The Relationship between Perceived Teacher Emotional Support, Online Academic Burnout, Academic Self-Efficacy, and Online English Academic Engagement of Chinese EFL Learners

Abstract

This study explores the impact of college students’ perceived teacher emotional support on English academic engagement in technology-enhanced online learning contexts. It examined whether 450 Chinese students’ perceptions of teacher emotional support were related to students’ English academic engagement and mediated by students’ online academic burnout and academic self-efficacy. All of the sample students were recruited to complete an online questionnaire, and some students and teachers were invited to do a semi-structured interview. SPSS24 and PROCESS3.5 macro were utilized to undertake the descriptive, correlational, and mediation analyses. The findings showed that teachers’ perceived emotional support and student academic efficacy positively affect students’ online English academic engagement. In contrast, online academic burnout negatively affects students’ online English academic engagement. Student academic efficacy partially mediated the effects of perceived teacher emotional support and student academic engagement in online learning contexts. The interviews confirmed the findings, and the influences and roles of teachers’ emotional support were described in terms of teacher and student dimensions in an interview. Directions and further implications for researchers, teachers, and educators are presented at the end of the study.

1. Introduction

In the era of knowledge and information explosion, various online platforms and digital media technologies have brought infinite convenience to students’ lives, learning, and socialization. Using various social learning apps and online platforms, such as Facebook, WeChat, and QQ, gives students a tremendous amount of information to communicate all the time [1,2]. The promotion of education informatization reform and various social emergencies have made blended learning and online communication the new normal of students’ learning lives. With the innovation of media technology and the application of communication devices, information production, storage, and dissemination have developed rapidly. While students enjoy the convenience of receiving and processing information, they also suffer from various negative emotions such as anxiety, aversion, stress, and burnout due to the rapid increase in the amount of information [1,2,3,4,5,6], thus failing to select and effectively process useful information rationally, or even intentionally avoiding information [7,8,9]. Negative academic emotions such as burnout harm students’ mental health and intention to use online learning devices, as well as their satisfaction and academic achievement [10].

On the one hand, the innovation of information technology and the wide application of innovative technology in education have promoted personalized, innovative tutoring and intelligent assessment and diagnosis in online learning [11,12], and the research on teachers’ commitment to students’ studies and learning satisfaction in online and blended learning has become an essential indicator for promoting online teaching reform and smart technology innovation [13,14]. On the other hand, the positioning of teachers’ roles and teacher–student interactions under information technology has become an essential indicator for implementing and promoting innovative education and online learning toward deep learning and personalized learning [15]. In the intelligent technology environment, technology integrates with our teaching resources, teaching tools, and teaching presentations and generatively develops self-adaptive and AI-style intelligent teaching analytics tools. As Nel proposes, teacher emotional power in the artificial age builds a harmonious and sustainable educational ecology that integrates teacher, student, and machine [16]. Teacher emotional support in the online environment is the emotional perception of the teaching scene and the emotional transmission and feedback between students and teachers. It is not only the emotional exploration and experience of teachers and students on the value of self-existence in the intelligent technology-assisted teaching environment, but also the embodiment of teachers’ emotional insight, management, and expression. The exploration of teachers’ online emotional support can help teachers’ emotional wisdom mining and self-sustainable growth in the intelligent technology environment.

English teachers’ support of and influence on students’ cognitive emotions, classroom interactions, and academic achievement have always been topics worth exploring in order to effectively alleviate various negative academic emotions in students’ online learning and reduce the cognitive load of students’ learning process in an intelligent online environment based on platforms and media software [17,18,19,20]. Just as teachers in online learning create a sense of belonging for students through teaching activities, online or blended learning builds online learning communities, and learners find a sense of self-presence in collaborative teaching, which leads to further reflection and dialogue and ultimately to understanding, analyzing, and problem-solving. Compared to the traditional face-to-face classroom, online teaching seems unable to convey teachers’ non-verbal behaviors at close range, or to enhance teachers’ pedagogical interactions through changes in spatial distances and body postures. Information technology has expanded the limitations of time, space, and mode of teachers’ teaching, such as tracking and feedback on students’ learning behaviors and classroom interactions through a variety of online interactive tools and assessment systems. For educators, how to give teachers an emotion pedagogical advantage in the online community on a broader scale, and how it can impact meaningfully on student self-evaluation and achievements is still unclear.

Does online learning teachers’ emotional motivation and support in terms of students’ perceptions and acceptance positively impact self-directed and deep learning? In what ways is student self-identity defined and the role of the instructor in the collaborative community manifested in online teaching and learning environments? Are teachers’ emotional support, encouragement, and assistance to students effective in building a positive collaborative learning climate and providing more support for students’ engagement and presentation of themselves, thereby enhancing students’ sense of self-presence and academic engagement and improving students’ collaboration and efficacy in online learning?

There is still a need for in-depth research on the emotional support aspect of teacher support, especially teacher’s attention to students’ academic and social emotions and the corresponding teacher’s emotional support in teacher–student interactions in the field of online language education at present with the development of intelligent technologies. Given this, this study explores the impact of students’ perceived teacher emotional support for online self-efficacy and academic burnout in online English learning environments, from a cognitive load and community of inquiry perspective, and the relationship between these variables and students’ academic engagement in an online learning community. Through questionnaires and semi-structured interviews, we try to explore the sources of academic burnout among online English learners and discuss the correlations among perceived teacher emotional support, online academic self-efficacy, online academic trickle-down, and online academic engagement to enrich the research dimensions of online academic engagement, deepen the empirical research on cognitive-emotional dimensions of online English learning, such as self-efficacy and burnout, and make suggestions for online English learning. The study also provides inspirational pedagogical suggestions and research insights on teachers’ perceived emotional support in online English learning.

2. Theoretical Background

2.1. Cognitive Load Theory

Proposed by psychologist John Sweller in 1988, cognitive load theory can be traced back to brain load or mental-load-related studies [21,22,23]. Cognitive load theory focuses on the learner’s influence on the conceptualization and cognitive processing of things through learning materials and instruction methods [24]. Cognitive load is the total amount of mental and cognitive resources of a learner during information processing [25] or the total amount of working memory and mental activity imposed on an individual during a specific working time [26]. Valcke (2002) proposed the metacognitive load theory based on the previous cognitive and metacognitive theories [27]. Van and Sweller (2005) categorized cognitive load into intrinsic, extrinsic, and related cognitive load [28]. As research deepened, cognitive load theory was viewed as a multidimensional construct. Cognitive load in learning tasks can lead to a decline in an individual’s cognitive ability, which reduces his or her efficiency in learning and working and has an impact on his or her attention, mood, cognitive ability, etc., [29] which shows that reasonable avoidance of the cognitive load phenomenon in teaching and learning is conducive to enhancing students’ learning efficiency and improving teachers’ instructional design.

