A review on application of mobile media in personalized special education
Vol 9, Issue 8, 2024, Article identifier:
VIEWS - 132 (Abstract) 118 (PDF)
Abstract
Personalized special education is an educational approach that focuses on the unique needs and abilities of students with special needs, making it ideal for inclusive classrooms. Research indicates that personalized learning can enhance learning outcomes, increase engagement, and improve overall experience. Mobile media, including digital content and apps, is crucial in special education for providing inclusive experiences. These devices offer various features and functions that can be customized to meet the unique needs of students with diverse learning requirements. Benefits of using mobile media include accessibility, interactive learning, inclusive education, assistive technologies, collaborative learning, increased engagement, motivation, and feedback and assessment. Assistive technology tools like text-to-speech capabilities enhance accessibility and facilitate learning for students with diverse needs. Therefore, the behavior characteristics of special children were analyzed, and the relevant use of mobile media was obtained through literature review, which provided theoretical support for the study of mobile media design for special children through edutainment.
Keywords
Full Text:
PDFReferences
1. Tetzlaff, L., Schmiedek, F., & Brod, G. (2021). Developing personalized education: A dynamic framework. Educational Psychology Review, 33, 863-882.
2. Kim, J. and Kim, K. (2023). Special education teacher training programs in south korea: current status and issues. Intervention in School and Clinic, 59(4), 287-291.
3. Thulin, E., Vilhelmson, B., & Schwanen, T. (2019). Absent friends? smartphones, mediated presence, and the recoupling of online social contact in everyday life.
4. Park, Y. (2011). A pedagogical framework for mobile learning: categorizing educational applications of mobile technologies into four types. The International Review of Research in Open and Distributed Learning, 12(2), 78.
5. Kraleva, R. (2017). Childibu – a mobile application for bulgarian children with special educational needs. International Journal on Advanced Science Engineering and Information Technology, 7(6), 2085.
6. Febtriko, A., Rahayuningsih, T., Sukri, S., & Saputra, H. T. (2018). Implementation of mobile robots approach for autistic children media therapy. International Journal of Engineering & Technology (UEA), 7, 95-98.
7. Nikolopoulou, K. and Kousloglou, M. (2019). Mobile learning in science: a study in secondary education in greece. Creative Education, 10(06), 1271-1284.
8. Kukulska‐Hulme, A. and Shield, L. (2008). An overview of mobile assisted language learning: from content delivery to supported collaboration and interaction. Recall, 20(3), 271-289.
9. Cho, K., Lee, S., Joo, M., & Becker, B. (2018). The effects of using mobile devices on student achievement in language learning: a meta-analysis. Education Sciences, 8(3), 105.
10. Ferris, T. (2004). User-centered design: an integrated approach. Ieee Transactions on Dependable and Secure Computing, 47(1), 75-77. https://doi.org/10.1109/tpc.2004.824283
11. Altaboli, A. and Lin, Y. (2011). Investigating effects of screen layout elements on interface and screen design aesthetics. Advances in Human-Computer Interaction, 2011, 1-10. https://doi.org/10.1155/2011/659758
12. Zhuang, Q., Xu, W., Yang, D., & Wei, N. (2022). Multimedia analysis of digital museum user interface based on goal-oriented theory and information fusion and intelligent sensing. Journal of Sensors, 2022, 1-17.
13. Ripalda, D., Guevara, C., & Garrido, A. (2020). Framework based on gestalt principles to design mobile interfaces for a better user experience. 158-165. https://doi.org/10.1007/978-3-030-51828-8_21
14. Myers, C. (2003). Software systems as complex networks: structure, function, and evolvability of software collaboration graphs. Physical Review E, 68(4). https://doi.org/10.1103/physreve.68.046116
15. France, R. and Rumpe, B. (2007). Model-driven development of complex software: a research roadmap. https://doi.org/10.1109/fose.2007.14
16. Zhang, W., He, J., & Song, K. (2021). Rethinking complexity for software code structures: a pioneering study on linux kernel code repository.
17. Qin, Y., Xu, C., Chen, Z., & Lu, J. (2019). Software testing for cyber-physical systems suffering uncertainty. Scientia Sinica Informationis, 49(11), 1428-1450. https://doi.org/10.1360/n112018-00305
18. Liu, Y. and Wilde, E. (2011). Personalized location-based services. https://doi.org/10.1145/1940761.1940829
19. Arfaoui, S., BELMEKKI, A., & Mezrioui, A. (2022). A privacy by design methodology application in telecom domain. International Journal of Communication Networks and Information Security (Ijcnis), 13(2). https://doi.org/10.17762/ijcnis.v13i2.4926
20. Ahnn, J. (2014). Scalable big data computing for the personalization of machine learned models and its application to automatic speech recognition service. https://doi.org/10.1109/bigdata.2014.7004349
21. Allen, M. L., Hartley, C., & Cain, K. (2016). iPads and apps as a tool to support children with autism spectrum disorder in academic writing: A literature review. Journal of Assistive Technologies, 10(1), 6-14.
