Article Per Year (5 Year)
p-Index From 2021 - 2026
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All Journal Journal of Hypermedia & Technology-Enhanced Learning
Resmi Darni
Department of Electronics Engineering, Faculty of Engineering, Universitas Negeri Padang, Padang
Computer Science & IT Education Electrical & Electronics Engineering Engineering Social Sciences Other

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An Artificial Intelligence-Based Mobile Application for Early Detection of Dyslexia Using Recurrent Neural Network Muhamad Fathur Rahman; Resmi Darni; Dony Novaliendry; Khairi Budayawan
Journal of Hypermedia & Technology-Enhanced Learning Vol. 4 No. 1 (2026): Journal of Hypermedia & Technology-Enhanced Learning—Future Education
Publisher : Sagamedia Teknologi Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58536/j-hytel.217

Abstract

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.
A Mobile Vocabulary Learning App for Engineering Contexts: Gamification, Self-Directed Learning, and User Progress Tracking Anita Nursi; Resmi Darni; Dony Novaliendry
Journal of Hypermedia & Technology-Enhanced Learning Vol. 3 No. 3 (2025): Journal of Hypermedia & Technology-Enhanced Learning—Next Horizon
Publisher : Sagamedia Teknologi Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58536/j-hytel.193

Abstract

Engineering students frequently face challenges in mastering technical English vocabulary due to its context-dependent and specialized nature. To address this issue, this study developed a mobile application that facilitates self-directed learning of domain-specific vocabulary through gamified activities. The application was designed using the Flutter framework and integrated with Firebase for real-time data management and user tracking. It features two interactive games—Word Guess and Word Match—as well as text-to-speech functionality, a limited hint system, and visual progress tracking through mastery counts and progress bars. Vocabulary items are organized into three thematic categories: General Terms, Tools, and Instructions, each structured through JSON-based data management for scalability and ease of maintenance. Empirical evaluations involving expert review yielded a high validity score (M = 4.6/5), confirming the application’s pedagogical soundness and technical stability. The integration of gamification and progress visualization significantly enhanced learner motivation, autonomy, and engagement. This study contributes a replicable model for combining Flutter–Firebase development with instructional design principles to advance technology-enhanced language learning in engineering education.
Co-Authors Anita Nursi Dony Novaliendry Khairi Budayawan Muhamad Fathur Rahman