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Article

Impact of Teenage EFL Learners’ Psychological Needs on Learning Engagement and Behavioral Intention in Synchronous Online English Courses

by
Sijing Zhou
*,
Huiling Zhu
and
Yu Zhou
College of Foreign Languages, Hunan University, Changsha 410082, China
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(17), 10468; https://doi.org/10.3390/su141710468
Submission received: 18 June 2022 / Revised: 16 August 2022 / Accepted: 18 August 2022 / Published: 23 August 2022
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Abstract

:
There is a relatively small body of literature that is concerned with the extent to which students are actively engaged in online English learning. To address this issue, the present study investigates 233 Chinese secondary school students attending online English courses during the pandemic and explores the degree to which learners are behaviorally, cognitively, emotionally, and socially engaged in synchronous online English courses. Three basic psychological needs (autonomy, competence, and relatedness) are examined as antecedents of learning engagement, and the behavioral intention is hypothesized as a learning outcome. This study takes a mixed-methods approach, integrating quantitative data from questionnaires and qualitative data from semi-structured interviews. A partial least squares (PLS)-structural equation modeling (SEM) technique was used to test hypotheses and the proposed research model. The quantitative findings indicate that, firstly, whereas the basic psychological needs predict students’ four dimensions of online learning engagement, competence is confirmed to be the strongest predicting factor. Secondly, behavioral intention is significantly influenced by students’ cognitive engagement and emotional engagement. Thirdly, thematic analysis of the qualitative data shows that students tend to have a lower level of engagement compared with a face-to-face classroom learning enviroment, and a more interesting and interactive online course design is crucial to the fulfillment of learners’ psychological needs of autonomy and relatedness in synchronous online English learning.

1. Introduction

The suspension of classroom learning during the coronavirus pandemic expanded into a broad spectrum of educational settings, from elementary to higher education in many parts of the world. This unexpected crisis brought about revolutionary changes in teaching and learning. A large number of learners were involved in a large-scale emergency online teaching and learning mode, which was identified as “Emergency Remote Teaching” (ERT) [1]. In China, ERT during the pandemic took the form of “synchronous online learning” in an effort to maintain learning in a time of crisis, when “learners attend live structures, and there are real-time interactions between educators and learners, and there is possibility of instant feedback” [2] (p. 7). For a majority of elementary and secondary school students, it was the first time ever to take part in online course learning, which was regarded as “unprecedented in scale and scope” in China [3] (p. 2). Meanwhile, it is estimated that China has the largest proportion of English-as-a-foreign-language (EFL) learners in Asia, and even in the world [4]. Investigation on Chinese EFL learners is supposed to contribute to a better understanding of online foreign language learning in other contexts and areas as well.
Although online learning has been widely investigated in recent years, one of the main concerns is to what extent students are actively engaged in online English learning. In the foreign language learning field, researchers pointed out that language learning is a complex and challenging process, especially in online environments, and it requires plenty of behavioral, cognitive, and affective factors to persist in it [5]. The concept of engagement is becoming increasingly popular in the educational field nowadays. Fredricks et al. [6] classified it into behavioral, cognitive, and emotional engagement, which could correspond with the complex language learning process. Although online teaching and learning is quickly and successfully conducted in China as an emergency method to not suspend educational activities, it poses challenges to both instructors and students to heighten engagement in online learning courses. As online teaching conducted during the pandemic is without careful preparation [1], it is a concern whether inexperienced online learners, who have never used online platforms for formal knowledge learning, really engage in online English courses. Very recently, Ji, Park, and Shin [7] confirmed a positive correlation between South Korean undergraduate students’ learning engagement and satisfaction in a synchronous online second language learning environment. However, there is still a relative paucity of empirical research focusing specifically on high school students’ engagement in synchronous online learning courses.
Moreover, online English teaching and learning during the pandemic usually took place in a home environment, which is different from traditional school learning [8]. Students may have different engagement experiences in the home online learning environment. Hence, it is of great significance to explore students’ perceptions and performance in the synchronous online learning practice. In fact, researchers have long recognized and emphasized the necessity of students’ learning engagement in online settings [9,10]. The authors of [11] mentioned the importance of engagement in improving learners’ academic achievement in online foreign language courses. Most of the current research is concerned with how to promote learning engagement by incorporating various technological devices [12]. However, little is known so far about “the subconscious sides of engagement and technology-driven real-time, authentic data that can be used to test hypothesis” [13] (p. 21). In particular, there are relatively limited studies that are conducted through a mixed-methods approach to investigate teenage learners’ engagement, particularly in synchronous online English learning environments.
This study aims to explore psychological reasons to predict online learning engagement from the perspective of basic psychological needs. As one of the sub-theories of self-determination theory, three basic needs include one’s fulfillment of autonomy, competence, and relatedness [14]. Satisfaction of these psychological needs is reported to enhance learners’ intrinsic motivation, and motivation could act as a force to encourage students to actively engage in the learning process [8]. These three needs are reported to be effective in predicting college students’ involvement in the mobile learning context [15], and they are found to be essential in predicting Chinese students’ academic achievements [16]. However, limited understanding is reached on secondary school students’ psychological needs during the pandemic, and few studies have concentrated on the impact of learners’ psychological needs on their engagement and behavioral intention of future English learning in the online education context.
To gain a deeper understanding of Chinese secondary school students’ learning engagement in synchronous online English courses, this study incorporates self-determination variables with engagement to predict teenage EFL learners’ behavioral intention. Specifically, the present research aims to answer the following four questions:
Research Question 1: What are the general profiles of Chinese secondary school students’ basic psychological needs and learning engagement in synchronous online English courses?
Research Question 2: What impact do students’ psychological needs (autonomy, competence, and relatedness) have on their learning engagement (i.e., behavioral, cognitive, emotional, and social) in synchronous online English courses?
Research Question 3: What is the relationship between students’ learning engagement and behavioral intention in synchronous online English courses?
Research Question 4: What are the individual, psychological, and contextual factors that influence students’ learning engagement and behavioral intention in synchronous online English courses?

