Article Per Year (5 Year)
p-Index From 2021 - 2026
0.562
P-Index
This Author published in this journals
All Journal Journal of Teaching and Learning Physics Madani: Multidisciplinary Scientific Journal
Fuadi, Muhammad Ashar
Unknown Affiliation
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Education Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Physics Public Health Social Sciences Other

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : Journal of Teaching and Learning Physics

 1 
ANALYZING COMPUTATIONAL THINKING SKILLS USING THE RASCH MODEL ABOUT LEARNING ENVIRONMENT AND GENDER Fuadi, Muhammad Ashar
Journal of Teaching and Learning Physics Vol 10, No 1 (2025): Journal of Teaching and Learning Physics (February 2025)
Publisher : UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/jotalp.v10i1.44108

Abstract

This research analyzes Computational Thinking abilities using the Rasch model in Futuhiyyah High School students regarding sound waves regarding the learning environment and gender. This type of mixed methods research uses a concurrent embedded design. Data were analyzed using Wright maps and different measures assisted by Minister software. The Computational Thinking ability profile of the class: Only 7 out of 27 students had very high abilities. They exceeded the measured value of the decomposition indicator question which was the most difficult question, namely 4.46. Students' Computational Thinking abilities in terms of the learning environment, 18.52% of students in boarding schools and 7.41% of non-cottage students were able to solve very difficult questions, namely decomposition indicators and algorithmic thinking by exceeding the respective indicator's measure values of 4.10 and 4. 46. Students' Computational Thinking ability in terms of gender, 7.41% of male students and 18.52% of female students were able to solve very difficult questions, namely indicators of decomposition and algorithmic thinking. The results of the different measures, boarding school students were higher in answering questions on indicators of abstraction, generalization, and decomposition, while non-residential students focused on evaluation indicators and algorithmic thinking. Male students were higher in answering questions on the indicators of abstraction, generalization, and algorithmic thinking, while female students were higher on the indicators of evaluation and algorithmic thinking.
ANALYZING COMPUTATIONAL THINKING SKILLS USING THE RASCH MODEL ABOUT LEARNING ENVIRONMENT AND GENDER Fuadi, Muhammad Ashar
Journal of Teaching and Learning Physics Vol. 10 No. 1 (2025): Journal of Teaching and Learning Physics (February 2025)
Publisher : UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/jotalp.v10i1.44108

Abstract

This research analyzes Computational Thinking abilities using the Rasch model in Futuhiyyah High School students regarding sound waves regarding the learning environment and gender. This type of mixed methods research uses a concurrent embedded design. Data were analyzed using Wright maps and different measures assisted by Minister software. The Computational Thinking ability profile of the class: Only 7 out of 27 students had very high abilities. They exceeded the measured value of the decomposition indicator question which was the most difficult question, namely 4.46. Students' Computational Thinking abilities in terms of the learning environment, 18.52% of students in boarding schools and 7.41% of non-cottage students were able to solve very difficult questions, namely decomposition indicators and algorithmic thinking by exceeding the respective indicator's measure values of 4.10 and 4. 46. Students' Computational Thinking ability in terms of gender, 7.41% of male students and 18.52% of female students were able to solve very difficult questions, namely indicators of decomposition and algorithmic thinking. The results of the different measures, boarding school students were higher in answering questions on indicators of abstraction, generalization, and decomposition, while non-residential students focused on evaluation indicators and algorithmic thinking. Male students were higher in answering questions on the indicators of abstraction, generalization, and algorithmic thinking, while female students were higher on the indicators of evaluation and algorithmic thinking.
Co-Authors Shaunata, Via Amalia