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Research Article

A systematic review of online personalized systems for the autonomous learning of people with cognitive disabilities

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Pages 174-205 | Received 14 Nov 2022, Accepted 21 Jul 2023, Published online: 13 Aug 2023

References

  • Afonseca, C., & Badia, S. B. (2013). Supporting collective learning experiences in special education. In 2nd ieee international conference on serious games and applications for health, Vilamoura, Portugal (pp. 1–7).
  • Akhuseyinoglu, K., & Brusilovsky, P. (2022). Exploring behavioral patterns for data-driven modeling of learners’ individual differences. Frontiers in Artificial Intelligence, 5, 23–32. Retrieved from https://www.frontiersin.org/articles/10.3389/frai.2022.807320
  • Albo, L., Barria-Pineda, J., Brusilovsky, P., & Hernandez-Leo, D. (2022). Knowledge-based design analytics for authoring courses with smart learning content. International Journal of Artificial Intelligence in Education, 32(1), 4–27. https://doi.org/10.1007/s40593-021-00253-3
  • Aldowah, H., Al-Samarraie, H., Alzahrani, A. I., & Alalwan, N. (2020). Factors affecting student dropout in MOOCs: A cause and effect decision-making model. Journal of Computing in Higher Education, 32(2), 429–454. https://doi.org/10.1007/s12528-019-09241-y
  • Alghabban, W. G., & Hendley, R. (2020). Adapting e-learning to dyslexia type: An experimental study to evaluate learning gain and perceived usability. In Proceedings of the 22nd hci international conference (p. 519–537). Berlin, Heidelberg: Springer-Verlag.
  • Alsobhi, A., & Alyoubi, K. (2019). Adaptation algorithms for selecting personalised learning experience based on learning style and dyslexia type. Data Technologies and Applications, 2(3), 65–82. https://doi.org/10.1108/DTA-10-2018-0092
  • Alsobhi, A., & Alyoubi, K. (2020). Learning styles and dyslexia types–understanding their relationship and its benefits in adaptive e-learning systems. International Journal of Interactive Mobile Technologies, 42(4), 123–135.
  • Arachchi, T. K., Sitbon, L., & Zhang, J. (2017). Enhancing access to e-learning for people with intellectual disability: Integrating usability with learning. In Ifip conference on human- computer interaction, Mumbai, India (pp. 13–32).
  • Assembly, U. G. (2006). Convention on the rights of persons with disabilities. Ga Res. Retrieved June 2023, from https://www.un.org/en/development/desa/population/migration/generalassembly/docs/globalcompact/A_RES_61_106.pdf
  • Basham, J., Hall, T., Carter, R., & Stahl, W. (2016). An operationalized understanding of personalized learning. Journal of Special Education Technology, 31(3), 126–136. https://doi.org/10.1177/0162643416660835
  • Benmarrakchi, F., El Kafi, J., Elhore, A., & Haie, S. (2017). Exploring the use of the ICT in supporting dyslexic students’ preferred learning styles: A preliminary evaluation. Education and Information Technologies, 22(6), 2939–2957. https://doi.org/10.1007/s10639-016-9551-4
  • Brennan, A., McDonagh, T., Dempsey, M., & McAvoy, J. (2022). Cosmic sounds: A game to support phonological awareness skills for children with dyslexia. IEEE Transactions on Learning Technologies, 15(3), 301–310. https://doi.org/10.1109/TLT.2022.3170231
  • Brusilovsky, P. (2001). Adaptive hypermedia. User Modeling and User-Adapted Interaction, 11(1), 87–110. https://doi.org/10.1023/A:1011143116306
  • Buzzi, M. C., Buzzi, M., Perrone, E., & Senette, C. (2019). Personalized technology-enhanced training for people with cognitive impairment. Universal Access in the Information Society, 18(4), 891–907. https://doi.org/10.1007/s10209-018-0619-3
  • Campos, J. D. S. U. B., & Almeida, A. M. P. (2021). Game mechanics for storytelling to support students with dyslexia: Teachers and students’ perspectives. Revista Praxis Educacional, 17(45), 1–23. https://doi.org/10.22481/praxisedu.v17i45.8345
  • Cano, A., Fernandez-Manjon, B., & Garcia-Tejedor, A. (2016). Downtown, a subway adventure: Using learning analytics to improve the development of a learning game for people with intellectual disabilities. In 16th ieee international conference on advanced learning technologies, Austin, TX, USA (pp. 125–129).
