Introduction In 2020, COVID-19 forced higher education institutions in many countries to turn to online distance learning. The trend of using online education has accelerated across the world. However, this change in the teaching mode has led to the decline of students' online learning quality and resulted in students being unable to do deep learning. Therefore, the current research, aimed at promoting deep learning in the online environment, constructed a theoretical model with learning self-efficacy and positive academic emotions as mediators, deep learning as the dependent variable, perceived TPACK support, peer support, technical usefulness, and ease of use as independent variables. Methods The theoretical model was verified by SPSS26.0 and smartPLS3.0, and to assess the measurement and structural models, the PLS approach to structural equation modeling (SEM) was performed. Results The study found that (a) positive academic emotions play a mediating role between perceived TPACK support and deep learning, perceived peer support and deep learning, and perceived technology usefulness and ease of use and deep learning; (b) learning self-efficacy plays a mediating role between perceived TPACK support and deep learning, perceived peer support and deep learning, and perceived technology usefulness and ease of use and deep learning. Discussion The findings of this study fill the gaps in the research on the theoretical models of deep learning in the online environment and provide a theoretical basis for online teaching, learning quality, and practical improvement strategies.

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What factors can support students' deep learning in the online environment: The mediating role of learning self-efficacy and positive academic emotions?

Emotions can influence and regulate learners’ attention, memory, thinking, and other cognitive activities. The similarities and differences between English and non-English majors in terms of English classroom learning engagement were compared, and the significant factors affecting the emotional, cognitive, and behavioral engagement of the two groups of students in the English classroom were different. English majors’ affective engagement in the classroom was not significant, which was largely related to their time and frequency of English learning. Traditional methods of learner emotion recognition suffer from low recognition rate, complex algorithms, poor robustness, and easy to lose key information about facial expression features. The paper proposes a convolutional neural network-based learner emotion recognition method, which includes three convolutional layers, three pooling layers, and one fully connected layer. In the future, the method will can be applied to the construction of smart learning environments, providing technical support for improving learner models, realizing emotional interactions, and mining learning behaviors, etc.

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Affective Cognition of Students’ Autonomous Learning in College English Teaching Based on Deep Learning

This study aims to (1) develop and validate the four-dimension model hypothesis of deep learning to better understand deep learning in language education; (2) investigate and promote deep learning by conducting a survey involving 533 college students in the online learning English as a foreign language (EFL) teaching context in China. Concretely, this study initially synthesized theoretical insights from deep learning in the education domain and related theories in the second language acquisition and thus proposed the four-dimension model hypothesis of deep learning involving the motivation of deep learning, the engagement of deep learning, the strategy of deep learning, and the directional competence of deep learning. This study subsequently undertook a questionnaire survey utilizing a standardized instrument to confirm the model hypothesis and further investigate the current status and salient differences in students' deep learning in online EFL teaching. Exploratory factor analysis (EFA), confirmation factor analysis (CFA), and Pearson's correlation test validated a positively correlated four-dimension model of deep learning with high composite reliability and good convergent validity. Moreover, the descriptive and inferential statistics revealed that the level of students' deep learning marginally reached the median, with the lowest mean of directional competence and the highest mean of motivation; students manifested more instructional motives, neglect of deploying skilled-based cognitive strategies, and deficiency of language application skills, etc.; there existed some significant differences between deep learning and four sub-dimensions across the grade, English proficiency, EFL course, and vision groups. Eventually, this study proffered primary reasons and five appropriate strategies to scaffold and promote students' deep learning in online EFL teaching. Hopefully, this study will be a pioneering effort to clear away the theoretical muddle of deep learning construct in language education and be illuminating to further improve effectiveness in the online EFL teaching context.

Understanding, Investigating, and promoting deep learning in language education: A survey on chinese college students' deep learning in the online EFL teaching context

The objective of this study is to examine the sense of social connectedness (SSC) and prosocial behavior (PB) of students in Guangxi higher education institutions in China and to further understand the factors influencing PB of higher education students. In this study, a total of 1,007 students were sampled from 8 Guangxi higher vocational schools through purposive sampling using questionnaires, of which 676 (67.1%) were male students and 331 (32.9%) were female students. This study further enriches self-determination theory by exploring the effects of teachers’ character teaching behaviors and social support on PB, using the SSC as an intrinsic motivation. In addition, the results of the study revealed that SSC, perceived teacher character teaching behaviors and perceived social support (PSS) were positively related to students’ PB. SSC affects PB not only directly, but also indirectly through the mediating role of perceived teacher character teaching behavior and perceived teacher support. Teacher character teaching behaviors and PSS also play a chain mediating role in the relationship between SSC and PB. Finally, this study provides strategies to optimize school character management for higher education students in order to meet their basic psychological needs and thus promote the production of PB.

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The influence of students’ sense of social connectedness on prosocial behavior in higher education institutions in Guangxi, China: A perspective of perceived teachers’ character teaching behavior and social support

Teaching endeavors to improve learners' well-being and educational engagement, as both factors contribute to progress facilitated by the integration of PP into the learning procedure, enhancing success in the academic cycle. Some studies have affirmed the significant effect of teacher support on learners' academic performance. Nevertheless, the precise mechanism linking teacher support, particularly emotional support, to student learning remains unclear. This research was structured to scrutinize the mediating influence of learner motivation in the connection between perceived emotional support and learners' engagement and well-being, employing the perspective of, a widely acknowledged psychological theory that centers on human motivation and the determinants influencing individuals to participate in specific behaviors, including learning. Consequently, the objective of this research was to assess the relationship between perceived emotional support, well-being, and student engagement with particular attention to the mediating role of motivation among 590 college students in Chinese universities. The results of the Structural Equation Model (SEM) indicate approximately 72% of the variations in perceived emotional support, well-being, engagement, and motivation. These relationships are significant at the 95% confidence level, indicating the robustness of the model in capturing the intricate dynamics among the variables within the educational context. Consequently, several implications are suggested for teachers, learners, teacher trainers, and other academic stakeholders. 

Investigating the role of perceived emotional support in predicting learners’ well-being and engagement mediated by motivation from a self-determination theory framework

The purpose of this research is to test the mediation effect of self-efficacy on college student's perception of teacher autonomy support and students' deep learning, and whether the peer support perceived by students can moderate the relationship between perceived teacher autonomy support and deep learning. A survey of 1,800 college students from a provincial undergraduate normal university in Guizhou Province in China was conducted through the revised Perceived Teacher Autonomy Support Scale, Deep Learning Scale, Self-Efficacy Scale, and Perceived Peer Support Scale (Mean age = 21 years old, SD = 1.34). Data use SPSS23.0, AMOS22.0 for descriptive analysis and correlation analysis, exploratory factor analysis (EFA) and confirmatory factor analysis (CFA), moderation effect, and mediation effect analysis. The research results show that after controlling for gender, major, and grade, self-efficacy partially moderates the connection between perceived teacher autonomy support and deep learning of college students. Moreover, perceived peer support mediates the relationship between perceived teacher autonomy support and students' self-efficacy.

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Perceived Teacher Autonomy Support and Students' Deep Learning: The Mediating Role of Self-Efficacy and the Moderating Role of Perceived Peer Support.

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Perceived Teacher Autonomy Support and Students' Deep Learning: The Mediating Role of Self-Efficacy and the Moderating Role of Perceived Peer Support.