Résumés
Abstract
Emerging technologies are enabling adaptive learning systems to develop. This specific system consists of several models, including a learner model, domain knowledge model, instructional model, learning analytics model, and adaptive engine model. This paper reviewed multiple studies and highlighted the importance of refining each model in the context of creating a conceptual framework. We also proposed a metacognitive auxiliary model and an adaptive assessment model. The objective is to advance research into logical transitions in the internal structure of an adaptive learning ecosystem through the interpretation of different approaches, technologies, and solutions that facilitate the decision-making processes.
Keywords:
- Ecosystem,
- adaptive learning,
- didactics,
- evaluation
Résumé
Les technologies émergentes permettent l’évolution des systèmes d’apprentissage adaptatifs. Ce système spécifique se compose de plusieurs modèles, notamment un modèle d’apprenant, un modèle de connaissance du domaine, un modèle d’enseignement, un modèle d’analyse d’apprentissage et un modèle de moteur adaptatif. Cet article recense plusieurs études et souligne l’importance d’affiner chaque modèle dans l’optique de la création d’un cadre conceptuel. Nous proposons également un modèle auxiliaire métacognitif et un modèle d’évaluation adaptatif. L’objectif est de faire progresser la recherche de transitions logiques dans la structure interne d’un écosystème d’apprentissage adaptatif grâce à l’interprétation de ces approches, de ces technologies et de ces solutions qui facilitent les processus décisionnels.
Mots-clés :
- Écosystème,
- apprentissage adaptatif,
- didactique,
- évaluation
Parties annexes
References
- Alam, A. (2023). Cloud-based E-learning: Scaffolding the environment for adaptive e-learning ecosystem based on cloud computing infrastructure. In S. C. Satapathy, J. C.-W. Lin, L. K. Wee, V. Bhateja, & T. M. Rajesh (Eds), Computer communication, networking and IoT (Lecture notes in networks and systems, Vol 459, pp. 1‑9). Springer. https://doi.org/kpxg
- Alam, A., & Mohanty, A. (2022). Metaverse and posthuman animated avatars for teaching-learning process: Interperception in virtual universe for educational transformation. In M. Panda, S. Dehuri, M. R. Patra, P. K. Behera, G. A. Tsihrintzis, S.-B. Cho, & C. A. Coello Coello (Eds.), Innovations in intelligent computing and communication – ICIICC 2022 (Communications in computer and information science, Vol. 1737, pp. 47‑61). Springer. https://doi.org/kpxh
- Anisimova, T. I., Sabirova, F. M., & Shatunova, O. V. (2020). Formation of design and research competencies in future teachers in the framework of STEAM education. International Journal of Emerging Technologies in Learning (IJET), 15(2), 204‑217. https://doi.org/10.3991/ijet.v15i02.11537
- 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). https://doi.org/10.3390/su14116442
- Aprilisa, E. (2020). Realizing Society 5.0 to face the Industrial Revolution 4.0 and teacher education curriculum readiness in Indonesia. Proceeding International Conference on Science and Engineering, 3, 543‑548. https://doi.org/10.14421/icse.v3.559
- Berding, F., Slopinski, A., Frerichs, R., & Rebmann, K. (2021). Opportunities for adaptive learning environments to promote sustainability-oriented innovation competence in vocational education and training. Journal of Sustainable Development, 14(2), 96‑110. https://doi.org/10.5539/jsd.v14n2p96
- Bozkurt, A., Karakaya, K., Turk, M., Karakaya, Ö., & Castellanos-Reyes, D. (2022). The impact of COVID-19 on education: A meta-narrative review. TechTrends, 66(5), 883‑896. https://doi.org/grk6cb
- Brandt, P. A. (2020). Cognitive semiotics: Signs, mind, and meaning. Bloomsbury.
