Article Preview
TopIntroduction
In 2017, the “13rd Five-Year Plan for the Development of National Education of China” highlighted that schools should utilize big data technology to collect, analyze, and provide feedback on teaching activities and student behavior to promote personalized learning and targeted teaching (The State Council, 2017). In June 2018, the National Conference on Undergraduate Education of Institutions of Higher Learning in the New Era highlighted the necessity of promoting public sharing of high-quality resources, changing the educational and teaching mode, and capturing the historical opportunities provided by information technology reform. Thus achieving “changing the track and overtaking” the quality of higher education (Ministry of Education, 2018).
In October 2019, the “Implementation Opinions on the Construction of First-class Undergraduate Courses” issued by the Ministry of Education in China stated that it is necessary to integrate modern information technology into teaching activities, resolve the challenges associated with innovation in teaching and learning modes, and eliminate stereotypes and formalization in the ubiquitous application of technology (Ministry of Education, 2019).
Since the corona virus disease 2019 (COVID-19) outbreak in 2020, online teaching activities have ensured that regular teaching activities continue to run smoothly during this period, as they remove the limitations of time and space (Liu & Zhang, 2020). There are, however, several disadvantages of online teaching, such as difficulties in supervision in the classroom, easy visual fatigue for students, inconvenient communication between teachers and students, and easy distraction of the students (Song & Xu, 2020). Later, blended learning, which combines online and offline teaching activities, became the mainstream teaching method. As a result, blended teaching has recently become widely adopted in two ways. The first type of teaching resource requires students to learn it independently, given that the students can learn independently. Consequently, offline teaching activities involve a lot of discussion in the classroom, resulting in an asymmetry in information between the teacher and the student. The second method relies on traditional classroom instruction with online resources utilized as supplementary materials for students to review after class and prepare lessons before class, thus making mixed teaching a mere formality.
As a result, our research examines how to acquire students’ learning performance data in time, adjust online and offline teaching designs, and implement accurate teaching. To fully exploit the advantages of new technology in teaching activities, this paper constructs a precision teaching paradigm and applies it in practice based on big data analysis of students’ learning behaviors.