scholarly journals Computational Thinking Processes and Their Congruence with Problem-Solving and Information Processing

2019 ◽  
pp. 65-78
Author(s):  
A. Labusch ◽  
B. Eickelmann ◽  
M. Vennemann
Keyword(s):  
Information Processing ◽  
Problem Solving ◽  
Computational Thinking ◽  
Thinking Processes

7. Computational thinking

2016 ◽  
pp. 119-127
Author(s):  
Subrata Dasgupta
Keyword(s):  
Cognitive Psychology ◽  
Information Processing ◽  
Problem Solving ◽  
Computational Thinking ◽  
Problem Solving Skills ◽  
Action Effect ◽  
Abstraction Level ◽  
System A ◽  
Human Creativity ◽  
Molecular Information

What is computational thinking? The AI researcher Paul Rosenbloom’s interpretation focuses on two relationships: interaction (a ‘reciprocal action, effect or influence’ between two entities); and implementation (to ‘put into effect’ a system A at a higher abstraction level in terms of interacting processes within a system B at a lower level of abstraction). ‘Computational thinking’ explains how computational thinking entails more than analytical and problem solving skills. It encompasses a way of imagining, by way of seeing analogies and constructing metaphors. The influence of computational thinking on cognitive psychology and connectionism, and the emergence of cognitive science and its impact on understanding human creativity and molecular information processing 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.

scholarly journals Introducing Computational Thinking Practices in Learning Science of Elementary Schools [Research-in-Progress]

10.28945/4327 ◽  
2019 ◽  
Keyword(s):  
Science Education ◽  
Problem Solving ◽  
Computational Thinking ◽  
Learning Science ◽  
Future Research ◽  
Full Potential ◽  
Science Program ◽  
Next Generation ◽  
Science Field ◽  
Problem Solving Skills

Aim/Purpose: Science is becoming a computational endeavor therefore Computational Thinking (CT) is gradually being accepted as a required skill for the 21st century science student. Students deserve relevant conceptual learning accessible through practical, constructionist approaches in cross-curricular applications therefore it is required for educators to define, practice and assess practical ways of introducing CT to science education starting from elementary school. Background: Computational Thinking is a set of problem-solving skills evolving from the computer science field. This work-in-progress research assesses the CT skills, along with science concepts, of students participating in a science program in school. The program pertains learning science by modeling and simulating real world phenomenon using an agent-based modeling practice. Methodology: This is an intervention research of a science program. It takes place as part of structured learning activities of 4th and 5th grade classes which are teacher-guided and are conducted in school. Both qualitative and quantitative evaluations are parts of the mixed methods research methodology using a variety of evaluation technique, including pretests and posttests, surveys, artifact-based interviews, in class observations and project evaluations. Contribution: CT is an emerging skill in learning science. It is requiring school systems to give increased attention for promoting students with the opportunity to engage in CT activities alongside with ways to promote a deeper understanding of science. Currently there is a lack of practical ways to do so and lack of methods to assess the results therefore it is an educational challenge. This paper presents a response to this challenge by proposing a practical program for school science courses and an assessment method. Findings: This is a research in progress which finding are based on a pilot study. The researches believe that findings may indicate improved degree of students' science understanding and problem-solving skills. Recommendations for Practitioners: Formulating computer simulations by students can have great potential on learning science with embedded CT skills. This approach could enable learners to see and interact with visualized representations of natural phenomena they create. Although most teachers do not learn about CT in their initial education, it is of paramount importance that such programs, as the one described in this research, will assist teachers with the opportunity to introduce CT into science studies. Recommendation for Researchers: Scientific simulation design in primary school is at its dawn. Future research investment and investigation should focus on assessment of aspects of the full Computational Thinking for Science taxonomy. In addition, to help teachers assess CT skills, new tools and criteria are required. Impact on Society: STEM related professions are lacking the man power required therefore the full potential of the economy of developed countries is not fulfilled. Having students acquire computational thinking skills through formal education may prepare the next generation of world class scientists and attract larger populations to these fields. Future Research: The inclusion of computational thinking as a core scientific practice in the Next Generation Science Standards is an important milestone, but there is still much work to do toward addressing the challenge of CT-Science education to grow a generation of technologically and scientifically savvy individuals. New comprehensive approaches are needed to cope with the complexity of cognitive processes related to CT.

scholarly journals AN OVERVIEW OF SWARM INTELLIGENCE IN ARTIFICIAL INTELLIGENT SYSTEMS

2021 ◽  
Vol 9 (08) ◽  
pp. 673-675
Author(s):  
Kalpana C. Dalwai ◽  
Keyword(s):  
Information Processing ◽  
Problem Solving ◽  
Swarm Intelligence ◽  
Intelligent Systems ◽  
Optimization Problems ◽  
Artificial Intelligent

Swarm intelligence refers to a kind of problem-solving ability that emerges in the interactions of simple information-processing units. The concept of a swarm suggests multiplicity, stochasticity, randomness, and messiness. Advancement of technology has led to problems that are complex and more challenging.Swarm intelligence techniques were mostly developed for solving optimization problems.

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.

An Information Processing Analysis of Expert and Novice Teachers’ Problem Solving

1990 ◽  
Vol 27 (3) ◽  
pp. 533-556 ◽  
Author(s):  
H. Lee Swanson ◽  
James E. O’Connor ◽  
John B. Cooney
Keyword(s):  
Information Processing ◽  
Problem Solving ◽  
Novice Teachers ◽  
Expert And Novice
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