thinking

Este trabajo presenta una herramienta Web libre y gratuita que facilita a cualquier centro educativo la enseñanza de conceptos básicos sobre robótica y programación y que, al mismo tiempo, permite desarrollar habilidades relacionadas con el pensamiento computacional: descomposición, abstracción, reconocimiento de patrones y pensamiento algorítmico. Dicha herramienta permite diseñar y personalizar un robot a través del uso de distintos tipos de sensores. Tras su creación, dicho robot se podrá poner a prueba en un entorno de simulación mediante distintos retos. En dicho entorno podremos definir el comportamiento del robot por medio de un lenguaje de programación visual basado en bloques. Dichos bloques permiten definir las acciones a llevar a cabo por el robot en función de la información recogida por los sensores con el objetivo de superar los desafíos propuestos. This work presents a free software tool that facilitates the teaching of basic robotics and programming concepts at any educational institution. At the same time, it allows the development of computational thinking skills to be carried out: decomposition, abstraction, pattern recognition and algorithmic thinking. This tool allows the design and configuration of a robot through the specification of different types of sensors. After designing the robot, its behaviour can be simulated by means of different challenges proposed to the user. This behaviour is defined through a block-based visual programming language. Blocks allow actions that the robot has to perform based on the information gathered by the different sensors to be defined in order to pass a challenge.

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