  • Cano, A. R., Fernandez-Manjon, B., & Garcia-Tejedor, A. (2018). Using game learning analytics for validating the design of a learning game for adults with intellectual disabilities. British Journal of Educational Technology, 49(4), 659–672. https://doi.org/10.1111/bjet.12632
  • Cardona-Reyes, H., Ortiz-Aguinaga, G., Barba-Gonzalez, M. L., & Munoz-Arteaga, J. (2021). User-centered virtual reality environments to support the educational needs of children with ADHD in the COVID-19 pandemic. IEEE Revista Iberoamericana de Tecnologias del Aprendizaje, 16(4), 400–409. https://doi.org/10.1109/RITA.2021.3135194
  • Chatti, M., & Muslim, A. (2019). The perla framework: Blending personalization and learning analytics. International Review of Research in Open & Distributed Learning, 20(1), 213–229. https://doi.org/10.19173/irrodl.v20i1.3936
  • Chatzara, K., Karagiannidis, C., & Stamatis, D. (2016). Cognitive support embedded in self-regulated e-learning systems for students with special learning needs. Education and Information Technologies, 21(2), 283–299. https://doi.org/10.1007/s10639-014-9320-1
  • Chen, S., & Wang, J.-H. (2021). Individual differences and personalized learning: A review and appraisal. Universal Access in the Information Society, 20(4), 833–849. https://doi.org/10.1007/s10209-020-00753-4
  • Cinquin, P.-A., Guitton, P., & Sauzeon, H. (2019). Online e-learning and cognitive disabilities: A systematic review. Computers and Education, 130, 152–167. https://doi.org/10.1016/j.compedu.2018.12.004
  • Cinquin, P.-A., Guitton, P., & Sauzeon, H. (2021a). Designing accessible MOOCs to expand educational opportunities for persons with cognitive impairments. Behaviour and Information Technology, 40(11), 1101–1119. https://doi.org/10.1080/0144929X.2020.1742381
  • Cinquin, P.-A., Guitton, P., & Sauzeon, H. (2021b). Toward truly accessible MOOCs for persons with cognitive impairments: A field study. Human Computer Interaction, 38(5), 352–373. https://doi.org/10.1080/07370024.2021.2008250
  • Corlu, D., Tasel, S., Turan, S. G., Gatos, A., & Yantac, A. E. (2017). Involving autistics in user experience studies: A critical review. In Proceedings of the 2017 conference on designing interactive systems, Edinburgh, United Kingdom (pp. 43–55).
  • De Araujo, E. C. J., & Andrade, W. (2021). A systematic literature review on teaching programming to people with cognitive disabilities. In 2021 ieee frontiers in education conference, Lincoln, NE, USA (pp. 1–8).
  • Dehghani, H. (2019). The effectiveness of a mobile application kalcal on the learning of mathematics in students with dyscalculia. In 2019 international serious games symposium (isgs), Tehran, Iran (pp. 1–6).
  • Felder, R., & Silverman, L. (1988). Learning and teaching styles in engineering education. Engineering Education, 78(7), 674–681.
