Enhancing socio-pedagogical engagement in Moodle through AI-driven personalized learning
Keywords:
Artificial Intelligence, Personalized Learning, Socio-Pedagogical Engagement, Moodle, Natural Language Processing, Educational TechnologyAbstract
In the rapidly evolving landscape of digital education, platforms like Moodle have become integral to facilitating learning and fostering student engagement. However, challenges persist in maintaining socio-pedagogical engagement and providing personalized support that caters to the diverse needs of learners. This study introduces an AI-driven system designed to enhance socio-pedagogical engagement within Moodle by delivering personalized messages and recommendations tailored to individual student profiles. Leveraging advanced Artificial Intelligence (AI) and Natural Language Processing (NLP) techniques, the proposed system analyses student interactions, performance metrics, and engagement patterns to generate customized content and communication strategies. The methodology involves the development of AI models trained on a combination of simulated datasets that mirror potential real-world scenarios within educational settings. These models are integrated into the Moodle platform to facilitate dynamic adaptation of learning materials and communication based on individual learner needs. Preliminary evaluations conducted through scenario-based testing indicate a significant potential for the AI-driven system to improve student engagement and learning outcomes. The system demonstrates the ability to identify disengagement early and respond with tailored interventions, thereby fostering a more inclusive and responsive educational environment. Moreover, the personalized recommendations have shown promise in addressing varied learning paces and styles, contributing to a more equitable learning experience. Despite the encouraging preliminary findings, the study acknowledges the limitations posed using fictional data and the need for validation through real-world implementation. Future research will focus on deploying the system within actual educational contexts to assess its efficacy comprehensively. Ethical considerations, particularly concerning data privacy and the handling of sensitive student information, will be paramount in these subsequent phases. This research contributes to the burgeoning discourse on the integration of AI in education, highlighting its potential to revolutionize socio-pedagogical engagement. By tailoring educational experiences to individual learner profiles, AI-driven systems like the one proposed can bridge gaps in engagement and support, paving the way for more personalized and effective digital education. The findings underscore the transformative potential of AI in enhancing educational equity and inclusivity, aligning with the contemporary shift towards learner-centered educational paradigms.References
Outoukarte I, Ben Fares S, Itouni H, et al (2023) Distance learning in the wake of COVID-19 in Morocco. Heliyon 9:e16523. https://doi.org/10.1016/j.heliyon.2023.e16523
Naseer F, Khan MN, Tahir M, et al (2024) Integrating deep learning techniques for personalized learning pathways in higher education. Heliyon 10:e32628. https://doi.org/10.1016/j.heliyon.2024.e32628
Logvinova OK (2016) Socio-pedagogical Approach to Multicultural Education at Preschool. Procedia - Social and Behavioral Sciences 233:206–210. https://doi.org/10.1016/j.sbspro.2016.10.203
Makitan V, Glušac D, Kavalić M, Stanisavljev S (2024) The socio-digital engagement of adolescents and their cognitive—Educational needs a case study: Serbia. Computers and Education Open 6:100170. https://doi.org/10.1016/j.caeo.2024.100170
Kanchon MdKH, Sadman M, Nabila KF, et al (2024) Enhancing personalized learning: AI-driven identification of learning styles and content modification strategies. International Journal of Cognitive Computing in Engineering 5:269–278. https://doi.org/10.1016/j.ijcce.2024.06.002
Koltovskaia S, Rahmati P, Saeli H (2024) Graduate students’ use of ChatGPT for academic text revision: Behavioral, cognitive, and affective engagement. Journal of Second Language Writing 65:101130. https://doi.org/10.1016/j.jslw.2024.101130
Kuronja M, Čagran B, Krajnc MS (2019) Teachers’ sense of efficacy in their work with pupils with learning, emotional and behavioural difficulties. Emotional and Behavioural Difficulties 24:36–49. https://doi.org/10.1080/13632752.2018.1530499
Vásquez-Bermúdez M, Aguirre-Munizaga M, Hidalgo-Larrea J (2023) Analysis of CoI Presence Indicators in a Moodle Forum Using Unsupervised Learning Techniques. In: Valencia-García R, Bucaram-Leverone M, Del Cioppo-Morstadt J, et al (eds) Technologies and Innovation. Springer Nature Switzerland, Cham, pp 27–38