2. Theoretical Background

2.1. Technology-Assisted Language Learning

Technology-assisted language learning takes a wide range of forms, for instance, computer-assisted language learning, mobile-assisted language learning, robot-assisted language learning, and virtue world language learning [17]. Foreign language education has embraced a wide range of technology to enhance efficiency and positive outcomes in EFL learning. On the one hand, technology adoption is a concerning aspect in explaining learner intention in English writing [18] and speaking [19]. Fathali and Okada [20] incorporated basic psychological needs into Technology Acceptance Model (TAM) in predicting Japanese EFL university students’ intention in the technology-enhanced out-of-class language learning environment. On the other hand, various studies have highlighted factors associated with learners’ learning experience, attempting to figure out how to improve the effectiveness of online language learning. For instance, Ma [21] observed that peer online feedback on Wiki writing was beneficial for English writing. Factors such as basic psychological needs [22], learning motivation, anxiety [23], and self-regulated learning [24] were also investigated in an online language learning environment.
In addition, a large volume of published studies is concerned with the efficiency of language learning assisted by technology. Employing a mobile learning system, Shadiev et al. [25] found out that Chinese learners regarded mobile learning systems as useful in improving their language learning performance in unfamiliar environments. Focusing on specific language skills, Tsai [26] confirmed the effectiveness of Google Translate in EFL writing. Similarly, Li [27] revealed the efficacy of the mobile learning application Baicizhan in improving Chinese EFL college students’ vocabulary learning achievement.
However, there is a relatively small body of research on the topic of engagement in the technology-assisted language learning field. Yang [28] developed an online learning system in order to explore EFL undergraduates’ engagement and their learning progress in such a context, which provides a general profile of students’ language learning experience. More recently, researchers [5] have proffered that language learning is a complex and challenging process, especially in online environments, requiring plenty of behavioral, cognitive, and affective factors to persist in it. As a consequence, more research should be carried out on learning engagement in order to grasp a much more comprehensive perspective on technology-assisted language learning.

2.2. Basic Psychological Needs

Self-determination theory (SDT) is a well-established macro theory on human motivation. SDT is one of the most comprehensive and widely supported theoretical approaches related to human motivation in educational settings [10,20], examining three universal and fundamental psychological human needs, that is, autonomy, competence, and relatedness. SDT contends that intrinsic motivation will be sustained and enhanced when these three needs are fulfilled [14,29]. Previous literature has focused on the validity of SDT in educational fields both in traditional classrooms [29] and online learning environments [30,31]. In some other studies, self-determination variables were viewed as antecedents influencing learners’ perceptions, such as perceived behavioral control, attitude [32], and behavioral intention [33].
In recent years, there is a surging interest in the associations between engagement and psychological needs in relevant studies. A large number of recent studies have discovered that the fulfillment of these psychological needs helps facilitate engagement and maintain satisfying learning outcomes or better achievement in various cultural contexts [31,34]. For instance, Lan and Hew [35] conducted a comparative study between Massive Open Online Courses (MOOC) completers and non-completers, revealing that basic psychological needs could positively predict MOOC participants’ behavioral, cognitive, and emotional engagement.
Moreover, Schneider et al. [36] concluded that autonomy is a strong catalyst for engagement in learning. The autonomy-supportive learning environment in educational settings leads to more active learning engagement and also higher levels of achievement [37,38]. Meanwhile, Fang et al. [39] found that competence need was the most influential factor in predicting learners’ engagement in MOOCs. Besides, Olivier et al. [40] documented that for most students, especially girls, relatedness is crucial in explaining behavioral, cognitive, and emotional engagement. Relatedness was reported to provide online learners a feeling of connectedness, which encouraged learners to exchange opinions with peers [39], and thus social engagement was enhanced.
As for foreign language education studies, Dincer et al. [41] employed a framework which helped to confirm that the three basic psychological needs within the self-determination framework are working as self-system factors influencing learners’ subsequent engagement. They posited that the psychological needs positively predicted EFL learners’ behavioral, cognitive, emotional, and agentic engagement. Agentic engagement refers to “learners’ constructive contribution to their learning activities” [41] (p. 2). More research is needed to determine whether psychological needs predict social engagement in online learning environment. Besides, basic psychological needs are proven to be able to predict dimensions of engagement among elementary school students [42] and university students [41] in EFL learning settings. Therefore, in accordance with the previous literature, we assume that:
Autonomy(H1), competence(H2), and relatedness(H3) significantly and positively influence Chinese secondary school students’ engagement in the following areas:
a.
Behavioral (H1a; H2a; H3a)
b.
Cognitive (H1b; H2b; H3b)
c.
Emotional (H1c; H2c; H3c)
d.
Social (H1d; H2d; H3d)

