Information Technology Education Journal
INTEC Journal is published by the Informatics and Computer Engineering Education Study Program at Makassar State University. INTEC Journal is published periodically three times a year, containing articles on research results and / or critical studies in the field of Informatics and Computer Engineering Education from students, lecturers, and practitioners from universities or research institutions. The INTEC journal already has a print version ISSN with the number 2809-798X in 2022 and an online version ISSN with the number 2809-7971. INTEC Journal contains articles on informatics and computer engineering education in particular: learning multimedia e-learning/blended learning, information system, artificial intelligence and robotics, embedded expert system, big data and machine learning, software and network engineering
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<![CDATA[Understanding the TPACK Framework Among Technical Students: Evidence from Civil Engineering and Planning at UNM ]]>
<![CDATA[Muhammad Haristo Rahman]]>;
<![CDATA[Mudarris]]>;
<![CDATA[Akmal Hidayat]]>
<![CDATA[ Information Technology Education Journal Vol. 4, No. 1, February (2025) ]]>
Publisher : <![CDATA[Jurusan Teknik Informatika dan Komputer ]]>
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DOI: 10.59562/intec.v4i1.7681
<![CDATA[In an era of digital transformation, the integration of technology in education has become indispensable—especially in technical disciplines such as civil engineering. This study explores how students in the Civil Engineering and Planning program at Universitas Negeri Makassar perceive their competence within the Technological Pedagogical and Content Knowledge (TPACK) framework. Utilizing a quantitative descriptive design, a structured survey was administered to 75 students to assess their self-reported proficiency in the domains of technological, pedagogical, and content knowledge. The results indicate strong confidence in the use of technology for content comprehension and problem-solving, while moderate scores were observed in pedagogical and integrative dimensions of TPACK. These findings reveal both strengths and gaps in students’ readiness to apply TPACK holistically, highlighting the need for curriculum innovation that balances technical fluency with pedagogical strategy. The study extends the application of the TPACK framework beyond teacher education, offering insights for advancing instructional competence in engineering education. Recommendations for future research include triangulated methods, longitudinal studies, and cross-disciplinary comparisons to enrich understanding of TPACK adoption in higher technical education.]]>
<![CDATA[Implementation of Interactive Video Content H5P to Increase Student Activeness in Receiving Praktik Plumbing Material ]]>
<![CDATA[Iriandy]]>;
<![CDATA[Ayu Saputri Bahar]]>;
<![CDATA[Andi Khaerun Niza]]>;
<![CDATA[Fitry Purnamasari]]>;
<![CDATA[Andi Muh Akbar Saputra]]>
<![CDATA[ Information Technology Education Journal Vol. 4, No. 1, February (2025) ]]>
Publisher : <![CDATA[Jurusan Teknik Informatika dan Komputer ]]>
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DOI: 10.59562/intec.v4i2.7794
<![CDATA[This study aims to improve student activeness and understanding in learning praktik plumbing through the application of interactive learning videos based on LUMI H5P. The method used is ADDIE model-based development which includes the stages of analysis, design, development, implementation, and evaluation. The interactive video was developed to support independent learning by inserting evaluation questions in the video material. The results show that the use of interactive content based on H5P can increase student learning motivation, accelerate task completion, and facilitate the evaluation process with automatic feedback. The implementation of this technology is also able to overcome the limitations of static document-based learning. In conclusion, the use of LUMI H5P in learning praktik plumbing significantly improves student engagement and understanding, and promotes more flexible and effective learning. It is recommended that the use of H5P interactive content be integrated more widely in various practical courses to support the quality of digital learning.]]>
<![CDATA[Comparison of Jupyter Interactive Notebook Media and PDF E-Module on Regression Materials for Vocational School Students ]]>
<![CDATA[Andi Qodrat Munandar]]>;
<![CDATA[Sukma Ayu]]>;
<![CDATA[Syamsinar]]>;
<![CDATA[Taswin Rahmat]]>;
<![CDATA[Titin Ulang Dari]]>;
<![CDATA[Wahyu Djuddah]]>
<![CDATA[ Information Technology Education Journal Vol. 4, No. 1, February (2025) ]]>
Publisher : <![CDATA[Jurusan Teknik Informatika dan Komputer ]]>
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DOI: 10.59562/tremz958
