Online English Learning Engagement among Digital Natives: The Mediating Role of Self-Regulation

Abstract

Because of the recent pandemic, students have needed to become skilled, adaptable, self-regulating, and flexible toward online learning. However, few researchers considered digital nativity (DN) when studying self-regulation and learning engagement. This study explored how Chinese digital natives regulated themselves in English learning. A total of 408 college English students volunteered, and partial least squares structural equation modeling (PLS–SEM) was used to process the questionnaire’s tested four hypotheses. The results showed that: (1) DN is related to online self-regulated English learning (OSEL); (2) OSEL is related to online student engagement (OSE); (3) DN is not related to OSE; and (4) OSEL is the mediator between DN and OSE. As such, the current findings should encourage e-learning designers and educators to equip students with both computer literacy and self-regulated competence for sustainable English learning development.

1. Introduction

Those born into a digital environment have been termed “digital natives”. As Prensky wrote, these people have grown up with computers, are comfortable multitasking, rely on graphics to communicate, and thrive on instant gratification [1]. Coffin and Pérez [2] wrote that digital natives who use digital applications efficiently boost their digital literacy. Hence, electronics have been integrated seamlessly into digital natives’ education systems. Technology has brought great changes to people’s lifestyles. In education, it has promoted educational reform, improved teaching efficiency, and promoted the sustainable development of education [3]. Recently, the prevalence of online education has caused a surge of interest in online learning engagement, and studies show that computers affected academic engagement in higher education [4,5,6]. Fonseca et al. [4] brought forward the idea that students tend to further their engagement when learning with digital devices, and Gulek and Demirtas [7] found evidence that linked computer-based learning to higher academic outcomes. However, opponents like Kuh and Hu [8] argued that student engagement has almost nothing to do with computers, and Malaney [9] held that too much reliance on the Internet is detrimental to students’ grades. Sustainable online language learning requires language teachers to promote a positive language learning environment to enhance students’ interest and motivation [10]. However, there is a research gap to be filled on the relationship between digital nativity and learning engagement.

Self-regulated learning strategies were one of the predictors of academic achievement [11]. When the SARS-CoV-2 virus led to an international pandemic, online learning via Zoom, Tencent Meeting, and Tencent Classroom became popular [12]. Unlike traditional classes, these classes transcend the limits of time and space [13], and, thus, these students must be more independent [14] and manage and regulate their learning effectively [15]. Many researchers believed that engagement is crucial to L2 students’ goals [16,17,18]. In synchronous online learning environments especially, these students are exposed to many distractions. Self-regulated learning, therefore, played an irreplaceable role in online second-language learning [19], especially in getting students engaged cognitively, affectively, and behaviorally. However, there is little research on how digital natives learn a foreign language online with the support of self-regulation.

This study investigated how digital natives learn English as a second language online and whether self-regulated learning influences their engagement. It was also necessary to shed light on how online self-regulated English learning (OSEL) was related to digital nativity (DN) and online student engagement (OSE).

To find out their relationships, this study proposed hypotheses based on previous studies and used partial least squares structural equation modeling (PLS-SEM) to analyze the research model.

Despite some recent attention toward OSE, the concept remains under-explored and deserves further empirical investigation. Although DN, OSEL, and OSE have been studied in recent years, the simultaneous association of these three constructs in the context of EFL does not seem to have been investigated in the relevant literature. To further enrich the literature in this area, this study’s hypothetical model exploration provides a useful account of student engagement in the EFL context. Furthermore, the hypothesized model may help second-language instructors and learners understand which variables influence engagement with English.

2. Literature Review

2.1. Digital Nativity (DN)

The thinking and behavior patterns of human beings changed due to the development of digital communication in the 1980s. Those born after 1980 are seen as “expert” computer users and have been labeled the “net generation” [20], “millennials” [21], and, more frequently, “digital natives” [1]. These people are regarded as native speakers of digital languages and are more adaptable to electronic devices and the Internet, whereas their parents’ generation is regarded as “digital immigrants” because they did not have access to computers and electronic products until they were adults [1]. However, a study conducted by Cameron [22] found that many first-year students born after the 1980s were not experts at using digital devices at university. If age were the only criterion, then the older generation would never surpass the younger [23]. Therefore, Bennett et al. [24] and Jones et al. [25] believed that digital students’ use of computers and learning preferences cannot be overgeneralized by age. To bridge the gap between digital natives and immigrants, Teo [26] constructed the Digital Natives Assessment Scale (DNAS), a self-reporting instrument to measure the digital degree of a target group. Teo et al. [27] and Huang et al. [28] validated this scale in China and Turkey, respectively. In addition, Chinese university teachers also reported that they demonstrate “digital native” features [29].

