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All Journal ELINVO (Electronics, Informatics, and Vocational Education) Indonesian Journal of Artificial Intelligence and Data Mining Ideguru: Jurnal Karya Ilmiah Guru
Ramadana, Muhammad Rifqy
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Computer Science & IT Education Electrical & Electronics Engineering Other

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Students' Cognitive Load on Computer Programming Instructional Process Using Example-Problem-Based Learning and Problem-Based Learning Instructional Model at Vocational High School Herlambang, Admaja Dwi; Ramadana, Muhammad Rifqy; Wijoyo, Satrio Hadi; Phadung, Muneeroh
Elinvo (Electronics, Informatics, and Vocational Education) Vol. 9 No. 2 (2024): November 2024
Publisher : Department of Electronic and Informatic Engineering Education, Faculty of Engineering, UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/elinvo.v9i2.57882

Abstract

This paper fills an essential gap in applying cognitive load theory in teaching computer programming within vocational settings. It is an important area to consider for improving students' learning processes who intend to enter the rapidly changing technology sector. This study assessed the distinct impacts of the instructional paradigms, specifically Example-Problem-Based Learning (EPBL) and Problem-Based Learning (PBL), on students' cognitive loads upon framing an iterative structure lesson on computer programming. Vocational programming education is chosen for this purpose because vocational education faces unique challenges in integrating practical skills development with theoretical understanding, and programming tasks involve high cognitive demands. In a quasi-experimental design, 68 vocational high school students were assigned to an EPBL (n = 34) and a PBL (n = 34) group. The measurement of ICL was operationalized by RPI, the ECL by ME, and the GCL by LO. The relationship among the various components of the cognitive load was tested using the Spearman correlation test. There are significant differences in the profile of cognitive load between the two groups: the EPBL group was always associated with the lower ECL and higher GCL. In other words, the present study is original because it systematically compares EPBL with PBL in the context of vocational programming education and provides empirical evidence based on instructional design decisions. These findings suggest a further refinement of the CLT within domain-specific contexts and practical guidelines for optimizing instructional strategies in computer programming education in vocational schools.
Analisis Kemampuan Matematika Siswa SMP BSS Ditinjau dari Tingkat Beban Kognitif dan Motivasi Intrinsik Yuliaristiawan, Ervan Dwi; Ramadana, Muhammad Rifqy; Soepriyanto, Yerry; Purnomo, Purnomo
Ideguru: Jurnal Karya Ilmiah Guru Vol 10 No 1 (2025): Edisi Januari 2025
Publisher : Dinas Pendidikan, Pemuda dan Olahraga Daerah Istimewa Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51169/ideguru.v10i1.1104

Abstract

Mathematics education in middle school was important for developing students' logical and analytical thinking abilities. This article explored the factors influencing students' mathematical abilities, particularly cognitive load and intrinsic motivation. Cognitive load referred to how much information students could process, which could hinder understanding if too high. Intrinsic motivation increased students' enthusiasm for learning and encouraged deeper understanding. This research analyzed the relationship between BSS middle school students' mathematical abilities and their levels of cognitive load and intrinsic motivation through data collection. Data analysis was conducted using multiple regression tests to determine the relationship between students' cognitive load and intrinsic motivation levels and their mathematical abilities both in mathematics subjects and simultaneously. The multiple regression statistical test showed that intrinsic motivation did not significantly affect students' mathematical abilities (sig.0.102>0.05), while students' cognitive load significantly affected their mathematical abilities but was weak (sig.0.021<0.05). Simultaneously, the influence of cognitive load and intrinsic motivation significantly affected students' mathematical abilities (0.01<0.05). This research provided suggestions for further research on the impact of cognitive load on students' mathematical abilities.
Analyzing Student Cognitive Engagement in AI-Based Learning using Prompting Techniques Ramadana, Muhammad Rifqy; Ulfa, Saida; Soepriyanto, Yerry
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 2 (2025): July 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i2.36160

Abstract

With the increasing integration of AI in education, understanding how students engage cognitively in AI-assisted learning is crucial. Cognitive engagement in AI-assisted learning is important because it helps students interact meaningfully with AI tools, process information critically, and enhance their learning outcomes through effective AI-driven feedback and responses. To improve response quality in AI, one effective method is utilizing prompting techniques, which guide AI to generate more accurate, relevant, and structured responses, enhancing student learning experiences. This research investigates students' cognitive engagement when learning with AI-based tools using different prompting techniques, including Zero-Shot, Chain of Thought, Interactive Prompting, and Elaborate Prompting. A total of 54 students participated, and their engagement was assessed using a cognitive engagement questionnaire. The results, analyzed through a One-Sample T-test, reveal that students demonstrate significantly positive in cognitive engagement when using prompting techniques in AI-based learning. Furthermore, the findings suggest that effective prompting enhances the quality of AI-generated responses, positioning AI Chatbots as valuable learning assistants. This study provides important insights into optimizing AI-based learning strategies, highlighting the role of prompting in fostering deeper student interaction and engagement with AI tools.
Co-Authors Admaja Dwi Herlambang, Admaja Dwi Phadung, Muneeroh Purnomo Purnomo Saida Ulfa Satrio Hadi Wijoyo Yerry Soepriyanto Yuliaristiawan, Ervan Dwi