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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 28 Documents
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Search results for , issue "Vol. 4, No. 3, August (2025)" : 28 Documents clear
Evaluating the Credibility of AI-Based Authentic Assessment in Early Numeracy Education: A Systematic Review Nursalim; Muhammad Ikhsan Sukaria
Information Technology Education Journal Vol. 4, No. 3, August (2025)
Publisher : Jurusan Teknik Informatika dan Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/intec.v4i3.10119

Abstract

This study systematically reviews the credibility of artificial intelligence (AI)–based authentic assessment in evaluating numeracy achievement among early-grade primary students. The growing use of AI in educational assessment—ranging from automated scoring to adaptive feedback—has raised questions regarding its validity, reliability, and fairness. Using a qualitative descriptive approach through a systematic literature review, this study synthesizes eighteen high-quality publications issued between 2020 and 2025 from major academic databases. Thematic analysis identifies three interrelated findings. First, algorithmic accuracy must be complemented by human validation to prevent bias and preserve contextual meaning. Second, collaboration between teachers and AI systems enhances interpretive credibility and fosters reflective learning practices. Third, integrating AI ethics and literacy within primary school curricula is essential to ensure fair and transparent assessment outcomes. The results indicate that credible AI-based evaluation depends not solely on computational precision but also on socially grounded interpretation supported by ethical and pedagogical principles. Theoretically, this study reinforces Vygotsky’s social constructivist perspective and the Assessment for Learning (AfL) paradigm, positioning AI as a cognitive scaffold that enables reflective interaction among students, teachers, and technology. Practically, it offers implications for educational policy in Indonesia’s Merdeka Curriculum, emphasizing teacher training, ethical guidelines, and algorithmic transparency to promote equitable digital learning ecosystems. This study uniquely integrates AI assessment credibility with the context of early numeracy, providing original insights and advancing theoretical discourse on ethical, human-centered, and contextually responsive digital assessment practices in primary education.
Determinants of Solar Photovoltaic Adoption among Mobile Micro Enterprises: A Technology Acceptance Model Approach Iriandy; Haedar, Ahmad Wahidiyat; Nurul Aliah; Ayu Saputri Bahar
Information Technology Education Journal Vol. 4, No. 3, August (2025)
Publisher : Jurusan Teknik Informatika dan Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/intec.v4i3.10258

Abstract

This study investigates the determinants influencing the adoption of solar photovoltaic (PV) systems among mobile micro-enterprises in Makassar, Indonesia. Drawing upon the Technology Acceptance Model (TAM) extended with two contextual constructs Energy Needs and Ease & Support the research employs a quantitative cross-sectional design involving 30 mobile coffee vendors. Reliability and regression analyses confirm the model’s robustness, indicating that Perceived Usefulness, Technology Acceptance, and Energy Needs significantly predict the intention to adopt PV systems, while Ease & Support shows no significant effect. The study extends the application of TAM to the informal micro-enterprise context, which has rarely been explored in renewable energy adoption research. Findings highlight that perceived economic benefits and energy necessity outweigh technical convenience in shaping adoption behavior. This suggests that micro-entrepreneurs’ motivation is primarily driven by practicality and efficiency rather than ease of use. The results provide policy insights for promoting inclusive green transitions through targeted micro-financing, technical training, and supportive urban regulations for small mobile vendors. Overall, this pilot investigation offers a validated framework for future empirical studies on renewable energy adoption within developing-country micro-enterprise sectors.
Improving Spatial Thinking Skills through GEE-Integrated STEM Learning: A Quasi-Experimental Study among Geography Undergraduates Haris, Haris; Syamsunardi; Abdul Mannan; Nurul Ilmi Rasjusti
Information Technology Education Journal Vol. 4, No. 3, August (2025)
Publisher : Jurusan Teknik Informatika dan Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/intec.v4i3.10463

