The Use of Adaptive Learning Technologies in e-Learning for Inclusive Education: A Systematic Review
DOI:
https://doi.org/10.57125/ELIJ.2024.03.25.05Keywords:
inclusive education, technology, electronic technologies, equitable learning environment, tools, overviewAbstract
The ongoing transformation of the education system involves the introduction of adaptive e-learning technologies at all levels, including inclusive education. The purpose of this study is to conduct a systematic review of scientific papers on e-learning, inclusion, and adaptive technologies. For this purpose, the PRISMA research approach was used to search for relevant sources and to initially process them. Main scientific bases: Scopus and Google Scholar. Date range: 2014–2023. The search for materials was carried out in stages: first, all duplicates were eliminated, a screening was performed, and sources were included based on 5 criteria (mainly content, methodology, and scientific novelty). The results show that the topic of using adaptive technologies, despite its potential, has not yet been covered in detail in scientific circles in recent years. The main methods of support and development of adaptive learning are co-teaching, individualized and differentiated learning. Besides, they should be used comprehensively to ensure a fair and inclusive environment. The conclusions indicate that the implementation of adaptive learning is important for successful inclusion. Among the adaptive learning technologies, various e-learning platforms, adaptive systems, communication tools, and assistive technologies are important. Together, they contribute to effective support for students with special educational needs.
References
Alamri, H. A., Watson, S., & Watson, W. (2021). Learning technology models that support personalization within blended learning environments in higher education. TechTrends, 65(1), 62–78. https://doi.org/10.1007/s11528-020-00530-3
Apoki, U. C., Hussein, A. M. A., Al-Chalabi, H. K. M., Badica, C., & Mocanu, M. L. (2022). The role of pedagogical agents in personalised adaptive learning: A review. Sustainability, 14(11), Article 6442. https://doi.org/10.3390/su14116442
Aulakh, K., Roul, R. K., & Kaushal, M. (2023). E-learning enhancement through educational data mining with Covid-19 outbreak period in backdrop: A review. International Journal of Educational Development, 101, Article 102814. https://doi.org/10.1016/j.ijedudev.2023.102814
Beem, H. R., Ampomah, C., Takyi, J., & Adomdza, G. K. (2023). Development of an online project-based learning design course for African first year students and its impact on self-efficacy levels. IEEE Transactions on Education, 66(5), 410–420. https://doi.org/10.1109/te.2023.3312968
Betlej, A., & Danileviča, A. (2022). Learning technologies for people with mild intellectual disabilities. From digital exclusion to inclusive e-education in network society. International Journal of Research in E-learning, 8(2), 1–20. https://doi.org/10.31261/ijrel.2022.8.2.07
Behl, A., Jayawardena, N., Pereira, V., Islam, N., Giudice, M. D., & Choudrie, J. (2022). Gamification and e-learning for young learners: A systematic literature review, bibliometric analysis, and future research agenda. Technological Forecasting and Social Change, 176, Article 121445. https://doi.org/10.1016/j.techfore.2021.121445
Bjekić, D., Obradović, S., Vučetić, M., & Bojović, M. (2014). E-teacher in inclusive e-education for students with specific learning disabilities. Procedia – Social and Behavioral Sciences, 128, 128–133. https://doi.org/10.1016/j.sbspro.2014.03.131
Björnsdóttir, K. (2019). Belonging to higher education: Inclusive education for students with intellectual disabilities. In M. R. Coleman & M. Shevlin (Eds.), Postsecondary Educational Opportunities for Students with Special Education Needs (pp. 126–136). London: Routledge. https://doi.org/10.4324/9781351107570-9
Bradac, V., & Walek, B. (2017). A comprehensive adaptive system for e-learning of foreign languages. Expert Systems with Applications, 90, 414–426. https://doi.org/10.1016/j.eswa.2017.08.019
Cinquin, P.-A., Guitton, P., & Sauzéon, H. (2019). Online e-learning and cognitive disabilities: A systematic review. Computers & Education, 130, 152–167. https://doi.org/10.1016/j.compedu.2018.12.004
Colchester, K., Hagras, H., Alghazzawi, D., & Aldabbagh, G. (2017). A survey of artificial intelligence techniques employed for adaptive educational systems within e-learning platforms. Journal of Artificial Intelligence and Soft Computing Research, 7(1), 47–64. https://doi.org/10.1515/jaiscr-2017-0004