Digital nativity is an emerging topic in education, and studies have found that digital nativity significantly interferes with students’ learning motivation [30], and it is also a predictor of online information search strategies [31]. Kabakci Yurdakul [32] noted that digital nativity was an asset for pre-service teachers’ Technological Pedagogical Content Knowledge (TPACK). According to Calvo-Ferrer [33], digital nativity affected students’ second-language vocabulary acquisition through digital games. Aharony and Gazit [5] found that the characteristics of digital natives improve information literacy, which means digital nativity can enhance the confidence of college students to go online to promote learning. Therefore, digital natives can promote their English learning.

2.2. Online Self-Regulated English Learning (OSEL)

Self-regulated learning denotes how students behave in meta-cognition, motivation, and behavior when learning independently [34], and includes planning, monitoring, and goal orientation [35]. Self-regulated learning strategies are associated with higher academic achievements [36,37]. The EU Council [38], for example, highlighted their necessary role in lifelong learning. Because of growing access to digital information, online learning has multiplied over the past three decades, such that research on self-regulation learning has shifted from the classroom to the digital world [39]. As a result, traditional teachers faced challenges [40], because online learning relied heavily on independent learning, not instruction, [15], which means that students cannot be monitored as they would be in a classroom.

Research has shown that self-regulated skills are conducive to online learning [41], and Hood et al. found them to be crucial in massive open online courses (MOOCs) [42], as did Alhazbi and Hasan [43]. Moreover, empirical studies on online self-regulated learning have been on the rise [44,45,46]. Zheng et al. [46] constructed a six-factor scale for English learning in a network environment, including goal setting, time management, environment structuring, help-seeking, task strategies, and self-evaluation. Studies have found students to be receptive to online learning [47], especially to learning English [48]. If students have good self-regulation skills, then they can manage their attention, working memory, and inhibitory control [49,50]. Another study found that digital nativity and information literacy predict online information search strategies [31], so DN may be related to OSEL. However, whether digital native characteristics influence online self-regulated English learning has not been studied.

2.3. Online Student Engagement (OSE)

Student engagement is the time and energy students put into educational activities [51]. To understand how students apply themselves, the National Survey of Student Engagement (NSSE) was designed. It consisted of five factors: academic challenge, supportive campus environment, enriched educational experience, student–faculty interaction, and active and collaborative learning in and out of the classroom [52]. Fredricks et al. [53] emphasized the multidimensional structure of student engagement, proposing a three-dimensional structure comprising emotional, cognitive, and behavioral engagement. Dixson [54] developed an OSE scale, based on social constructivism, which verified the four-dimensional structure of skill, emotion, participation, and performance. This scale was later used to explore the relationship between students’ nonverbal behaviors and online course engagement [55].

2.4. The Relationships among DN, OSEL, and OSE

With the advent of computer-based learning, classroom interaction [56] and learning engagement [57] have risen. Research by Gosper et al. [58] found that students used social media to boost university academic performance, and studies during the pandemic found that student adaptability was positively related to student engagement [59]. The shift from classroom to online learning brought significant challenges [60], but studies show that self-regulation correlated positively with study engagement [39] and that this relationship carried over to online learning [61]. Thus, we hypothesize that DN would associate with OSEL.

The arrival of big data has augmented online student learning. Fonseca et al. [4] pointed out a positive correlation between technology and student engagement. Similarly, a study by Chen et al. [62] showed that the “intelligent use” of electronic devices [6] correlated positively with investment in online learning. Boulianne & Theocharis [63] also presented that digital media use was associated with engagement in both offline and online political activities. In English, Calvo-Ferrer [33] revealed that “thriving on instant gratifications and rewards” was positively related to EFL vocabulary acquisition. In the study of educational psychology, Philp & Duchesne [64] demonstrated the existence of engagement in second-language learning. Moreover, Yu et al. [65] have shown that mobile learning technology contributed to EFL learning engagement and outcomes. Therefore, we hypothesize that DN is significantly related to OSE.