2.3. Engagement and Behavioral Intention

Acting as a meta-construct [6], engagement refers to one’s active involvement and ongoing efforts during the learning process to perform well and achieve desired learning goals in academic settings [35,43,44]. Engagement has been measured as an inclusive unidimensional construct [45], whereas from a dichotomy perspective, scholars divided it into psychological and behavioral components in their research [46,47]. Fredricks et al. [6] proposed that as a multidimensional notion, engagement includes three different subcategories, i.e., behavioral engagement, cognitive engagement, and emotional/affective engagement. Behavioral engagement is usually a common and frequent criterion aspect. It refers to positive conduct and participation in learning tasks, such as adhering to classroom norms, attending classes, and doing homework [6]. Cognitive engagement refers to the adoption of learning strategies to acquire knowledge in the learning activity [41,45]. Emotional engagement refers to the degree to which students enjoy and hold an affective attitude towards teachers, peers, the course, and their learning experience [12,48]. Wang et al. [10] further expanded the dimensions of engagement and maintained that social engagement is conceptually valid and should also be included as one of the engagement dimensions because social interaction is considered pivotal in the context of classroom tasks. Social engagement refers to “either enjoying/participating in or withdrawing from collaboration or social interaction with peers and instructors” [49] (p. 3). It is a relatively new dimension being integrated into the engagement framework.
A growing body of literature has proffered that in general, engagement could positively predict students’ learning performance and achievement [6,45]. Both in traditional and online classrooms, high levels of engagement could effectively reduce learners’ behavioral problems and prevent dropout rates or absenteeism [41,50]. In particular, Xiong et al. [51] found out that in a MOOC context, university student engagement could significantly predict their retention and sustainability in the course. In the same vein, Jung and Lee [44] also reached similar conclusions in the K-MOOCs setting. In Saudi Arabia, EFL learners reported high levels of engagement in online language courses during the coronavirus pandemic [52]. Moreover, Dincer et al. [41] revealed that higher engagement could predict higher achievement and less absenteeism in the Turkish EFL context. Based on the findings reported in previous research, we hypothesize that:
H4a.
Behavioral engagement significantly and positively influences Chinese secondary school students’ behavioral intention.
H4b.
Cognitive engagement significantly and positively influences Chinese secondary school students’ behavioral intention.
H4c.
Emotional engagement significantly and positively influences Chinese secondary school students’ behavioral intention.
H4d.
Social engagement significantly and positively influences Chinese secondary school students’ behavioral intention.

2.4. Proposed Research Model

Grounded on SDT, the learning engagement framework, and relevant literature, the research model (Figure 1) is proposed for the present study, aiming to explore four dimensions of engagement in predicting students’ behavioral intention with learners’ basic psychological needs as the independent variables in a synchronous online English learning context. Based on the above literature review, research hypotheses are therefore developed, and the research model is displayed as follows:

3. Methodology

3.1. Research Context and Participants

A convenience sampling technique is employed in this study. A total of 233 Chinese secondary school students in mainland China, who mainly came from Henan, Jiangxi, and Anhui provinces, took part in the current investigation during the COVID-19 period. As shown in Table 1, the participants consisted of 137 females (58.8%) and 96 males (41.2%). Senior one students constituted 36.9%, senior two 53.2%; the rest were senior three students, aging from 15 to 18 years old (mean age 16.7 years). English, as a compulsory subject, is taught in both primary and secondary schools in China. An EFL learner in China generally takes English courses for 3–6 years in primary school, and then for 6 years in secondary school (junior and senior high school). At the time of the survey, the majority of participants, about 90.2%, had taken synchronous online English courses for two months and even much longer.
Meanwhile, learners use varying learning platforms to take online English courses. However, the content of instruction is comparable as the English courses are supposed to be designed under the guidance of English curriculum standards for senior high schools (2017 edition) issued by China’s Ministry of Education. The synchronous online English courses were supposed to last for 45 min each day, aiming to improve learners’ four English skills of listening, speaking, reading, and writing. However, due to instructors’ personal teaching styles and individualized course designs, there might be different emphasis.
The data also revealed that students had abundant online learning experiences during the pandemic. Students were informed that their participation was voluntary. The online English course they took during the crisis period was credit-receiving. Similar to a traditional English classroom, the total online learning mode in the context was still of instructor-led style and with synchronous characteristics.

3.2. Measurement Instruments

The online questionnaire used in the research consists of four parts: demographic information, basic psychological needs scale, student engagement questionnaire, and behavioral intention. There were 31 items in total. Demographic information concerns participants’ age, gender, educational level, duration in synchronous online English courses, and online learning platforms. A basic psychological needs scale was adapted from Sun et al. [46] and [53], which includes three subscales, i.e., fulfillment of autonomy, fulfillment of competence, and fulfillment of relatedness. The student engagement questionnaire comprises four sub-constructs: behavioral engagement, cognitive engagement, emotional engagement, and social engagement, which was adapted from recently validated scales developed by Reeve [54], Strong et al. [55], Bergdahl et al. [49], and Liu et al. [56]. The construct “behavioral intention” was derived from Joo et al. [57] and Liu et al. [58]. The 5-point Likert-type scale was employed in the questionnaire, which ranged from 1 (strongly disagree) to 5 (strongly agree). The final version contains 31 items and it was distributed to Chinese secondary school students via Wenjuanxing (https://www.wjx.com)(accessed on 6 April 2020), which is a Chinese commercial online survey service provider and is a widely-used platform for distributing and collecting surveys in China. The adapted questionnaire is presented in Appendix A.
Ten open-ended questions were designed for semi-structured interviews aiming to delve into participants’ perceptions about their synchronous online learning experiences. The draft interview questions were examined by two experts in terms of clarity and validity.