De Medio, C., Limongelli, C., Sciarrone, F., & Temperini, M. (2020). MoodleREC: A recommendation system for creating courses using the moodle e-learning platform. Computers in Human Behavior, 104, Article 106168. https://doi.org/10.1016/j.chb.2019.106168
Eljak, H., Ibrahim, A. O., Saeed, F., Hashem, I. A. T., Abdelmaboud, A., Syed, H. J., ... Elsafi, A. (2023). E-learning based cloud computing environment: A systematic review, challenges, and opportunities. IEEE Access, 12, 7329–7355. https://doi.org/10.1109/access.2023.3339250
El-Sabagh, H. A. (2021). Adaptive e-learning environment based on learning styles and its impact on development students' engagement. International Journal of Educational Technology in Higher Education, 18(1), Article 53. https://doi.org/10.1186/s41239-021-00289-4
Essa, S. G., Celik, T., & Human-Hendricks, N. (2023). Personalised adaptive learning technologies based on machine learning techniques to identify learning styles: A systematic literature review. IEEE Access, 11, 48392–48409. https://doi.org/10.1109/access.2023.3276439
Ezzaim, A., Dahbi, A., Haidine, A., & Aqqal, A. (2023). AI-based adaptive learning: A systematic mapping of the literature. JUCS - Journal of Universal Computer Science, 29(10), 1161–1198. https://doi.org/10.3897/jucs.90528
Gandolfi, E., Ferdig, R. E., & Kratcoski, A. (2021). A new educational normal an intersectionality-led exploration of education, learning technologies, and diversity during COVID-19. Technology in Society, 66, Article 101637. https://doi.org/10.1016/j.techsoc.2021.101637
Hillel, T., Bierlaire, M., Elshafie, M. Z. E. B., & Jin, Y. (2021). A systematic review of machine learning classification methodologies for modelling passenger mode choice. Journal of Choice Modelling, 38, Article 100221. https://doi.org/10.1016/j.jocm.2020.100221
Hocine, N., & Sehaba, K. (2023). A systematic review of online personalized systems for the autonomous learning of people with cognitive disabilities. Human–Computer Interaction, 39(3-4), 174–205. https://doi.org/10.1080/07370024.2023.2242364
Horne, E. T., Monaco, M. K., Cannon, S. E., & Roberts, C. E. (2019). Using performance-based assessments as part of quality assurance system for program improvement. In K. Winter, H. Pinter, & M. Watson (Eds.), Performance-Based Assessment in 21st Century Teacher Education (pp. 1–20). IGI Global. https://doi.org/10.4018/978-1-5225-8353-0.ch001
Hubalovsky, S., Hubalovska, M., & Musilek, M. (2019). Assessment of the influence of adaptive E-learning on learning effectiveness of primary school pupils. Computers in Human Behavior, 92, 691–705. https://doi.org/10.1016/j.chb.2018.05.033
Ingavélez-Guerra, P., Otón-Tortosa, S., Hilera-González, J., & Sánchez-Gordón, M. (2021). The use of accessibility metadata in e-learning environments: A systematic literature review. Access in the Information Society, 22(2), 445–461. https://doi.org/10.1007/s10209-021-00851-x
Kakosimos, K. E. (2015). Example of a micro-adaptive instruction methodology for the improvement of flipped-classrooms and adaptive-learning based on advanced blended-learning tools. Education for Chemical Engineers, 12, 1–11. https://doi.org/10.1016/j.ece.2015.06.001
Kardan, A. A., Aziz, M., & Shahpasand, M. (2015). Adaptive systems: A content analysis on technical side for e-learning environments. Artificial Intelligence Review, 44(3), 365–391. https://doi.org/10.1007/s10462-015-9430-1
Kem, D. (2022). Personalised and adaptive learning: Emerging learning platforms in the era of digital and smart learning. International Journal of Social Science and Human Research, 5(2), 385–391. https://doi.org/10.47191/ijsshr/v5-i2-02
Khamparia, A., & Pandey, B. (2019). Association of learning styles with different e-learning problems: a systematic review and classification. Education and Information Technologies, 25(2), 1303–1331. https://doi.org/10.1007/s10639-019-10028-y
Kolekar, S. V., Pai, R. M., & Pai, M. M. M. (2018). Adaptive user interface for Moodle based e-learning system using learning styles. Procedia Computer Science, 135, 606–615. https://doi.org/10.1016/j.procs.2018.08.226
Labonté, C., & Smith, V. R. (2022). Learning through technology in middle school classrooms: Students’ perceptions of their self-directed and collaborative learning with and without technology. Education and Information Technologies, 27, 6317–6332. https://doi.org/10.1007/s10639-021-10885-6