Liew et al. (2017) stated that teachers’ enthusiasm in multi-media instructional environments is emotional, motivational, and cognitive. Teachers’ enthusiasm for teaching (verbal and nonverbal) significantly enhances students’ emotions, helps students to participate and actively engage in online learning effectively, and creates an effective emotional link [30]. Altinpulluk et al. (2020) focused on the effects of video instruction in open and distance learning instruction on students’ cognitive load regarding their working memory [31]. As Bahari et al. (2021) pointed out, cognitive load affects the interaction between learners and the learning environment, which affects the optimization or hindrance of information processing in working memory [32].

Research on teacher role positioning, and the emotional aspects of cognitive load theory across different geographies and teacher types deserves to be explored in depth in anticipation of contributing to research on emotion regulation and recognition in online learning design to enhance student interest, motivation, and engagement in learning, which in turn positively affects cognitive load. We investigate whether, in the online teaching environment of various mobile terminals and informatization platforms and in the face of massive online English learning resources, the students’ psychological and cognitive resource carrying capacity will produce loads on their physiology and psychology, and whether the teachers’ emotions can effectively promote their learning efficiency and mobilize their language learning emotions, so as to promote the improvement of their cognitive ability and learning input. This is worthy of in-depth excavation and research.

2.2. Online Academic Burnout

Burnout initially refers to the negative psychological state that people present under chronic stress and is a manifestation of emotional fatigue in information users. Social media burnout is associated with negative emotions, mental health, and intention to use and satisfaction, and it harms academic achievement [10,33]. In the current era of advanced information and communication, people share, communicate, and transfer information and insights through social networking sites, WeChat, forums, and various apps. Social media platforms play an essential role in people’s lives and learning. Social media platforms such as TikTok, Kuaishu, Rain Classroom, QQ, and DingTalk are powerful online English learning assistants. However, the intensive and excessive use of information media in online learning can lead to academic burnout [34,35]. Freudenberger introduced the concept of burnout in 1974, which refers to the state in which a worker in the workplace “fails, runs out of energy, or is exhausted as a result of excessive exertion of energy, stamina, or power” [36].

Burnout was then further explained by Maslach and Jackson (1981) in the development of the Maslach Burnout Inventory (MBI) as a psychological reaction resulting from coping with work stress over time [37]. Academic burnout, derived from occupational burnout, is viewed as a psychological, emotional, and physical response triggered by the demands of academic studying [38]. In online learning environments, the mental, emotional, and physical exhaustion that learners experience due to prolonged overuse of social media can be interpreted as online academic burnout. Salanova et al. (2005) applied the Maslach Burnout Inventory to the field of education. They constructed three dimensions for measuring academic burnout: fatigue (perceived stress and psychological burden of academic demands), inefficacy (lack of confidence in academic competence), and detachment (loss of interest and motivation in learning) [39]. Ugwu et al. (2013) conducted a study on the relationship between academic burnout, self-efficacy, and academic engagement among Nigerian undergraduates, and the results showed that students’ academic burnout was negatively correlated with academic engagement [40].

Gao (2016) stated that a negative online academic mood harmed online academic engagement [41]. Evers et al. (2022) studied the relationship between social media burnout and students’ academic efficiency [42]. The study showed that excessive use of social media leads to sleep deprivation and sleep disruption, which creates academic burnout and hinders students’ academic efficiency. Based on previous related studies, academic burnout as a negative academic emotion is associated with students’ mental health, intention to use, and satisfaction, and it hurts students’ academic achievement. In the online language education environment, social media is not only a source of learning materials for students but also a medium for student–teacher and student–student interactions. It is necessary to examine the impact of social media on students’ academic achievements and their perceptions of online learning and its related dimensions to examine their physical and mental knowledge, academic emotions regarding academic and online learning more systematically.

2.3. Perceived Teacher Emotional Support in the Inquiry Communities

House (1988) refined the social support categories (emotional, instrumental, informational, and evaluative) and stated that emotional support is essential to social support [43]. Wills (1991), in his article Social Support and Interpersonal Relationships, introduced the concept of social support, which can be categorized into emotional and instrumental support [44]. Teacher support is defined as the application of the concept of social support to instruction and learning situations [45]. Teacher emotional support focuses on teachers’ help and concern for students in education [46]. Ryan and Patrick (2001) stated that emotional support could be understood as a teacher’s caring, understanding, concern, and friendly behavior towards students [47]. Teachers’ perceptions of and responses to students’ emotions and needs have been the focus of the research on teacher emotional support. Sckenke et al. (2017) claimed that a teacher’s emotional support is vital in promoting good teacher–student relationships, enhancing students’ autonomy and teacher–student interactions [48]. Hamre and Pianta (2010) proposed the perceived Teacher Emotional Support Framework based on the Classroom Observation System [49]. It consists of three dimensions: positive climate, teacher sensitivity, and respect for students. Positive climate focuses on the degree of teacher instruction and teacher–student interaction; teacher sensitivity is the teacher’s response to students’ emotions and needs in relation to academic; and respect for students reflects the teacher’s understanding of and respect for students’ learning autonomy and individuality.

Sustainability and motivation in online learning are essential factors that promote deep learning and higher order thinking; negotiated and dialogic learning communities are extremely valuable for an excellent online learning experience and the construction of academic emotions toward academic studying [50,51,52]. Teacher emotion in online teaching is technology-enabled care and dialog based on human ontology, which follows the teaching subject’s value needs, emotional experience, reflection, and growth. It is a new model of teacher emotional thinking and practice based on technological care rather than manipulation. Sakiz et al. (2012) focused on the issue of students’ perceptions of teachers’ emotional support and constructed a model of students’ perceived emotional support from teachers [53]. With the continuous innovation of education informatization, scholars have paid attention to teachers’ emotional support in online teaching environments. Li and Lei (2012) explored the theory and practice of teacher emotional support from a distance [54]. The study showed that teacher emotional support and teacher–student interactions in technology-mediated environments enhance students’ positive academic emotions, learning autonomy, academic engagement, and self-emotional regulation.

