Mohamedhen Ahmed Salem, Alfazi Abdullah, Arfaoui Nouha, Ejbali Ridha, Nanne Mohamedade Farouk
Department Mathematics and Computer Science, Faculty of Science and Technology, University of Nouakchott, Nouakchott, Mauritania.
Department of Computer Science, College of Computer Engineering and Sciences in Al-Kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia.
Heliyon. 2024 Oct 11;10(20):e39088. doi: 10.1016/j.heliyon.2024.e39088. eCollection 2024 Oct 30.
The rapid increase of online educational content has made it harder for students to find specific information. E-learning recommender systems help students easily find the learning objects they require, improving the learning experience. The effectiveness of these systems is further improved by integrating deep learning with multi-agent systems. Multi-agent systems facilitate adaptable interactions within the system's various parts, and deep learning processes extensive data to understand learners' preferences. This collaboration results in custom-made suggestions that cater to individual learners. Our research introduces a multi-agent system tailored for suggesting learning objects in line with learners' knowledge levels and learning styles. This system uniquely comprises four agents: the learner agent, the tutor agent, the learning object agent, and the recommendation agent. It applies the Felder and Silverman model to pinpoint various student learning styles and organizes educational content based on the newest IEEE Learning Object Metadata standard. The system uses advanced techniques, such as Convolutional Neural Networks (CNN) and Multilayer Perceptrons (MLP), to propose learning objects. In terms of creating personalized learning experiences, this system is a considerable step forward. It effectively suggests learning objects that closely match each learner's personal profile, greatly enhancing student engagement and making the learning process more efficient.
在线教育内容的迅速增加使得学生更难找到特定信息。电子学习推荐系统帮助学生轻松找到他们所需的学习对象，提升学习体验。通过将深度学习与多智能体系统相结合，这些系统的有效性得到进一步提高。多智能体系统促进系统各部分之间的适应性交互，而深度学习处理大量数据以了解学习者的偏好。这种协作产生了针对个体学习者的定制化建议。我们的研究引入了一种多智能体系统，该系统专为根据学习者的知识水平和学习风格推荐学习对象而量身定制。该系统独特地由四个智能体组成：学习者智能体、导师智能体、学习对象智能体和推荐智能体。它应用费尔德和西尔弗曼模型来确定学生的各种学习风格，并根据最新的IEEE学习对象元数据标准组织教育内容。该系统使用卷积神经网络（CNN）和多层感知器（MLP）等先进技术来推荐学习对象。在创造个性化学习体验方面，这个系统向前迈出了重要一步。它有效地推荐与每个学习者个人资料紧密匹配的学习对象，极大地提高了学生的参与度，并使学习过程更加高效。