  • Felix, V., Mena, L., Ostos, R., & Maestre, G. (2017). A pilot study of the use of emerging computer technologies to improve the effectiveness of reading and writing therapies in children with down syndrome. British Journal of Educational Technology, 48(2), 611–624. https://doi.org/10.1111/bjet.12426
  • Fernandez-Lopez, A., Rodriguez-Fortiz, M. J., Rodriguez-Almendros, M. L., & Martinez- Segura, M. J. (2013). Mobile learning technology based on iOS devices to support students with special education needs. Computers and Education, 61, 77–90. https://doi.org/10.1016/j.compedu.2012.09.014
  • Fleming, N., & Mills, C. (1992). Not another inventory, rather a catalyst for reflection. To Improve the Academy, 11(1), 137–155. Retrieved from. https://onlinelibrary.wiley.com/doi/abs/10.1002/j.2334-4822.1992.tb00213.x
  • Fortes, R. P., Lima Salgado, A. D., Souza Santos, F., Agostini Do Amaral, L., & Nogueira da Silva, E. A. (2017). Game accessibility evaluation methods: A literature survey. In International conference on universal access in human-computer interaction, Vancouver, Canada (pp. 182–192).
  • Gupta, T., Aflatoony, L., & Leonard, L. (2021). A helping hand: Design and evaluation of a reading assistant application for children with dyslexia. In Proceedings of the 33rd australian conference on human-computer interaction, Melbourne, VIC, Australia (pp. 242–251).
  • Herrera-Pavo, M. A. (2021). Collaborative learning for virtual higher education. Learning, Culture & Social Interaction, 28, 437–467. Retrieved from https://www.sciencedirect.com/science/article/pii/S2210656120301082
  • Heumader, P., Murillo-Morales, M., Miesenberger, K., Kouroupetroglou, G., Mavrou, K., Manduchi, R., Covarrubias Rodriguez, M., & Penaz, P. (2022). Buddy - A Personal Companion to Match People with Cognitive Disabilities and AT. In Proceedings of the 18 international Conference on Computers Helping People with Special Needs, Lecco, Italy (pp. 275–283). https://doi.org/10.1007/978-3-031-08645-8_32
  • Hocine, N. (2019). Personalized serious games for self-regulated attention training. In Adjunct publication of the 27th conference on user modeling, adaptation and personalization, Larnaca, Cyprus (pp. 251–255).
  • Hocine, N., Gouaich, A., Cerri, S., Mottet, D., Froger, J., & Laffont, I. (2015). Adaptation in serious games for upper-limb rehabilitation: An approach to improve training outcomes. User Modeling and User-Adapted Interaction, 25(1), 65–98. https://doi.org/10.1007/s11257-015-9154-6
  • Honey, P., & Mumford, A. (2000). The learning styles helper’s guide. Peter Honey Publications Maidenhead.
  • Huipeng, S., Sivaparthipan, C. B., & Thanjai, V. (2022). Interactive teaching using human-machine interaction for higher education systems. Computers & Electrical Engineering, 100, 107–121. https://doi.org/10.1016/j.compeleceng.2022.107811
  • Imam, K., & Widya, K. (2021). Construction of students’ mathematical knowledge in the zone of proximal development and zone of potential construction. European Journal of Educational Research, 10(1), 341–351. https://doi.org/10.12973/eu-jer.10.1.341
  • Kinnebrew, J., Biswas, G., Sulcer, B., & Taylor, R. (2013). Investigating self-regulated learning in teachable agent environments. In R. Azevedo & V. Aleven (Eds.), International handbook of metacognition and learning technologies (pp. 451–470). Springer New York.
  • Korkman, M., & Kemp, S. (2010). Essentials of nepsy-ii assessment. John Wiley & Sons.
  • Lambert, S. (2020). Do MOOCs contribute to student equity and social inclusion? a systematic review 2014–18. Computers and Education, 145, 103–113. https://doi.org/10.1016/j.compedu.2019.103693
  • Lan, Y.-J., Hsiao, I. Y., & Shih, M.-F. (2018). Effective learning design of game-based 3d virtual language learning environments for special education students. Journal of Educational Technology & Society, 21(3), 213–227.
  • Lee, Y. L., Kwon, J., Kim, Y. T., & Shin, S.-J. (2015). Effects of an intelligent robot on number of words and length of sentences uttered by children with autism. In Proceedings of the international convention on rehabilitation engineering and assistive technology, Midview City, Singapore (pp. 1–4).