Under technology-enabled online or blended teaching environments, learners’ online learning engagement is an essential indicator of academic achievement and learning satisfaction, among others [55]. Online learners’ various emotional experiences toward learning tasks and interactions, such as pleasure, anxiety, burnout, etc., can be regarded as emotional engagement or a degree of online learning emotional presence [56]. As Reyes et al. (2012) expressed in their study of the emotional environment of the classroom, when teachers meet students’ needs, foster positive relationships, and create a positive emotional environment in the classroom, students are more eager and engaged in their learning, and academic achievement is likely to increase as a result [57]. Zhao et al. (2018) examined the positive impact of teaching emotional support on online learners’ burnout [58].

Li et al. (2019) conducted an empirical study on secondary school students’ motivation, academic burnout, and perceived teacher emotional support, which showed that perceived teacher emotional support negatively affects academic burnout [59]. It can be seen that teachers’ emotional support is not unidirectional. It must be perceived and understood by students as care, respect, love, and help, and only when students perceive and understand teachers’ emotional support can it impact students’ cognition and emotions, thus promoting their learning effectiveness. Lobo (2023) examined the links between academic resilience, perceived teacher emotional support, and school engagement and showed that instructors’ emotional support promoted college students’ resilience and engagemen in online contexts [60]. He et al. (2023) stated that perceived emotional support is one of the driving factors that enhances students’ continuous learning intention in e-learning [61]. Zhou et al. (2023) conducted an empirical study on the perceived teacher emotional support and social engagement of Chinese students, and the empirical study indicated that self-efficacy mediates between perceived teacher emotional support and interactive engagement for English learners [62].

As a result of innovations in information presentation and interaction, technology-enabled online and hybrid education provides an open and diverse conceptualization of communication spaces and better facilitates reflection, dialogue, and collaborative inquiry. This constructs a dynamic model of negotiation, which is the community of inquiry framework co-founded by Garrison and Anderson [63]. The framework originally consisted of three main elements: social, cognitive, and pedagogical presence. Cleveland-Innes et al. (2012) added the element of emotional presence to the original framework [64]. Emotional presence refers to the external representation of individual emotions in individuals and learning communities, with interactions related to learning technology, course content, students, and instructors. Rienties et al. (2014) refined emotional presence to obtain a community of inquiry theoretical model, wherein emotional presence is categorized into independent emotional, socially relevant emotional, and pedagogically relevant emotional zones [65]. The emotional support of teachers in the field of informatized teaching and intelligent education reflects teachers’ emotional presence. The perception, awareness, integration, and decision-making of teachers’ emotions based on technological empowerment not only build a new emotional teacher–student, student–student, and teacher–student–technology synergy, but we also expect them to present the linkage of fields in terms of dialog and negotiation so as to build the space of emotional interaction [66].

2.4. Online Learning Engagement

Learning engagement is one of the crucial indicators for evaluating online courses or online education. Schaufeli et al. (2002) defined learning engagement as students’ active, sustained, and focused state of mind during the learning process and categorized it into three dimensions: motivation, energy, and concentration [67]. Scholars have refined learning engagement into behavioral, cognitive, and emotional engagement [68,69,70]. Student learning effectiveness in online learning engagement [71,72], willingness to learn online [73], instructor support [74], self-efficacy [75,76], and learning emotions [77] are among the contemporary concerns of online learning input. Philp and Duchesne (2016) defined the effort and dedication students put into English language learning as English language learning input, which consists of the four-dimensional constructs of behavioral, cognitive, emotional, and social inputs [78]. Scholars have attempted to describe and explore the intrinsic mechanisms of online learners’ learning engagement from multiple dimensions. Given the dynamic and autonomous nature of learning, Montenego (2017) proposed the concept of agentic input, which became a new dimension to measure learning input [79]. Learning engagement is a multifaceted construct [80]. Suhaimi and Hussin (2017) expressed that the stress brought about by the overuse of mediated information technology hinders the learner’s effectiveness and ability to learn and acts as a negative influence [81]. It can be seen that understanding learners’ receptivity to information positively promotes learners’ learning engagement, social media engagement, and peer engagement [82]. Cai and Jia (2020) explored the effects of academic self-efficacy on online learning engagement and its mechanisms, and their findings showed that academic self-efficacy positively predicted online learning engagement [83]. Kuo et al.’s (2021) empirical study on the relationship between students’ online learning engagement and self-efficacy under MOOC learning also showed that different types of online self-efficacy had a positive effect on students’ emotional, behavioral, and cognitive engagement, as well as behavioral and cognitive inputs, and that online self-efficacy plays a crucial role in online academic engagement [84].

2.5. Academic Self-Efficacy

Self-efficacy is derived from Bandura’s Social Cognitive Theory and is a belief in a person’s ability to accomplish a task and desired goal [85]. Bandura (1993), in a subsequent study of self-efficacy and functioning in cognitive development, noted that students’ beliefs about regulating and completing academic tasks are positively associated with students’ motivation, academic mood, and academic achievement [86]. Self-efficacy is closely related to using computers, networks, and information and communication devices (ICTs) in online social environments. Individuals’ perceptions of their ability to use ICT are viewed as ICT self-efficacy [87]. Self-efficacy in the context of online learning is students’ perceptions and beliefs about ICT and online learning when they are in an online learning environment. Park and Flowerday (2015) explored the effect of the interaction between academic mood and learning engagement in multimedia learners [88]. The study showed that negative moods, such as confusion and anxiety, lead to lower engagement and effort and adversely affect cognitive processes; students showing positive emotions and a strong sense of self-efficacy are usually more willing to take on challenging tasks and are more likely to use appropriate strategies when faced with problems or challenges [86,89]. Rohmani and Andriani (2021) investigated the relationship between academic self-efficacy and information burnout among first year students who participated in online learning [90]. The study showed that the high intensity and fatigue of online learning affected students’ perceptions of academic achievement and confidence in learning, creating burnout. Yang et al. (2021) showed that academic self-efficacy mediated the relationship between academic burnout and students’ academic satisfaction [91]. Fariborz et al. (2019) found that self-efficacy played an essential role in the relationship between academic stress, stress response, and academic burnout [92]. The information environment in online learning environments impacts students’ academic mood, cognition, and learning effectiveness.