Engaged students value the self-regulation of their learning process, and these students also pay attention to lectures, participate actively, and make a clear effort to learn [53]. Student-perceived support, such as timely teacher feedback and peer facilitation, was positively correlated with student engagement [66]. It is known that students are supposed to apply various self-regulated skills, depending on the learning context [67]. Digital natives, for example, have to adapt their self-regulation to perform better in online learning, which is important for their sustainable development either in the job market or academic career. To test the relationship between DN, OSEL, and OSE, the following research model was constructed (Figure 1).

In this model, we proposed the following hypotheses:

H1. 

DN is significantly related to OSEL.

H2. 

DN is significantly related to OSE.

H3. 

OSEL is significantly related to OSE.

H4. 

OSEL is the mediator between DN and OSE.

3. Methodology

3.1. Overall Design

This paper explored the influence of DN characteristics (independent variable) on OSE (dependent variable) and analyzed the mediating role of OSEL in an English learning context.

To maintain content validity [68], all three constructs DN, OSEL, and OSE were adapted from the literature [27,46,54]. Following the suggestions of Podsakoff et al. [69], common method biases (CMBs) were appropriately offset by the following measures. First, the working definition of these constructs was redefined, and the statements of each specific measure were rewritten. DN measures were adapted from Teo [27], OSEL measures were adapted from Zheng et al. [46], and OSE measures were adapted from Dixson [54]. Both DN and OSEL have Chinese versions; hence, the original Chinese questionnaires were used in this research. Second, the back-translation suggested by Brislin [70] was used to ensure consistency between the Chinese and English versions, although minor adjustments were made to suit the Chinese context. Third, before sending out the questionnaire, two experienced native Chinese English teachers were invited to test the questionnaire’s validity and examine the translation. Next, a draft version was sent to six students who participated in a college online English class for prestudy and revision. Based on the feedback, this questionnaire was improved. All items were designed on a 7-point Likert scale, ranging from “not at all true for me” (1) to “very true for me” (7) (see Appendix A).

3.2. Instruments

3.2.1. Digital Nativity Assessment Scale (DNAS)

Teo [27] validated a Chinese version of the DNAS: C-DNAS. It comprised 21 items in four subscales: growing up with technology (5 questions), comfortable with multitasking (6 questions), reliant on graphics for communication (5 questions), and thriving on instant gratifications and rewards (5 questions). Internal consistency (Cronbach’s α) in this study was recalculated as 0.756.

3.2.2. Online Student Engagement (OSE) Scale

The OSE scale [54] used in this study consisted of 4 items—skills, emotion, participation, and performance—consisting of 19 questions. During this study, the overall Cronbach’s α was 0.908.

3.2.3. Online Self-Regulated English Learning Scale (OSEL)

The OSEL scale used [46] was adapted from Barnard-Bark et al. [67] and had six factors: goal setting, time management, environment structuring, help-seeking, task strategies, and self-evaluation. The recalculated Cronbach’s α was 0.934.

3.3. Data Collection

The online questionnaire distribution website wenjuanxing (www.wjx.com) (accessed on 2 August 2022). was adopted for data collection. A convenience sampling method was used with the help of 21 college English teachers from the north of China. As Ball points out [71], even though an online survey may have a poor response rate, deceptive information, and duplicate responses, it reduces person-to-person contact, which is an important factor during the pandemic. To attenuate the possible disadvantages of the online questionnaire, the following methods were adopted. It was distributed by English teachers through QQ instant messaging, Tencent meetings, and online communities to students who had an online English learning experience, thereby ensuring a maximum response. All the participants were informed of the research purpose and that they had the right to withdraw at any time. The responses that were the same for every item were deleted. We also eliminated answers from the same IP address [72] and those who answered the whole questionnaire in less than 90 s.

The data were collected from 2 August to 10 August 2022. A total of 454 students volunteered and 408 valid responses were retained for the following analysis. The sample data met the minimum sample size of 88 required by SEM, according to Christopher [73].

3.4. Data Analysis Method

Smart PLS version (V3.3.9) was used to perform data analysis. The hypothesized model was made of outer and inner constructs, and each observed variable had its latent variables. Reflective constructs were assessed by testing factor loading, convergent validity, composite reliability, average variance extracted, and discriminate validity. The PLS method is appropriate for exploring new research models [74] and it has fewer measurement demands compared with covariance-based structural equation modeling (SEM). If the research problem is new and ready to be developed, PLS–SEM is an alternative to CB–SEM because it can be used to deal with the complex relationships between latent variables and indicators, such as reflective and formative measurement models [75]. Therefore, we used PLS–SEM to analyze the data.

