Journal of Hypermedia & Technology-Enhanced Learning

Interactive Macromedia Flash–Based Learning Media for K3LH Instruction in Vocational Electronics Education

2026-02-28T20:28:51+07:00

This study developed interactive learning media using Macromedia Flash to enhance Occupational Health, Safety, and Environmental Awareness (K3LH) instruction within the Basic Electronics curriculum in vocational high schools. The research employed a Research and Development approach guided by the 4D model (Define, Design, Develop, and Disseminate) to produce a validated multimedia learning product aligned with the Merdeka Curriculum. During the Define stage, needs assessment and learner analysis revealed low student engagement and conceptual difficulties with electronics safety topics, underscoring the need for technology-based instructional innovation. The designed media integrated text, visuals, animations, audio, and interactive assessments to support autonomous and student-centred learning. Expert validation involving material and media specialists resulted in high validity scores of 94.7% and 94.8%, indicating strong curriculum alignment, conceptual clarity, interface organisation, and language quality. Field testing with Grade 10 Electronics Engineering students yielded a practicality score of 85.1%, confirming that the media was easy to operate, pedagogically relevant, visually engaging, and effective in supporting understanding and participation. These outcomes demonstrate that Macromedia Flash-based learning media remain highly functional for vocational classroom environments, particularly where technological infrastructure is limited. The study concludes that the developed media can serve as a robust instructional tool to strengthen student motivation, improve comprehension of K3LH concepts, and enrich technology-enhanced learning in vocational electronics education. Future research is recommended to expand implementation across different school contexts and migrate the product to modern digital platforms to improve scalability and accessibility.

Interactive Multimedia Learning for Vocational Electronics Education: Development, Validation, and Classroom Implementation

2026-02-28T20:28:48+07:00

This study presents the design, development, and empirical evaluation of an interactive multimedia learning application for basic electronics instruction in vocational education. To address persistent challenges such as passive learning, limited conceptual understanding, and low learner engagement, the research employed the 4D (Define, Design, Develop, and Disseminate) development model to create a digital learning environment in Adobe Animate. The application integrates dynamic animations, vector-based visualizations, explanatory text, and embedded interactive assessments to support conceptual change and active learning. Content and media validity were assessed through expert evaluations from subject-matter and instructional media specialists, while usability and practicality were examined through field tests involving Grade X students in an Audio-Video Engineering program. The findings show high levels of content accuracy, pedagogical alignment, and interface usability. Practicality results indicate strong learner acceptance, intuitive navigation, and consistent engagement during classroom implementation. Observational and questionnaire data reveal positive effects on students’ learning motivation and participatory behaviors. This study provides empirical evidence of the pedagogical value of interactive multimedia for competency-based technical education and offers a replicable development framework for integrating digital learning media in vocational settings. The findings hold practical and theoretical implications for educators, curriculum designers, and policymakers seeking to improve learning quality in technology-oriented vocational education.

Improving Students’ Digital Literacy in a Suburban Elementary School: An Action Research Study within a Community Service Program

2026-02-28T20:28:53+07:00

The increasing integration of digital technology in elementary education presents both opportunities and risks for students’ cognitive, social, and behavioral development. This study aimed to examine the digital literacy status of elementary school students in terms of engagement, understanding, behavior, digital awareness, and reflection, and to enhance these dimensions through a classroom-based seminar intervention implemented as action research within a community service program. The study involved 18 sixth-grade students from a suburban elementary school in Banten Province, Indonesia. An educational and participatory approach was employed, incorporating structured material presentations, interactive discussions, guided practical activities, visual media, and formative quizzes. The findings indicate that the seminar-based intervention contributed to measurable improvements in students’ digital engagement, understanding, behavior, and digital awareness. However, the development of critical reflection required further iterative cycles of action-based learning. These results provide empirical support for participatory classroom interventions as a viable strategy for strengthening digital literacy as a foundational competency at the elementary school level.

An Artificial Intelligence-Based Mobile Application for Early Detection of Dyslexia Using Recurrent Neural Network

2026-02-28T21:13:14+07:00

Dyslexia is a neurodevelopmental learning disorder that significantly affects children’s reading and writing skills despite normal intelligence, and delayed identification may lead to long-term academic and psychosocial consequences. Existing dyslexia screening methods rely heavily on expert-driven assessments that are time-consuming, subjective, and difficult to scale in non-clinical settings. Although recent studies have explored artificial intelligence (AI) approaches for dyslexia detection, many remain limited to single-modality data, offline analysis, or non-mobile implementations, restricting their practical applicability for early screening. This study aimed to develop an AI-based mobile application for early dyslexia detection by leveraging sequential text and speech data through a Recurrent Neural Network (RNN) architecture, specifically the Gated Recurrent Unit (GRU). A Research and Development (R&D) methodology was employed, encompassing requirements analysis, system design, GRU model training, mobile application development with Flutter, and system integration with a RESTful backend and a MySQL database. The GRU model was trained on preprocessed reading text and voice recordings to capture temporal patterns associated with dyslexia-related reading behaviors. Experimental results indicate that the proposed model achieved reliable classification performance in identifying dyslexia-related patterns, while the mobile application successfully delivered real-time screening results and maintained longitudinal assessment records. The findings demonstrate that integrating lightweight sequential deep learning models into mobile platforms offers a scalable and accessible solution for early dyslexia screening, supporting independent use by parents and educators outside clinical environments.