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Information Technology Education Journal
Published by Universitas Negeri Makassar
ISSN : 28097971     EISSN : 2809798X     DOI : -
Core Subject : Science, Education,
Computer Science & IT Education
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
Arjuna Subject : Ilmu Sosial - Pendidikan
Articles 250 Documents
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Enhancing the Digital Classroom Experience through the Integration of Interactive Flat Panel and Tug of Knowledge as Cloud-Based Learning Media Ayu Saputri Bahar; Iriandy
Information Technology Education Journal Vol. 4, No. 4, November (2025)
Publisher : Jurusan Teknik Informatika dan Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/intec.v4i4.11189

Abstract

This study aims to develop and implement cloud-based learning media through the integration of the Interactive Flat Panel (IFP) and Tug of Knowledge in order to enhance the digital learning experience in the classroom. The study employed a Research and Development (R&D) design using the ADDIE model, which consists of the stages of analysis, design, development, implementation, and evaluation. The research participants comprised 32 students from the Family Welfare Education Study Program, Faculty of Engineering, Universitas Negeri Makassar. Data were collected through observations of student engagement, documentation of learning interactions, and analysis of task outcomes, which were subsequently analyzed using descriptive quantitative methods based on a 1–5 response scale. The results revealed an increase in student engagement, as indicated by an average engagement score of 4.35, as well as an improvement in group discussion participation of approximately 28% compared to learning conditions prior to the implementation of the media. In addition, 87.5% of students provided positive responses to the use of this media, particularly with regard to interactivity, collaboration, and learning motivation. These findings indicate that the integration of the Interactive Flat Panel and Tug of Knowledge as cloud-based learning media is effective in creating a more interactive, collaborative, and measurable digital learning experience and is aligned with the needs of 21st-century learning.
Predicting Generative AI–Based Learning Among Students: The Roles of Adaptive Learning Motivation, Technology Openness, and Digital Collaboration Readiness Daud Mahande, Ridwan
Information Technology Education Journal Vol. 5, No. 1, February (2026)
Publisher : Jurusan Teknik Informatika dan Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/intec.v5i1.2601

Abstract

The integration of Generative Artificial Intelligence (AI) in higher education presents opportunities and challenges related to student readiness as the main users of technology. This study aimed to analyze the role of adaptive learning motivation, technology openness, and digital collaboration readiness in predicting student perceptions of Generative AI-based learning. A quantitative approach with an explanatory design was used through a survey of 370 students from the Faculty of Engineering, State University of Makassar, Indonesia. The data were analyzed using Partial Least Squares structural equation modeling (PLS-SEM). The results showed that the three predictor variables had a positive and significant effect on students' perception of Generative AI-based learning, with adaptive learning motivation being the most dominant factor. In addition, a pattern of tiered relationships was found, in which adaptive learning motivation affects openness to technology, which further strengthens the readiness for digital collaboration. The research model explained 60.8% of students' perceptions of AI-based learning. These findings confirm that the success of Generative AI integration is not only determined by technological readiness but also by students' psychological and digital readiness. This study contributes to expanding the model of learner readiness in the AI-based education ecosystem.
Comparison of Jupyter Interactive Notebook Media and PDF E-Module on Regression Materials for Vocational School Students Andi Qodrat Munandar; Sukma Ayu; Syamsinar; Taswin Rahmat; Titin Ulang Dari; Wahyu Djuddah
Information Technology Education Journal Vol. 4, No. 1, February (2025)
Publisher : Jurusan Teknik Informatika dan Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/tremz958

Abstract

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.
The Impact of Gamification on Network Security Learning on the Motivation and Learning Completeness of Vocational School Students Alifhia Pratiwi Farham; Sahrul; Suci; Suhail; Suriadi; Wahyu Ardiansa
Information Technology Education Journal Vol. 4, No. 1, February (2025)
Publisher : Jurusan Teknik Informatika dan Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/q3w3s457

Abstract

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. 
The Effect of GNS3-Based Virtual Laboratory on Dynamic Routing Configuration Competency of Vocational High School Aisyah Maydina; Nurul Fahira S; Nurul Fatimah Mas'ud; Nurul Istiqamah; Nurul Izzati Inayah; Nurul Janna
Information Technology Education Journal Vol. 4, No. 1, February (2025)
Publisher : Jurusan Teknik Informatika dan Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/r3cxkr62

Abstract

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.
The Effect of Local Dataset-Based Computer Vision Practice and Data Augmentation on Data Literacy of Vocational School Students Andi Khaedar AR; Riska Aprilia; Riskah; Riswandi; Riswani Nurkhatima; Rosmiah Rahman
Information Technology Education Journal Vol. 4, No. 1, February (2025)
Publisher : Jurusan Teknik Informatika dan Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/2fzkws38

