IJIE (Indonesian Journal of Informatics Education)
Journal Title IJIE (Indonesian Journal of Informatics Education) Initials IJIE Frequency 2 Issues Per Year (July and December) DOI 10.20961/ijie Online ISSN 2549-0389 Editor-in-Chief Cucuk Budiyanto Publisher Informatics Education Department, Faculty of Teacher Training and Education, Universitas Sebelas Maret Accreditation Sinta 3 IJIE (Indonesian Journal of Informatics Education) is a scientific journal promoting the study of, and interest in, informatics education. The journal publishes empirical papers on information systems, informatics, technology learning, and distance learning. It is an international journal published by the Informatics Education Department, Faculty of Teacher Training and Education, Universitas Sebelas Maret, Indonesia, biannually in July and December (ISSN: 2549-0389 (Online)). Important Notice This OJS2 website remains the primary official website of the Indonesian Journal of Informatics Education (IJIE). New issues, journal information, and new manuscript submissions will continue to be managed and published through this website. The previously planned migration of IJIE from OJS2 to OJS3 has been cancelled. Therefore, authors who have not yet submitted their manuscripts should submit them through this OJS2 website. Authors whose manuscripts were submitted through the OJS3 website but have not entered the editorial process are requested to submit or resubmit their manuscripts through this OJS2 website. Manuscripts that have already been submitted through the OJS3 website and have entered the editorial or peer-review process will continue to be processed through that website. Authors of these manuscripts do not need to resubmit them through OJS2. The IJIE OJS3 website at https://journal.uns.ac.id/ijie/ is not a cloned or unauthorized website. It is an official website that was prepared for the planned migration of IJIE to OJS3 before the migration was cancelled.
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Exploring In-Service and Pre-Service Teacher Digital Competence in Secondary Education: A Review
Background: Digital transformation has accelerated the integration of technology in education, positioning teacher digital competency (TDC) as a critical determinant of school readiness in the digital age. Purpose : To bridge the gap in existing literature that frequently evaluates pre-service and in-service educators separately, this systematic literature review aims to comprehensively map TDC in secondary education by examining its core characteristics, development initiatives, and discipline-specific applications. Method: Following PRISMA 2020 guidelines, a systematic search of the Scopus database was conducted, selecting 20 empirical papers published between 2020 and 2025 for thematic synthesis. Result: The synthesis revealed three core insights: (1) TDC extends beyond operational IT skills to encompass pedagogical implementation, teacher willingness, and social impact; (2) competency development relies heavily on teacher training, institutional school support, and proactive university curricula; and (3) practical applications are currently concentrated in STEM courses, with growing needs in non-STEM disciplines. Conclusion: Teacher education programs must systematically embed digital pedagogy into their curricula to align educator readiness with real-world secondary school demands. Despite methodological limitations from relying on a single database, this study provides a foundational roadmap for future mixed-methods research.
Copyright (c) 2026 Rizka Latifah, Cucuk Wawan Budiyanto, Dewi Anjarsari http://creativecommons.org/licenses/by-sa/4.0
A Theory of Planned Behaviour-Based Analysis of IT Specialisation Choices in a South African Historically Disadvantaged Institution
Decisions to specialise in Information Technology (IT)-based courses have notably gained prominence in shaping academic pathways among students, more specifically within historically disadvantaged institutions (HDIs) within South Africa, influenced by persistent socio-economic factors. While research has examined factors influencing career decisions, including specialisation decisions, in IT, there has been minimal use of theory-based frameworks, particularly within resource-constrained contexts of higher education. In this regard, this research aims to investigate the factors influencing IT students' specialisation decisions at a historically disadvantaged institution in South Africa, applying the Theory of Planned Behaviour as the framework for this inquiry. The research employed a cross-sectional design and was conducted among 104 second- or third-year Information Technology students recruited through stratified random sampling. The research findings revealed that attitudes toward IT specialisation decisions have had the greatest influence, relative to subjective norms, while perceived behaviour control has had relatively less influence. Overall, 34.1% of the variation in specialisation decisions was accounted for in the study. As this study draws on the Theory of Planned Behaviour to examine specialisation decisions in IT at historically disadvantaged institutions in South Africa, it aims to make a relevant contribution within the field of informatics education.
