RETRACTED: Interactive teaching using human-machine interaction for higher education systems

计算机科学 异步通信 软件部署 班级(哲学) 高等教育 多媒体 远程教育 数学教育 心理学 人工智能 政治学 计算机网络 操作系统 法学
作者
Huipeng Shang,C. B. Sivaparthipan,ThanjaiVadivel
出处
期刊:Computers & Electrical Engineering [Elsevier]
卷期号:100: 107811-107811 被引量:40
标识
DOI:10.1016/j.compeleceng.2022.107811
摘要

Advances in Interactive Teaching Methodology (ITM) quickly transformshigher education and learning where ITM incorporates web-based face-to-face instruction. IfITM policies are expanding, teachers, lecturers, and administrators must discuss the theoretical foundations of ITM Research. The intellectually enhanced ability makesmore learning choices in some adverse circumstances.Using methods such as hands-on demonstrations, audio-visual aids, and regular teacher-student interaction, teachers actively involve their students in their learning through interactive Teaching. Students are constantly urged to take an active role in class discussions.This paper examines the Interactive Teaching Framework using Human-Machine Interaction (ITF-HMI) for online education Higher Education Systems.In the literature survey, the critical hypotheses used in prior research are the ad hoc models for embracing technologies, the performance studies of knowledge systems, the unified ideology of ITM deployment, the utilization of technology, and the propagation of progress theories. In higher education institutions' instructional phase, sophisticated information and communication technologies with virtual technologies allow an integrated collaboration. This framework discusses how the MOOC platform allows organizing e-learning, taking electronic classroom classes, following online courses, and carrying out synchronous and asynchronous learning. The online tasks created permitted the students to track their progress in all educational activities. The experiment findings helped direct a follow-up analysis in this area with a high acceptance rate amongst the participants. The simulation result of the proposed method enhances performance analysis (98.27%), prediction analysis (97.45%), accuracy analysis (96.27%), resiliency analysis (94.9%), efficiency analysis (98.8%).
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