3.3. Research Design, Data Collection, and Data Analysis

This study takes a mixed-methods approach, integrating quantitative data from self-reported questionnaires and qualitative data from semi-structured interviews. The questionnaire was presented to students mainly via Wechat or QQ messages during April 2020. The questionnaire respondents were informed of the aim of this survey, and their participation was voluntary and anonymous. Finally, 233 Chinese secondary school students completed the questionnaire. Subsequently, one-to-one online interviews were conducted among five respondents who were willing to participate in order to further reflect learners’ perceptions about their synchronous online learning experience. Several focal questions were asked during the interviews. Mandarin or Chinese dialects were used to allow participants to express their opinions more thoroughly. Interviews were all audio-recorded for later transcription and coding, on average, lasting 21.9 min with a standard deviation of 3.2.
Quantitative data were analyzed by SPSS 20.0 and structural equation modeling (SEM) software smart partial least squares (PLS) 2.0. Finally, 192 samples were used to test the hypotheses and proposed research model because any case with a z score greater than |2.0| is deemed as an outlier that may exert possible interference with the results [30]. SPSS is employed for descriptive analysis. PLS-SEM is a second-generation regression technique that utilizes confirmatory factor analysis and linear regression to run both the measurement and structural models at the same time [59]. PLS-SEM was used to assess the underlying structure of the measurement model in this study for several reasons. First, it is appropriate for complex structural relations with many latent and manifest constructs when compared with other SEM approaches [60]. Second, the primary concern of this research is theory development and prediction, and according to Hair et al. [61], the PLS-SEM approach is a better choice.
The analysis of the qualitative data was conducted in four steps. At first, the five audio recordings were carefully transcribed and read through. Next, the verbatim transcriptions were initially coded. Then, these initial codes were categorized into different themes. Coding and categorizing were mainly finished by the first author. The others examined and reviewed the two processes to guarantee that the two processes were consistent and that the data were pertinent to the assigned codes and themes. Finally, emerging themes were reviewed and discussed by the three authors to prevent overlapping.

4. Results

4.1. Results of Quantitative Analysis

4.1.1. Descriptive Statistics

Table 2 displays the SPSS analysis results of descriptive statistics of all constructs. As reported in the table, all constructs’ means are greater than the midpoint of 3, ranging from 3.319 to 3.922, illustrating that most of the students show aspiring attitudes towards these eight variables in the current research. Chinese secondary school students deemed their basic psychological needs were relatively satisfied in the synchronous online English courses. Besides, they rated themselves as active participants in the online courses in behavioral, cognitive, emotional, and social aspects. As can be seen from Table 2, the standard deviations range from 0.657 to 0.902, indicating a narrow spread around the mean. As reported in Table 2, for all of the constructs, both the Skewness and Kurtosis meet the required standards [62], suggesting the data is normally distributed.

4.1.2. Measurement Model

The measurement model was tested in terms of reliability and validity. As displayed in Table 3, all items show factor loadings higher than 0.70, suggesting adequate individual item reliability. The composite reliabilities (CR) were above 0.7 and Cronbach’s alpha coefficients also exceeded 0.7, identifying a satisfactory internal consistency [60]. The constructs in the research model had a good convergent validity, as the average variance extracted (AVE) values in Table 3 were all above the recommended value of 0.5 [63]. According to Fornell–Larcker Criterion, the square roots of the AVE for a particular construct should be higher than any correlation values with any other latent variables [63]. Table 4 showed that the square roots of the AVE for each construct are greater than its correlation with all other constructs. For instance, the square root of the AVE for the construct AUTO is 0.820, which is greater than its correlation with all other constructs. Therefore, the constructs in the research model are considered to have acceptable discriminant validity.