2.6. Research Questions

Based on previous related literature, this study takes a unique approach to examining students’ online English academic engagement. It focuses on the interplay between perceived teacher emotional support, students’ learning efficacy in online learning, and online learning burnout, guided by the cognitive load theory and the inquiry community theory. The study aims to uncover the underlying mechanisms of these factors and provide practical suggestions for enhancing students’ online English learning efficiency. To this end, the study addresses the following three research questions:

RQ 1. 

What are the general trends of perceived teacher emotional support, student online academic burnout, online academic efficacy, and online English academic engagement?

RQ 2. 

What is the relationship between the four factors of perceived teacher emotional support, student online academic burnout, online academic efficacy, and online English academic engagement?

RQ 3. 

Do student online academic burnout and online academic efficacy mediate the relationship between perceived teacher emotional support and online English academic engagement?

3. Research Methods

3.1. Research Subjects

The students in the class surveyed by the questionnaire are learners who usually use the intelligent teaching platform or various kinds of learning software and textbook-supporting apps in English learning inside and outside the classroom. They undertake online learning inside and outside the classroom and complete specific online English learning tasks. The lecturer informed the students of the basic information and the anonymous and voluntary filling out of the questionnaire, etc. After asking for the student’s consent, the questionnaire was released as an electronic questionnaire through Questionnaire Star in WeChat and QQ groups from 22 November 2023 to 13 December 2023. This time, 480 questionnaires were distributed to universities in four provinces: Henan, Beijing, Guizhou, and Sichuan, and 450 questionnaires were retrieved after excluding incomplete or identical questionnaires and questionnaires that were too short to be filled in, resulting in a recovery rate of 93.8%. From

Table 1. Social demographic frequencies of the sample (N = 450).

		Frequency	Percentage
Gender	Male	129	28.7
	Female	321	71.3
	Total	450	100
Study section	Undergraduate	270	60
	Postgraduate	180	40
	Total	450	100
Major	Liberal arts	191	42.4
	Science	259	57.6
	Total	450	100

, it can be seen that there are 270 undergraduates and 180 graduate students, each accounting for 60% and 40%, with a gender ratio of 71.3% for female and 28.7% for male students, of which 42.4% are in liberal arts and 57.9% are in science. All these can be seen in File S1 in Supplementary Materials.

Based on the results of the questionnaire and the principle of voluntary typicality, we invited a total of 8 graduate students and 14 undergraduate students from different geographical regions for a two-week semi-structured interview with the students surveyed in the questionnaire. To reflect the situation of emotional support more perfectly and objectively, we conducted semi-structured interviews with teachers after finishing the student interviews. Based on the comprehensive considerations of title, teaching age, geographic area, type of instruction, and gender, we conducted face-to-face or telephone interviews with seven English teachers for one week.

3.2. Procedure of the Study

The study was conducted based on the consent of the school and the subjects by distributing the questionnaire online, collecting the relevant data, and subsequently analyzing the questionnaire-related scales and data based on SPSS 25.0 and its built-in Process 3.3 data analysis software. Firstly, SPSS was used to conduct validation factor analysis and test the scales’ discriminant validity to detect the scale structure’s internal consistency. Secondly, SPSS was used to conduct descriptive statistics and correlation analysis to examine the scales’ reliability and the questionnaire data distribution and to analyze the relationship between the relevant variables. Finally, based on the results of the students’ consent and questionnaire analysis, we conducted structured interviews with the class to ask questions related to the student feedback on the perceived emotional support of the teacher and the causes of students’ online academic burnout. Interviews were conducted based on the questionnaire and the study participants through WeChat scripts, voice chats, and face-to-face, and the interview data were finally organized and transformed into interview questions and answers.

The interviews were conducted in face-to-face interviews and by telephone WeChat, etc., and we fully solicited the volunteering of the students and their question-and-answer methods. After explaining the concept of emotional support to the students and clarifying the interview process and the principles of data use, the students were individually interviewed on the following seven questions. Interview Question 1: What kind of online learning atmosphere makes you express yourself and join in classroom interactions actively? Interview Question 2: What are some of the emotional needs you have in online learning? For example, being noticed, being encouraged, looking forward to collaboration, eager to show off, and so on. How would you like your teacher to better understand and fulfill your emotional needs in online learning? Interview Question 3: Can you perceive the teacher’s concern, guidance, and help in your online English learning? Please use some specific examples to illustrate your teacher’s emotional concern to go over study problems or challenges in online learning. Interview Question 4: How does the teacher’s emotional support differ from face-to-face teaching in the online learning environment? Do teachers provide emotional support more effectively in online foreign language learning environments? Interview Question 5: Do you feel that the teacher’s emotional support contributes to your self-confidence and sense of self-worth? Interview Question 6: Do you feel that the teachers’ emotional support has helped you in your online learning? In what ways was it manifested? Did you feel more confident to express your ideas boldly because of the teacher’s encouragement? For example, teachers encourage and praise expressions and character in interactive comments. Interview Question 7: What do you think are the barriers to emotional support between students and teachers? Are there any suggestions you can give to help teachers provide better emotional support?

The outline of the specific interviews about teachers involves the following four questions. Interview Question 1: How do you recognize when students need emotional support? For example, signals in the instructional environment or student learning data in online learning or other? What specific behaviors or signals reflect that students need emotional support? Interview Question 2: What tools or strategies do you prefer to use to enhance emotional communication in online teaching? Interview Question 3: How would you characterize the role of emotional support in students’ personality development and academic growth? For example, the impact on students’ constructs of assertiveness, on students’ engagement and motivation to learn, etc. Interview Question 4: What insights have you gained about your teaching in the process of providing emotional support? How have you learned and improved your skills and strategies in emotional support? (e.g., building better relationships with students through evaluating and giving feedback on their emotional support; establishing a harmonious atmosphere for online discussions through dialogic negotiation with students).

4. Research Instrument

Starting from the demographic section, which includes students’ gender, learning experience, and major, this questionnaire aimed to collect information about students’ online learning emotional perceptions and engagement experiences. Thus, the three scales were designed to measure students’ social media burnout, academic self-efficacy, and online English academic engagement in English learning contexts.