Abstract

This study examines the effectiveness of integrating Google Earth Engine (GEE) into a STEM-based learning framework to enhance the spatial thinking skills of geography undergraduates. A quasi-experimental design involving 60 students was implemented, with one class assigned to the experimental group receiving GEE-integrated STEM instruction and the other to the control group receiving conventional instruction. Spatial thinking outcomes were measured using pretest–posttest cognitive assessments and four performance indicators: spatial representation, spatial relation, spatial analysis, and spatial application. Data analysis included descriptive statistics, normality and homogeneity tests, paired-sample t-tests, independent-sample t-tests, Cohen’s d effect size, and ANCOVA controlling for pretest scores. The results indicate that the experimental group achieved significantly higher posttest scores than the control group (t(58) = 10.24, p < 0.001), with a large effect size (d = 2.64). ANCOVA further confirmed the robustness of the treatment effect after adjusting for initial differences (F(1,57) = 101.99, p < 0.001). Indicator-level analysis also showed consistent improvement across all spatial thinking components. This study is limited by its single-institution context, quasi-experimental design, and potential novelty effects associated with geospatial technology integration. Future research should involve larger and more diverse samples to validate these findings.
Students’ Perceptions of Lecturers’ TPACK Competence in Digital Project-Based Learning Mustari S. Lamada; Sulaiman, Dwi Rezky Anandari; Shabrina Syntha Dewi; Aulyah Zakilah Ifani
Information Technology Education Journal Vol. 4, No. 3, August (2025)
Publisher : Jurusan Teknik Informatika dan Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/intec.v4i3.10479

Abstract

The acceleration of digital transformation in higher education requires lecturers to demonstrate strong Technological Pedagogical and Content Knowledge (TPACK), especially when implementing Project-Based Learning (PjBL). However, limited empirical evidence exists on how students perceive lecturers’ digital-integrative competence within project-based contexts in Southeast Asia. This study aims to examine students’ perceptions of lecturers’ TPACK competence in integrating digital technology within PjBL settings. Using a quantitative survey design, data were collected from 42 engineering students at Universitas Negeri Makassar. Descriptive statistics and Spearman correlation analyses were conducted across seven TPACK dimensions. Results revealed that students rated lecturers’ TPACK competence from moderate to high, with the strongest correlation between Pedagogical Content Knowledge (PCK) and overall TPACK (rₛ = 0.809, p < 0.001). This finding underscores the importance of combining pedagogical and content expertise to enhance digital instruction. The study implies that institutional training focused on pedagogical–technological integration may further strengthen lecturers’ digital teaching performance.
Generative AI Writing Tools and Academic Writing in Higher Education: A Systematic Review of Empirical and Review Studies Shabir, Achmad
Information Technology Education Journal Vol. 4, No. 3, August (2025)
Publisher : Jurusan Teknik Informatika dan Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/intec.v4i3.10494

Abstract

The rapid diffusion of generative artificial intelligence (AI) writing tools such as ChatGPT, Grammarly, and related systems has intensified debates in higher education about their pedagogical value, risks, and long-term consequences for academic writing. This study reports a systematic review of empirical and review studies published between January 2023 and October 2025 that examine how AI writing tools and closely related AI applications influence writing and writing-related outcomes in higher-education settings. Following PRISMA 2020 guidelines, database searches identified 1,032 records; after deduplication and screening of titles and abstracts, 213 full texts were assessed and 102 studies met the inclusion criteria. From these, a focal corpus of 40 articles that most directly addressed generative AI tools, automated written feedback, or academic writing in higher education was subjected to in-depth coding and thematic synthesis. Across the writing-focused primary studies, AI-based feedback and generative tools were frequently associated with improvements in surface-level aspects of writing such as grammatical accuracy, cohesion, and fluency, while the broader corpus highlighted perceived benefits for efficiency, personalization, and formative support. At the same time, many studies reported concerns about overreliance on AI, reduced metacognitive engagement, academic integrity, and gaps in institutional governance. Owing to substantial heterogeneity in tools, designs, and outcome measures, the review does not compute new pooled effect sizes and instead offers a narrative, thematically structured synthesis. Key limitations include the short time window, the predominance of review-type studies, and concentration in specific disciplines and regions. Overall, the findings suggest that generative AI writing tools function most productively as supports within guided, reflective pedagogy rather than as stand-alone replacements for human writing instruction.
AI-Driven Adaptive Pathways in Virtual Learning Environments: A PRISMA-Based Systematic Literature Bakri, Hasrul
Information Technology Education Journal Vol. 4, No. 3, August (2025)
Publisher : Jurusan Teknik Informatika dan Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/intec.v4i3.10515

