scholarly journals Non-Deterministic Computational Thinking

2021 ◽  
Vol 9 (8) ◽  
pp. 271-283
Author(s):  
Leandro Ferreira Paz ◽  
Cristiano Gomes Carvalho ◽  
Andréia dos Santos Sachete ◽  
Marcele Teixeira Homrich Ravasio ◽  
Ricardo Antonio Rodrigues ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Problem Solving ◽  
Computational Thinking ◽  
Vital Role ◽  
Large Field ◽  
Daily Lives ◽  
Deterministic Dynamics ◽  
Educational Paradigm ◽  
Algorithmic Thinking

An educational paradigm that has improved problem-solving capacity is computational thinking, which uses characteristics such as decomposition, abstraction, pattern recognition, and algorithmic thinking. However, most of the resources developed under this paradigm are deterministic. However, the current world is not linear. Non-deterministic dynamics play a vital role in today's world. Decisions about the same fact can cause different events, and students must be prepared to live with such uncertainties. This article discusses challenges and possibilities in the development of non-deterministic computational thinking resources. This work shows a large field of research yet to be worked on, with new possibilities and a great potential to connect new resources with the students' daily lives.

scholarly journals Using Grey-based Mathematical Equations of Decision-making as Teaching Scaffolds: from an Unplugged Computational Thinking Activity to Computer Programming

2018 ◽  
Vol 2 (2) ◽  
pp. 29-46
Author(s):  
Meng-Leong How ◽  
Chee-Kit Looi
Keyword(s):  
Decision Making ◽  
Problem Solving ◽  
Computational Thinking ◽  
Computer Code ◽  
Daily Lives ◽  
Decision Making Processes ◽  
Mathematical Equations ◽  
Problem Solving Strategies ◽  
Software Code ◽  
Crucial Part

Computational Thinking (CT) is pervasive in our daily lives and is useful for problem-solving. Decision-making is a crucial part of problem-solving. In the extant literature, problem-solving strategies in educational settings are often conveniently attributed to intuition; however, it is well documented that computer programmers might even have difficulty describing about their intuitive insights during problem-solving using natural language (such as English), and subsequently convert what has been described using words into software code. Hence, a more analytical approach using mathematical equations and descriptions of CT is offered in this paper as a potential form of rudimentary scaffolding, which might be useful to facilitators and learners of CT-related activities. In the present paper, the decision-making processes during an unplugged CT activity are delineated via Grey-based mathematical equations, which is useful for informing educators who may wish to explain to their learners about the various aspects of CT which are involved in the unplugged activity and simultaneously use these mathematical equations as scaffolds between the unplugged activity and computer code programming. This theoretical manuscript may serve as a base for learners, should the facilitator ask them to embark on a software programming activity that is closely associated to the unplugged CT activity.

First-year engineering students' computational reasoning abilities

2022 ◽  
Vol 1 (2) ◽  
Author(s):  
Concepcion Rebollar ◽  
Carolina Varela ◽  
Olatz Eugenio
Keyword(s):  
Critical Thinking ◽  
Problem Solving ◽  
Engineering Students ◽  
Computational Thinking ◽  
First Year ◽  
Reasoning Abilities ◽  
New Students ◽  
Measurement And Evaluation ◽  
Computational Reasoning ◽  
Algorithmic Thinking

Computational thinking is an essential skill set for today's students, given the digital age in which we live and work (CT). Without a precise definition, it is generally understood to be a collection of abilities and attitudes required to deal with difficulties in any aspect of life, whether or not a computer is involved. Measurement and evaluation of students' progress in CT abilities are critical, and this can only be done using instruments that have been tested and shown to work before. New students at the Basque Country's University of the Basque Country's Engineering Degrees are tested for critical thinking, algorithmic thinking, problem solving, cooperation and creativity using a previously proven tool.