4.1.3. Structural Model

The proposed model was tested to examine research hypotheses and explanatory power. Figure 2 visualizes the results of the structural model, including path coefficient estimation and predictive capacity (R2). Note that the statistically insignificant paths are represented by dotted lines, which also means that there are no values visible in Figure 2. A bootstrapping procedure was employed to examine whether the path coefficients are at a significant value, and the sub-sample was set to be 5000 [61]. The path from autonomy to BE (β = 0.373, t = 4.520, p < 0.001), to CE (β = 0.381, t = 5.691, p < 0.001), to EE (β = 0.300, t = 2.546, p < 0.05), to SE (β = 0.278, t = 3.003, p < 0.01) are all found to be significant, supporting H1a, H1b, H1c, and H1d. Competence is also shown to significantly and positively influence BE (β = 0.320, t = 4.293, p < 0.001), CE (β = 0.547, t = 8.870, p < 0.001), EE (β = 0.508, t = 4.932, p < 0.001), and SE (β = 0.456, t = 6.276, p < 0.001), thus H2a, H2b, H2c, and H2d are all supported. As for relatedness, it is positively related with BE (β = 0.269, t = 4.214, p < 0.001) and SE (β = 0.250, t = 3.054, p < 0.01), however, paths from relatedness to CE (β = 0.023, t = 0.441) and to EE (β = 0.084, t = 1.232) are shown to be insignificant. Thus, H3a and H3d are supported whereas H3b and H3c are not. It is shown that CE (β = 0.251, t = 2.268, p < 0.05) and EE (β = 0.566, t = 5.499, p < 0.001) have great effects on BI whereas BE (β = −0.150, t = 1.548) and SE (β = 0.046, t = 0.458) are found to be insignificant to behavioral intention, suggesting that H4b and H4c are valid whereas H4a and H4d are not supported. Table 5 is a summary of the structural model assessment.
Figure 2 reports R2 for the five constructs in the proposed model, including behavioral, cognitive, emotional and social engagement, and behavioral intention, which are 0.666, 0.742, 0.624, 0.701, and 0.502, respectively. In other words, autonomy, competence, and relatedness explained about 66.6% of the total variance in behavioral engagement, about 74.2% in cognitive engagement, about 62.4% in emotional engagement, and 70.1% in social engagement, which indicates a strong explanatory power. Similarly, behavioral, cognitive, emotional, and social engagement explained about 50.2% of the total variance in behavioral intention, showing a moderate explanatory power. In addition, the results indicate that competence proved to be the most important factor influencing four dimensions of engagement.

4.2. Results of Qualitative Analysis

Five interviewees showed diversified attitudes to emergency synchronous online English courses. Five themes regarding learners’ psychological needs, learning engagement, and behavioral intention were identified in their responses.
“Interaction” was the first important theme generated from the interviews, indicating learners’ psychological need for relatedness and social engagement. All of the five respondents indicated that interaction in online courses was a key factor in keeping them interested and interacting with teachers and classmates and was beneficial to improving their English learning. For instance, participant 1 said, “I’m free to express myself when interacting with classmates, and some new ideas occur when we communicate with each other.” However, compared to F-T-F (face-to-face) classroom English learning, three participants (P2, P3, P4) showed a preference for interaction in F-T-F classroom English learning. Interaction was reported to be less frequent and less convenient in online courses. For instance, P5 commented that he felt interaction in his online English course was not very useful and further suggested that online teacher–student interaction would have been more effective if it had been in the form of danmu, where students’ comments were superimposed on and scroll across the screen so that the teacher could get to know their feedback in a timely manner.
The second important theme that emerged was “learning atmosphere”, which marked the learners’ psychological need for relatedness as well. Participants indicated a strong sense of distance and isolation in online English courses. All of the five respondents mentioned that compared with F-T-F classrooms, online English courses maintained a weak learning atmosphere as classmates and teachers were far away. The weak atmosphere negatively influenced their learning engagement. However, one interviewee (P1) held that he enjoyed the relaxed atmosphere which made him less nervous in online courses.
The third theme, “teachers’ course design” emerged, which was perceived as a key factor influencing learners’ emotional and behavioral engagement. As one interviewee (P2) said, “Teaching was mainly conducted based on the prepared PPT, and teachers didn’t give relevant examples, which was very boring.” Another student (P1) stated that “If English teachers kept talking by themselves, students will be easily absent-minded and even mind-wandering in the synchronous online English courses.” Three of the participants (P1, P2, P3) consequently expressed their desire for more interesting course designs. One interviewee (P3) commented, “If my teacher delivers some new English knowledge or designs some interesting instructional activities, I would like to actively engage in the online English course.” This revealed the necessity for better course designs in promoting students’ learning engagement.
In addition, a common view was expressed that disturbance often occurred, which resulted in distraction or disengagement in online English courses. Reasons can be concluded into two major aspects: personal traits (lack of self-discipline/interest in English) and external environment (temptation of entertainment apps/family disturbance).
As for behavioral intention, three interviewees (P2, P3, P4) stated they would choose F-T-F learning because it was more effective and productive. On the other hand, two participants (P1, P5) preferred online learning, mainly because they felt less anxious in an online English course. Furthermore, they (P1, P5) proposed that the combination of online and offline might be able to offer them the best of both worlds.

