With the gradual popularization of mobile learning, it has become a trend for college students to use learning applications (APPs) for learning, but the learning effect has always been a concern. Since college students have different learning purposes, strategies, skills, and habits, responses to these differences have been an urgent problem. This paper takes college students using an English vocabulary APP to study as the survey object. It sets up the influencing factors model of the English vocabulary learning effect based on UTAUT2. Then, we analyzed the factors influencing the learning effect of using an English vocabulary APP and studied its mechanism. The results show that on the one hand, the influence degree of influencing factors is habit, facilitation condition, price value, effort expectancy, and performance expectancy from high to low, and all the above factors have a significant positive impact on the learning effect of college students using English vocabulary APP. On the other hand, gender, grade, and major have a moderating effect on English vocabulary learning, and there are differences among different genders, grades, and majors. Finally, suggestions are put forward from the perspective of APP construction and students’ differences to enhance and improve the learning effect of English vocabulary.

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With the rapid development in online education and the recurrence of COVID-19 around the world, people have temporarily turned to online education. To identify influencing factors of online learning behavior and improve online education, this study used CiteSpace to visually analyze research on influencing factors of online learning behavior on WoS. It discusses the research status, hotspots, and trends. Then, through cluster analysis and literature interpretation, the paper summarizes the types of online learning behavior and the influencing factors of different online learning behaviors from positive and negative dimensions. The findings of this paper are as follows. (1) The number of studies on the influencing factors of online learning behavior has increased in the last decade, especially after the outbreak of COVID-19. The research countries and institutions in this field lack contact and cooperation. (2) Online learning behaviors mainly include online learning engagement behavior, continuous behavior, procrastination behavior, and truancy behavior. (3) Online learning engagement behavior is mainly affected by perceived usefulness, perceived ease of use, individual characteristic differences, and other factors. (4) Online learning continuous behavior is mainly affected by quality, perceived usefulness, learning self-efficacy, and other factors. (5) The influencing factors of online learning procrastination mainly include learning environment, individual characteristics, social support, and pressure. (6) The main influencing factors of online learning truancy behavior are social interaction, participation, and learner control. At the end of this paper, according to the action mode of the influencing factors of online learning behavior, some suggestions for teaching improvement are put forward from the two perspectives of promoting positive online learning behavior and avoiding negative online learning behavior, which can provide a reference for teachers and schools in the future when conducting online education.

The evaluation of ecological sustainability is significant for high-quality urban development and scientific management and regulation. Taking the Chengdu urban agglomeration (CUA) as the research object, this paper combined the three-dimensional ecological footprint model (3DEF) and random forest to evaluate the ecological sustainability of the study area and identify the influencing factors. The study results indicate that: (1) From 2000 to 2019, the ecological sustainability of Chengdu urban agglomeration was divided into four types, and the overall ecological sustainability of this region showed a downward trend. The areas with higher ecological sustainability were mainly distributed in the northern part of the urban agglomeration (Mianyang City) and the southern part (Leshan City and Ya’an City), while the cities in the central region (Chengdu City, Meishan City, and Ziyang City) had lower ecological sustainability. (2) The main factors affecting the ecological sustainability of urban agglomerations are industrial wastewater discharge, industrial smoke (powder) dust discharge, and green coverage of built-up areas, followed by urbanization and population size. Through this study, we have two meaningful findings: (a) Our research method in this paper provides a new way to study the factors affecting the ecological sustainability of urban agglomerations. (b) The results of the identification of influencing factors might be the reference for urban environmental infrastructure construction and urban planning.

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