- Brühwiler, C., & Vogt, F. (2020). Adaptive teaching competency: Effects on quality of instruction and learning outcomes. Journal for Educational Research Online, 12(1), 119‑142. http://waxmann.com/artikelART103914
- Brusilovsky, P., Sosnovsky, S., & Thaker, K. (2022). The return of intelligent textbooks. AI Magazine, 43(3), 337‑340. https://doi.org/10.1002/aaai.12061
- Bull, S. (2020). There are open learner models about! IEEE Transactions on Learning Technologies, 13(2), 425‑448. https://doi.org/10.1109/TLT.2020.2978473
- Capuano, N., & Caballé, S. (2020). Adaptive learning technologies. AI Magazine, 41(2), 96‑98. https://doi.org/10.1609/aimag.v41i2.5317
- Carayannis, E. G., & Morawska-Jancelewicz, J. (2022). The futures of Europe: Society 5.0 and Industry 5.0 as driving forces of future universities. Journal of the Knowledge Economy, 13(4), 3445‑3471. https://doi.org/kpxj
- Carlon, M. K. J., & Cross, J. S. (2022). Knowledge tracing for adaptive learning in a metacognitive tutor. Open Education Studies, 4(1), 206‑224. https://doi.org/kpzj
- Clemente, J., Yago, H., de Pedro-Carracedo, J., & Bueno, J. (2022). A proposal for an adaptive recommender system based on competences and ontologies. Expert Systems with Applications, 208, Article 118‑171. https://doi.org/10.1016/j.eswa.2022.118171
- Corbett, F., & Spinello, E. (2020). Connectivism and leadership: Harnessing a learning theory for the digital age to redefine leadership in the twenty-first century. Heliyon, 6(1), Article e03250. https://doi.org/10.1016/j.heliyon.2020.e03250
- Darmaji, D., Mustiningsih, M., & Arifin, I. (2019). Quality management education in the Industrial Revolution Era 4.0 and Society 5.0. In A. V. Valdes et al. (Eds.), Proceedings of the 5th International Conference on Education and Technology (ICET 2019) (pp. 565‑570). Atlantis Press. https://doi.org/10.2991/icet-19.2019.141
- Edwards, J., & Kaimal, G. (2016). Using meta-synthesis to support application of qualitative methods findings in practice: A discussion of meta-ethnography, narrative synthesis, and critical interpretive synthesis. The Arts in Psychotherapy, 51, 30‑35. https://doi.org/10.1016/j.aip.2016.07.003
- El Guabassi, I., Bousalem, Z., Al Achhab, M., Jellouli, I., & EL Mohajir, B. E. (2018). Personalized adaptive content system for context-aware ubiquitous learning. Procedia Computer Science, 127, 444‑453. https://doi.org/10.1016/j.procs.2018.01.142
- Fuller, A., Fan, Z., Day, C., & Barlow, C. (2020). Digital twin: Enabling technologies, challenges and open research. IEEE Access, 8, 108952‑108971. https://doi.org/ggzg9m
- Grugeon-Allys, B., Chenevotot-Quentin, F., & Pilet, J. (2022). Using didactic models to design adaptive pathways to meet students’ learning needs in an online learning environment. In P. R. Richard, M. P. Vélez, & S. Van Vaerenbergh (Eds.), Mathematics education in the age of artificial intelligence: How artificial intelligence can serve mathematical human learning (pp. 141‑165). Springer. https://doi.org/kpxm
- Guerra Hollstein, J. D. (2018). Open learner models for self-regulated learning: Exploring the effects of social comparison and granularity [Doctoral dissertation, University of Pittsburgh, USA]. D-Scohlarship. http://d-scholarship.pitt.edu/33626
- Hooshyar, D., Pedaste, M., Saks, K., Leijen, Ä., Bardone, E., & Wang, M. (2020). Open learner models in supporting self-regulated learning in higher education: A systematic literature review. Computers & Education, 154, Article 103878. https://doi.org/10.1016/j.compedu.2020.103878
- Kabir, K. S., Kenfield, S. A., Van Blarigan, E. L., Chan, J. M., & Wiese, J. (2022). Ask the users: A case study of leveraging user-centered design for designing just-in-time adaptive interventions (JITAIs). In S. Santini (Ed.), Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (Vol. 6, No 2, article 59). https://doi.org/kpxn
