Scholarlink

<p>In the modern era, the development of artificial intelligence and machine learning is improving education by collecting, analysing, and correcting every interaction that occurs in both real and virtual classrooms and enabling teachers to address each student’s unique pain points. However, assessing students’ progress and customizing education present serious difficulties with conventional approaches. In order to enhance e-learning design, the artificial intelligence-driven effective teaching–learning model (AID-ETLM) has been proposed in this study. Additionally, this study makes use of ensemble methods with deep neural networks (DNN) to forecast student performance, learner learning style, and the teacher-student learning process. To increase the robustness of the student and instructor network without compromising performance, two key objectives concerning prediction scores and gradients of instances are defined. When compared to other current approaches, the experimental results demonstrate that the suggested AID-ETLM method improves the students’ account, satisfaction, and interaction.&#xa0;</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Artificial Intelligence-Based an Efficient Methodology for Organizing the Process of Teaching and Learning

  • 期刊论文

  • 发表日期：

    2026年7月31日

查看全文
  • Nashwan Adnan Othman

摘要

In the modern era, the development of artificial intelligence and machine learning is improving education by collecting, analysing, and correcting every interaction that occurs in both real and virtual classrooms and enabling teachers to address each student’s unique pain points. However, assessing students’ progress and customizing education present serious difficulties with conventional approaches. In order to enhance e-learning design, the artificial intelligence-driven effective teaching–learning model (AID-ETLM) has been proposed in this study. Additionally, this study makes use of ensemble methods with deep neural networks (DNN) to forecast student performance, learner learning style, and the teacher-student learning process. To increase the robustness of the student and instructor network without compromising performance, two key objectives concerning prediction scores and gradients of instances are defined. When compared to other current approaches, the experimental results demonstrate that the suggested AID-ETLM method improves the students’ account, satisfaction, and interaction.