22. Dargut, C., & Erdem, A. (2021). The effect of mobile social story maps on developing cognitive and social skills for children with autism spectrum disorder. Computers & Education, 166, 104153.
23. Must, A., Phillips, S., Curtin, C., Bandini, L. G., & Barish, M. E. (2015). The effect of obesity on health-related quality of life in youth with autism spectrum disorders. Pediatrics, 136(6), 1040-1047.
24. Ntalindwa, T., Al-Samarraie, H., & Dawes, T. (2019). Exploring the use of Information Communication Technologies (ICT) among children with autism spectrum disorder (ASD): A qualitative study. Education and Information Technologies, 24(2), 1253-1277.
25. Alam, M. (2023). Investigating the impact of a gamified learning analytics dashboard: student experiences and academic achievement. Journal of Computer Assisted Learning, 39(5), 1436-1449. https://doi.org/10.1111/jcal.12853
26. Shadiev, R., Liu, T., & Hwang, W. (2019). Review of research on mobile‐assisted language learning in familiar, authentic environments. British Journal of Educational Technology, 51(3), 709-720. https://doi.org/10.1111/bjet.12839
27. Shen, L., Xie, B., & Shen, R. (2014). Enhancing user experience in mobile learning by affective interaction. https://doi.org/10.1109/ie.2014.63
28. Kaasinen, E., Roto, V., Roloff, K., Väänänen-Vainio-Mattila, K., Vainio, T., Maehr, W. … & Shrestha, S. (2009). User experience of mobile internet. 175-194. https://doi.org/10.4018/978-1-60960-499-8.ch011
29. Ickin, S., Wac, K., Fiedler, M., Janowski, L., Hong, J., & Dey, A. (2012). Factors influencing quality of experience of commonly used mobile applications. Ieee Communications Magazine, 50(4), 48-56. https://doi.org/10.1109/mcom.2012.6178833
30. Zhang, Y., Liu, Y., Li, W., Peng, L., & Yang, C. (2020). A study of the influencing factors of mobile social media fatigue behavior based on the grounded theory. Information Discovery and Delivery, 48(2), 91-102. https://doi.org/10.1108/idd-11-2019-0084
31. Utomo, U., Rapisa, D., Damastuti, E., & Susanti, D. (2023). Development of sign language application pesan kuliah with material substance modification based on student characteristics with hearing impaired. Journal of Icsar, 7(1), 136. https://doi.org/10.17977/um005v7i12023p136
32. Genc, Z., Aydin, S., & Aydin, H. (2021). The effectiveness of mobile applications in special education: A systematic review. International Journal of Special Education, 36(1), 45-59.
33. May, D., Lensing, K., Tekkaya, A., Grosch, M., Berbuir, U., & Petermann, M. (2014). What students use: results of a survey on media use among engineering students.. https://doi.org/10.1109/fie.2014.7044002
34. Song, S., Tan, K., & Awang, M. (2021). Generic digital equity model in education: mobile-assisted personalized learning (mapl) through e-modules. Sustainability, 13(19), 11115.
35. Andriah, P., & Amir, Z. (2021). The impact of mobile learning tools on mathematics learning outcomes: A systematic review. Journal of Educational Technology & Society, 24(2), 123-134.
36. Nasution, D. (2021). Mobile media for language learning: A review of recent studies. Language Learning & Technology, 25(1), 56-68.
37. Demirbilek, M. (2010). Investigating attitudes of pre-service teachers towards the use of educational technology. Journal of Educational Computing Research, 42(3), 305-325.
38. Huang, C., & Yu, S. (2019). Enhancing student engagement and motivation through mobile learning. Interactive Learning Environments, 27(4), 465-477.
39. Huang, R., Joo, K. P., & Lee, K. (2020). The effects of collaborative learning with mobile technologies on student learning in different environments. Computers & Education, 143, 103682.
40. Hasan, M. (2023). Diversity and pluralism in arab media education curricula: an analytical study in light of unesco standards. Humanities and Social Sciences Communications, 10(1).
41. Shaheen, N., Khan, R., Yasmeen, R., & Sajid, M. (2021). “probing in the complexities of the integrated undergraduate medical curriculum”: a qualitative exploratory study. Journal of the Pakistan Medical Association.
DOI: https://doi.org/10.59429/esp.v9i8.2910
(132 Abstract Views, 118 PDF Downloads)
Refbacks
- There are currently no refbacks.
Copyright (c) 2024 Beili Wang, Saiful Hasley Ramli, Samsilah Roslan, Ahmad Rizal Abdul Rahman, Ziming Li
This work is licensed under a Creative Commons Attribution 4.0 International License.