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 Improving Novice Students’ Computational Thinking Skills by Problem-Solving and Metacognitive Techniques

2021 ◽  
Vol 20 (6) ◽  
pp. 88-108
Author(s):  
Nor Hasbiah Ubaidullah ◽  
Zulkifley Mohamed ◽  
Jamilah Hamid ◽  
Suliana Sulaiman ◽  
Rahmah Lob Yussof
Keyword(s):  
Problem Solving ◽  
Cognitive Skills ◽  
Teaching And Learning ◽  
Intervention Program ◽  
Computational Thinking ◽  
Thinking Skills ◽  
Metacognitive Skills ◽  
Thinking Skill ◽  
Novice Students ◽  
Programming Courses

Admittedly, the teaching and learning of programming courses in the computer science and information technology programs have been extremely challenging. Currently, most instructors depend on either the problem-solving technique or the metacognitive technique to help students develop a range of cognitive skills, including metacognitive skills, which are important in the development of a strong computational thinking skill required for 21st-century learning. Studies focusing on the practices of instructors in using both techniques are scarce, thus motivating the researchers to carry out this study. This study was based on a qualitative approach involving a case-study design in which five (5) male and five (5) female instructors were selected from 10 pre-university centers in Malaysia as the respondents and participants in an intervention program. The research instruments used were an interview checklist and intervention guidelines. As anticipated, the findings showed that the activities of each technique could only help students develop certain sub-skills of the computational thinking skill, thus underscoring the need for instructors to integrate both techniques in their teaching practices. Thus, it could be reasoned that using either the metacognitive technique or the problem-solving technique alone would not be sufficient to help students develop strong computational thinking skills, as each technique has its strengths and weaknesses. Therefore, it becomes imperative for instructors to leverage the strengths of both techniques by integrating both of them in the teaching and learning of programming courses.

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.

The Role of Artificial Intelligence Within the Scope of Digital Transformation in Enterprises

2020 ◽  
pp. 122-146 ◽  
Author(s):  
M. Hanefi Calp
Keyword(s):  
Artificial Intelligence ◽  
Problem Solving ◽  
Digital Transformation ◽  
New Era ◽  
Artificial Intelligence Techniques ◽  
Reasoning Problem ◽  
Analytical Thinking ◽  
Effective Service

Digital transformation, which is the beginning of a new era, and performed in order to provide a more effective service, has become a compulsory situation for the enterprises that take into account the increasing corporate volumes. However, the processes and technologies used in this transformation may change according to the enterprise volume and needs. At this point, activities that implement artificial intelligence technologies will make significant contributions to digital transformation. Artificial intelligence technologies serve many purposes such as search, reasoning, problem-solving, perception, learning, estimating, analytical thinking, optimization, and planning. The purpose of this chapter is to demonstrate the effects of artificial intelligence techniques on the processes of digital transformation utilized in enterprises by considering the difficulties experienced in the realization of digital transformation. It is expected that the study will provide a perspective for other studies on digital transformation and thus create an awareness.

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.

Exploring Factors That Influence Computational Thinking Skills in Elementary Students’ Collaborative Robotics

2021 ◽  
pp. 073563312199247
Author(s):  
William H. Stewart ◽  
Youngkyun Baek ◽  
Gina Kwid ◽  
Kellie Taylor
Keyword(s):  
Problem Solving ◽  
Intrinsic Motivation ◽  
Computational Thinking ◽  
Thinking Skills ◽  
Influential Factors ◽  
Educational Setting ◽  
School Students ◽  
Collaborative Robotics ◽  
Significant Relationships ◽  
5Th Grade

Recently educational robotics has expanded into curriculum beyond traditional STEM fields, and which can also be used to foster computational thinking (CT) skills. Prior research has shown numerous interdisciplinary benefits related to CT, however, these influential factors have often been investigated with relatively few variables. This study investigated factors that may lead to 4th and 5th grade elementary school students’ development of computational thinking skills in collaborative robotics activities by hypothesizing a model which proposed that a problem solving inventory, intrinsic motivation, and enjoyment were the main predictors of computational thinking skills. The model was then tested by surveying students with several psychometric inventories where a revised model was then constructed. The study found significant relationships between perceived competence and enjoyment, and learning motivation, and intrinsic motivation. Another important finding was that problem solving was a significant predictor of computational thinking skills. Results were interpreted with reference to implications for possible means of improving learning outcomes when using collaborative robotics in an educational setting.

scholarly journals Discovering the Role of Problem-Solving and Discussion Techniques in the Teaching Programming Environment to Improve Students' Computational Thinking Skills

2021 ◽  
Vol 11 (12) ◽  
pp. 615-623
Author(s):  
Nor Hasbiah Ubaidullah ◽  
◽  
Zulkifley Mohamed ◽  
Jamilah Hamid ◽  
Suliana Sulaiman
Keyword(s):  
Problem Solving ◽  
Teaching And Learning ◽  
Computer Programming ◽  
Computational Thinking ◽  
Structured Interview ◽  
Thinking Skills ◽  
Survey Method ◽  
Teaching Techniques ◽  
University Lecturers ◽  
Programming Courses

Computational thinking skill is one of the essential abilities to be learned and perfected by students of this century. Studies have shown that in the teaching and learning of programming courses, discussion and problem-solving techniques have been widely used. However, studies based on the suitability of such teaching techniques for the development of the computational thinking skills of students are, however, lacking. In this context, this research was conducted to define the teaching techniques used by university lecturers when teaching a computer programming subject and to explore how the techniques can influence the development of the computational thinking skills of students. This research was based on a combination of qualitative and quantitative approaches involving a semi-structured interview and a survey method, respectively. The research sample consisted of eight (8) university lecturers recruited from several Malaysian public universities, who had been teaching computer science to undergraduates. The results showed that in teaching computer programming, a majority of the respondents used discussion and problem-solving methods, with each assisting students to gain computer programming skills and learn certain components of computational thinking. As such, it is recommended that teaching practitioners incorporate the discussion and problem-solving techniques in the teaching and learning of programming courses. The incorporation of such strategies will help students develop good computer programming and computational thinking skills encompassing all the fundamental elements. The results also revealed that the respondents had no experience in using the metacognitive technique. As such, it is also proposed that future research should focus on this technique to investigate any possible effects that it may have on the growth of the computer programming and computational thinking skills of undergraduates.

Sign in / Sign up

Export Citation Format

Share Document