  • Lewis, P., Noble, S., & Soiffer, N. (2010). Using accessible math textbooks with students who have learning disabilities. In Proceedings of the 12th international acm sigaccess conference on computers and accessibility, Orlando, Florida, USA (pp. 139–146).
  • Lichtenberger, E., Volker, M., Kaufman, A., & Kaufman, N. (2006). Assessing gifted children with the Kaufman assessment battery for children second edition (KABC-II). Gifted Education International, 21(2), 99–126. https://doi.org/10.1177/026142940602100304
  • Lingyu, L., & Andrea, R. (2021). Conceptualizing teacher agency for inclusive education: A systematic and international review. Teacher Education and Special Education, 44(1), 42–59. https://doi.org/10.1177/0888406420926976
  • Mavroudi, A., Giannakos, M., & Krogstie, J. (2018). 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
  • Mazon, C., Clement, B., Roy, D., Oudeyer, P.-Y., & Sauzeon, H. (2022). Pilot study of an intervention based on an intelligent tutoring system (its) for instructing mathematical skills of students with asd and/or id. Education and Information Technologies, 1–30. https://doi.org/10.1007/s10639-022-11129-x
  • Morales-Villaverde, L., Caro, K., Gotfrid, T., & Kurniawan, S. (2016). Online learning system to help people with developmental disabilities reinforce basic skills. Association for Computing Machinery.
  • Neamtu, R., Camara, A., Pereira, C., & Ferreira, R. (2019). Using artificial intelligence for augmentative alternative communication for children with disabilities. In D. Lamas, F. Loizides, L. Nacke, H. Petrie, M. Winckler, & P. Zaphiris (Eds.), Human-Computer Interaction – INTERACT 2019 (pp. 234–243). https://doi.org/10.1007/978-3-030-29381-9_15
  • Nuckols, C., & Nuckols, C. (2013). The diagnostic and statistical manual of mental disorders,(DSM-5). American Psychiatric Association.
  • OCECD. (2022). Ocecd: Cognitive Disability. https://www.ocecd.org/disability-definitions.aspx. (Retrieved: March 28, 2023)
  • Ostiz-Blanco, M., Lallier, M., Grau, S., Rello, L., Bigham, J., & Carreiras, M. (2018). Jellys: Towards a videogame that trains rhythm and visual attention for dyslexia. In Proceedings of the 20th international acm sigaccess conference on computers and accessibility, Galway, Ireland (pp. 447–449).
  • Paas, F., & van Merrienboer, J. J. (2020). Cognitive-load theory: Methods to manage working memory load in the learning of complex tasks. Current Directions in Psychological Science, 29(4), 394–398. https://doi.org/10.1177/0963721420922183
  • Page, M., McKenzie, J., Bossuyt, P., Boutron, I., Hoffmann, T., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E. … Whiting, P. (2021). The prisma 2020 statement: An updated guideline for reporting systematic reviews. Systematic Reviews, 10(1), 1–11. https://doi.org/10.1186/s13643-021-01626-4
  • Pejman, S., Ahmed, E., & Olga, D. T. (2022). Individualization in serious games: A systematic review of the literature on the aspects of the players to adapt to. Entertainment Computing, 41, 100–128. https://doi.org/10.1016/j.entcom.2021.100468
  • Pennington, R., Ault, M. J., Schuster, J., & Sanders, A. (2011). Using simultaneous prompting and computer-assisted instruction to teach story writing to students with autism. Assistive Technology Outcomes and Benefits, 7(1), 24–38.
  • Petretto, D. R., Carta, S. M., Cataudella, S., Masala, I., & Mascia, M. L. (2021). The use of distance learning and e-learning in students with learning disabilities: A review on the effects and some hint of analysis on the use during COVID-19 outbreak. Clinical Practice and Epidemiology in Mental Health: CP & EMH, 17(1), 92–102. https://doi.org/10.2174/1745017902117010092
  • Popov, M., & Ivanova, T. (2020). Knowledge model for developing, searching and using personalized learning content for learners, having dyslexia disability. In Proceedings of the 21st international conference on computer systems and technologies’ 20, Ruse, Bulgaria (pp. 258–265).
