Classroom research is an important means of improving teaching quality. The current classroom research methods mainly include traditional classroom research based on human intelligence and automatic analysis classroom research based on machine intelligence, but these two methods are insufficient. Although the classroom research method based on human-machine hybrid intelligence can solve these problems to a certain extent, there are still certain limitations. Based on this, this paper firstly proposed a digital transformation theory of classroom research based on human-machine hybrid intelligence, which mainly included the following five parts of audio and video collection, cutting and storage based on human-machine hybrid intelligence, automatic voice recognition based on machine intelligence, classroom log generation based on human intelligence, generation of digital materials for classroom research based on human-machine hybrid intelligence, classroom research based on human intelligence. Then, the paper carried out the application studies on digital transformation of classroom research have been conducted for 16 demonstration lessons of Chinese language teaching in middle schools. It was found that the digital transformation theory of classroom research proposed in this paper can quickly generate numerous digital materials of classroom research, with the advantages of multi-functionality, efficiency, and flexibility, which met the needs of daily classroom research. Through research, this paper was aimed to provide referable implementation framework for the digital transformation of classroom research.

With technology scaling into nanometer regime, rampant process variations impact visible influences on leakage power estimation of very large scale integrations (VLSIs). In order to deal with the case of large inter- and intra-die variations, we induce a novel theory prototype of the statistical leakage power analysis (SLPA) for function blocks. Because inter-die variations can be pinned down into a small range but the number of gates in function blocks is large(>1000), we continue to simplify the prototype. At last, we induce the efficient methodology of SLPA. The method can save much running time for SLPA in the low power design since it is of the local-updating advantage. A large number of experimental data show that the method only takes feasible running time (0.32 s) to obtain accurate results (3 σ -error <0.5% on maximum) as function block circuits simultaneous suffer from 7.5%(3 σ /mean) inter-die and 7.5% intra-die length variations, which demonstrates that our method is suitable for statistical leakage power analysis of VLSIs under rampant process variations.