<![CDATA[This study aims to compare the effectiveness of interactive Jupyter Notebook media and PDF-based e-modules in improving vocational high school (SMK) students’ understanding of regression concepts. The research responds to the need for more interactive computational learning environments in teaching statistical modeling, particularly regression, which plays an important role in data analysis and applied mathematics. A quasi-experimental study using a pretest–posttest control group design (INTEC) was conducted with 68 eleventh-grade students. The experimental group (n = 34) learned regression using interactive Jupyter Notebook integrating Python simulations and real-time visualization, while the control group (n = 34) used structured PDF e-modules. Data were collected through a validated 30-item regression concept test (α = 0.88). Statistical analyses included paired and independent sample t-tests and Cohen’s d effect size. The experimental group achieved a significantly higher posttest mean (M = 84.62) than the control group (M = 72.15), with p < 0.001 and a large effect size (d = 1.69), indicating superior conceptual gains. The study was limited to one school and a four-week intervention. The findings support integrating interactive coding environments in vocational regression learning.]]>
<![CDATA[The Impact of Gamification on Network Security Learning on the Motivation and Learning Completeness of Vocational School Students ]]>
<![CDATA[Alifhia Pratiwi Farham]]>;
<![CDATA[Sahrul]]>;
<![CDATA[Suci]]>;
<![CDATA[Suhail]]>;
<![CDATA[Suriadi]]>;
<![CDATA[Wahyu Ardiansa]]>
<![CDATA[ Information Technology Education Journal Vol. 4, No. 1, February (2025) ]]>
Publisher : <![CDATA[Jurusan Teknik Informatika dan Komputer ]]>
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DOI: 10.59562/q3w3s457
<![CDATA[This study aims to examine the impact of gamification using badge and leaderboard features in network security learning on students’ motivation and learning mastery at vocational high school (SMK). This research employed an experimental study method with a control group and an experimental group. The experimental class received learning treatment through gamification (badge–leaderboard), while the control class received conventional instruction. Data were collected using motivation questionnaires and learning outcome tests, then analyzed using statistical tests to determine differences between groups. The results indicate that the implementation of gamification significantly improved students’ learning motivation and increased learning mastery in network security subjects compared to conventional learning. Students in the experimental class demonstrated higher engagement, competitiveness, and active participation during the learning process, which positively affected their academic achievement. Therefore, gamification (badge–leaderboard) can be considered an effective instructional strategy to enhance motivation and learning mastery in vocational network security education. ]]>
<![CDATA[The Effect of GNS3-Based Virtual Laboratory on Dynamic Routing Configuration Competency of Vocational High School ]]>
<![CDATA[Aisyah Maydina]]>;
<![CDATA[Nurul Fahira S]]>;
<![CDATA[Nurul Fatimah Mas'ud]]>;
<![CDATA[Nurul Istiqamah]]>;
<![CDATA[Nurul Izzati Inayah]]>;
<![CDATA[Nurul Janna]]>
<![CDATA[ Information Technology Education Journal Vol. 4, No. 1, February (2025) ]]>
Publisher : <![CDATA[Jurusan Teknik Informatika dan Komputer ]]>
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DOI: 10.59562/r3cxkr62
<![CDATA[This study aims to examine the effect of a GNS3-based virtual laboratory on students’ competency in dynamic routing configuration in a Vocational High School Computer and Network Engineering program. The study addresses the limited availability of physical networking devices in vocational schools, which often constrains students’ practical skills development. A quasi-experimental design using a pretest–posttest control group approach was employed. The participants consisted of 64 eleventh-grade students divided into an experimental group (n = 32) and a control group (n = 32). The experimental group received instruction using GNS3 virtual lab simulations, while the control group used conventional teaching methods with limited hardware practice. Data were collected through a validated performance-based test measuring routing configuration skills (RIP and OSPF). Independent sample t-test analysis revealed a statistically significant difference between the posttest scores of the experimental and control groups (p < 0.05). The experimental group achieved a higher mean score (M = 85.47) compared to the control group (M = 74.12). The findings indicate that the GNS3 virtual laboratory significantly improves students’ dynamic routing configuration competency. However, limitations include short intervention duration and single-school sampling. The study contributes to vocational education by providing empirical evidence supporting simulation-based learning integration in networking instruction.]]>
<![CDATA[The Effect of Local Dataset-Based Computer Vision Practice and Data Augmentation on Data Literacy of Vocational School Students ]]>
<![CDATA[Andi Khaedar AR]]>;
<![CDATA[Riska Aprilia]]>;
<![CDATA[Riskah]]>;
<![CDATA[Riswandi]]>;
<![CDATA[Riswani Nurkhatima]]>;
<![CDATA[Rosmiah Rahman]]>
<![CDATA[ Information Technology Education Journal Vol. 4, No. 1, February (2025) ]]>
Publisher : <![CDATA[Jurusan Teknik Informatika dan Komputer ]]>
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DOI: 10.59562/2fzkws38