  • Porayska-Pomsta, K., Alcorn, A., & Avramides, K. (2018). Blending human and artificial intelligence to support autistic children’s social communication skills. ACM Transactions on Computer-Human Interaction, 25(6), 1–35. https://doi.org/10.1145/3271484
  • Regier, D., Kuhl, E., & Kupfer, D. (2013). The DSM-5: Classification and criteria changes. World Psychiatry, 12(2), 92–98. https://doi.org/10.1002/wps.20050
  • Rocha, T., Nunes, R., Barroso, J., & Martins, P. (2019). Using game-based technology to enhance learning for children with learning disabilities: A pilot study. In Proceedings of the 2019 3rd international conference on education and e-learning, Barcelona, Spain (pp. 89–94).
  • Roldan-Alvarez, D., Martin, E., & Haya, P. (2021). Collaborative video-based learning using tablet computers to teach job skills to students with intellectual disabilities. Education Sciences, 11(8), 437–452. https://doi.org/10.3390/educsci11080437
  • Rosalind, P. (2010). Emotion research by the people, for the people. Emotion Review, 2(3), 250–254. https://doi.org/10.1177/1754073910364256
  • Sehaba, K. (2012). Sharing experiences between learners with different profiles: Adaptation of interaction traces. In 2012 ieee 12th international conference on advanced learning technologies, Rome, Italy (p. 488–492).
  • Serge, G., Cecile, G., & Rosseni, D. (2018). Personalizing learning: A critical review of language learning with mobile phones and social networking sites. Journal of Advanced Research in Dynamical & Control Systems, 10(2), 1782–1786.
  • Shaban, A., & Pearson, E. (2019). A learning design framework to support children with learning disabilities incorporating gamification techniques. In the 2019 chi conference on human factors in computing systems (pp. 1–6). New York, NY, USA: Association for Computing Machinery.
  • Shaban, A., & Pearson, E. (2020). Evaluation of user experience and cognitive load of a gamified cognitive training application for children with learning disabilities. In Proceedings of the 17th international web for all conference (pp. 1–10). New York, NY, USA: Association for Computing Machinery.
  • Shi, Z., Groechel, T. R., Jain, S., Chima, K., Rudovic, O., & Mataric, M. (2022). Toward personalized affect-aware socially assistive robot tutors for long-term interventions with children with autism. ACM Transactions on Human-Robot Interaction (THRI), 11(4), 1–28. https://doi.org/10.1145/3526111
  • Siemens, G. (2019). Learning analytics and open, flexible, and distance learning. Distance Education, 40(3), 414–418. https://doi.org/10.1080/01587919.2019.1656153
  • Siti, I., & Rabiah, A. K. (2011). Edutism: An assistive educational system for the treatment of autism children with intelligent approach. In Visual informatics: Sustaining research and innovations (pp. 193–204). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-25200-6_19
  • Skinner, B. F. (1961). Teaching machines. Scientific American, 205(5), 90–106. https://doi.org/10.1038/scientificamerican1161-90
  • Standen, P., Brown, D., Taheri, M., Galvez Trigo, M., Boulton, H., Burton, A., Hallewell, M. J., Lathe, J. G., Shopland, N., Blanco Gonzalez, M. A., Kwiatkowska, G. M., Milli, E., Cobello, S., Mazzucato, A., Traversi, M., & Hortal, E. (2020). An evaluation of an adaptive learning system based on multimodal affect recognition for learners with intellectual disabilities. British Journal of Educational Technology, 51(5), 1748–1765. https://doi.org/10.1111/bjet.13010
  • Tinto, V. (1998). Colleges as communities: Taking research on student persistence seriously. The Review of Higher Education, 21(2), 167–177. https://doi.org/10.1353/rhe.1998.a30046
  • Torrente, J., Del Blanco, A., Marchiori, E., Moreno-Ger, P., & Fernandez-Manjon, B. (2010). E- adventure: Introducing educational games in the learning process. In Education engineering conference, Madrid, Spain (pp. 121–126). IEEE Education Society.