Abstract

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.
The Effectiveness of AI Learning Using Teachable Machine on the Understanding of Classification Concepts in Vocational School Students Amir; Yuliani; Nurwahidah; Rafikah Sary; Rahmat Al Qadri Basri; Rika Atirah
Information Technology Education Journal Vol. 4, No. 1, February (2025)
Publisher : Jurusan Teknik Informatika dan Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/wtsn4e36

Abstract

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
The Effectiveness of Think–Pair–Share in Teaching AI Ethics and Bias on Reducing Vocational High School Students’ Misconceptions: A Quasi-Experimental Study Ahmad Husain Rs; Nurhaerah Damir; Nurhidayat Tasrif; Nurul Musfira; Nurul Sukmawati R; Nuryaumil Amalia Jais
Information Technology Education Journal Vol. 4, No. 1, February (2025)
Publisher : Jurusan Teknik Informatika dan Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/intec.v4i1.2501

Abstract

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.  
The Effectiveness of Scenario-Based Learning Using Wireshark on Packet Analysis and Network Troubleshooting Skills Ahmad Firdaus; Nur Fadila; Nur Fitri; Nur Reski Amelia Sikki; Nurfaedah Masdan; Nurfaikah
Information Technology Education Journal Vol. 4, No. 2, May (2025)
Publisher : Jurusan Teknik Informatika dan Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/intec.v4i2.2501

Abstract

This study investigated the effectiveness of Scenario-Based Learning (SBL) using Wireshark in improving students’ packet analysis and network disturbance diagnostic skills. The study addressed the gap between theoretical networking instruction and students’ limited ability to interpret real network traffic and troubleshoot practical cases. The key argument is that integrating authentic troubleshooting scenarios with professional packet analysis tools enhances higher-order analytical competence. A quantitative pre-experimental one-group pretest–posttest design was employed. Thirty-two second-year networking students participated in a four-week intervention consisting of structured scenario modules using Wireshark (version 4.x) in a controlled laboratory environment. Data were collected through a 20-item performance-based test measuring conceptual understanding, packet interpretation, and network diagnosis. Statistical analysis included descriptive statistics, Shapiro–Wilk normality testing, paired-sample t-test, and Cohen’s d effect size calculation. The mean score increased from 56.41 (SD = 8.72) in the pretest to 78.63 (SD = 7.95) in the posttest. The paired-sample t-test revealed a significant difference, t(31) = 15.87, p < 0.001. The calculated effect size (Cohen’s d = 2.80) indicated a very large effect. The highest gains were observed in packet interpretation and network diagnosis components. These results confirm that SBL using Wireshark significantly improves analytical and troubleshooting competence. The absence of a control group, limited sample size (N = 32), short intervention duration, and single-institution context may restrict generalizability. Future studies employing randomized or longitudinal designs are recommended. This study provides empirical evidence of integrating scenario-based pedagogy with industry-standard packet analysis tools in networking education. It offers a replicable instructional framework that bridges theory and authentic technical practice.
The Impact of Incident-Based Learning on Network Security Practicum: Effects on Analytical and Response Skills A. Sri Bungsu Asma; Ahmad Syawaluddin; Muflihah Qanita H; Muh. Yasyfin Farhan; Muh.Kidfari; Muhammad Arsyad Meru; Muhammad Fadli Kasman
Information Technology Education Journal Vol. 4, No. 4, November (2025)
Publisher : Jurusan Teknik Informatika dan Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/intec.v4i4.11190

Abstract

The purpose of this study is to examine the effectiveness of Incident-Based Learning (IBL) in improving students' analytical and incident response skills within network security practicum courses. The research addresses the gap in existing cybersecurity education by testing the impact of IBL compared to conventional practicum approaches, with the aim to better prepare students for dynamic and unstructured cybersecurity threats. This study employed a true experimental design with a pretest–posttest control group format. Two groups of undergraduate students participated in the study: one exposed to Incident-Based Learning and the other following a conventional practicum approach. Data were collected using performance-based assessments that measured analytical skills and incident response abilities, followed by statistical analysis using paired and independent samples t-tests, and MANOVA. The results revealed that students in the IBL group showed significantly higher improvements in both analytical skills (t(58) = 2.94, p < 0.01) and incident response skills (t(58) = 3.20, p < 0.01) compared to the control group. The MANOVA confirmed a significant multivariate effect (p < 0.05) of IBL on both competencies. While the findings suggest that IBL enhances practical cybersecurity competencies, the study's limitations include its sample size and scope, which may impact the generalizability of results to broader educational contexts. This study contributes to the field by providing empirical evidence on the effectiveness of IBL in cybersecurity education. It offers a valuable framework for improving network security curricula and paves the way for future research on integrating IBL into various technical education domains

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