5. Discussion and Implications

5.1. Discussion

5.1.1. Basic Psychological Needs Are Effective Predictors of Teenage EFL Learners’ Online Learning Engagement

The findings of the structural model results confirm that basic psychological needs are essential antecedents for engagement. More specifically, autonomy and competence showed significant influence on behavioral, cognitive, emotional, and social engagement. This result is in accordance with that of Dincer et al. [41] who conducted their research in a traditional EFL classroom and Buil et al.’s [12] research in a market course. The evidence that autonomy positively predicts social engagement corroborates with the ideas of Dincer et al. [38], which suggested that the autonomy-supportive language environment resulted in high levels of participation in an English-speaking class.
Meanwhile, competence has positive effects on social engagement as well. It is argued that, influenced by Chinese culture, students are usually involved in teacher-centered classes in English learning [4]. Chinese students are often reported to be timid and shy to express themselves in class [64]. As the synchronous online courses were still in the instructor-led style, the classroom activities and tasks were usually assigned by English instructors. However, the feeling of competence in an online learning context brings students more confidence in class; as a consequence, they are inclined to take part in these activities and tasks when encouraged by teachers.
Moreover, relatedness is confirmed to have a significant impact on learner behavioral engagement, which is consistent with those of prior research [12,35,41]. Besides, relatedness is shown to be an important indicator of social engagement, although there is relatively limited research focusing on the direct association between relatedness and social engagement. Fang et al. [39] proffered that relatedness gives online learners a sense of connectedness in the community which may encourage them to exchange opinions and to search for help from their instructors and peers in the MOOC learning context. It is true that in such an unexpected crisis, Chinese secondary school students were anxious and alienated, and they needed to communicate with classmates and teachers so as to ease their unstable moods; therefore, a sense of relatedness encouraged them to get more social engagement.
However, in contrast to earlier findings [35,40], relatedness shows no significant impact on cognitive and emotional engagement. One possible explanation might be that in Olivier et al.’s [40] study, participants were students with behavioral and social problems, and thus more sensitive to a classroom climate. Therefore, the association between relatedness and engagement is much closer. However, in the current study, students are not with such problems, and therefore the impact of relatedness on engagement may not be as strong as in the mentioned research. Although this result differs from some studies [12,35], it is in accordance with Eseryel et al.’s findings [65], which indicated there was no relationship between relatedness and engagement in digital game-based learning even if the concept of engagement was treated as a unidimensional notion. In addition, the results indicate that competence proved to be the most influential factor in the four dimensions of engagement, which is in agreement with the data obtained in Fang et al.’s [39] research.

5.1.2. Cognitive and Emotional Engagement as Positive Factors on Teenage EFL Learners’ Behavioral Intention

Cognitive and emotional engagement are shown to have a significant impact on behavioral intention or on future learning intention. It can thus be suggested that learners tend to be more willing to continue their e-learning when they are cognitively and emotionally engaged in online English courses.
However, both behavioral engagement and social engagement have no significant influence on students’ behavioral intention. Two main reasons might explain this observation. Firstly, Chinese secondary students are reported to have a relatively high level of engagement in the emergency online learning environment, as some of them are highly motivated to learn well under the constant pressure of high-stake tests and a college entrance examination at the end of the school year. However, they still prefer face-to-face classroom language learning. Aguilera-Hermida [66] discovered that students preferred face-to-face learning to emergency online instruction during the pandemic, which may account for students’ low behavioral intention in online learning. Besides, social interaction in online courses is inconvenient compared with face-to-face instruction, which might result in learners’ discouraging social engagement experience; as a consequence, they are less likely to continue online English courses. Secondly, the insignificant relation between behavioral engagement and outcome variables is not unprecedented, for example, Dincer et al. [41] also mentioned that there was no association between behavioral engagement and achievement in a foreign language learning context.

5.1.3. Student-Generated Problems and Comments on Improving Engagement

First of all, the qualitative data suggest that social interaction is a critical aspect of online English learning. All of the participants expressed a desire for better social engagement in their online learning experience, which is correspondent with Dincer et al.’s [41] findings in a traditional classroom. As proposed in their research, foreign language learners are especially in need of in-class interactions because they usually have no English-relevant interactions after class. Chinese EFL learners have the same problem in online courses and their learning is usually led by teachers.
Secondly, the sense of distance results in a weaker learning atmosphere in an online learning environment, thus leading to a lower level of emotional engagement (e.g. lacking a sense of relatedness) and behavioral engagement (e.g. absent-mindedness), causing a decline in certain English skills. This finding elicited from interview data supports evidence from observations that online practice during COVID-19 lacked emotional attachment [8]. Chinese EFL learners are usually accustomed to group learning environments since early childhood. It is normal that they had some unsatisfactory experiences when suddenly moving to online learning, which can be regarded as a form of individual learning.
Lastly, transferring from the F-T-F classroom to synchronous online learning mode also posed challenges to English teachers, since it is more difficult to cognitively engage students. The qualitative finding that respondents were expecting a more interesting course design was consistent with that of Chiu [8] who pointed out that instructional designs and teaching resources should be more effective in an online context.

5.2. Implications and Future Research

Although the current research is set in China, it entails important implications for other EFL contexts in both theoretical and practical ways.
In the light of theoretical implications, the amalgamated model confirms the validity of the basic needs theory in enhancing learners’ engagement in the synchronous online English learning environment. Furthermore, as far as we know, it is among the first to assess the effects of each type of engagement to describe more detailed information about how secondary school students are engaged in the emergency online learning context. Thus, the present study extends the acknowledged conclusion that the fulfillment of fundamental psychological needs fuels student engagement in learning [35,65] by investigating the distribution of each dimension of engagement in detail. Furthermore, in previous research, the notion of engagement was often viewed as a unified single construct which is usually the integration of behavioral, cognitive, and emotional engagement. This study further identifies each dimension of engagement and also incorporates social engagement into the proposed framework, figuring out that emotional engagement is the most essential one in predicting Chinese secondary school students’ behavioral intention.
In terms of practical implications, EFL teachers should pay attention to learners’ psychological needs in online courses since the study shows that learners’ motivational needs, such as autonomy and relatedness, are important factors in facilitating learners’ engagement in the emergency online learning environment. To enhance autonomy, teachers can design different types of in-class activities or tasks so as to offer options and students will have the autonomy to choose the one that they are interested in. Meanwhile, instructors are suggested to improve their course design by incorporating more cooperative learning tasks and giving interactive feedback, which are supposed to increase students’ relatedness. On the other hand, emotional engagement plays a great role in influencing participants’ behavioral intentions. EFL instructors should pay special attention to students’ emotional aspects in the online learning environment. For instance, teachers are suggested to promote students’ interests and motivation by offering authentic tasks that reflect real-life situations in online courses [17].
The current research has several limitations. First, the data we collect is from self-reported perceptions, thus, additional measures such as log data and profiles are necessary to examine students’ learning behaviors. Second, this study doesn’t include actual learning outcomes such as grades in the proposed model. Longitudinal studies and experimental investigations are needed to estimate more variations in learners’ online learning experiences. Thirdly, the sample is a middle-size group of high school students in China; as a result, it may not be applicable to other learner groups or students from other countries. Thus, more cross-cultural research should be carried out in the synchronous online language learning field. In addition, as one of the dimensions of the engagement framework, social engagement has been relatively less studied than the others, and therefore, further research should be conducted to explore how social engagement may influence learners’ adoption of online language learning.