  • Trausan-Matu, S., Maraschi, D., & Cerri, A. S. (2002). Ontology-centered personalized presentation of knowledge extracted from the web. In S. A. Cerri, G. Gouarderes, & F. Paraguacu (Eds.), International conference on intelligent tutoring systems (pp. 259–269). Springer Berlin Heidelberg.
  • Tsiopela, D., & Jimoyiannis, A. (2014). Pre-vocational skills laboratory: Development and investigation of a web-based environment for students with autism. Procedia Computer Science, 27, 207–217. https://doi.org/10.1016/j.procs.2014.02.024
  • Vallerand, R., Blais, M., Briere, N., & Pelletier, L. (1989). Construction et validation de l’echelle de motivation en education (eme). Canadian Journal of Behavioural Science/Revue Canadienne des Sciences du Comportement, 21(3), 323–245. https://doi.org/10.1037/h0079855
  • VanLehn, K. (2011). The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educational Psychologist, 46(4), 197–221. https://doi.org/10.1080/00461520.2011.611369
  • Vasalou, A., Khaled, R., Holmes, W., & Gooch, D. (2017). Digital games-based learning for children with dyslexia: A social constructivist perspective on engagement and learning during group game-play. Computers and Education, 114, 175–192. https://doi.org/10.1016/j.compedu.2017.06.009
  • Vitomir, K., Srecko, J., Dragan, G., Marek, H., & George, S. (2017). Content analytics: The definition, scope, and an overview of published research. In C. Lang, G. Siemens, A. Wise, & D. Gasevic (Eds.), Handbook of learning analytics (pp. 77–92). Society for Learning Analytics Research.
  • Vullamparthi, A. J., Khargharia, H., & Babu, N. (2011). A smart tutoring aid for the autistic- educational aid for learners on the autism spectrum. In 2011 ieee international conference on technology for education, Chennai, India (pp. 43–50).
  • Vygotsky, L. (1978). Interaction between learning and development. Readings on the Development of Children, 23(3), 34–41.
  • Vygotsky, L. S., & Cole, M. (1978). Mind in society: Development of higher psychological processes. Harvard university press.
  • W3C. (2022). W3c Accessibility Standards Overview. https://www.w3.org/WAI/standards-guidelines. (Retrieved: March 28, 2023)
  • Wills, C. (2014). DSM-5 and neurodevelopmental and other disorders of childhood and adolescence. Journal of the American Academy of Psychiatry and the Law Online, 42(2), 165–172.
  • Worthen, M. (2016). The future of personalized learning for students with disabilities. State Education Standard, 16(3), 35.
  • Yalcin, O. N., Lalle, S., & Conati, C. (2022). An intelligent pedagogical agent to foster computational thinking in open-ended game design activities. In 27th international conference on intelligent user interfaces (pp. 633–645). New York, NY, USA: Association for Computing Machinery.
  • Yeung, G., Afshan, A., Quintero, M., Martin, A., & Spaulding, M. (2019). Towards the development of personalized learning companion robots for early speech and language assessment. In 2019 annual meeting of the american educational research association. Toronto, Canada.
  • Zare, S. (2011). Personalization in mobile learning for people with special needs. In International conference on universal access in human-computer interaction, Orlando, FL, USA (pp. 662–669).
  • Zhang, L., Fu, Q., Swanson, A., Weitlauf, A., Warren, Z., & Sarkar, N. (2018). Design and evaluation of a collaborative virtual environment (comove) for autism spectrum disorder intervention. ACM Transactions on Accessible Computing (TACCESS), 11(2), 1–22. https://doi.org/10.1145/3209687

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