4. Results

4.1. Demographic Information

Participants’ demographic information is provided in

Table 1. Demographic information of participants (n = 408).

Variable		Frequency	Percentage (%)
Sex	Female Male	212 196	52.0 48.0
Age	17–19	193	47.3
	20–22	200	49.0
	23–25	12	2.9
	26–28	2	0.5
	>28	1	0.2
Education	Freshman	104	25.5
	Sophomore	199	48.8
	Junior	57	14.0
	Senior	33	8.0
	Master	15	3.7
Internet experience (years)	1–5	59	14.5
	6–10	245	60.0
	11–15	101	24.8
	>15	3	0.74
Subject background	Foreign Language	46	11.3
	Science and Engineering	205	50.2
	Literature and History	57	14.0
	Economics and Management	100	24.5
Total		408	100

. Significantly, 85.5% of the participants had used the Internet for more than five years, and the participants were enrolled in different majors.

4.2. Measurement Models

In this study, Smart PLS (V3.3.9) was used to evaluate the measurement model. First, the model had good validity and reliability since the Cronbach’s alpha (CA) was greater than 0.6 and the composite reliability (CR) was greater than 0.7 [76]. The CA ranged from 0.687 to 0.910, and the CR ranged from 0.865 to 0.931 (

Table 2. Results for the measurement model.

	Cronbach’s Alpha	CR	AVE
GT	0.789	0.876	0.702
MULT	0.806	0.873	0.632
RG	0.870	0.911	0.719
IGR	0.774	0.869	0.689
SKILL	0.867	0.904	0.655
EMOTION	0.860	0.900	0.642
PAR	0.910	0.931	0.693
PERF	0.687	0.865	0.762
PLAN	0.899	0.926	0.715
CONG	0.806	0.873	0.633
ENVIR	0.864	0.902	0.647
TIME	0.907	0.931	0.730
HELP	0.857	0.897	0.635
META	0.855	0.916	0.686

), indicating good internal consistency and, thus, reliability. Second, we adopted factor and cross loading to measure discriminant and convergent validity. Each construct’s factor loading was higher than the cross loading of other variables, demonstrating enough discriminant validity [68,77]. Each factor loading was more than 0.708, which means that this model was reliable [78] (see Appendix B).

Additionally, the average variance extracted (AVE) square root for each variable was used to analyze the discriminant validity further. The AVE values exceeded 0.5, indicating a good internal convergence [76]. The square root of the AVEs was greater than the correlations among all the constructs, which showed that the measurement model had good discriminant validity [68,79] (see Appendix C).

4.3. Structural Models

4.3.1. Common Method Bias

When conducting quantitative research, common method bias (CMB) should be considered. Following the suggestion of Kock [80], we conducted Harman’s Single Factor Test. All items were analyzed by the principal component factor in SPSS. The results (see

Table 3. Harman’s single factor test.

Criterion	Acceptability
Harman’s Single Factor Test	37.537% variance proportion

) showed that the first cumulative value was less than 40%, which revealed no CMB issues [81,82]. Second, following Podsakoff et al. [69] and Williams et al. [83], we added a common method factor in the PLS model with indicators for all principal constructs and calculated the variance of each indicator explained by the substantive principal constructs and the method. As shown in Appendix D, the results indicate that the mean substantive explained variance was 0.497, while the mean method-based variance was 0.044. The ratio of substantive variance to method variance was approximately 11:1. In addition, most of the method factor loadings were not significant. Given the small magnitude and insignificance of the method variance, we concluded that the method is unlikely to be a serious problem in this study, according to Liang et al. [84].

4.3.2. Explanatory Power

The coefficient of determination (R²) in

Table 4. R² and R² adjusted.

	R2	R2 Adjusted
DN → OSEL	0.035	0.033
DN → OSE	0.657	0.655
OSEL → OSE

indicates the explanatory power of an independent variable on the dependent variable. The explanatory power of OSEL was 0.033, which was weak, while the R² value of OSE (0.655) had substantial explanatory power [85].

4.3.3. Cross-Validated Redundancy of Constructs

The predictive value Q² was used to examine the predictive power of the inner model, and a positive Q² was desirable [85]. As shown in

Table 5. Construct cross-validated redundancy.

	SSO	SSE	Q2 (=1 − SSE/SSO)
DN	1632	1632
OSE	1632	797.689	0.511
OSEL	2448	2385.929	0.025

, the values were all above 0, so the predictive relevance was enough.