- Kabudi, T., Pappas, I., & Olsen, D. H. (2021). AI-enabled adaptive learning systems: A systematic mapping of the literature. Computers and Education: Artificial Intelligence, 2, Article 100017. https://doi.org/10.1016/j.caeai.2021.100017
- Kinsner, W. (2021). Digital twins for personalized education and lifelong learning. In Proceedings of the 2021 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE). https://doi.org/gr9w7v
- Lamya, A., Mohamed, K., & Mohamed, E. (2022). Personalization between pedagogy and adaptive hypermedia system. In M. Lazaar, C. Duvallet, A. Touhafi, & M. Al Achhab (Eds.), Proceedings of the 5th International Conference on Big Data and Internet of Things (pp. 223‑234). Springer. https://doi.org/kpxp
- Lhafra, F. Z., & Abdoun, O. (2023). Adaptive collaborative learning process in a hybrid model. In A. E. Hassanien, V. Snášel, M. Tang, T.-W. Sung, & K.-C. Chang (Eds.), Proceedings of the 8th International Conference on Advanced Intelligent Systems and Informatics 2022 (pp. 26‑38). Springer. https://doi.org/kpxq
- Lim, L., Bannert, M., van der Graaf, J., Singh, S., Fan, Y., Surendrannair, S., Rakovic, M., Molenaar, I., Moore, J., & Gašević, D. (2023). Effects of real-time analytics-based personalized scaffolds on students’ self-regulated learning. Computers in Human Behavior, 139, Article 107547. https://doi.org/10.1016/j.chb.2022.107547
- Lindsay, A., & Petrick, R. P. A. (2022, June). Incremental domain model acquisition with a human in the loop [Paper presentation]. 32nd International Conference on Automated Planning and Scheduling – Workshop on Knowledge Engineering for Planning and Scheduling. http://icaps22.icaps-conference.org/...
- Martin, F., Chen, Y., Moore, R. L., & Westine, C. D. (2020). Systematic review of adaptive learning research designs, context, strategies, and technologies from 2009 to 2018. Educational Technology Research and Development, 68(4), 1903‑1929. https://doi.org/gmwqjf
- Mayrhuber, E., & Krauss, O. (2022). User profile-based recommendation engine mitigating the cold-start problem. In Proceedings of the 2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME). https://doi.org/kpzc
- Mihai, S., Yaqoob, M., Hung, D. V., Davis, W., Towakel, P., Raza, M., Karamanoglu, M., Barn, B., Shetve, D., Prasad, R. V., Venkataraman, H., Trestian, R., & Nguyen, H. X. (2022). Digital twins: A survey on enabling technologies, challenges, trends and future prospects. IEEE Communications Surveys & Tutorials, 24(4), 2255‑2291. https://doi.org/grhz4s
- Nguyen, T.-M., Quach, S., & Thaichon, P. (2022). The effect of AI quality on customer experience and brand relationship. Journal of Consumer Behaviour, 21(3), 481‑493. https://doi.org/10.1002/cb.1974
- Park, E., Ifenthaler, D., & Clariana, R. B. (2023). Adaptive or adapted to: Sequence and reflexive thematic analysis to understand learners’ self-regulated learning in an adaptive learning analytics dashboard. British Journal of Educational Technology, 54(1), 98‑125. https://doi.org/10.1111/bjet.13287
- Pokhrel, S., & Chhetri, R. (2021). A literature review on impact of COVID-19 pandemic on teaching and learning. Higher Education for the Future, 8(1), 133‑141. https://doi.org/gfgv
- Polat, L., & Erkollar, A. (2021). Industry 4.0 vs. Society 5.0. In N. M. Durakbasa & M. G. Gençyılmaz (Eds.), Digital conversion on the way to Industry 4.0 – Selected papers from ISPR2020 (pp. 333‑345). Springer. https://doi.org/kpzd