Educational Robotics and Computational Thinking Development

2020 ◽  
pp. 310-338
Author(s):  
Timoleon Theofanellis ◽  
Evagelia Voulgari ◽  
Savvas Tsolakis
Keyword(s):  
Pattern Recognition ◽  
Problem Solving ◽  
Computational Thinking ◽  
Robotic Systems ◽  
Educational Robotics ◽  
Two Systems ◽  
Do It Yourself ◽  
Problem Solving Process ◽  
The Way

Computational thinking (CT) is a problem-solving process that refers to characteristics such as de-composition, abstraction, pattern recognition, and algorithms. This chapter focuses on educational robotics and their use in developing CT. Firstly, the importance of CT is analyzed along with the way it is applied in the classroom. It goes on discussing the way the introduction of educational robotic systems in education affect CT and the importance of the do-it-yourself philosophy. It presents two widely used educational robotic systems follows, Arduino and Lego EV3, along with examples of their relationship with CT development. The chapter finishes with a comparison of the two systems regarding the easiness and difficulties of using them.

scholarly journals Exploring Students Computational Thinking based on Self-Regulated Learning in the Solution of Linear Program Problem

2019 ◽  
Vol 8 (1) ◽  
pp. 30
Author(s):  
Diah Nuraisa ◽  
Amalina Nur Azizah ◽  
Dian Nopitasari ◽  
Swasti Maharani
Keyword(s):  
Pattern Recognition ◽  
Problem Solving ◽  
Computational Thinking ◽  
Linear Program ◽  
Program Problem ◽  
Self Regulated Learning ◽  
Linear Program Problem ◽  
Regulated Learning ◽  
Depth Interviews

This study aims to analyze the students computational thinking in the solution of the linear program problem based on self-regulated learning. The data were collected by self-regulated learning questionnaire, computational thinking test, and depth interviews. This study was conducted in SMAN 10 Tangerang. Computational thinking in students with high and medium levels of self-regulated learning has no difference. Students still make a solution that is fixated with linear program problem-solving procedures in general, that is using examples, substitution, and elimination. In solving problems, students can reach the stages of decomposition and pattern recognition only. Students still do not evaluate the results of their work. Algorithmic performed is less coherent because the abstraction has not been done. The recommendation for further research is the need for research that can develop student abstraction in solving problems. Besides, there is also a need for research that analyzes the reflective of students in computational thinking when solving problems.

Coding as a tooL for forming ComputationaL thinking of students

2020 ◽  
pp. 4-10
Author(s):  
L. L. Bosova
Keyword(s):  
Pattern Recognition ◽  
Elementary School ◽  
Elementary School Students ◽  
Computational Thinking ◽  
Social Aspects ◽  
Digital Environment ◽  
School Students ◽  
Digital Society ◽  
Algorithmic Thinking

The article outlines the developmental and social aspects of teaching coding to schoolchildren: the development of thinking, the formation of new values of the digital society, understanding the rules of behavior in the digital environment. Coding is a powerful tool for developing computational (algorithmic) thinking. The article shows a variant of teaching coding, which allows schoolchildren to independently invent algorithms, develop their thinking, and improve their abilities. Namely, an example of a sequence of coding tasks on the topic "Integers and operations on them" for elementary school students is considered in detail. Tasks solutions (programs) are given in Python. The emphasis is on how, on the basis of a specially organized sequence of tasks, to provide the student with the opportunity to develop their abilities to perform decomposition, abstraction, pattern recognition, algorithmization, modeling, assessment.

scholarly journals The Importance of Computational Thinking to Train Structured Thinking in Problem Solving

2021 ◽  
Vol 6 (1) ◽  
pp. 113
Author(s):  
Rian Andrian ◽  
Rizki Hikmawan
Keyword(s):  
Pattern Recognition ◽  
Problem Solving ◽  
Everyday Life ◽  
Computational Thinking ◽  
Computational Tools ◽  
The World

Ability to do problem solving will be greatly influenced by how the flow of thinking in decomposing a problem until it finds the root of the problem so that it can determine the best solution. There is currently a growing recognition around the world that all fields require a prerequisite ability, namely to think logically, in a structured manner, and use computational tools to rapidly model and visualize data. This ability is known as Computational Thinking (CT). In this study, the author applied the computational thinking key concept in a case study to train structured thinking in problem solving. Computational thinking key concept includes Decomposition, Pattern recognition, Abstraction, and lastly use algorithms when they design simple steps to solve problems. Based on our case study that has been model, the result shows us that Computational Thinking can be used to train structured thinking in problem solving in everyday life

scholarly journals PROSES BERPIKIR KOMPUTASI SISWA SMP DALAM MEMECAHKAN MASALAH POLA BILANGAN DITINJAU DARI PERBEDAAN JENIS KELAMIN

