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Muhammad Taufik
Prodi Pendidikan Fisika, FMIPA, Universitas Mataram
Agriculture, Biological Sciences & Forestry Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Physics Public Health Social Sciences Other

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AI-Driven Optimization of Pascal Programming Instruction for Undergraduate Physics Students at University of Mataram Muhammad Taufik; Muhammad Zuhdi; Syahrial Ayub; Joni Rokhmat; Hisbulloh Als Mustofa
International Journal of Contextual Science Education Vol. 2 No. 2 (2024): April - June 2024
Publisher : Postgraduate Program, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/ijcse.v2i2.640

Abstract

Teaching computational physics and developing programming skills remains a significant challenge for many undergraduate programs worldwide. This study presents an innovative approach implemented at the University of Mataram, Indonesia, to optimize Pascal programming instruction for physics students. Various artificial intelligence (AI) techniques were utilized to assist students in developing more complex programs. This enabled the generation of customized lesson plans with interactive tutorials, coding exercises, and simulations tailored to each student's needs. Throughout the semester, the system continuously monitored student progress and adjusted instructional materials accordingly. The analysis of Mann-Whitney test results for Computational Physics scores among Classes A, B, and C revealed no statistically significant differences between the groups, with median scores consistently at 80 and p-values exceeding 0.05. However, an examination of the theoretical section of the final examination showed an overall improvement in average scores compared to the previous year, with Class A achieving the highest mean score of 84%. Additionally, the practical programming section demonstrated increased pass rates across all three classes, with Class B achieving the highest pass rate at 92%, followed by Class A at 88% and Class C at 82%. Student feedback indicated high levels of satisfaction with the approach, citing increased engagement and motivation. The study highlights the potential of leveraging AI techniques in generating personalized programming examples for computational physics education, enhancing comprehension of theoretical concepts, and facilitating the development of practical programming skills.
Co-Authors 'Ardhuha, Jannatin Ahmad Busyairi, Ahmad Ahmad Harjono Al Fiyatoen Sevtia Arifuddin Arifuddin Aris Doyan Aris Doyan Bakti Sukrisna Elfatihan Egista Fazriatun Fazriatun Fitrotul Jamil G. Gunawan, G. Hairul Imam Hairunisyah Sahidu Hairunnisyah Sahidu Haris Munandar Hikmawati Hisbulloh Als Mustofa I Wayan Gunada I Wayan Gunada Intan Nur Maulida Isni Sahrianti Izlayani Putri Izzatun Nidda Jannatin Ardhuha Jannatin ‘Ardhuha Joni Rokhmat Kosim Kosim . Linda Juliarti Lisdah Wati Muh. Makhrus Muhammad Zuhdi Naf’atuzzahrah Naf’atuzzahrah Ni Nyoman Sri Putu Verawati Nurbaiti Nurbaiti Nurmala Hidayah Raudah Anjani Ronodirdjo, Muhammad Zuhdi Saprizal Hadisaputra Satutik Rahayu Selviana Haerani Susilawati - Syahrial Ayub Syamsuddin Syamsuddin Syamsul Hakim Wahyudi Wahyudi Wahyudi Wahyudi Wahyudi Wahyudi Wiwik Kurniawati