Anggraeni DM, Sole FB (2018) E-Learning Moodle, Media Pembelajaran Fisika Abad 21. Jurnal Penelitian dan Pengkajian Ilmu Pendidikan: e-Saintika 1:57–65. https://doi.org/10.36312/e-saintika.v1i2.101
Rivas A, Fraile JM, Chamoso P, et al (2019) Students Performance Analysis Based on Machine Learning Techniques. In: Uden L, Liberona D, Sanchez G, Rodríguez-González S (eds) Learning Technology for Education Challenges. Springer International Publishing, Cham, pp 428–438
Chang Y-C, Li J-W, Huang D-Y (2022) A Personalized Learning Service Compatible with Moodle E-Learning Management System. Applied Sciences 12:3562. https://doi.org/10.3390/app12073562
Chiu TKF, Xia Q, Zhou X, et al (2023) Systematic literature review on opportunities, challenges, and future research recommendations of artificial intelligence in education. Computers and Education: Artificial Intelligence 4:100118. https://doi.org/10.1016/j.caeai.2022.100118
Bernard J, Popescu E, Graf S (2022) Improving online education through automatic learning style identification using a multi-step architecture with ant colony system and artificial neural networks. Applied Soft Computing 131:109779. https://doi.org/10.1016/j.asoc.2022.109779
Ismail H, Hussein N, Harous S, Khalil A (2023) Survey of Personalized Learning Software Systems: A Taxonomy of Environments, Learning Content, and User Models. Education Sciences 13:741. https://doi.org/10.3390/educsci13070741
OECD (2023) Shaping Digital Education: Enabling Factors for Quality, Equity and Efficiency. Organisation for Economic Co-operation and Development, Paris
Jiang Z, Zhou J (2020) Ethical Considerations and Challenges of AI in Higher Education: Analysis from the Perspective of International Organizations. In: Peters MA, Heraud R (eds) Encyclopedia of Educational Innovation. Springer Nature, Singapore, pp 1–6
Maier U, Klotz C (2022) Personalized feedback in digital learning environments: Classification framework and literature review. Computers and Education: Artificial Intelligence 3:100080. https://doi.org/10.1016/j.caeai.2022.100080
Abulibdeh A, Zaidan E, Abulibdeh R (2024) Navigating the confluence of artificial intelligence and education for sustainable development in the era of industry 4.0: Challenges, opportunities, and ethical dimensions. Journal of Cleaner Production 437:140527. https://doi.org/10.1016/j.jclepro.2023.140527
Rane N (2024) Enhancing the quality of teaching and learning through Gemini, ChatGPT, and similar generative Artificial Intelligence: Challenges, future prospects, and ethical considerations in education. TESOL and Technology Studies 5:1–6. https://doi.org/10.48185/tts.v5i1.1000
Chima Abimbola Eden, Onyebuchi Nneamaka Chisom, Idowu Sulaimon Adeniyi (2024) Integrating AI in education: Opportunities, challenges, and ethical considerations. Magna Sci Adv Res Rev 10:006–013. https://doi.org/10.30574/msarr.2024.10.2.0039
Gligorea I, Cioca M, Oancea R, et al (2023) Adaptive Learning Using Artificial Intelligence in e-Learning: A Literature Review. Education Sciences 13:1216. https://doi.org/10.3390/educsci13121216
Published
Issue
Section
License
Copyright (c) 2024 Aammou Souhaib, Tagdimi Zakaria, Touis Tarik
This work is licensed under a Creative Commons Attribution 4.0 International License.
The BRAJETS follows the policy for Open Access Journals, provides immediate and free access to its content, following the principle that making scientific knowledge freely available to the public supports a greater global exchange of knowledge and provides more international democratization of knowledge. Therefore, no fees apply, whether for submission, evaluation, publication, viewing or downloading of articles. In this sense, the authors who publish in this journal agree with the following terms: A) The authors retain the copyright and grant the journal the right to first publication, with the work simultaneously licensed under the Creative Commons Attribution License (CC BY), allowing the sharing of the work with recognition of the authorship of the work and initial publication in this journal. B) Authors are authorized to distribute non-exclusively the version of the work published in this journal (eg, publish in the institutional and non-institutional repository, as well as a book chapter), with acknowledgment of authorship and initial publication in this journal. C) Authors are encouraged to publish and distribute their work online (eg, online repositories or on their personal page), as well as to increase the impact and citation of the published work.