- Rahim, M. N. (2021). Post-pandemic of Covid-19 and the need for transforming Education 5.0 in Afghanistan higher education. Utamax: Journal of Ultimate Research and Trends in Education, 3(1), 29‑39. https://doi.org/10.31849/utamax.v3i1.6166
- Raj, N. S., & Renumol, V. G. (2022). A systematic literature review on adaptive content recommenders in personalized learning environments from 2015 to 2020. Journal of Computers in Education, 9(1), 113‑148. https://doi.org/gpxbfw
- Ramírez-Mera, U., & Tur, G. (2023). Metacognitive skills and emotions in the construction of personal learning environments. Revista de Educación a Distancia (RED), 23(71). https://doi.org/10.6018/red.526831
- Saadati, Z., & Barenji, R. V. (2023). Toward Industry 5.0: Cognitive cyber-physical system. In A. Azizi & R. V. Barenji (Eds.), Industry 4.0: Technologies, applications, and challenges (pp. 257‑268). Springer. https://doi.org/kpzf
- Sakkinah, I. S., Hartanto, R., & Permanasari, A. E. (2022). Hypermedia learning environment development to enhance self-regulated learning based on self-monitoring skills. Journal Nasional Teknik Elektro dan Teknologi Informasi, 11(2), 97‑104. https://doi.org/10.22146/jnteti.v11i2.3480
- Sarıyalçınkaya, A. D., Karal, H., Altinay, F., & Altinay, Z. (2021). Reflections on adaptive learning analytics: Adaptive learning analytics. In A. Azevedo, J. M. Azevedo, J. O. Uhomoibhi, & E. Ossiannilsson (Eds), Advancing the power of learning analytics and Big Data in education (p. 61‑84). IGI Global. https://doi.org/10.4018/978-1-7998-7103-3.ch003
- Shawky, D., & Badawi, A. (2018). A reinforcement learning-based adaptive learning system. In A. E. Hassanien, M. F. Tolba, M. Elhoseny, & M. Mostafa (Eds.), Proceedings of the International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2018) (pp. 221‑231). Springer. https://doi.org/10.1007/978-3-319-74690-6_22
- Siemens, G. (2005). Connectivism: A learning theory for the digital age. Instructional Technology & Distance Learning, 2(1). https://itdl.org/...
- Sobocinski, M., Malmberg, J., & Järvelä, S. (2022). Exploring adaptation in socially-shared regulation of learning using video and heart rate data. Technology, Knowledge and Learning, 27(2), 385‑404. https://doi.org/gkq6r3
- Somyürek, S., Brusilovsky, P., & Guerra, J. (2020). Supporting knowledge monitoring ability: Open learner modeling vs. open social learner modeling. Research and Practice in Technology Enhanced Learning, 15, Article 17. https://doi.org/gpkm5z
- Sun, J., Tian, Z., Fu, Y., Geng, J., & Liu, C. (2021). Digital twins in human understanding: A deep learning-based method to recognize personality traits. International Journal of Computer Integrated Manufacturing, 34(78), 860‑873. https://doi.org/gmghsp
- Van der Graaf, L., Dunajeva, J., Siarova, H., & Bankauskaite, R. (2021). Research for CULT Committee – Education and youth in post-COVID-19 Europe – Crisis effects and policy recommendations. European Parliamentary Research Service (EPRS). http://europarl.europa.eu/...
- Yilma, B. A., Panetto, H., & Naudet, Y. (2021). Systemic formalisation of Cyber-Physical-Social System (CPSS): A systematic literature review. Computers in Industry, 129, Article 103458. https://doi.org/10.1016/j.compind.2021.103458
- Zargane, K., Erradi, M., & Khaldi, M. (2023). Design and implementation of collaborative pedagogical scenarios for adaptive learning. In M. Khaldi (Ed.), Handbook of research on scripting, media coverage, and implementation of e-learning training in LMS platforms (pp. 242‑250). IGI Global. https://doi.org/10.4018/978-1-6684-7634-5.ch010
- Zhang, N., Bahsoon, R., & Theodoropoulos, G. (2020). Towards engineering cognitive digital twins with self-awareness. In Proceedings of the 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (p. 38‑91). https://doi.org/grvgpg