Exploring Computer Science with MicroworldsEX to Learn Geometry and Logo Programming Code

2019 ◽  
pp. 593-598
Author(s):  
Thomas Walsh Jr.
Keyword(s):  
Problem Solving ◽  
Computer Science ◽  
Programming Language ◽  
Meta Analysis ◽  
Programming Environments ◽  
Problem Solving Skills ◽  
Cognitive Problem ◽  
Logo Programming ◽  
Guided Instruction ◽  
Bureau Of Labor Statistics

Future employment of computer-programming jobs will be best for applicants with experience in different languages and coding tools (Bureau of Labor Statistics, 2018). Empirical and meta-analysis research studies support of teaching Logo programming in developing student cognitive problem-solving skills has been documented. Using guided instruction with teacher-mediated scaffolding Exploring Computer Science with MicroworldsEX (Walsh, 2013-2017) has been found as an effective method in preparing students using the Logo code programming language to create geometric graphic, animation, and gaming projects. More research is needed to study teacher scaffolding and mediation skills to support learning Logo coding and transfer to other domains including other programming environments.

scholarly journals Improving problem-solving skills through logo programming language

2015 ◽  
Vol 41 (3) ◽  
pp. 52-64 ◽  
Author(s):  
Bens Pardamean ◽  
Teddy Suparyanto ◽  
Evelyn Evelyn
Keyword(s):  
Problem Solving ◽  
Programming Language ◽  
Problem Solving Skills ◽  
Logo Programming

Algorithmic Puzzles

2011 ◽  
Author(s):  
Anany Levitin ◽  
Maria Levitin
Keyword(s):  
Problem Solving ◽  
Computer Science ◽  
Algorithm Design ◽  
Divide And Conquer ◽  
Algorithmic Problem ◽  
Design Strategies ◽  
Job Interviews ◽  
Skill Levels ◽  
Problem Solving Skills ◽  
Solution Methods

While many think of algorithms as specific to computer science, at its core algorithmic thinking is defined by the use of analytical logic to solve problems. This logic extends far beyond the realm of computer science and into the wide and entertaining world of puzzles. In Algorithmic Puzzles, Anany and Maria Levitin use many classic brainteasers as well as newer examples from job interviews with major corporations to show readers how to apply analytical thinking to solve puzzles requiring well-defined procedures. The book's unique collection of puzzles is supplemented with carefully developed tutorials on algorithm design strategies and analysis techniques intended to walk the reader step-by-step through the various approaches to algorithmic problem solving. Mastery of these strategies--exhaustive search, backtracking, and divide-and-conquer, among others--will aid the reader in solving not only the puzzles contained in this book, but also others encountered in interviews, puzzle collections, and throughout everyday life. Each of the 150 puzzles contains hints and solutions, along with commentary on the puzzle's origins and solution methods. The only book of its kind, Algorithmic Puzzles houses puzzles for all skill levels. Readers with only middle school mathematics will develop their algorithmic problem-solving skills through puzzles at the elementary level, while seasoned puzzle solvers will enjoy the challenge of thinking through more difficult puzzles.

scholarly journals The Online Learning and Students’ Fear of COVID-19: Study in Malaysia and Pakistan

2021 ◽  
Vol 22 (4) ◽  
pp. 1-21
Author(s):  
Farhat Munir ◽  
Aizza Anwar ◽  
Daisy Mui Hung Kee
Keyword(s):  
Online Learning ◽  
Problem Solving ◽  
Social Presence ◽  
Process Model ◽  
Sampling Technique ◽  
Snowball Sampling ◽  
Online Data ◽  
Problem Solving Skills ◽  
Cognitive Problem ◽  
The Relationship