Copyright (c) 2026 Nqobile Thobile Mpulo, Khumbuzile Mzobe, Zinhle Mkhize, Kudakwashe Maguraushe http://creativecommons.org/licenses/by-sa/4.0
Leveraging on AI Tools in Hybrid Learning for Inclusive Learning Analytics: Evidence from a South African Disadvantaged University
Background: The rapid integration of Artificial Intelligence (AI) into higher education has transformed hybrid learning environments by enabling personalised, data-driven, and inclusive teaching and learning practices. AI-powered learning analytics offer opportunities to monitor student engagement, predict learning outcomes, and provide timely interventions, thereby addressing the diverse needs of students in increasingly complex educational settings. Despite these advancements, there is limited understanding of how AI-enabled learning analytics can effectively promote inclusivity and equitable learning experiences within hybrid learning environments. Purposes: This study explores the utilisation of AI tools in hybrid learning environments to advance inclusive learning analytics. Specifically, it examines how AI-enabled technologies support personalised learning, enhance student engagement, improve academic performance, and facilitate informed pedagogical decision-making in higher education. Method: A qualitative research approach was adopted to gain in-depth insights into participants' experiences and perceptions of AI-supported learning analytics. Data were collected from participants across two university campuses through semi-structured interviews and analysed using thematic analysis. The study was informed by the theoretical foundations of learning analytics, inclusive education, and educational data science. Results: The findings revealed two dominant themes: personalised learning and academic performance enhancement. Participants reported that AI-powered tools, including adaptive learning platforms, predictive analytics, automated feedback systems, and intelligent dashboards, enabled tailored learning experiences, continuous feedback, and early identification of at-risk students. These capabilities improved learner engagement, supported academic success, and empowered educators to implement targeted interventions. The findings further demonstrated that AI-driven learning analytics can foster greater inclusivity by accommodating students with diverse socio-economic, educational, and technological backgrounds, thereby promoting equitable access to learning opportunities. Conclusion: The significance of this research lies in its contribution to the growing body of knowledge on AI-enhanced learning analytics and its potential to move beyond traditional student performance monitoring towards more equitable, student-centred educational practices. The study also responds to the increasing demand for flexible and inclusive learning models in higher education, particularly within contexts characterised by large class sizes, diverse student populations, and limited resources.
Copyright (c) 2026 Oluwatosin Bamigboye http://creativecommons.org/licenses/by-sa/4.0
Integrating Artificial Intelligence (AI) in Principals’ Communication Practices for Teachers Efficiency in Public Secondary Schools
Background: The growing advancement of Artificial Intelligence (AI) has increasingly influenced educational leadership, particularly in enhancing communication practices, administrative efficiency, and teacher support. Despite the global recognition of AI as a tool for improving school management, its practical integration into principals’ communication practices in Nigerian public secondary schools remains underexplored. Purpose: This study investigated the integration of Artificial Intelligence (AI) in principals’ communication practices and its association with teachers’ efficiency in public secondary schools in Osun State, Nigeria. Method: A cross-sectional descriptive survey design was adopted. Data were collected from 256 teachers selected through multistage sampling techniques using a structured questionnaire, and analysed using descriptive and regression statistics. Results: The findings revealed that the awareness of AI integration in principals’ communication practices was relatively high (Mean =2.63). However, principals still relied and depended largely on traditional forms of communication, such as face-to-face interaction, official memos, staff meetings, and circulars (Mean =2.98). The study further showed a low level of actual integration of AI tools in principals’ communication practices (Mean = 2.42), indicating that AI awareness was mainly conceptual with limited practical application. Meanwhile, there was a relatively high level of teachers’ efficiency (Mean = 2.99). The regression analysis indicated there was a significant association between principals’ AI-supported communication practices and teachers’ efficiency (B = 0.611, β = 0.652, t = 13.65, p = 0.000). The coefficient of determination (R² = 0.425) indicated that principals’ AI-supported communication practices accounted for 42.5% of the observed variation in teachers’ efficiency. Conclusion/Implications: The study therefore concluded that principals’ AI-supported communication practices were significantly associated with teachers’ efficiency, although such association does not imply causation. The study implies that strengthening digital infrastructure, enhancing professional development, and establishing clear policy frameworks may support effective AI integration in school administration and improve communication efficiency in public secondary schools.
