An effective analysis of online education model based on artificial intelligence

Abstract

With the development of artificial intelligence in education, online education has been recognized by the society as a new teaching method. It can make full use of the advantages of the network across regions, and make full use of the advantages of network technology to share the resources of colleges and universities, which is a promising educational method. In response to the demand of online education for learner information, this paper proposes the learner model Neighbor Mean Variation Multi-Objective Particle Swarm Optimization-Genetic Algorithm (NMVMOPSO-GA). This model includes the learner’s learning interest sub-model, the learner’s cognitive ability sub-model and the learner’s knowledge sub-model. The modelling techniques of the three sub-models are discussed separately, and their status and role in the online education system are analyzed. At the same time, for the knowledge model that reflects the learner’s learning progress and knowledge mastery, a learner knowledge sub-model constructed with Bayesian networks is proposed. The neighbor mean mutation operator is introduced to optimize the multi-objective particle swarm optimization algorithm and improve the convergence performance and stability of the multi-objective particle swarm optimization algorithm. We study the application of multi-objective particle swarm optimization algorithm in online course resource generation service. Through simulation experiments, it is verified that the multi-objective particle swarm optimization algorithm can improve the performance and stability of online course resource generation.

Exploring Artificial Intelligence Architecture in Data Cleaning Based on Bayesian Networks

“…Thus, the occurrence of random events A and B can be calculated. The causal event is for event A and event B, and the event's P ðAjBÞ indicates the probability that incident A will occur in event B. Co-probability: The combination can be represented by P ðA, BÞ or P ðABÞ [10], the probability that event A will occur, such as event B. Edge event: The edge result is the result of a single event A or only B, which can be expressed as P ðAÞ and P ðBÞ.…”

Analysis of Factors Influencing the Use of Chinese Online Education Platform: A DEMATEL-ISM Based Approach

“…proposed that various paradigms in the field of online education under the background of big data continue to emerge and have achieved very good results [15]. Peng et al believe that to make full use of the current network technology, network education as a new education method is gradually recognized by the society [7]. Jia et al proposed that with the popularization of 5G and cloud computing technology, online education has ushered in another wave of development [4].…”

“…As the result element. The calculation of centrality M i and cause degree N i is shown in formulas ( 6) and (7).…”

English Multimode Production and Usage by Artificial Intelligence and Online Reading for Sustaining Effectiveness

“…The research covers these problems and concentrates further issues of web intelligence like intelligent web services, semantic markup, and web mining, along with recommendations on how to use novel attempting-based techniques and study testing issues in artificial intelligence in education. Peng [34] has proposed that the critical role of artificial intelligence in virtual teaching has been documented via the public as a novel training technique. The request of virtual instruction for student statistics, learners' prototype neighbor mean variation multiobjective particle swarm optimization-genetic algorithm is recommended.…”

“…The collected work speaks numerous significant theories of dispersed AI. An exposed information system is a put forward resolving the issue of matching the usage of two publications Title of the paper Year [3] AI-based English multimodal online reading [4] Open information systems semantics [5] AI review [6] AI-based conversational agents for chronic conditions [7] AI for education [8] AI applications in higher education [9] AI-based survey on lip-reading techniques [10] AI for in virtual learning environments [11] AI chatbots for improving English grammar skills [12] Multimodal technologies in precision education [13] English teaching practice based on AI [14] Beyond traditional literacies [15] Multimodal learning analytics [16] AI innovation in education [17] ESL learners in online reading [18] AI pronunciation through sound processing technology [19] Multimodal intelligence [20] Inclusive education in the age of AI [21] Review of AI in education [22] AI in education from 2010 to 2020 [23] Students' well-being by their readiness for the AI [24] AI and multimodal data in the service of human decision-making [25] Eye-tracking and AI to enhance motivation and learning [26] AI to higher vocational English teaching [27] Blind user and the tyflos assistive prototype [28] AI, smart classrooms, and online education [29] 21st-century skills and the fourth industrial revolution [30] AI and the academy's loss of purpose [31] Academic performance prediction using AI [32] Impact of AI on teaching and learning in higher education [33] Web intelligence and AI in education [34] Analysis of online education based on AI [35] My teacher is a machine…”