4.1. Online Academic Burnout Questionnaire

The students’ online academic burnout questionnaire was measured by items from the Maslach Burnout Inventory (MBI) mentioned in Li et al.’s (2021) [34] learning burnout scale for Chinese foreign language learners and the social media burnout scale developed by Han (2018) [93]. The questionnaire selected two dimensions of emotional burnout and alienation from the original questionnaire and replaced Facebook with the option of using online English resources and devices. The questionnaire consisted of five items, including three dimensions of emotional burnout (four items), isolation (three items), and ineffectiveness (two items), and was scored on a five-point Likert scale from 1 (strongly disagree) to 5 (strongly agree). The reliability of the questionnaire was good, with a Cronbach coefficient of 0.891 and a structural validity KMO value of 0.876. The questionnaire had good structural validity. The reliability and validity of this questionnaire are originated from File S1 from Supplementary Materials.

4.2. Perceived Teacher Emotional Support Questionnaire

This questionnaire draws on the questionnaires used by Pianta et al. (2012) Classroom Assessment Scoring System (CLASS) [94] and Yang et al. (2022) in Perceived Teacher Emotional Support to investigate students’ perceptions and experiences of teacher emotional support in an online English learning environment [20]. The questionnaire had a total of 11 items, focusing on issues such as teachers’ positive teaching scenario settings, teachers’ perceptions of students’ online learning difficulties, and teachers’ respect for students’ personal needs. The questionnaire was scored on a five-point Likert scale from 1 (strongly disagree) to 5 (strongly agree). The adapted questionnaire had good reliability and validity with a Cronbach coefficient of 0.956; the structural validity KMO value was 0.947. The reliability and validity of this questionnaire are originated from File S1 from Supplementary Materials.

4.3. Academic Self-Efficacy Questionnaire

Based on the adaptation of the Academic Self-Efficacy Scale developed by Pintricj and DeGroot (1990) [95] and the Chinese College Students’ Online Learning Self-Efficacy Scale developed by Xie et al. (2011) [96], the Online Learning Self-Efficacy Questionnaire was designed to investigate the students’ perceptions and self-confidence in their own learning abilities and learning behaviors, etc., when they were learning English online. The questionnaire is divided into two dimensions, competence and behavioral efficacy, with ten items. Sense of competence focuses on an individual’s perception of his or her abilities, learning expectations, and goals; the sense of behavior is the manipulation and effective use of online learning activities and behaviors. The questionnaire uses a five-point Likert scale, with scores ranging from 1 (strongly disagree) to 5 (strongly agree), and the higher the score, the stronger the online learning self-efficacy. The Cronbach coefficient of the adapted questionnaire was 0.891. The KMO value was 0.922, showing the adapted questionnaire’s reliability and validity. The reliability and validity of this questionnaire are rooted in File S1 from Supplementary Materials.

4.4. Online English Academic Engagement Questionnaire

This questionnaire utilized the revised Student Learning Engagement Questionnaire developed by Wang et al. (2016) [97]. After drawing on Fredricks and McColskey’s (2012) [70] and Philp and Duchesne’s (2016) divisions of learning engagement, online English learning engagement was divided into four dimensions: cognitive, behavioral, emotional, and social [78]. The questionnaire replaced classroom learning in the original questionnaire with online English learning and converted the relevant reverse questions. The questionnaire consisted of cognitive inputs (four items), behavioral inputs (two items), emotional inputs (six items), and social inputs (four items), with sixteen items on a five-point Likert scale from 1 (strongly disagree) to 5 (strongly agree). The adapted questionnaire had good reliability and validity with a Cronbach coefficient of 0.950; the structural validity KMO value was 0.955. The reliability and validity of this questionnaire are rooted in File S1 from Supplementary Materials.

5. Data Analysis and Results

5.1. Overall Trends and Correlation Analysis of Online Academic Self-Efficacy, Online Academic Burnout, Online English Academic Engagement, and Perceived Teacher Emotional Support

Through SPSS descriptive analysis, this study explored the overall trends of the four factors of online academic self-efficacy, online academic burnout, online English academic engagement, and perceived teacher emotional support to answer Research Question 1.

Table 2. Descriptive statistics and correlation analysis among variables.

	OSE	OAB	OEE	PTE
OSE	1
OAB	−0.141 **	1
OEE	0.690 **	−0.143 **	1
PTE	0.475 **	−0.113 *	0.722 **	1
Mean	3.1431	2.8946	3.4208	3.6349
SD	0.65134	0.7429	0.65034	0.73255
Skewness	0.140	−0.089	0.243	0.053
Kurtosis	1.156	0.665	1.084	0.14
Cronbach’s Alpha	0.891	0.891	0.950	0.956

** At the 0.01 level (two-tailed), the correlation is significant. * At the 0.05 level (two-tailed), the correlation is significant. OEE: online English academic engagement, PTE: perceived teacher emotional support, OSE: online academic efficacy, OAB: online academic burnout.

shows each variable’s mean, standard deviation, skewness, kurtosis, and correlation. Based on Table 2, the mean of each variable is 3.1431, 2.8946, 3.4208, and 3.6349, respectively. The figures from Table 2 show that the overall level of perceived teacher support is high, and the mean of online academic burnout is relatively low, indicating that its overall level is low. Given that the skewness of online academic burnout is −0.089, a one-sample Kolmogorov–Smirnov test was conducted to examine the normal distribution of this data set. From

Table 3. One-sample Kolmogorov–Smirnov test.

		Burnout 1	Burnout 2	Burnout 3	Burnout 4	Alienation 1	Alienation 2	Alienation 3	Inefficiency 1	Inefficiency 2
Number of cases		450	450	450	450	450	450	450	450	450
Normal parameters a,b	Mean	2.96	2.78	2.69	2.75	2.94	2.68	2.64	3.38	3.23
	SD	0.99	1.06	1.02	1.02	1.02	0.10	1.06	0.97	1.01
Most extreme difference	Absolute	0.23	0.21	0.23	0.23	0.23	0.25	0.21	0.20	0.20
	Positive	0.20	0.19	0.20	0.20	0.21	0.22	0.19	0.20	0.19
	Negative	−0.23	−0.21	−0.23	−0.23	−0.23	−0.25	−0.21	−0.19	−0.20
Test statistic		0.23	0.21	0.23	0.23	0.23	0.25	0.21	0.20	0.20
Asymptotic significance (two-tailed)		0.000 c	0.000 c	0.000 c	0.000 c	0.000 c	0.000 c	0.000 c	0.000 c	0.000 c

a Test distribution is normal. b Calculated from the data. c Reilly’s significance correction.