Copyright (c) 2026 Busuyi Francis Olowo, Babatunde Femi Akinyode, Oluwafunmilayo Grace Oso http://creativecommons.org/licenses/by-sa/4.0
Beyond 'Game Over': Engineering a Failure-Free FSM Architecture for Emotion Regulation in Serious Games
The increasing prevalence of stress and anxiety among adolescents has highlighted the need for digital learning environments that support emotion regulation and psychological safety. However, many existing serious games still inherit performance-oriented mechanics, including scoring systems and game-over states, which may conflict with reflective and supportive interaction goals. To address this architectural gap, this study presents the design and development of Logisense: Fractured Mind, a narrative-driven serious game that employs consequence-driven interactions rather than punitive failure mechanisms. Using a Design and Development (D&D) framework, the system was implemented in RPG Maker MV through an Event-Driven Finite State Machine (FSM) architecture that manages dialogue progression and environmental transitions. Instead of conventional scoring, player choices dynamically modify a hidden psychological pressure parameter that controls shifts between the stable Virtual Real World and the distorted Logisphere environment. Internal alpha validation through structured scenario walkthroughs confirmed that the architecture correctly executed state transitions, reflective breathing events, and alternative narrative endings without triggering system-enforced game-over conditions. The results demonstrate the technical feasibility of implementing a failure-free interaction model while maintaining logical narrative progression and gameplay continuity. Consequently, the proposed architecture provides a reproducible blueprint for developing psychologically safe serious games and establishes a foundation for future empirical investigations concerning emotion regulation and digital learning environments.
Copyright (c) 2026 Muhamad Akda Fathul Barri, Reisa Aulia Sodikin, Nuur Wachid Abdul Majid, Ayu Permata Sari http://creativecommons.org/licenses/by-sa/4.0
Assessing Gender Differences in Technological Readiness and AI Adoption Intentions among Pre-Service Teachers
Background: Artificial Intelligence (AI) is increasingly transforming instructional processes across educational settings, creating a need to understand the factors associated with its adoption among future educators, particularly across gender groups. Purpose: This study examined the relationships among technological readiness, attitude toward AI, and behavioural intention to adopt AI among pre-service teachers, and investigated whether gender moderates these relationships. Specifically, the study aimed to investigate the extent to which technological readiness is related to attitudes toward AI and behavioural intention to adopt AI, and whether these relationships vary between male and female pre-service teachers. Method: A quantitative cross-sectional survey design was employed, and data were collected from 378 undergraduate pre-service teachers enrolled in teacher education programmes at public universities in southwestern Nigeria using a structured online questionnaire administered through Google Forms. Data were analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM) with SmartPLS 4, including multigroup analysis to examine gender differences. Results: The findings indicate that technological readiness was not significantly associated with behavioural intention to adopt AI (β = .092, t = 1.945, p = .052), and no significant gender differences were found in this relationship (t = 0.479, p = .630). However, technological readiness showed a significant positive association with attitude toward AI (β = .075, t = 2.330, p < .05), with significant gender differences in the strength and direction of this relationship (t = 3.991, p < .05). The relationship was positive among female pre-service teachers but negative among male pre-service teachers. Conclusion: These findings highlight the importance of strengthening technological confidence and promoting equitable AI integration initiatives in teacher education programmes. The study further highlights the need for teacher educators, informatics educators, and policymakers to create inclusive learning environments that support positive attitudes and engagement with AI technologies among future educators.
Copyright (c) 2026 Oluwanife Segun Falebita, Petrus Jacobus KOK http://creativecommons.org/licenses/by-sa/4.0