Cultivating innovative talent has become a primary path for China to develop science and technology and solve “bottleneck” technical problems. Catering to the trend of educational reform in the intelligent age, the use of robotics to enhance students’ creativity shows richer practical value and contemporary significance. In this study, 48 China and foreign experimental studies and 6,057 samples are coded using the meta-analysis method, and the findings are as follows: First, the overall effect of educational robots on students’ creativity is 0.576, reaching a medium or above level of influence. Among them, the effect of promoting students’ innovative, practical ability is the most obvious, and the degree of influence on the quality of innovative personality is average. Second, from the perspective of the academic stage, the effect of educational robots on the creativity of students in junior middle schools and primary schools is more obvious. Third, in terms of disciplines, robot courses impose the best effect on students’ creativity. Fourth, among teaching themes, prototype creation has the most impact on students’ creativity, and the level is above the intermediate level. Fifth, in terms of the choice of teaching methods, inquiry teaching can better stimulate students’ creativity. Finally, compared with ordinary classrooms, the laboratory environment is more optimistic for developing students’ creativity. Combined with field research, four practical suggestions are put forward.
As an important teaching method in the era of education informatization 2.0, the smart classroom has become a hot topic in terms of whether it can really improve students’ learning effectiveness. Many scholars have carried out empirical studies in this field, but the conclusions are not the same. By the use of meta-analysis, this study quantitatively analyzes 48 experimental and quasi-experimental studies in China and other countries to explore the smart classroom’s effect and influencing factors. The results show that, on the whole, the smart classroom has a better-than-average promotion effect on students’ learning effectiveness; from the perspective of moderating variables, the learning outcome of smart classrooms is influenced by the factors of learning stage, knowledge type, teaching cycle, class scale, and equipment condition, but is not moderated by the factors of subject, teaching strategy, and evaluation tool. Specifically, smart classrooms can become a suitable partner for large-class teaching, short-term courses, and experimental courses. However, the misuse of technology and equipment should be avoided in the application process.
Online knowledge-sharing is the key link to individuals in the digital era. Postgraduates are the core members of the future knowledge-based society. Exploring factors affecting postgraduates’ online knowledge-sharing behavior is of significance promoting their fair enjoyment of digital dividends and contributing to the construction of Digital China. However, little literature exists on this topic. This study examines factors resulting in postgraduates’ differentiated online knowledge-sharing behavior from two dimensions: social structure and individual initiative. The results of the questionnaire survey of 501 postgraduates show that: First, not all postgraduates are aborigines of the digital age; and structural factors (gender, school type, location, father’s occupation, and father’s education) have a significant impact on postgraduates’ online knowledge-sharing behavior. These factors also have a greater impact on the quality of online knowledge sharing than in a quantity sense. Second, the individual initiative factor (information literacy) also has a significant impact on postgraduates’ online knowledge-sharing behavior. It has a greater impact on the quantity of online knowledge- sharing than in a quality sense. Third, there is a Matthew effect under the internet context, and certain effects of structural factors on online knowledge-sharing behavior are indirectly generated through individual initiative factors. Participants with privileged structural status show a higher level of information literacy, which further encourages them to be more active in online knowledge-sharing behavior and facilitates their high-quality production.
In the context of the highly demanding trend of equitable and quality education, digital technology has outlined a blueprint for sharing resources across spatial and temporal boundaries and learner differences. The hybrid classroom implemented on university campuses during the pandemic demonstrates the tremendous shaping power of digital technology on teaching and learning across different groups, time, and space. This study investigates two types of learners, Clone Classroom and Global Hybrid Classroom at Tsinghua University, and finds that intrapersonal, interpersonal, and institutional dimensions of classroom climate all significantly affect learners’ learning outcomes. However, the influences at the three dimensions differ in degree, with interpersonal factors outweighing institutional factors and institutional factors outweighing individual factors. Furthermore, in individual factors, information literacy and tech-assisted support in institutional factors have the weakest impact on learning outcomes; institutional factors mediate individual and interpersonal factors influencing higher order cognition development. To avoid the pitfalls of techno-centrism, this study suggests promoting an insight of technology for humanity and embedding technology into teaching to better empower teacher development and student learning experience.
With the in-depth development of internet plus education and the COVID-19 pandemic, online learning has become a universal teaching method in the world. However, the issue of superficial and low quality in online learning is still widespread. From the internal and external conditions of online deep learning, this study reveals the theory mechanism of how the online learning environment and teacher support, as external conditions, affect students’ emotions and learning motivation, and then affect students’ internal learning state. On this basis, an online deep teaching model is constructed, which consists of three stages: online asynchronous self-study before class, online synchronous live broadcasting teaching in class, and online asynchronous expansion after class. Meanwhile, a one term long online teaching practice is carried out in the course named the Instructional System Design. The results show that, in the learning process level, the online deep teaching model can effectively improve students’ online learning engagement, promote students to use deep learning methods and deep learning motivation to learn, and enhance the depth of online interaction between students. At the level of learning results, students’ critical thinking and online academic performance have been significantly improved.