, the online academic burnout data showed a normal distribution. The figures from Table 2 and Table 3 are originated from File S2 in Supplementary Materials.

To examine the two-by-two relationship between the variables and answer Research Question 2, we used Pearson correlation analysis to explore the linear relationship between the two-by-two variables and multiple regression analysis to observe the effects of multiple independent variables (perceived teacher emotional support, online academic efficacy, and online academic burnout) on the dependent variable (online English academic engagement) and their degree of influence. As shown in Table 2, the four variables in the questionnaire showed a significant two-by-two correlation: online academic burnout was negatively correlated with online academic efficacy (−0.141 **), negatively correlated with online English academic engagement (−0.143 **), and negatively correlated with perceived teacher emotional support (−0.113 *); perceived teacher emotional support was positively correlated with online academic efficacy (0.475 **) and positively correlated with online English academic engagement (0.722 **); and online academic efficacy was positively correlated with online English academic engagement (0.690 **).

The VIF values for the three independent variables can be seen in

Table 4. Summary of multiple linear regression.

		Unstandardized Coefficients		Standardized Coefficients	t	Significance	Collinear Statistics		R2	Sample Independence
		B	Direct Error	Beta			Tolerance	VIF		D-W
OEE	Constant	0.439	0.131		3.346	0.001			0.678	1.908
	PTE	0.451	0.027	0.508	16.608	0.000	0.772	1.295
	OSE	0.445	0.031	0.446	14.515	0.000	0.766	1.305
	OAB	−0.02	0.024	−0.022	−0.821	0.412	0.977	1.023

OEE: online English academic engagement, PTE: perceived teacher emotional support, OSE: online academic efficacy, OAB: online academic burnout.

as 1.295, 1.305, and 1.023, with values less than 10, showing no covariance between the variables. The overall multiple regression model for each variable is significant, and at least one independent variable significantly affects the dependent variable. The figures in Table 4 are from File S2 in Supplementary Materials. The significance of the model in Table 4 shows that the variables are correlated. However, from the specific values of the significance and the value of the standardized coefficient Beta, it can be found that the significance of online academic burnout is 0.412, exceeding the criterion of 0.05. Its explanatory power for online English academic engagement must be more substantial in the regression model. The other two factors have strong explanatory power. The Beta coefficients of the three independent variables indicate that the change in units of this independent variable increases by 0.508 units, increases by 0.446 units, and decreases by 0.022 units on online English academic engagement. In contrast, the other independent variables remain unchanged. Based on the Durbin–Watson test, its value of 1.908 is less than 2, showing independence between the samples. The R-square value is 0.678, so the three independent variables, namely, teachers’ emotional support, online academic efficacy, and online academic burnout, can explain 67.8% of the change in the dependent variable of online English academic engagement.

5.2. Mediating Effects of Perceived Teacher Emotional Support, Online Academic Self-Efficacy, Online Academic Burnout, and Online English Academic Engagement

To answer the third Research Question, Process V3.5 (Model 4), an analytical tool developed by Hayes, was used to investigate the direct and indirect effects of perceived teacher emotional support on online English language academic engagement. Based on the examination of two mediators, online academic self-efficacy and online academic burnout, the study used the deviation-corrected nonparametric positive percentile Bootstrap method to explore the direct and indirect effects of perceived teacher emotional support on online academic engagement in English, as well as 5000 interactions with 95% confidence intervals for self-help sampling to validate the mediator and whether or not the model was significant (see

Table 5. Partial mediation model analysis.

Item	Symbol	Meaning	Effect	95% CT		p-Value
				Lower Limit	Upper Limit
PTE → OSE → OEE	a × b	Indirect effect	0.1880	0.1288	0.2476	0.000
PTE → OSE	a	X → M 1	0.4753	0.3500	0.4952	0.000
OSE → OEE	b	M 1 → Y	0.4450	0.3847	0.5052	0.000
PTE → OEE	c’	Direct effect	0.4510	0.3977	0.5044	0.000
PTE → OEE	c	Total effect	0.6413	0.5843	0.6983	0.000
PTE → OAB → OEE	a × b	Indirect effect	0.0022	−0.0036	0.0153	0.691
PTE → OAB	a	X → M 2	−0.1129	−0.2080	−0.0209	0.0166
OAB → OEE	b	M 2 → Y	−0.0195	−0.0663	0.0373	0.4121

¹ indicates that PTE has a significant indirect effect on OEE through OSE. ² is used to explain the indirect effect of PTE on OEE through OAB.

). As seen in Table 5 and File S3 in Supplementary Materials, perceived teacher emotional support was a significant predictor of the mediating variable, online academic efficacy (β = 0.4753, p < 0.001), with a t-value of >1.96. Perceived teacher emotional support was a significant predictor of the mediating variable, online academic burnout (β = −0.0.1129, p < 0.05), with a t-value of <−1.96. If both variables were mediated, the perceived teacher emotional support positively predicted online English academic engagement (β = 0.5080, p < 0.001). Furthermore, online academic self-efficacy had a positive predictive effect on online English academic engagement (β = 0.4457, p < 0.001). Online academic burnout, however, was not a significant predictor (β = −0.195, p > 0.05). The mediating effect of online academic burnout was insignificant if the confidence interval for online academic burnout contained zero. Perceived teacher emotional support significantly predicted online English academic engagement (total effect β-value = 0.7224, p < 0.001, t-value > 1.96). The direct effect of perceived teacher emotional support on online English academic engagement was 0.4510, accounting for 70.33% of the total effect. The indirect effect of perceived teacher emotional support on online English academic engagement through online academic efficacy was 0.1880, which accounted for 28.85% of the total effect. Therefore, online academic efficacy is partially mediated (see Figure 1).

5.3. Influencing Factors of Perceived Teacher Emotional Support

In the semi-structured interview, referring to the question “What kind of online learning atmosphere do you think makes you feel like actively expressing yourself and joining in classroom interactions?” students used the following expressions, “The teacher uses humorous phrases and combines them with the current affairs or some interest things”, “The teacher encourages and supports expression verbally or in writing”, “The teacher recognizes my expression and does not criticize or deny me freely”, “The teacher gives me timely rewards such as extra credit or praises me in class.“, etc. All the student interviewees prefer to gain teachers’ feedback, especially words of confirmation and emoticons. Students mentioned that they are digital natives who like to use emojis or stickers to show their intimacy, approval, or identification. They prefer to have a relaxed and dialogic climax. All of the above students’ responses reflect the questionnaire’s perceived teacher support in the creation of the classroom environment, the creation of a favorable learning atmosphere, etc., which can be seen from the questionnaires and interviews in the students’ emotional feedback on the creation of a positive classroom atmosphere and the needs of the students with regard to the teachers of the online English classroom. Students felt that relaxed and autonomous classroom communication would make them less nervous and less afraid of making mistakes.

