Influence of Blockchain Technology Application in Education on Online Teaching Resources Sharing

Abstract

With the continuous expansion of educational view, blockchain technology develops into the cutting-edge applied science and technology of educational information construction. Blockchain technology can effectively solve such problems as information security and trust crisis in the current application of big data in online education by virtue of its characteristics of decentralization, traceability, multi-party consensus mechanism, and high trust. Blockchain technology is conducive to protecting online educational resources, strengthening the transparency of online teaching resources, simplifying the copyright transaction process, and thus enhancing the scientific and technological innovation ability of teachers. Studies regarding the blockchain technology application in education were analyzed, and the influences of the four aspects of the blockchain technology application in education on online teaching resource sharing were explored. Results showed that the Cronbach α coefficient of the questionnaire designed in this research was 0.856, and the KMO value was 0.859, indicating its good reliability and validity. The cross-institutional learning record storage space, learning certificate management mechanism, and collaboratively developed educational ecosystem of the blockchain technology application in education obviously promoted online teaching resource sharing at a significance level of 5%. The results are important reference values for strengthening the integration of educational resources under blockchain technology, improving the application mechanism of online digital educational resources, and improving the accurate service quality of online educational resources.

Enhancing Online Teaching Effectiveness Through Computer Vision Analysis of Teacher Expressions and Gestures in Educational Videos

“…This operation ensures that spatial information is fully considered and integrated before temporal modeling, enabling subsequent temporal processing to more effectively capture the dynamic changes of features. Suppose the output features of ConvLSTM at each time step are represented by G=[G (1) , G (2) ,..., G (S) ], and the feature sequence extracted by ConvLSTM is represented by C, then:…”

“…With the rapid development of online education, more and more students are acquiring knowledge through internet platforms. This mode of learning not only breaks the limitations of time and space but also greatly enriches educational resources [1][2][3][4]. However, in an online learning environment, teachers cannot directly observe students' reactions, and students find it difficult to intuitively feel the teacher's emotions and teaching enthusiasm, which to some extent affects the teaching effectiveness [5][6][7][8][9].…”

Enhancing Students' Metacognition via AI-Driven Educational Support Systems

“…Artificial intelligence (AI), identified as one of the most powerful technologies currently available, has been extensively utilized in various domains, with the field of education being particularly notable. Traditional educational methods frequently struggle to meet the requirements of modernized, personalized, and intelligent education, whereas the incorporation of AI technology is perceived to offer a more expansive developmental sphere for education [9][10][11][12][13][14]. Metacognition, a pivotal educational concept, represents the capability of students for self-regulation, self-reflection, and self-management, playing an indispensably critical role in their learning process [15,16].…”