MATHEdunesa ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 95-103
Author(s):  
Lintang Sekar Danindra ◽  
Masriyah
Keyword(s):  
Pattern Recognition ◽  
High School Students ◽  
Problem Solving ◽  
Junior High School ◽  
Settlement Patterns ◽  
Female Student ◽  
Computational Thinking ◽  
Male Student ◽  
Thinking Skills ◽  
Decomposition Process

One of the thinking skills needed in the 21st century is computational thinking skills. There are several factors that can affect students' computational thinking processes, including gender. The differences in the sexes of students allow for differences in computational thinking skills of each student. This research is a descriptive research with a qualitative approach aimed to describe the computational thinking process of junior high school students in solving the problem of number patterns in terms of gender differences. This research was conducted in class VIII-G of SMPN 13 Surabaya.The subjects in this research were one male student and one female student. The results of this study are the process of thinking about the comparison of male and female student with high mathematical abilities through the installation of decomposition, introduction to patterns, algorithmic thinking and generalization of patterns and abstractions. Male student in the decomposition process chose to read over and over the problem solving problem. In the process of pattern recognition, he created rows and columns to recognize completion patterns and check again. In the process of thinking algorithms, he mentioned logical steps that are in accordance with the identified settlement patterns. In the process of generalizing patterns and abstractions, he written general patterns and conclusions answers to problem solving problems and is very sure of the answers obtained. Female student in the decomposition process, she read while underlining important information on problem solving problems. In the process of pattern recognition, she separated every important information that exists and unifies every information with the same characteristics to recognize the pattern of completion and re-check. In the process of thinking algorithms, she mentioned logical steps that are in accordance with the identified settlement patterns. In the process of generalizing patterns and abstractions, she written general patterns and conclusions answers to problem solving. Keywords : Computational Thinking, Number Patterns, Gender.

scholarly journals Examining Trajectories of Elementary Students’ Computational Thinking Development Through Collaborative Problem-Solving Process in a STEM-Integrated Robotics Program

2021 ◽  
Vol 14 (1) ◽  
pp. 27-42
Author(s):  
Yi-Chun Hong ◽  
Yingxiao Chen ◽  
Yu-Fen Yang
Keyword(s):  
Problem Solving ◽  
Cognitive Skills ◽  
Computational Thinking ◽  
Video Data ◽  
Collaborative Problem Solving ◽  
Study Approach ◽  
Collaborative Problem ◽  
Problem Solving Process ◽  
K 12 ◽  
Algorithmic Thinking

Developing K-12 students’ computational thinking (CT) skills is essential. Building on the existing literature that has emphasized programming skill development, this study expands the focus to examine students’ use of underlying CT cognitive skills during collaborative problem-solving processes. A case study approach was employed to examine video data of 5th graders engaging in an integrated-STEM robotics curriculum. The findings reveal that students applied algorithmic thinking most frequently and prediction the least. They recorded most debugging behaviors initially in the problem-solving process, but after accumulating more experiences their uses of other CT skills, including algorithmic thinking, pattern recognition, and prediction, increased. Implications for developing young learners’ CT skills to solve real-world problems are discussed.

The Interplay Between Mathematical and Computational Thinking in Primary School Students’ Mathematical Problem-Solving Within a Programming Environment

2021 ◽  
pp. 073563312097993
Author(s):  
Zhihao Cui ◽  
Oi-Lam Ng
Keyword(s):  
Problem Solving ◽  
Mathematical Thinking ◽  
Mathematics Learning ◽  
Mathematical Problem ◽  
Computational Thinking ◽  
Programming Environment ◽  
Primary School Students ◽  
School Students ◽  
Mathematical Concepts ◽  
Block Based

In this paper, we explore the challenges experienced by a group of Primary 5 to 6 (age 12–14) students as they engaged in a series of problem-solving tasks through block-based programming. The challenges were analysed according to a taxonomy focusing on the presence of computational thinking (CT) elements in mathematics contexts: preparing problems, programming, create computational abstractions, as well as troubleshooting and debugging. Our results suggested that the challenges experienced by students were compounded by both having to learn the CT-based environment as well as to apply mathematical concepts and problem solving in that environment. Possible explanations for the observed challenges stemming from differences between CT and mathematical thinking are discussed in detail, along with suggestions towards improving the effectiveness of integrating CT into mathematics learning. This study provides evidence-based directions towards enriching mathematics education with computation.

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