At the same time, emotional encouragement and affirmation from the teacher would increase students’ confidence, thus making them more enthusiastic and expressive. It can be seen that teachers’ perception of students’ emotions in perceived teacher emotional support is a positive predictor of students’ motivation and learning efficacy. The questionnaire explored whether the teacher responded promptly to students’ difficulties and allowed them to express their views. In the interviews, we consulted with teachers about which situations, instruction tasks, or sessions they show their affirmation, encouragement, or praise in. The students expressed that it occurred during their feedback assignments, classroom interactions, and group presentations. Regarding which specific teacher emotional support made them more confident, proactive, or relaxed, students reported that teachers’ positive feedback and proactive questioning made them feel more valued and gave them more opportunities to express their doubts or share their ideas boldly.

In response to the questionnaire’s mention of students’ perception of the teacher’s concern, guidance, and help in their online English learning, what do students think or do in the interview when they express what they think or do when perceiving a teacher’s emotional support? Student A expressed that when his teacher noticed his hesitation or lack of confidence in speaking, his teacher would help him carefully and did not let him feel embarrassed. He felt more relaxed in the rest of the study session and was more willing to join in group discussions and express his opinions. Student B expressed that her efforts were recognized for extra credit, and the teacher thought she was active in the classroom, which made her more confident in expressing her ideas. All the student interviewees were willing to accept the teacher’s emotional support, since it was beneficial for them to build self-confidence. Some students stated that even though technology provides everything and aids them, they still have the desire to negotiate with others, especially teachers or authorities. Technology cannot truly understand them. Students’ classroom engagement, motivation, and enthusiasm for learning are all indispensable factors in promoting students’ academic self-confidence and efficacy, which are closely related to students’ emotional support from their teachers.

We also interviewed groups of teachers in online English language teaching. Teacher A expressed that when learning English online, a part of the data of students’ online feedback or interaction is based on the interactive information from the intelligent platform or mobile APP. This kind of information can visually show the number of interactions and active states of students in the classroom through backstage data collection. Therefore, it is more capable of discovering the students’ points of interest and blind spots in terms of attention to the students than a traditional classroom. Teacher B’s feedback on current information technology in relation to students is that they are actually very good at typing and sharing, and their copying and pasting is also perfect, which leads to the teaching of students to answer some of the same expressions of a problem. Technology-enabled teacher emotional support can create a good classroom atmosphere and stimulate students’ learning initiative, self-confidence, and points of interest. However, it is essential to pay attention to information in online English learning in terms of how to balance the teacher’s emotional support of the cultivation of student’s cognitive abilities, such as concentration and learning beliefs in online English learning, to carry out in-depth learning more efficiently, mobilize students’ positive emotions, and enhance learning effects.

6. Discussion

This study focused on the effects of perceived teacher emotional support on students’ negative academic emotions with regard to academic and online academic burnout in an online teaching environment. It examined the effects of perceived teacher emotional support on students’ online academic efficacy, with the final test mediated by the question of whether perceived teacher emotional support affects students’ online English academic engagement, thus examining the correlation of the four factors. The findings of this study argued that perceived teacher emotional support could play a role in attenuating or eliminating negative academic emotions such as online academic burnout, which is consistent with the findings of Li et al.‘s (2019) study of secondary school students’ comprehension of teachers’ emotional support and its impact on academic burnout [59]. Perceived teacher emotional support in this study had a positive predictive effect on online academic efficacy, and the above findings are consistent with those in Yang et al.’s (2022) study of perceived teacher emotional support among college students [20]. Teachers’ concern for students’ difficulties and completion of online learning tasks will help students to construct self-confidence. It is known from the study that perceived teacher emotional support has an impact on both students’ academic efficacy and online academic burnout, which is consistent with the formulation of academic burnout mentioned in the findings of Zhao et al. (2018), Huang et al. (2018), Li et al. (2019), Suhaimi and Hussin (2017), and Ugwu et al. (2018) [40,56,58,59,81]. The stress wrought by media information technology hinders learners’ effectiveness and ability to learn. In this study, online academic burnout harms online academic engagement. This study used academic efficacy as a mediator to examine the effect of perceived teacher emotional support, ultimately, on students’ online English academic engagement. This is consistent with the results of Zhou et al.’s (2023) empirical study of perceived teacher emotional support and social engagement among students, where self-efficacy mediated the relationship between perceived teacher emotional support and interactive engagement among English learners [62]. This finding also aligns with Yang et al.‘s (2021) study in which academic self-efficacy mediated the representation between academic burnout and students’ academic satisfaction [91]. The amount of direct effect and the percentage of total effect (0.410, 70.33%) of perceived teachers’ emotional support on online English academic engagement in the questionnaire makes it easy to see its importance for online English academic engagement. This finding is consistent with the findings of Li and Lei (2012) and Li et al. (2019) [54,59]. Teachers’ emotional support helps students to overcome online learning problems and difficulties once correctly perceived and understood by students.

Regarding the causes of online academic burnout, based on interviews and questionnaires, there are mainly the following triggering factors. First, when excessive learning information challenges or even burdens students’ cognition, students’ enthusiasm and self-confidence decline, and their learning commitment is also affected. Second is the impact of students’ personality traits on online commitment. Students who are more confident in themselves are usually better informed to complete various communicative tasks and are more willing to join in group discussions. Traditional Chinese values emphasize respect for teachers and elders as well as face-saving, especially when there are conflicts. Students usually adopt the principles of avoidance or politeness and humility when questioned or challenged. This makes the teachers’ emotional support emerge mostly in the form of encouragement, while Chinese students prefer to get answers to questions and authority issues from the group’s decision-makers or mentors. Teachers’ traditional authoritative and didactic representations of emotion are no longer appropriate for today’s students; negotiation and dialog are the essence of online communities. This finding also aligns with the study of He et al., 2023, Zhou et al., 2023, and Xiao and Zhou, 2014 [61,62,66].

