Research on the Application of Data-Driven Smart Teaching in Programming Courses

The insufficient data collection and low teacher-student interaction efficiency in programming courses are common problems encountered. In order to solve the problems, this study presents a data-driven smart teaching model (DD_STM) with a four-layers architecture, which are teaching interaction layer, data collection and storage layer, intelligent computing layer and intelligent decision application layer. Moreover, a smart teaching platform has been developed to support the implementation of DD_STM. Empirical study from “Foundations of Python Programming instruction” indicates that the smart teaching model significantly improves teacher-student interaction efficiency and promotes higher level of students learning participation. The positive outcomes may be attributed to the timely response from the smart platform and the effective feedback on students’ difficulties, which enhance students’ initiative in learning and thereby promote greater engagement in the learning process.