Abstract

The static nature of conventional Virtual Learning Environments (VLEs) often fails to address the diverse needs of individual learners, limiting the potential for personalized education. The integration of Artificial Intelligence (AI) offers promising solutions through adaptive learning pathways. This systematic literature review aims to synthesize current evidence on AI-driven adaptive pathways in VLEs by examining: (1) types of AI algorithms used, (2) adaptation mechanisms employed, (3) impacts on learning outcomes and engagement, and (4) emerging trends and innovations. Following the PRISMA guidelines, a comprehensive search was conducted across five major databases (Scopus, Web of Science, IEEE Xplore, ACM Digital Library, and Google Scholar) for publications between 2015 and 2024. From 2,150 initially identified records, 78 studies met the inclusion criteria after rigorous screening and quality assessment using the JBI critical appraisal tools. Findings indicate that deep learning, neural networks, and natural language processing are the most commonly used AI techniques for detecting learning styles and dynamically recommending content. AI-based adaptive systems consistently improve academic performance, motivation, and engagement by up to 25% compared to traditional static VLEs. However, significant challenges related to data privacy, algorithmic bias, and infrastructural readiness persist. AI-driven adaptive pathways hold transformative potential for creating more personalized and effective digital learning experiences. Successful implementation requires addressing ethical and technical barriers through multidisciplinary collaboration and the development of inclusive, sustainable frameworks.
Effectiveness of the Flipped-Blended Learning Model in Computer and Network Engineering Education Sam, Noer Ekafitri
Information Technology Education Journal Vol. 4, No. 3, August (2025)
Publisher : Jurusan Teknik Informatika dan Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/intec.v4i3.10590

Abstract

Research on Flipped-Blended Learning in vocational education has grown in recent years; however, empirical evidence focusing specifically on Computer and Network Engineering (TKJ) competencies in Indonesian vocational high schools remains limited. Previous studies primarily examined general blended learning implementation without addressing learning challenges unique to TKJ, such as limited practical time, low preparedness for autonomous learning, and the need for stronger conceptual mastery prior to hands-on practice. This study aims to evaluate the effectiveness of a Flipped-Blended Learning model implemented through a LMS at SMKN YPPP Wonomulyo and SMKN 1 Tinambung. Using a quasi-experimental one-group pretest–posttest design, data were collected from 30 students through cognitive tests, observation sheets, questionnaires, and LMS usage logs. Results indicate a medium-to-high increase in cognitive outcomes based on normalized gain. Student activity categorized as very active, particularly in accessing digital resources, completing online assignments, and participating in LMS-based discussions. These findings demonstrate that the Flipped-Blended Learning model effectively enhances conceptual understanding, active engagement, and independent learning in TKJ subjects. This study contributes by providing student-centered evidence on the integration of LMS-based flipped learning in vocational engineering contexts. Practical implications suggest that teachers need to systematically design pre-class digital materials and optimize face-to-face sessions for collaborative problem-solving and practice.
Programming Experience and Computational Thinking Skills: A Study on Prospective PPG Pre-Service Informatics Engineering Teachers Wahyuni, Maya Sari; Pratama, Muhammad Isbar; Abdal, Nurul Mukhlisah
Information Technology Education Journal Vol. 4, No. 3, August (2025)
Publisher : Jurusan Teknik Informatika dan Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/intec.v4i3.10609

Abstract

Computational Thinking (CT) is a core competency emphasized in Indonesia’s Kurikulum Merdeka, yet variations in pre-service teachers’ programming backgrounds may influence their CT readiness. This study examined the relationship between programming experience and CT skills among pre-service informatics teachers enrolled in the PPG Prajabatan program at Universitas Negeri Makassar. Using a correlational design, data were collected from 153 participants, with programming experience measured through a validated Python-based questionnaire and CT skills assessed using Bebras tasks covering key CT dimensions. The findings show a strong positive association between programming experience and CT performance, with active project involvement and frequent practice emerging as the most influential components. Programming experience also served as a significant predictor of CT skills, accounting for a substantial proportion of variance. These results highlight the importance of sustained programming engagement in supporting CT development and suggest that project-oriented coding activities should be more systematically integrated into pre-service informatics teacher education and PPG curricular practices

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