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Realizing the organic combination of scale and personalization is the main task in the implementation of China’s Education Modernization 2035. In the post-pandemic era, online courses have become an effective supplementary form of large-scale education and teaching. However, online courses have not solved the “temperature” problem between teachers and students. That is, teachers cannot analyse and accurately intervene in learners’ emotions in real time. Thus, students’ learning emotions are not addressed in a timely, effective manner. This, therefore, negatively impacts learning performance, learning perception, and high-level thinking ability (Zhu et al., 2022).
It is also a challenge to understand effective personalized emotional analysis in online education. The ministry of education, along with six other departments, stressed that the construction of online learning spaces and platforms should be supported by artificial intelligence (AI) to build a high-quality education system. Future large-scale education initiatives must identify ways to implement intelligent technologies to promote the teaching of online courses. For both theoretical and practical reasons, we must optimize existing emotion-computing technologies, carry out effective emotion recognition, and adapt to online education with new needs.
Due to their low information literacy and poor self-discipline, primary school students often need the assistance of their parents in an e-learning (home) environment. Both students and parents must, therefore, commit to e-learning resources (Drossel et al., 2020). As a stakeholder group in the promotion of basic education informatization, parents’ attitudes toward their children’s informatization learning is a factor that affects the smooth development of school informatization teaching (Su, 2021). During the COVID-19 pandemic, primary school students attended at-home schools, providing extensive characteristics and participation for review (Zheng et al., 2021). Researchers were then able to explore and understand parents’ attitudes toward e-learning (Cahyadi et al., 2021).
This article combines emotion recognition and the live classroom, taking the online live classroom as the research carrier to map the relationship between expression and emotion, the relationship between emotion and learning state, and the individual learning state and overall learning state in online live classroom learning (Cheng et al., 2020; Lim & Jung, 2019). This information provides relevant theoretical and technical support for improving the teaching effect of the online live classroom (Tian & Tang, 2022).
This article uses the live information learning system model with feedback function to analyse students’ emotions (attitudes) under the background of accepting large-scale online learning and multimedia technology. In addition, 3,793 parents of primary school students were given questionnaires and in-depth interviews. First, it aimed to find out whether large-scale online learning during the pandemic impacted parents’ attitudes toward their children’s e-learning. Second, it studied whether parents’ gender, age, educational background, and e-learning experience affected their attitudes toward their children’s e-learning.