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.

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 The Effect of Using STEAM Approach on Developing Computational Thinking Skills among High School Students in Jordan

2020 ◽  
Vol 14 (14) ◽  
pp. 80
Author(s):  
Rana Al-Haj Bedar ◽  
Muhannad Anwar Al-Shboul
Keyword(s):  
High School ◽  
High School Students ◽  
Private School ◽  
Computational Thinking ◽  
Thinking Skills ◽  
Control Group ◽  
School Students ◽  
Ordnance Survey ◽  
Educational Challenges ◽  
Algorithmic Thinking

The growing demand for combining digital technology with learning practices has surpassed the use of technology or learning how to use it into the process of enhancing learners’ intellectual levels and scaffolding their understanding by focusing on skills that include thought processes gathered in what is called computational thinking. On the other hand, educational challenges promote the search for new instructional tools and approaches. Consequently, learning shall be extended by superimposing science, technology, engineering, arts, and mathematics (STEAM) approach in the instructional practices. The aim of this paper is to show how STEAM approach can develop the computational thinking among high school learners in Jordan. The main skills of computational thinking included: algorithmic thinking, abstraction, decomposition, and generalization. The sample of this study involved 32 high school students in a private school in Amman. The experimental group studied geography skills in a STEAM approach that included the use of online resources such as LightBot maze and the Ordnance Survey maps (OS) website. The control group studied the same content but through conventional method. Findings showed a significant development in the computational thinking especially in algorithmic thinking and abstraction. Thus a STEAM approach learning environment is one of the effective methods of teaching that improved computational thinking.

The relationship between physical education and sports school students' positive thinking skill levels and their attitudes to learning: Comparison by gender and years of exercising

2020 ◽  
Vol 8 (4) ◽  
pp. 754-760
Author(s):  
Alparslan Ince ◽  
Keyword(s):  
High School Students ◽  
Physical Education ◽  
Quantitative Research ◽  
Thinking Skills ◽  
Positive Thinking ◽  
Thinking Skill ◽  
School Students ◽  
Skill Levels ◽  
Quantitative Research Methods ◽  
The Relationship

The aim of this study was to compare the relationship between physical education and sports high school students' positive thinking skill levels and attitudes of learning in terms of gender and years of doing sports. The study is a descriptive method, one of the quantitative research methods. The study group consisted of 280 (age: 20.98 ± 1.390) university students from School of Physical Education and Sports in Ordu university. As a result, it was concluded that the students' positive thinking skills were at a high level, and the nature of learning, anxiety, expectation, and openness to learning sub-dimensions of the attitude tolearning scale were at high levels. It was concluded that there is a statistically significant and positive relationship between the nature of learning, Expectation, and openness to learning, and positive thinking skill from sub-dimensions of the attitude to learning scale, but there is a negatively significant relationship between anxiety and positive thinking skills

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.

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