E-Schoolbag is an integrated platform presented on the portable digital devices for educational purposes. Successful and sustainable integration of e-Schoolbag into the classroom often demands teachers to be equipped with essential knowledge, skills, and willingness to employ it on a voluntary basis. The purpose of this study was to examine if teacher’s TPACK abilities could predict their inclination to use e-Schoolbag. A total of 1185 teachers from 45 primary and secondary schools in China participated in this study. A structural equation model was established to examine if and how the TPACK framework might affect TAM. Results showed that the teachers’ TPACK level had a large impact on their acceptance of e-Schoolbag through perceived usefulness (PU) and ease of use (EOU). Specifically, the technological knowledge (TK) component was more significantly correlated with the e-Schoolbag acceptance than Pedagogical knowledge (PK) and content knowledge (CK). The path coefficient value further showed that TPACK affected the EOU more than the PU.

The rapid development of artificial intelligence has driven the transformation of educational evaluation into big data-driven. This study used a systematic literature review method to analyzed 44 empirical research articles on the evaluation of big data education. Firstly, it has shown an increasing trend year by year, and is mainly published in thematic journals such as educational technology, science education, and language teaching. Chinese and American researchers have made the greatest contributions in this field. Secondly, the algorithmic models for big data education evaluation research are diverse, the text modality is the most popular, the evaluation subjects are mainly college students, with fewer primary and secondary school students, and science is the discipline that most commonly applies big data education evaluation. The evaluation objectives of big data education evaluation mainly focus on five aspects: high-order thinking analysis, learning performance prediction, learning emotion recognition, teaching management decision-making, and evaluation mode optimization, and the text modality is widely used for data collection in high-order thinking analysis; regardless of the evaluation objectives, higher education students are the most widely evaluated objects; the science discipline is the main field of using big data technology to empower teaching evaluation. Thirdly, the current research topics of big data education evaluation mainly focus on online learning behavior and environmental participation evaluation, process assessment of learning motivation and emotional analysis, development and optimization of subject domain big data models, cognitive diagnosis and high-order thinking skills evaluation, and design of learning analysis frameworks based on data mining.