Abstract
Adaptive learning represents a wide field of application of artificial intelligence technologies. The aim of this concept is to cover the different profiles of learners in order to minimize disorientation and increase the rate of engagement and motivation among them. The adaptation is not a single task that can be done alone or is only limited to the assimilation phase. To this end, we have implemented an intelligent and coherent adaptive learning model that accompanies the learner throughout their learning process. The proposed model consists of four phases: identification of learning styles, proposal of adaptive learning activities, recommendation of assessment activities and the implementation of an adaptive remediation strategy. Each phase of this model is based on artificial intelligence technology while respecting the pedagogical context. In order to measure the effectiveness of the proposed model, we set up a real experiment with learners.
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References
Boussakssou, M., Hssina, B., Erittali, M.: Towards an adaptive e-learning system based on Q-learning algorithm. Procedia Comput. Sci. 170, 1198–1203 (2020)
Kolekar, S.V., Pai, R.M., Manohara Pai, M.M.: Rule based adaptive user interface for adaptive e-learning system. Educ. Inf. Technol. 24, 613–641 (2018).
Ali, M., Hussein, A., Al-Chalabi, H.K.: Pedagogical agents in an adaptive E-learning system. SAR J. – Sci. Res. 3(1), 24–30 (2020)
Shershneva, V., Vainshtein, Y., Kochetkova, T., Esin, R.: Technological approach to development of adaptive e-learning system. SHS Web Conf. 66, 01014 (2019)
Vagale, V., Niedrite, L., Ignatjeva, S.: Implementation of personalized adaptive E-learning system. Baltic J. Mod. Comput. 8(2), 293–310 (2020)
Sweta, S.: Adaptive E-learning system. In: Modern Approach to Educational Data Mining and Its Applications. SAST, pp. 13–24. Springer, Singapore (2021). https://doi.org/10.1007/978-981-33-4681-9_2
El-Sabagh, H.A.: Adaptive e-learning environment based on learning styles and its impact on development students’ engagement. Int. J. Educ. Technol. High. Educ. 18(1), 1–24 (2021). https://doi.org/10.1186/s41239-021-00289-4
Alameen, A., Dhupia, B.: Implementing adaptive e-learning conceptual model: a survey and comparison with open source LMS. Int. J. Emerg. Technol. Learn. (iJET). 14, 28 (2019)
Sumak, B., Podgorelec, V., Karakatic, S., Dolenc, K., Sorgo, A.: Development of an autonomous, intelligent and adaptive e-learning system. In: 2019 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO) (2019)
El Fazazi, H., Elgarej, M., Qbadou, M., Mansouri, K.: Design of an adaptive e-learning system based on multi-agent approach and reinforcement learning. Eng. Technol. Appl. Sci. Res. 11, 6637–6644 (2021)
Chen, L., Chen, P., Lin, Z.: Artificial intelligence in education: a review. IEEE Access. 8, 75264–75278 (2020)
Pushpa, M.: ACO in e-Learning: towards an adaptive learning path. Int. J. Comput. Sci. Eng. 4(3) (2012)
Kachitvichyanukul, V.: Comparison of three evolutionary algorithms: Ga, PSO, and de. Ind. Eng. Manage. Syst. 11, 215–223 (2012)
Shobha, G., Rangaswamy, S.: Machine learning. Handbook of Statistics. 38, 197–228 (2018)
Felder, R.M., Silverman, L.K.: Learning and teaching styles in engineering education. Eng. Educ.78, 674–81(1988)
Astolfi, J.-P.: L’erreur, UN Outil Pour Enseigner, 12edn. Issy-les-Moulineaux, ESF éditeur (2014)
Markowski, M., Yearley, C., Bower, H.: Collaborative learning in practice (CLIP) in a London maternity ward-a qualitative pilot study. Midwifery 111, 103360 (2022)
Lhafra, F.Z., Abdoun, O.: Integration of evolutionary algorithm in an agent-oriented approach for an adaptive e-learning. Int. J. Electr. Comput. Eng. 13, 1964–1978 (2023)
Lhafra, F.Z., Abdoun, O.: Design and implementation of a serious game based on recommender systems for the Learning Assessment Process at primary education level. In: Lecture Notes on Data Engineering and Communications Technologies, pp. 200–210 (2022) https://doi.org/10.1007/978-3-031-15191-0_19
Lhafra, F.Z., Abdoun, O.: Hybrid approach to recommending adaptive remediation activities based on assessment results in an e-learning system using machine learning. In: Advanced Intelligent Systems for Sustainable Development (AI2SD 2020). pp. 679–696 (2022)
Lhafra, F.Z., Abdoun, O.: Adaptive Collaborative Learning Process in a Hybrid Model. In: Lecture Notes on Data Engineering and Communications Technologies. vol 152. Springer, ISBN 978–3–031–20601–6, (2023) https://doi.org/10.1007/978-3-031-20601-6_3
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Lhafra, F.Z., Abdoun, O. (2023). Towards an Adaptive Learning Process Using Artificial Intelligence Technologies. In: Motahhir, S., Bossoufi, B. (eds) Digital Technologies and Applications. ICDTA 2023. Lecture Notes in Networks and Systems, vol 668. Springer, Cham. https://doi.org/10.1007/978-3-031-29857-8_3
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DOI: https://doi.org/10.1007/978-3-031-29857-8_3
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