Methods, Metrics, and Taxonomies for Evaluating Accessibility and Usability in Digital Learning Systems for Deaf and Hard-of-hearing Users: Implications for Inclusive Engineering and Technical Education
DOI:
https://doi.org/10.16920/jeet/2026/v40is1/26151Keywords:
Accessibility, DHH, Digital Learning Systems, Engineering Education, Evidence Taxonomy, Quality Assurance, Technical Education, Usability.Abstract
Digital learning systems are increasingly used to support inclusive technical education for deaf and hard-of-hearing (DHH) students, yet evidence regarding their accessibility and usability remains fragmented. This study employed a PRISMA-based Systematic Literature Review (SLR), supported by bibliometric analysis, quality assessment, and thematic synthesis. The findings show that evaluation practices remain dominated by technical performance, while accessibility assessment, direct user involvement, and learning-outcome evaluation are less consistently reported. Based on these patterns, an Evaluation Evidence Taxonomy and evidence-gap map were developed and subsequently synthesised into a Design Knowledge Taxonomy. These outputs integrate technical performance, accessibility, usability, user evidence, context of use, and learning outcomes within a structured evaluation perspective. The resulting taxonomies provide an evidence-based foundation for evaluating digital learning systems intended to support inclusive technical education and serve as intermediate research artefacts for subsequent QA Model development.
Downloads
References
Abbood, A. M. H. (2024). Effectiveness of cooperative learning using multimedia in some physical abilities and basic skills for junior players in basketball. ASEAN Journal of Physical Education and Sport Science, 3(1), 7–16.
Ahn, S., Kim, J., Shin, C., & Hong, J.-H. (2025). Visualizing speech styles in captions for deaf and hard-of-hearing viewers. International Journal of Human-Computer Studies, 194, 103386.
Al Husaeni, D. N., & Rahmat, M. (2023). Computational bibliometric analysis: Can digital transformation improve the quality of Islamic learning? ASEAN Journal of Religion, Education, and Society, 2(2), 123–138.
Al-Saidi, M., Ballagi, Á., Hassen, O. A., & Saad, S. M. (2024). Type-2 neutrosophic Markov chain model for subject-independent sign language recognition: A new uncertainty–aware soft sensor paradigm. Sensors, 24(23), 7828.
Ariya, P., Yensathit, Y., Thongthip, P., Intawong, K., & Puritat, K. (2024). Assisting hearing and physically impaired students in navigating immersive virtual reality for library orientation. Technologies, 13(1), 2.
Ariyanti, L. S., & Nandiyanto, A. B. D. (2022). Distance teaching of stress materials to junior high school students using digital media. ASEAN Journal of Community Service and Education, 1(1), 7–14.
Babalian, V., & Slutu, S. (2025). Real-time sign language-to-speech translation system with AI-powered wearable technology. IEEE Access, 13(September), 151652–151663.
Berrezueta-Guzman, S., Daya, R., & Wagner, S. (2025). Virtual reality in sign language education: opportunities, challenges, and the road ahead. Frontiers in Virtual Reality, 6, 1625910.
Cabanillas-Carbonell, M., Cusi-Ruiz, P., & Prudencio-Galvez, D. (2022). Mobile application with augmented reality to improve the process of learning sign language. IJIM, 16(11), 51–64.
Clark, J., Barton, B., Albarqouni, L., Byambasuren, O., Jowsey, T., Keogh, J., Liang, T., Moro, C., & O’Neill, H. (2025). Generative artificial intelligence use in evidence synthesis: A systematic review. Research Synthesis Methods, 16, 601–619.
Coster, M. De, Shterionov, D., Herreweghe, M. Van, & Dambre, J. (2024). Machine translation from signed to spoken languages: State of the art and challenges. Universal Access in the Information Society, 23(3), 1305–1331.
Escudeiro, P., Galasso, B., Teixeira, D., Gouveia, M. C., & Escudeiro, N. (2022). Inclusive MOOC--educational content for deaf people, a Portuguese proof of concept. Cypriot Journal of Educational Sciences, 17(4), 1285-1303.
González-Rodríguez, J. R., Córdova-Esparza, D. M., Terven, J., & Romero-González, J. A. (2024). Towards a bidirectional Mexican sign language–Spanish translation system: A deep learning approach. Technologies, 12(1), 7.
Hashi, A. O., Mohd Hashim, S. Z., Mirjalili, S., Kebande, V. R., Al-Dhaqm, A., Nasser, M., & A Samah, A. B. (2025). A hybrid CNN-transformer framework optimized by Grey Wolf algorithm for accurate sign language recognition. Scientific Reports, 15(1), 43550.
Heck, G. S., & Ferraro, J. L. (2025). Accessible practices in science museums and their impact on the science capital of visually and hearing-impaired people. Rev. Int. de Pesquisa em Didática das Ciências e Matemática (RevIn), 6, e025011.
