Evaluation and Optimization of Computer Multimedia Assisted Network Teaching Information Ecosystem

At present, the continuous progress in contemporary multimedia innovation on optimization of computer-assisted network teaching with the evaluation of digital learning assets improves students in their cognitive structures by including diverse media aspects that promote learning and memory. The significant challenges in digital multimedia aided information education are corrective maintenance in classroom instructors on the use of audio-visual, poor quality of visuals, and applications that can suddenly lapse into a date. This paper introduces technology in Computer Multimedia-assisted Network Teaching based on Big Data (CMNT-BD) network that considerably enhances the accuracy of the interactive development model. BD-based teaching modes can significantly influence student learning, according to parametric studies. In addition to presenting essential knowledge to learners' better-suited forms of learning, it allows for individualized educational experiences. A design technique for a digital learning system is provided in the theoretical analysis of the education platform with the attributes and purposes of the IoT in multimedia technology. This study compares the Internet of Things (IoT) online course on multimedia technologies to a standard online class as a final step. Analytic teaching resources can be effectively connected and communicated using this strategy, resulting in greater integration and a greater connection between the various teaching materials. This conclusion proposes corresponding suggestions to optimize the teaching information ecosystem in the multimedia environment.