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Online education uses a variety of information technology strategies, including big data, multimedia, and artificial intelligence, to carry out distance education on an internet platform (Yang et al., 2010; Bahmani et al., 2012). Some studies have examined how two-tier online community learning and heterogeneous teams influence the knowledge performance of online work community organizations in the presence and absence of leader forgetting (Wu et al., 2021). The development of online education has not only injected new power into traditional education but has also brought movement toward educational reform and development.
In recent years, various computer-related technologies have been developed, and the internet era has also transformed into a big data era. In particular, the emergence of multimedia has triggered a revolution in education models. Online education has flourished, in general. It breaks the traditional education model and realizes leapfrog teaching on the internet in different periods (Yu et al., 2018; Min, 2022; Wang et al., 2019a; Zhang et al., 2017). There is much useful information on many social networking sites for consumers to compare products. Sentiment analysis is considered suitable for summarizing opinions (Ng et al., 2021). The same user can learn in different places and at different times, and this has gradually become the mainstream means of education.
The number of online education users in China is growing rapidly. The establishment of open universities has enabled millions of adults to continue their education. Simultaneously, the emergence of many learning websites has provided new opportunities for users who want to continue improving themselves (Dwivedi et al., 2018; Lin et al., 2021; Wan & Niu, 2016; Ni & Ni, 2020). Prior studies have proposed dynamic measurements and evaluation frameworks for hotel customer satisfaction through sentiment analysis of online reviews (Gang & Chenglin, 2021). When learning a topic, most of the corresponding learning resources can be found on the internet, and many can be used freely. With the rapid increase in the number of online users and course resources, online education platforms are generating much browsing data every day, which increases the platform load. To enable online users to accurately find the course resources they need, the course recommendation system was created. Course recommendation systems can provide users with course resources that meet both their characteristics and their interests and preferences. Therefore, the course recommendation system has become a new and mainstream form of personalized information service and (Liu et al., 2019).
Internet of Things (IoT) technology solves the limitations of space and distance in traditional education and helps realize the openness and sharing of educational resources (Wang, 2015; Bahmani et al., 2011; Duan, 2019). The high-quality educational resources owned by educational institutions are no longer limited to the scope of their activities but can be widely disseminated around the world through the network. As the storage locations and learning methods of educational resources have changed, learners can freely choose learning content by breaking through the restrictions brought about by time and place through online teaching (Imran et al., 2016). This change in learning style causes learners to change from traditional passive learning to active learning. Learners can choose the learning content and grasp the learning rhythm freely according to their own needs. At the same time, it also makes learners’ abilities more diversified (Gan & Zhang, 2020; Li et al., 2022; Xu et al., 2016).