Khraisha, Q., Put, S., Kappenberg, J., Warraitch, A., & Hadfield, K. (2024). Can large language models replace humans in systematic reviews? Evaluating GPT-4’s efficacy in screening and extracting data from peer-reviewed and grey literature in multiple languages. Research Synthesis Methods, 15(4), 616–626.
Kim, N., Choe, H., Lee, S., & Kang, C. (2025). Design and implementation of a 3D Korean sign language learning system using pseudo-hologram. Applied Sciences, 15(16), 8962.
Li, J., Xu, J., Liu, Y., Xu, W., & Li, Z. (2024). Enhancing the applicability of sign language translation. IEEE Transactions on Mobile Computing, 23(9), 8634–8648.
Loaiza-Ramírez, I., & González-Beltrán, B. A. (2026). Digital accessibility guidelines for deaf and hard of hearing persons: A state-of-the-art review article. Universal Access in the Information Society, 25(1), 4.
Lynch, S., Brunton, J., & Farrell, O. (2026). Experiences of disabled students in online education: A systematic review. Distance Education, 47(1), 70–90.
Millett, P. (2021). Accuracy of speech-to-text captioning for students who are deaf or hard of hearing. Journal of Educational, Pediatric & (Re)Habilitative Audiology, 25, 1.
Musayaroh, S., Asmiati, N., Utami, Y. T., Mulia, D., Sidik, S. A., Abadi, R. F., Pratama, T. Y., Maslahah, S., & Pramudyo, A. S. (2023). A digital accessibility and inclusive design-based e-module in higher education: Does it work in a classroom with a deaf student? ASEAN Journal of Community and Special Needs Education, 2(1), 55–60.
Nandiyanto, A. B. D., & Yulistiani, F. (2024). How technical language supports students’ understanding in engineering education: Definitions and step-by-step interpretation of dimensionless parameters (Re, Pr, Pe, Nu, Gr, Ra, Bi, and Fo) in heat transfer. International Journal of Language Education, 8(4), 885–900.
Nedjar, I., & Mohammed, M. (2024). Interactive system based on artificial intelligence and robotic arm to enhance Arabic sign language learning in deaf children. Education and Information Technologies, 29, 24563–24580.
Ogundele, A. G., Umar, I. Y., & Idris, A. M. (2025). The emergence of new technologies in metalwork/automobile industries: Issues, challenges and opportunities in emanating from for delivery of technical education on a pandemic era. ASEAN Journal for Science Education, 4(1), 1–6.
Panopoulos, I., Topalis, E., Petrellis, N., & Hadellis, L. (2025). Greek sign language detection with artificial intelligence. Electronics, 14(16), 3241.
Perea-Trigo, M., Vázquez-Enríquez, M., Benjumea-Bellot, J. C., Alba-Castro, J. L., & Álvarez-García, J. A. (2025). Sign language anonymization: Face swapping versus avatars. Electronics, 14(12), 2360.
Pérez-Arteaga, J., Letón, E., Pérez Martín, J., Molanes-López, E., & Rodríguez-Ascaso, A. (2025). Assessment of captions’ quality by students of a MOOC on accessibility of digital materials. Computers and Education Open, 9, 100279.
Rastgoo, R., Kiani, K., & Escalera, S. (2021). Sign language recognition: A deep survey. Expert Systems with Applications, 164, 113794.
Reyes, J. I., Meneses, J., & Xavier, M. (2023). Suitability of online higher education for learners with disabilities: The students’ voices. Journal of Special Education Technology, 38(3), 370–383.
Shah, C. (2025). From prompt engineering to prompt science with humans in the loop. Communications of the ACM, 68(6), 54–61.
Shahin, N., & Ismail, L. (2024). From rule-based models to deep learning transformers architectures for natural language processing and sign language translation systems: Survey, taxonomy and performance evaluation. Artificial Intelligence Review, 57(10), 271.
Shin, J., Musa Miah, A. S., Hasan, M. A. M., Hirooka, K., Suzuki, K., Lee, H. S., & Jang, S. W. (2023). Korean sign language recognition using transformer-based deep neural network. Applied Sciences, 13(5), 3029.
Sivunen, U. (2025). Lived experiences of language use and (in) accessibility among multilingual deaf and hard-of-hearing adolescents and young people in technology-enhanced learning in Finland. Deafness & Education International, 27(4), 340-366.
Susilawati, A. (2024). A bibliometric analysis of global trends in engineering education research. ASEAN Journal of Educational Research and Technology, 3(1), 103–110.
Tan, S., Khan, N., An, Z., Ando, Y., Kawakami, R., & Nakadai, K. (2024). A review of deep learning-based approaches to sign language processing. Advanced Robotics, 38(23), 1649–1667.
Violet, I. M., & Sri, R. L. (2025). A comprehensive survey on recent advances and challenges in sign language recognition systems. Discov. Artif. Intell., 5(1), 419.
Downloads
Published
How to Cite
Issue
Section
Access to login into the old portal (Manuscript Communicator) for Peer Review-