<![CDATA[This study aims to examine the effect of computer vision practice based on local datasets and data augmentation techniques on vocational high school (SMK) students’ data literacy. The research employed a pre-experimental design using a one-group pretest–posttest model (INTEC). Participants consisted of 34 eleventh-grade students from a Software Engineering program. Students were engaged in hands-on computer vision activities involving image classification using locally collected datasets representing contextual objects from their surrounding environment. The learning intervention also integrated data augmentation techniques, including image rotation, flipping, and brightness adjustment, to enhance dataset variability and model robustness. Data literacy was measured using a validated test instrument covering four indicators: data collection, data cleaning, data transformation, and data interpretation. Statistical analysis using paired-sample t-tests revealed a significant improvement in students’ data literacy scores after the intervention (p < 0.001), with a large effect size. The findings indicate that contextual computer vision practice combined with data augmentation strategies effectively strengthens students’ understanding of data processing and analytical thinking skills. This study contributes to the development of applied AI learning models in vocational education and supports the integration of authentic data-driven practices to enhance digital competencies in the era of artificial intelligence.]]>
<![CDATA[The Effectiveness of AI Learning Using Teachable Machine on the Understanding of Classification Concepts in Vocational School Students ]]>
<![CDATA[Amir]]>;
<![CDATA[Yuliani]]>;
<![CDATA[Nurwahidah]]>;
<![CDATA[Rafikah Sary]]>;
<![CDATA[Rahmat Al Qadri Basri]]>;
<![CDATA[Rika Atirah]]>
<![CDATA[ Information Technology Education Journal Vol. 4, No. 1, February (2025) ]]>
Publisher : <![CDATA[Jurusan Teknik Informatika dan Komputer ]]>
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DOI: 10.59562/wtsn4e36
<![CDATA[This study investigates the effectiveness of Artificial Intelligence (AI) learning using Teachable Machine in improving vocational high school (SMK) students’ understanding of classification concepts. The research employed a quasi-experimental design with a nonequivalent control group involving two classes: an experimental class that learned AI classification concepts through Teachable Machine and a control class that received conventional instruction. The learning process in the experimental group emphasized hands-on activities, including data collection, labeling, model training, testing, and evaluation, enabling students to directly experience the workflow of machine learning classification. Data were collected using pretest and posttest instruments designed to measure students’ conceptual understanding. The results of independent sample t-test analysis revealed a statistically significant difference in posttest scores between the two groups (p < 0.05), with the experimental group achieving higher mean scores and greater learning gains. These findings indicate that integrating Teachable Machine into AI instruction enhances students’ conceptual comprehension by providing interactive, visual, and experiential learning opportunities. The study concludes that AI-based learning using Teachable Machine is an effective instructional strategy to support the development of classification concept understanding in vocational education contexts]]>
<![CDATA[The Effectiveness of Think–Pair–Share in Teaching AI Ethics and Bias on Reducing Vocational High School Students’ Misconceptions: A Quasi-Experimental Study ]]>
<![CDATA[Ahmad Husain Rs]]>;
<![CDATA[Nurhaerah Damir]]>;
<![CDATA[Nurhidayat Tasrif]]>;
<![CDATA[Nurul Musfira]]>;
<![CDATA[Nurul Sukmawati R]]>;
<![CDATA[Nuryaumil Amalia Jais]]>
<![CDATA[ Information Technology Education Journal Vol. 4, No. 1, February (2025) ]]>
Publisher : <![CDATA[Jurusan Teknik Informatika dan Komputer ]]>
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DOI: 10.59562/intec.v4i1.2501
<![CDATA[Misconceptions about AI ethics and algorithmic bias remain prevalent among vocational high school students, particularly the belief that AI systems are inherently objective and neutral. This study investigates whether the Think–Pair–Share (TPS) cooperative learning model is more effective than conventional lecture-based instruction in reducing misconceptions and improving conceptual understanding of AI ethics and bias. The study addresses the need for empirically validated pedagogical strategies in secondary-level AI ethics education. A quasi-experimental non-equivalent control group pretest–posttest design was employed involving 68 eleventh-grade vocational students (34 experimental; 34 control). The experimental group received four weeks of TPS-based instruction, while the control group received lecture-based instruction covering identical content. A validated two-tier diagnostic test (20 items; α = 0.87) measured misconceptions across five domains: algorithmic bias, data representativeness, fairness and discrimination, transparency and accountability, and human oversight. Data were analyzed using paired and independent samples t-tests, normalized gain scores, and Cohen’s d effect size. The TPS group demonstrated significantly higher normalized gain (M = 0.63, SD = 0.12) compared to the lecture group (M = 0.32, SD = 0.15), t(66) = 9.14, p < .001, with a large effect size (d = 1.45). The greatest misconception reduction occurred in the fairness and discrimination domain (70%). Both hypotheses were supported. The study was limited to one school and short-term intervention duration, restricting generalizability and long-term retention analysis. This study provides empirical evidence supporting TPS as an effective instructional strategy for AI ethics education in vocational contexts and contributes a validated diagnostic instrument for measuring AI bias misconceptions. ]]>
