scholarly journals Exploring Computational Thinking Skills Training Through Augmented Reality and AIoT Learning

2021 ◽  
Vol 12 ◽  
Author(s):  
Yu-Shan Lin ◽  
Shih-Yeh Chen ◽  
Chia-Wei Tsai ◽  
Ying-Hsun Lai
Keyword(s):  
Artificial Intelligence ◽  
Augmented Reality ◽  
Positive Impact ◽  
Computational Thinking ◽  
Skills Training ◽  
Thinking Skills ◽  
Educational Systems ◽  
Learning Intention ◽  
Planning And Design ◽  
Competence Training

Given the widespread acceptance of computational thinking (CT) in educational systems around the world, primary and higher education has begun thinking about how to cultivate students' CT competences. The artificial intelligence of things (AIoT) combines artificial intelligence (AI) and the Internet of things (IoT) and involves integrating sensing technologies at the lowest level with relevant algorithms in order to solve real-world problems. Thus, it has now become a popular technological application for CT training. In this study, a novel AIoT learning with Augmented Reality (AR) technology was proposed and explored the effect of CT skills. The students used AR applications to understand AIoT applications in practice, attempted the placement of different AR sensors in actual scenarios, and further generalized and designed algorithms. Based on the results of the experimental course, we explored the influence of prior knowledge and usage intention on students' CT competence training. The results show that proposed AIoT learning can increase students' learning intention and that they had a positive impact on problem solving and comprehension with AR technology, as well as application planning and design.

Computational Thinking and Mathematics

2016 ◽  
pp. 853-865
Author(s):  
Thiago Schumacher Barcelos ◽  
Ismar Frango Silveira
Keyword(s):  
Problem Solving ◽  
Computer Science ◽  
Basic Education ◽  
National Curriculum ◽  
Computational Thinking ◽  
Thinking Skills ◽  
Educational Systems ◽  
Curriculum Guidelines ◽  
And Mathematics ◽  
The One

On the one hand, ensuring that students archive adequate levels of Mathematical knowledge by the time they finish basic education is a challenge for the educational systems in several countries. On the other hand, the pervasiveness of computer-based devices in everyday situations poses a fundamental question about Computer Science being part of those known as basic sciences. The development of Computer Science (CS) is historically related to Mathematics; however, CS is said to have singular reasoning mechanics for problem solving, whose applications go beyond the frontiers of Computing itself. These problem-solving skills have been defined as Computational Thinking skills. In this chapter, the possible relationships between Math and Computational Thinking skills are discussed in the perspective of national curriculum guidelines for Mathematics of Brazil, Chile, and United States. Three skills that can be jointly developed by both areas are identified in a literature review. Some challenges and implications for educational research and practice are also discussed.

Computational Thinking and Mathematics

2014 ◽  
pp. 922-934
Author(s):  
Thiago Schumacher Barcelos ◽  
Ismar Frango Silveira
Keyword(s):  
Problem Solving ◽  
Computer Science ◽  
Basic Education ◽  
National Curriculum ◽  
Computational Thinking ◽  
Thinking Skills ◽  
Educational Systems ◽  
Curriculum Guidelines ◽  
And Mathematics ◽  
The One

On the one hand, ensuring that students archive adequate levels of Mathematical knowledge by the time they finish basic education is a challenge for the educational systems in several countries. On the other hand, the pervasiveness of computer-based devices in everyday situations poses a fundamental question about Computer Science being part of those known as basic sciences. The development of Computer Science (CS) is historically related to Mathematics; however, CS is said to have singular reasoning mechanics for problem solving, whose applications go beyond the frontiers of Computing itself. These problem-solving skills have been defined as Computational Thinking skills. In this chapter, the possible relationships between Math and Computational Thinking skills are discussed in the perspective of national curriculum guidelines for Mathematics of Brazil, Chile, and United States. Three skills that can be jointly developed by both areas are identified in a literature review. Some challenges and implications for educational research and practice are also discussed.

scholarly journals LearningML: A Tool to Foster Computational Thinking Skills Through Practical Artificial Intelligence Projects

2020 ◽  
Vol 20 (63) ◽  
Author(s):  
Juan David Rodríguez García ◽  
Jesús Moreno-León ◽  
Marcos Román-González ◽  
Gregorio Robles
Keyword(s):  
Artificial Intelligence ◽  
Ethical Issues ◽  
Computational Thinking ◽  
Thinking Skills ◽  
Lessons Learned ◽  
Supervised Machine Learning ◽  
Hands On ◽  
The Face ◽  
Multiple Levels ◽  
Critical Citizens