6. Conclusions

The current research attempts to contribute to foreign language education literature by concentrating on four dimensions of engagement and incorporating psychological needs as antecedents while regarding behavioral intention as the following outcome. The study has found that basic psychological needs are significant constructs in predicting all four dimensions of engagement. More specifically, competence seems to have the strongest impact on engagement constructs. Besides, the study suggests that it is of great importance to facilitate emotional engagement in order to sustain their behavioral intention and future English learning. In addition, qualitative data reveals that social engagement is of great significance to online English learners.
The insights gained from this study are supposed to be of assistance to both practitioners and policymakers on how to effectively promote EFL learners’ synchronous online learning experiences and thus ensure the quality of language learning in a time of crisis. The findings on the aspect of Chinese secondary school students’ engagement may also be of referential significance in the study of other adolescence elsewhere in the world, especially in Asian regions.

Author Contributions

Conceptualization: S.Z. and H.Z.; methodology: S.Z. and H.Z.; formal analysis: S.Z. and H.Z.; writing—original draft preparation, S.Z. and H.Z.; writing—review and editing, H.Z., Y.Z. and S.Z.; All authors have read and agreed to the published version of the manuscript.

Funding

This research was partly funded by Social Science Foundation of Hunan University ( No. 541109140124) and Education Department of Hunan Province ( No. HNJG-2021-0036 and No. HNJG-2021-0372).

Institutional Review Board Statement

The study was conducted according to established research principles of Hunan University.

Informed Consent Statement

Informed consent was obtained from all participants involved in the study.

Data Availability Statement

The data used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Acknowledgments

Our sincere thanks go to three anonymous reviewers for their valuable comments and detailed suggestions. We also thank the participants who have voluntarily completed our questionnaires and interviews.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table
Table A1. Constructs and items in the questionnaire.
Table A1. Constructs and items in the questionnaire.
ConstructItemsSource
Behavioral intentionsBI1: I intend to completely switch over to the e-learning platform to learn English.Liu et al. (2009) [58] & Joo, So, Kim (2018) [57]
BI2: I intend to increase my use of the e-learning platform to learn English in the future.
BI3: If e-learning platform becomes diverse in the future, I intend to use it to learn English frequently even after graduation.
Fulfillment of the need for autonomyFNA1: I feel like I can make a lot of inputs to deciding how I learn English online.Sun et al. (2019) [46] & Agnesia (2010) [53]
FNA2: If it were up to me whether or not to do the online English learning task, I would still have done it.
FNA3: I did online English class tasks because I wanted to.
Fulfillment of the need for competenceFNC1: When learning English online, I get many chances to show my capability.Sun et al. (2019) [46]
FNC2: When learning English online, I often feel very capable.
FNC3: I feel very competent in learning English online.
Fulfillment of the need for relatednessFNR1: People are pretty friendly towards me when I am learning English online.Sun et al.(2019) [46]
FNR2: I really like the people learning English online with me.
FNR3: I get along with people when I am learning English online.
Behavioral engagementBehaE1: When I’m in the online English class, I listen very carefully.Reeve (2013) [54]
BehaE2: I try hard to do well in online English class.
BehaE3: When learning English online, I work as hard as I can.
Cognitive engagementCogE1: I try to make all the different ideas fit together and make sense when learning English online.Reeve(2013) [54]
CogE2: When doing work for online English class, I try to relate what I’m learning to what I already know.
CogE3: I make up my own examples to help me understand the important concept I study when learning English online.
Emotional engagementEmoE1: When we work on something in online English class, I feel interested.Reeve (2013) [54]
EmoE2: Online English class is fun.
EmoE3: I enjoy learning new things in online English class.
EmoE4: When learning English online, I feel good.
Social engagementSocE1: I felt comfortable interacting with other participants when learning in the online English class.Strong et.al (2012) [55] & Bergdahl et.al. (2020) [49] & Liu et.al (2010) [56]
SocE2: I felt comfortable participating in the online English class discussions, like answering instructor’s questions.
SocE3: I am satisfied with my English teachers’ use of online platform (e.g., QQ/Wechat/DingTalk) to keep track of my progress /give feedback.
SocE4: When learning English online, I engage in simultaneous learning interaction with others via online platform (e.g., QQ/Wechat/DingTalk)

