Academic self-efficacy is an important condition to ensure learning effectiveness and improve the sustainability of online learning quality. The present study intended to examine the relationship among online teacher support, academic procrastination, and academic self-efficacy by employing a questionnaire survey and structural equation modeling in the online learning environment. We report on survey data from N = 827 college students from China. The results showed the following: that online teacher support and its emotional, cognitive, and autonomy support dimensions have significant positive effects on academic self-efficacy, and it can predict the academic self-efficacy of college students through the mediating effect of academic procrastination. Therefore, in order to enhance the academic self-efficacy of online learners, it is crucial to increase online teacher support, provide guidance and motivation to online learners, reduce academic procrastination, and improve the emotional, cognitive, and autonomy support perceived by students in online courses and learning activities. The findings contribute to the exploration of the impact and mechanism of online teacher support regarding the academic self-efficacy of learners, providing theoretical and empirical support for enhancing academic self-efficacy in online learning and the development of a sustainable quality of teaching.

This paper investigates the resource allocation problem for a wireless-powered backscatter communication network where the sensors can utilize the radio frequency (RF) signals from a hybrid access point (H-AP) to transmit data and store energy. We assume that the sensors receive RF signals from the H-AP and transmit data to the sensors’ gateway by backscattering techniques. The sensors harvest energy from the RF signals, where a portion of the energy is harvested to transmit data to satisfy the gateway data demand, and the rest of the energy is stored in their batteries. In order to allocate the overall storage energy among sensors fairly, we propose a resource allocation problem based on the Nash bargaining solution (NBS) to maximize the product of the storage energy of each sensor. We prove that the energy allocation problem can be equivalent to a convex problem which can be solved by the interior-point method. Simulation results show that the proposed algorithm achieves a good balance between fairness and efficiency with other benchmark algorithms.