<![CDATA[Analysis of the Acceptance of Generative AI Use in Academic Tasks Using the UTAUT Model ]]>
<![CDATA[Nurhikma]]>;
<![CDATA[Rabiatul Adawiah]]>;
<![CDATA[Rachmat Hidayat Bachtiar]]>;
<![CDATA[Rahma Agustini Putri]]>;
<![CDATA[Rahmadinar Kadir]]>;
<![CDATA[Rahmat Hidayat]]>
<![CDATA[ Information Technology Education Journal Vol. 4, No. 1, February (2025) ]]>
Publisher : <![CDATA[Jurusan Teknik Informatika dan Komputer ]]>
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DOI: 10.59562/intec.v4i1.2503
<![CDATA[This study aims to examine students’ acceptance of Generative Artificial Intelligence (AI) in academic tasks using the Unified Theory of Acceptance and Use of Technology (UTAUT). The rapid integration of Generative AI tools in higher education raises important questions regarding the determinants of students’ behavioral intention and actual usage. This study argues that performance-related perceptions are the primary drivers of adoption. Design/methods/approach – A quantitative explanatory design was employed using a survey of 210 undergraduate students who had experience using Generative AI for academic purposes. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Measurement evaluation included outer loadings, Cronbach’s Alpha, Composite Reliability, and Average Variance Extracted (AVE), while structural relationships were tested using bootstrapping with 5000 resamples. Findings – Performance Expectancy significantly influenced Behavioral Intention (β = 0.41, p < 0.001), followed by Effort Expectancy (β = 0.27, p < 0.001) and Social Influence (β = 0.18, p = 0.003). Behavioral Intention strongly affected Use Behavior (β = 0.53, p < 0.001), and Facilitating Conditions also had a significant direct effect (β = 0.29, p < 0.001). The model explained 62% of the variance in Behavioral Intention and 58% in Use Behavior. Research implications/limitations – The study was limited to a single institution and relied on self-reported cross-sectional data, which may restrict generalizability and causal inference. Originality/value – This study extends UTAUT to the context of Generative AI in academic assignments and provides empirical evidence of its predictive power in emerging AI-based educational technologies.]]>
<![CDATA[The Effect of Generative AI as a Coding Assistant in Deep Learning Practicum on Code Quality and Conceptual Understanding ]]>
<![CDATA[Nurhidayah]]>;
<![CDATA[Alimin]]>;
<![CDATA[Ohfit Rijei]]>;
<![CDATA[Owentianus Nouvic]]>;
<![CDATA[Putra Langlang Buana]]>;
<![CDATA[Putra Rajawijaya]]>
<![CDATA[ Information Technology Education Journal Vol. 4, No. 1, February (2025) ]]>
Publisher : <![CDATA[Jurusan Teknik Informatika dan Komputer ]]>
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DOI: 10.59562/intec.v4i1.2502
<![CDATA[The rapid adoption of Generative AI as a coding assistant in programming education raises critical pedagogical questions regarding its impact on learning quality. This study investigates whether the use of Generative AI in a deep learning practicum enhances students’ code quality and conceptual understanding or merely improves productivity without meaningful comprehension. A quasi-experimental pretest–posttest control group design was employed involving 60 undergraduate students enrolled in a Deep Learning course. The experimental group (n = 30) used Generative AI tools (ChatGPT/GitHub Copilot) during practicum sessions, while the control group (n = 30) relied on conventional resources. Instruments included a validated conceptual understanding test (α = 0.87) and an analytic code quality rubric based on ISO/IEC 25010 standards (κ = 0.82). Data were analyzed using independent samples t-tests and MANOVA at α = 0.05. Results show that the experimental group achieved significantly higher posttest conceptual scores (M = 78.63) than the control group (M = 72.10), t(58) = 3.34, p = 0.001, d = 0.86. Code quality scores were also significantly higher (20.77 vs. 18.12 out of 25), t(58) = 4.57, p < 0.001, d = 1.18. MANOVA confirmed a significant combined effect (Wilks’ Λ = 0.71, p < 0.001). The study was limited to a single institution and a six-week intervention period, which may restrict generalizability and long-term interpretation. This research provides controlled experimental evidence demonstrating that Generative AI can enhance both technical code quality and conceptual mastery in deep learning education, contributing empirical guidance for responsible AI integration in computing curricula]]>