El uso de sistemas de inteligencia artificial en múltiples niveles de la sociedad ofrece nuevas y prósperas oportunidades, pero también introduce nuevos riesgos y cuestiones éticas que deben abordarse. Sostenemos que la introducción de contenidos de inteligencia artificial en las escuelas a través de proyectos prácticos es el camino a seguir para educar ciudadanos conscientes y críticos, para despertar vocaciones entre los jóvenes, y para fomentar las habilidades de pensamiento computacional de los estudiantes. Sin embargo, la mayoría de las plataformas educativas de programación existentes carecen de algunas características necesarias para desarrollar proyectos completos de IA y, en consecuencia, se requieren nuevas herramientas. En este artículo presentamos LearningML, una nueva plataforma dirigida al aprendizaje automático supervisado, una de las técnicas de IA más exitosas que se encuentra en la base de casi todas las aplicaciones actuales de IA. Este trabajo describe las principales funcionalidades de la herramienta y discute algunas decisiones tomadas durante su diseño, para el que hemos tenido en cuenta las lecciones aprendidas al revisar trabajos anteriores realizados para introducir la IA en la escuela y el análisis de otras soluciones que permiten proyectos prácticos de IA. También se presentan los próximos pasos en el desarrollo de LearningML, que se centran en la validación, tanto aparente como instruccional, de la herramienta. The use of artificial intelligence systems in multiple levels of society offers new and thriving opportunities, but also introduces new risks and ethical issues that should be dealt with. We argue that the introduction of artificial intelligence contents at schools through practical, hands-on, projects is the way to go in order to educate conscientious and critical citizens, to awaken vocations among youth people, as well as to foster students’ computational thinking skills. However, most existing programming platforms for education lack some required features to develop complete AI projects and, consequently, new tools are required. In this paper we present LearningML, a new platform aimed at learning supervised Machine Learning, one of the most successful AI techniques that is in the basis of almost every current AI application. This work describes the main functionalities of the tool and discusses some decisions taken during its design, for which we took into account the lessons learned while reviewing previous works carried out for introducing AI in school and from the analysis of other solutions that enable practical AI projects. The next steps in the development of LearningML are also presented, which are focused on both the face and instructional validation of the tool.

scholarly journals Educational Robotics in Primary School: Measuring the Development of Computational Thinking Skills with the Bebras Tasks

Informatics ◽  
2019 ◽  
Vol 6 (4) ◽  
pp. 43 ◽  
Author(s):  
Chiazzese ◽  
Arrigo ◽  
Chifari ◽  
Lonati ◽  
Tosto
Keyword(s):  
Primary School ◽  
Positive Impact ◽  
Computational Thinking ◽  
Fourth Grade ◽  
Thinking Skills ◽  
Research Field ◽  
Primary School Children ◽  
Control Group ◽  
Educational Robotics ◽  
K 12

Research has shown that educational robotics can be an effective tool to increase students’ acquisition of knowledge in the subjects of science, technology, engineering, and mathematics and promote, at the same time, a progression in the development of computational thinking (CT) skills in K–12 (kindergarten to 12th grade) education. Within this research field, the present study first sought to assess the effect of a robotics laboratory on the acquisition of CT-related skills in primary school children. The study also aimed to compare the magnitude of the effect of the laboratory across third- and fourth-grade students. For the purpose of the study, a quasi-experimental post-test-only design was adopted, and a group of 51 students, from third- and fourth-grade classrooms, participating in the robotics laboratories, were compared to a control group of 32 students from classrooms of the same grades. A set of Bebras tasks was selected as an overall measure of CT skills and was administered to children in both the intervention and control groups. Overall, the results showed that programming robotics artefacts may exert a positive impact on students’ learning of computational thinking skills. Moreover, the effect of the intervention was found to be greater among third-grade children.

Assessing K-9 Teachers' Computational Thinking Skills

2022 ◽  
pp. 467-487
Author(s):  
LeChen Zhang ◽  
Jalal Nouri
Keyword(s):  
Computational Thinking ◽  
Compulsory Education ◽  
Principal Component ◽  
Thinking Skills ◽  
Expert Review ◽  
Competence Training ◽  
Expert Review Panel ◽  
Ct Test ◽  
Teacher Ability ◽  
The Relationship

Many national curricula have incorporated computational thinking (CT) into compulsory education. Teacher ability to deliver the revised curriculum determines whether these new skills can be successfully integrated into teaching. Therefore, it is crucial to examine teacher readiness. This study measured Swedish K-9 teacher CT skills through a CT test validated by an expert review panel and a principal component analysis. Additionally, we engaged statistical analyses to examine the relationship between the teachers' background and their CT test scores, as well as their self-reported ability to teach CT. The result demonstrated the teachers' proficiency in different types of CT skills. Another finding revealed that the type of programming language mastered by teachers was associated with both their CT test score and self-reported ability to teach CT. This CT test can support teachers to identify specific areas for professional development and may facilitate the school management to plan teachers' competence training strategically.