Hussein, A. A., & Al-Chalabi, H. K. M. (2020). Pedagogical agents in an adaptive E-learning system. SAR Journal – Science and Research, 3, 24–30. https://doi.org/10.18421/sar31-04
Marín, V. I., de Benito, B., & Darder, A. (2020). Technology-enhanced learning for student agency in higher education: A systematic literature review. Interaction Design and Architecture(s), (45), 15–49. https://doi.org/10.55612/s-5002-045-001
Marone, V., & Heinsfeld, B. D. (2023). “Everyone pursuing their dreams”: Google's and Microsoft's discourse on educational technology. Computers and Education Open, 4, Article 100138. https://doi.org/10.1016/j.caeo.2023.100138
Mavroudi, A., Giannakos, M., & Krogstie, J. (2017). Supporting adaptive learning pathways through the use of learning analytics: Developments, challenges and future opportunities. Interactive Learning Environments, 26(2), 206–220. https://doi.org/10.1080/10494820.2017.1292531
Megahed, M., & Mohammed, A. (2020). Modeling adaptive E-Learning environment using facial expressions and fuzzy logic. Expert Systems with Applications, 157, Article 113460. https://doi.org/10.1016/j.eswa.2020.113460
Moriña, A. (2019). Inclusive education in higher education: Challenges and opportunities. In M. R. Coleman & M. Shevlin (Eds.), Postsecondary Educational Opportunities for Students with Special Education Needs (pp. 3–17). London: Routledge. https://doi.org/10.4324/9781351107570-2
Nabizadeh, A. H., Gonçalves, D., Gama, S., Jorge, J., & Rafsanjani, H. N. (2020). Adaptive learning path recommender approach using auxiliary learning objects. Computers & Education, 147, Article 103777. https://doi.org/10.1016/j.compedu.2019.103777
Özyurt, Ö., & Özyurt, H. (2015). Learning style based individualized adaptive e-learning environments: Content analysis of the articles published from 2005 to 2014. Computers in Human Behavior, 52, 349–358. https://doi.org/10.1016/j.chb.2015.06.020
Peng, H., Ma, S., & Spector, J. M. (2019). Personalized adaptive learning: An emerging pedagogical approach enabled by a smart learning environment. Smart Learning Environments, 6(1), Article 9. https://doi.org/10.1186/s40561-019-0089-y
Persico, D., Manca, S., & Pozzi, F. (2014). Adapting the Technology Acceptance Model to evaluate the innovative potential of e-learning systems. Computers in Human Behavior, 30, 614–622. https://doi.org/10.1016/j.chb.2013.07.045
Pratama, A. (2021). Modification of the technology acceptance model in the use of Google classroom in the COVID-19 Era: A case studies in junior high schools. Cypriot Journal of Educational Sciences, 16(5), 2598–2608. https://doi.org/10.18844/cjes.v16i5.6336
Starks, A. C., & Reich, S. M. (2022). “What about special ed?“: Barriers and enablers for teaching with technology in special education. Computers & Education, 193, Article 104665. https://doi.org/10.1016/j.compedu.2022.104665
Timothy, A. O., Abdulwab, I., Temitope, J. I., & Shade, J. (2020). Reviews on machine learning based adaptive mobile learning system. International Journal of Computer Applications, 176(38), 1–6. https://doi.org/10.5120/ijca2020920498
Truong, H. M. (2016). Integrating learning styles and adaptive e-learning system: Current developments, problems and opportunities. Computers in Human Behavior, 55, 1185–1193. https://doi.org/10.1016/j.chb.2015.02.014
Ünlüsoy, A., Leander, K. M., & de Haan, M. (2021). Rethinking sociocultural notions of learning in the digital era: Understanding the affordances of networked platforms. E-Learning and Digital Media, 19(1), 78–92. https://doi.org/10.1177/20427530211032302
Warin, T., & Stojkov, A. (2021). Machine learning in finance: A metadata-based systematic review of the literature. Journal of Risk and Financial Management, 14(7), Article 302. https://doi.org/10.3390/jrfm14070302
Wen, Z. A., Silverstein, E., Zhao, Y., Amog, A. L., Garnett, K., & Azenkot, S. (2020). Teacher views of math e-learning tools for students with specific learning disabilities. In T. Guerreiro, H. Nicolau, & K. Moffatt (Eds.), ASSETS '20: The 22nd International ACM SIGACCESS Conference on Computers and Accessibility (Article 44). Association for Computing Machinery. https://doi.org/10.1145/3373625.3417029
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Suren Gevorgyan
This work is licensed under a Creative Commons Attribution 4.0 International License.