The COVID-19 pandemic has forced millions of students to stay indoors and adapt to the new normal, namely distance learning at home, placing online learning in the spotlight. However, students’ motivation for online learning and its effectiveness in skill development during the COVID-19 pandemic has not been widely studied. This study examined the relationship between students’ fear of COVID-19 and students’ social presence in online learning while investigating the parallel mediating role of student psychological motivation and cognitive problem-solving skills related to online learning. The participants were 472 university students in Malaysia and Pakistan. An online data collection technique using Google Forms was employed. Faculty members of the universities were asked to share the survey with their students. Moreover, using a snowball sampling technique, students were requested to share the survey with their friends. SPSS Statistics (Version 21)  was employed to do preliminary data analysis, AMOS (Version 21) software was used to conduct confirmatory factor analysis using a maximum likelihood estimation, and Hayes’ PROCESS model was used to examine proposed hypotheses. The results show that only cognitive problem solving mediates the relationship between fear of COVID-19 and students’ social presence in online learning in Malaysian samples. In Pakistan, cognitive problem solving and psychological motivation mediate the relationship between fear of COVID-19 and students’ social presence in online learning. The study found that developing cognitive problem-solving skills and providing psychological motivation could enhance their engagement with online learning.

scholarly journals University Students' Attitude and Anxiety Towards Computer Science Education through Computational Thinking

2021 ◽  
Vol 12 (6) ◽  
pp. 755-760
Author(s):  
Youngseok Lee Et.al
Keyword(s):  
Science Education ◽  
Problem Solving ◽  
Computer Science ◽  
Learning Effect ◽  
Computer Science Education ◽  
Computational Thinking ◽  
Average Score ◽  
Problem Solving Skills ◽  
Programming Education ◽  
Learning Technique

Background/Objectives: In the 21st century, communication and collaboration between people is an important element of talent. As artificial intelligence (AI), the cutting edge of computer science, develops, AI and collaboration will become important in the near future. Methods/Statistical analysis: To achieve this, it is necessary to understand how artificial AI based on computer science works, and how problem-based programming education is effective in computer science education. In this study, 177 college students who received programming education focused on problem-solving learning were identified with computational thinking (CT) at the beginning of the semester, and their satisfaction and post-education satisfaction survey showed that their attitudes and interests influenced their education. Findings: To pretest the learners, they were diagnosed using a measurement sheet. The learners’ current knowledge statuses were checked, and the correlation between the evaluation results, based on what was taught according to the problem-solving learning technique, was analyzed according to the proposed method. The analysis of the group average score of the learners showed that the learning effect was significant. The results of the measures of the students’ CT at the beginning of the semester were correlated with problem-solving learning, teaching method, lecture satisfaction, and other environmental factors. The ability to solve a variety of problems using CT will become increasingly important, so if students seek to improve their satisfaction with problem-solving learning techniques for computer science education, it will be possible for universities to develop convergence talent more efficiently. Improvements/Applications: if you pursue a problem-solving learning technique and a way to improve students’ satisfaction, it will help students improve their problem-solving skills. If the method of deriving and improving computational thinking ability in this paper is applied to computer education, it will induce student interest, thereby increasing the learning effect.

A Brief Discussion on Incentives and Barriers to Computational Thinking Education

2022 ◽  
pp. 253-269
Author(s):  
Hüseyin Özçınar
Keyword(s):  
Computer Science ◽  
Programming Language ◽  
Primary Education ◽  
Computer Programming ◽  
Computational Thinking ◽  
Thinking Skill ◽  
Logo Programming ◽  
Basic Concepts ◽  
The 1960S ◽  
Algorithmic Thinking

The idea that computational thinking or algorithmic thinking should be taught to everyone dates back to the 1960s. First in 1960s, Alan Perlis argued that computer programming should be taught to everyone because it can be used as a mental tool for understanding and solving every kind of problem. In 1980s, under the leadership of Seymour Papert, students at the level of primary education were attempted to be taught LOGO programming language with the aim of gaining procedural thinking skill. After the publication of Jeannette Wing's “computational thinking” in Communications of the ACM in 2006, the idea that the basic concepts of computer science should be learned by all was started to be debated widely again. In the present paper, the justifications for teaching computational thinking and applicability of teaching computational thinking within the context of existing conditions will be discussed.

Implementation of Game-Based Learning to Enhance Students’ Problem Solving Skills—A Meta Analysis

2018 ◽  
Vol 24 (6) ◽  
pp. 4474-4477
Author(s):  
Nurfazliah Muhamad ◽  
Jamalludin Harun ◽  
Megat Aman Zahiri Megat Zakaria ◽  
Shaharuddin Md Salleh
Keyword(s):  
Problem Solving ◽  
Meta Analysis ◽  
Game Based Learning ◽  
Problem Solving Skills
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