Assessing K-9 Teachers' Computational Thinking Skills

2020 ◽  
pp. 124-144
Author(s):  
LeChen Zhang ◽  
Jalal Nouri
Keyword(s):  
Computational Thinking ◽  
Compulsory Education ◽  
Principal Component ◽  
Thinking Skills ◽  
Expert Review ◽  
Competence Training ◽  
Expert Review Panel ◽  
Ct Test ◽  
Teacher Ability ◽  
The Relationship

Many national curricula have incorporated computational thinking (CT) into compulsory education. Teacher ability to deliver the revised curriculum determines whether these new skills can be successfully integrated into teaching. Therefore, it is crucial to examine teacher readiness. This study measured Swedish K-9 teacher CT skills through a CT test validated by an expert review panel and a principal component analysis. Additionally, we engaged statistical analyses to examine the relationship between the teachers' background and their CT test scores, as well as their self-reported ability to teach CT. The result demonstrated the teachers' proficiency in different types of CT skills. Another finding revealed that the type of programming language mastered by teachers was associated with both their CT test score and self-reported ability to teach CT. This CT test can support teachers to identify specific areas for professional development and may facilitate the school management to plan teachers' competence training strategically.

scholarly journals Engineering Courses on Computational Thinking Through Solving Problems in Artificial Intelligence

2017 ◽  
Vol 7 (3) ◽  
pp. 34
Author(s):  
Piyanuch Silapachote ◽  
Ananta Srisuphab
Keyword(s):  
Artificial Intelligence ◽  
Problem Solving ◽  
Computational Thinking ◽  
Thinking Skills ◽  
Machine Intelligence ◽  
Collaborative Problem Solving ◽  
Learning To Learn ◽  
Dynamic Learning ◽  
Analytical Thinking ◽  
Mathematical Foundations

Computational thinking sits at the core of every engineering and computing related discipline. It has increasingly emerged as its own subject in all levels of education. It is a powerful cornerstone for cognitive development, creative problem solving, algorithmic thinking and designs, and programming. How to effectively teach computational thinking skills poses real challenges and creates opportunities. Targeting entering computer science and engineering undergraduates, we resourcefully integrate elements from artificial intelligence (AI) into introductory computing courses. In addition to comprehension of the essence of computational thinking, practical exercises in AI enable inspirations of collaborative problem solving beyond abstraction, logical reasoning, critical and analytical thinking. Problems in machine intelligence systems intrinsically connect students to algorithmic oriented computing and essential mathematical foundations. Beyond knowledge representation, AI fosters a gentle introduction to data structures and algorithms. Focused on engaging mental tool, a computer is never a necessity. Neither coding nor programming is ever required. Instead, students enjoy constructivist classrooms designed to always be active, flexible, and highly dynamic. Learning to learn and reflecting on cognitive experiences, they rigorously construct knowledge from collectively solving exciting puzzles, competing in strategic games, and participating in intellectual discussions.

scholarly journals Augmented Reality, Virtual Reality and Artificial Intelligence in Orthopedic Surgery: A Systematic Review

2021 ◽  
Vol 11 (7) ◽  
pp. 3253
Author(s):  
Umile Giuseppe Longo ◽  
Sergio De Salvatore ◽  
Vincenzo Candela ◽  
Giuliano Zollo ◽  
Giovanni Calabrese ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Virtual Reality ◽  
Augmented Reality ◽  
Orthopaedic Surgery ◽  
Resident Training ◽  
Comprehensive Search ◽  
Virtual And Augmented Reality ◽  
Orthopaedic Training ◽  
Meta Analyses

Background: The application of virtual and augmented reality technologies to orthopaedic surgery training and practice aims to increase the safety and accuracy of procedures and reducing complications and costs. The purpose of this systematic review is to summarise the present literature on this topic while providing a detailed analysis of current flaws and benefits. Methods: A comprehensive search on the PubMed, Cochrane, CINAHL, and Embase database was conducted from inception to February 2021. The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines were used to improve the reporting of the review. The Cochrane Risk of Bias Tool and the Methodological Index for Non-Randomized Studies (MINORS) was used to assess the quality and potential bias of the included randomized and non-randomized control trials, respectively. Results: Virtual reality has been proven revolutionary for both resident training and preoperative planning. Thanks to augmented reality, orthopaedic surgeons could carry out procedures faster and more accurately, improving overall safety. Artificial intelligence (AI) is a promising technology with limitless potential, but, nowadays, its use in orthopaedic surgery is limited to preoperative diagnosis. Conclusions: Extended reality technologies have the potential to reform orthopaedic training and practice, providing an opportunity for unidirectional growth towards a patient-centred approach.

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