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Sustainability 14 10468 g001 550
Figure 1. Proposed research model.
Figure 1. Proposed research model.
Sustainability 14 10468 g001
Sustainability 14 10468 g002 550
Figure 2. Proposed model with results. Note: * means p < 0.05, ** means p < 0.01, *** means p < 0.001.
Figure 2. Proposed model with results. Note: * means p < 0.05, ** means p < 0.01, *** means p < 0.001.
Sustainability 14 10468 g002
Table
Table 1. Background of participants.
Table 1. Background of participants.
N (%)
GenderFemale137 (58.8)
Male96 (41.2)
Educational levelSenior 186 (36.9)
Senior 2124 (53.2)
Senior 323 (9.9)
Duration in synchronous online English courses<2 weeks2 (0.9)
About 3 weeks1 (0.4)
About 1 month20 (8.6)
About 2 months126 (54.1)
>2 months84 (36.1)
Table
Table 2. Descriptive analysis.
Table 2. Descriptive analysis.
ConstructsMeanStandard DeviationSkewnessKurtosis
Autonomy3.8910.659−0.3080.147
Competence3.4530.795−0.1810.500
Relatedness3.9220.657−0.4771.197
Behavioral intention3.3190.902−0.3730.271
Behavioral engagement3.6910.763−0.2970.184
Cognitive engagement3.6080.740−0.3070.771
Emotional engagement3.5080.762−0.5020.852
Social engagement3.6810.693−0.1760.074
Table
Table 3. Factor loadings, AVE, CR, and Cronbach’s alpha.
Table 3. Factor loadings, AVE, CR, and Cronbach’s alpha.
ConstructsIndicatorsFactor LoadingsAVEComposite ReliabilityCronbach’s Alpha
AUTOAuto10.7170.6730.8600.754
Auto20.886
Auto30.848
BEBE10.8100.7490.8990.831
BE20.875
BE30.908
CECE10.8730.7140.8820.799
CE20.868
CE30.792
CompComp10.8470.7070.8790.793
Comp20.829
Comp30.847
EEEE10.8570.6730.8910.838
EE20.868
EE30.767
EE40.785
RelatRelat10.8770.6770.8620.757
Relat20.730
Relat30.853
SESE10.7120.6200.8670.795
SE20.807
SE30.831
SE40.795
BIBI10.7640.7290.8890.814
BI20.906
BI30.885
Note: AUTO = autonomy; BE = behavioral engagement; CE = cognitive engagement; Comp = competence; EE = emotional engagement; Relat = relatedness; SE = social engagement; BI = behavioral intention.
Table
Table 4. Inter-construct correlations and discriminant validity.
Table 4. Inter-construct correlations and discriminant validity.
AVEAUTOBECECompEERelatSEUI
AUTO0.6730.820
BE0.7490.7360.865
CE0.7140.7550.7670.845
Comp0.7070.6590.6920.8090.841
EE0.6730.6820.7110.7940.7450.820
Relat0.6770.5670.6300.4960.4680.4920.823
SE0.6200.7200.7110.7660.7570.7690.6210.788
BI0.7290.4700.4770.6200.5880.6930.2510.5660.854
Note: AUTO = autonomy; BE = behavioral engagement; CE = cognitive engagement; Comp = competence; EE = emotional engagement; Relat = relatedness; SE = social engagement; BI = behavioral intention.
Table
Table 5. Summary of hypothesis tests.
Table 5. Summary of hypothesis tests.
HypothesisPathβt-ValueSupported
H1aAutonomy—BE0.3734.520Yes
H1bAutonomy—CE0.3815.691Yes
H1cAutonomy—EE0.3002.546Yes
H1dAutonomy—SE0.2783.003Yes
H2aCompetence—BE0.3204.293Yes
H2bCompetence—CE0.5478.870Yes
H2cCompetence—EE0.5084.932Yes
H2dCompetence—SE0.4566.276Yes
H3aRelatedness—BE0.2694.214Yes
H3bRelatedness—CE0.0230.441No
H3cRelatedness—EE0.0841.232No
H3dRelatedness—SE0.2503.054Yes
H4aBE—BI−0.1501.548No
H4bCE—BI0.2512.268Yes
H4cEE—BI0.5665.499Yes
H4dSE—BI0.0460.458No
Note: BE = behavioral engagement; CE = cognitive engagement; EE = emotional engagement; SE= social engagement; BI = behavioral intention.
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Zhou, S.; Zhu, H.; Zhou, Y. Impact of Teenage EFL Learners’ Psychological Needs on Learning Engagement and Behavioral Intention in Synchronous Online English Courses. Sustainability 2022, 14, 10468. https://doi.org/10.3390/su141710468

AMA Style

Zhou S, Zhu H, Zhou Y. Impact of Teenage EFL Learners’ Psychological Needs on Learning Engagement and Behavioral Intention in Synchronous Online English Courses. Sustainability. 2022; 14(17):10468. https://doi.org/10.3390/su141710468

Chicago/Turabian Style

Zhou, Sijing, Huiling Zhu, and Yu Zhou. 2022. "Impact of Teenage EFL Learners’ Psychological Needs on Learning Engagement and Behavioral Intention in Synchronous Online English Courses" Sustainability 14, no. 17: 10468. https://doi.org/10.3390/su141710468

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