The traditional discourse and non-verbal communication of teachers’ body gestures, eyes, and other emotional expressions are being refined and revolutionized with the updating of information technology. Information media technology empowers teachers with intelligent information assistants. With AI smart teacher assistants and interactive tools, teachers create a sense of emotional presence [66]. Interactive discussion boards and emoticons in online learning were among the emotional representations, experiences, and interactions recognized by students and teachers in the interviews. It can be seen that the teachers’ emotional support in the online teaching environment realizes the spatial and temporal sense of the teachers’ emotional support, i.e., through synchronous and asynchronous exchanges online, the students can feel the teachers’ attention to their English learning from the teachers’ interactive feedback and the teachers’ evaluation of the learning data and interactive participation, thus increasing their internal emotional feelings of being paid attention to, being recognized, and being supported, and reducing the number of students who cannot be targeted in the large classroom during the English teaching process. It also reduces students’ inability to express and solve their English learning problems in large classes and effectively connects with teachers to seek inspirational help. The development and innovation of technology in the presentation and perception of online teachers’ emotional support will affect the students’ English learning effectiveness and commitment to learning online.

As the ratio of the direct and indirect effects of online teachers’ emotional support on students’ English online academic engagement in the questionnaire shows, it is easy to see that teachers can perceive the critical impact of online emotional support on students’ English online academic engagement. In the technology-enabled online English education environment, online learning platforms, learning materials, learning data, etc., are constantly being updated. However, how the technical support aspect of online teachers’ emotional support can be perfectly embedded into current online English learning is an issue worthy of in-depth consideration. Since technology empowers English teaching, and eye movement, brain electricity, bio-wave, and various big data analysis tools have had a radical impact on English learners and the learning environment, what is the position and significance of the teachers’ emotional support function regarding their role in English online education in the field of technological empowerment today? How do we embody the teacher’s educational nurturing function in addition to knowledge transfer and technological innovation? How do we fully embody the functions of caring, consideration, respect, and empathy in emotional education in educational evaluation and the educational process? These are the questions that we, as educators and researchers, still need to address and reflect on when students complain about the lack of warmth in online classrooms.

7. Limitations

Due to the limitations of research conditions, research area, research subjects, and funding, the research still needs to explore the four sub-dimensions of the factors of perceived teacher emotional support, online academic burnout, online academic efficacy, and online English learning commitment. This study does not involve research on perceived teacher emotional support of students in different courses, groups, and cultural backgrounds. The research on online academic emotions only focuses on negative academic emotions. In contrast, positive academic emotions significantly impact students’ motivation and enthusiasm for learning and need more attention. In addition, the analysis of different students and cultural groups can reflect on the mechanism of teacher emotional support, its impact on students’ learning outcomes, and the development of a multifaceted approach to perceived teacher emotional support. In comparison with non-technical students, technical students may have various opinions about online learning environments due to differences in professional backgrounds and information technology literacy. There may be differences in emotional acceptance styles and evaluations of emotional perceptions with non-technical students, which also suggests a more in-depth direction for conducting research on teachers’ emotional support, investigating whether differences in information literacy and technology acceptance have different impacts on perceived teacher emotional support as well as on students’ self-efficacy and engagement in learning.

8. Implications and Conclusions

Due to the intervention of media information technology and various innovative terminal platforms, online English language teaching has been regarded as a teaching mode that can cross the boundaries of time and space, effectively promote learners’ enthusiasm and motivation, and enhance students’ self-directed learning strategies (Park and Flowerday, 2015; Pishghadam et al., 2023 [19,88]). However, with adverse problems in online learning, such as decreased motivation and satisfaction and lack of attention to learning, negative academic emotions regarding academia in online English language teaching and their implications have attracted increasing attention from teachers and researchers. Given the reflections presented in the questionnaires and interviews, this paper theoretically explores the mechanisms by which perceived teacher emotional support affects the achievement aspects of online English language learning, focusing on the dimensions of online academic burnout and academic efficacy. Practically, this study focuses on the dimensional aspects of perceived teacher emotional support in online learning environments and empirically investigates the influence of students’ academic mood, learning efficacy, and academic engagement, as well as the influence factors of perceived teacher emotional support.

The present study further explores theoretically the role of teacher emotional support in learners’ emotional experience and transfer in the age of online learning and intelligence. The study connects the perceivability of teacher emotional support with teacher emotional presence, thus deepening the research on teacher emotional power development and its enhancement strategies. Student online engagement, satisfaction, and self-efficacy are precisely the positive outcomes that follow the positive impact of a teacher’s emotional power. In a technologically intelligent world, there is a potential for alienation and estrangement of emotions between teaching subjects in online education. The question of how to establish new ways of learning that are negotiated, dialogic, and critical is also an important aspect of building healthy self-perceptions by freeing people from being controlled by the constraints of technology. Research on emotional support for online instructors has an integral role to play in the synergy of teaching and student–teacher sustainability. Teachers’ emotional support in online education comprises listening and negotiating, and it points to a harmonious educational ecology of teacher–student–technology.

Analyses across different student and cultural groups can provide further insights into the mechanisms of teacher emotional support, its impact on student learning outcomes, and the multifaceted development of perceived teacher emotional support. Case studies or experimental research on different curricula and teaching activities will expand the practical exploration of teacher emotional research, thus enriching students’ perceptions and descriptions of teacher emotional support and effectively guiding teachers’ classroom practice. The use and perception of perceived teacher emotional support in different teaching scenarios, instruction activities, and objects are aspects of perceived teacher emotional support that need to be given more attention. The role of media technology in perceived teacher emotional support and its assistive mechanism is an aspect of online English learning that should be addressed. Currently, digital-media-assisted teaching focuses on supporting teachers’ teaching techniques, teaching materials platforms, and teaching interactions with relatively more instrumental support. However, the expression and presentation of teachers’ emotional support needs improvement. In addition, future research on teacher emotional support is closely related to the current development of information media. Intelligent learning has given rise to teachers’ educational decision-making inside and outside the classroom, AI-assisted intelligent support, and cognitive outsourcing. It has also raised new topics for research in the field of teachers’ emotional support, which are worth our attention and exploration. The alleviation of information technology pressure and cognitive load in technology-enabled foreign language teaching, constructing pleasant emotional experiences, and cultivating positive social emotions and language cognitive abilities are precisely the direction of teachers’ continuous efforts in constructing emotional support.