In addition, before testing the path hypotheses, the goodness of fit (GOF) of the measurement model was taken into account [86]. As suggested by Tenenhaus et al. [87], we used Equation (1) to test this:

$$\mathrm{GOF}=\sqrt{\overline{\mathrm{communality}}\times \overline{{\mathrm{R}}^2}}$$

(1)

A higher GOF means better fitness. Based on Equation (1), the fitness index of the model was 0.531. Therefore, the model was suitable for exploration.

$$\mathrm{GOF}=\sqrt{\overline{\mathrm{communality}}\times \overline{{\mathrm{R}}^2}}=\sqrt{0.824\times 0.344}=0.531$$

4.3.4. Hypotheses and Results

Table 6. Results of path coefficients.

Hypotheses	Path	Original Sample (O)	Sample Mean (M)	Standard Deviation (STDEV)	T Value	p Values	Supported (YES/NO)
H1	DN → OSE	0.042	0.041	0.036	1.157	0.247	NO
H2	DN → OSEL	0.187	0.186	0.052	3.581	0.000	YES
H3	OSEL → OSE	0.802	0.802	0.031	25.827	0.000	YES

and

Table 7. Results of the indirect effect.

Mediation Hypothesis		Effect			Mediation Test
	Path	Direct	Indirect	Total	Sobel	Aroian	Goodman	Supported (YES/NO)
H4	DN → OSEL → OSE	0.042	0.150	0.192	5.092	5.089	5.096	YES

present the results of the research hypotheses. Before we got the results, a bootstrapping 5000 resampling approach was used, and the findings revealed that DN was not related to OSE (T = 1.157); this did not conform to Hypothesis 1. In contrast, DN correlated positively with OSEL (T = 3.581), which confirmed Hypothesis 2. Additionally, OSEL was significantly related to OSE. Because the T-value was greater than 1.96, Hypothesis 3 was supported. In Table 7, OSEL was shown to be a mediator between DN and OSE (T = 3.450). The path coefficient for the indirect impact of DN on OSE through OSEL was 0.150. The mediation test was also analyzed by Sobel [88], Aroian [89], and Goodman [90]. All the Z-values resulting from the Sobel Z test were lower than the threshold of 1.96 [88]. Meanwhile, DN did not significantly relate to OSE. Therefore, we concluded that OSEL was a full mediator between DN and OSE.

5. Discussion

This study used DN, OSEL, and OSE as latent variables to explore how students behaved in online English learning contexts, and PLS was used to analyze the relationships among the variables.

5.1. Relationship between Digital Nativity and Online Self-Regulated English Learning

First, it was found that DN was significantly related to OSEL. This indicates that Chinese online English students exhibited more digital learning characteristics and had a greater tendency to adopt self-regulated strategies to reduce obstacles to online learning. Our model confirmed assumptions that DN students were “experts” in self-regulation and online learning [91], which supports the opinions of Evans [92], who said that advancements such as mobile phones empower students when they learn. In a broader sense, this finding revealed that university students also controlled themselves in online education. In the same vein, digital attributes, such as visual feedback, can effectively reduce delays in self-regulated learning [93]. This finding also resonated with several studies [50,94,95] which shows self-regulated learning strategies to be closely related to digital literacy. Following Gabrielle et al. [96], it was concluded that self-regulated learning was positively related to computer use both in the physical and virtual environments. Online self-regulated students are fully committed to learning and willing to ask for help; thus, they achieve academically in online synchronous and asynchronous courses [43]. In the 21st century, online English students fulfill their learning objectives by self-regulation and maintain these skills because of the frequent use of English for both academic and instrumental purposes [97].

5.2. Relationship between Digital Nativity and Online Student Engagement

Second, it was revealed that DN was not significantly related to OSE. Few studies have examined the relationship between the characteristics of DNs and OSE for English learning, and this study found no significant correlation between them. Previous research on technological skills and learning engagement has yielded mixed results. For example, one study showed that electronic devices were related to student engagement [4], and appropriate use of them correlated positively with OLE [6]. Another study proposed that online learning students have high levels of engagement [8]. Contrarily, Kirschner and Karpinski discovered that digital media use brought grades down [98], and spending too much time on the Internet decreased academic performance [99]. Moreover, students fail to concentrate on learning material when using laptops and smartphones simultaneously [100]. Previous studies [101,102,103] also showed that the use of laptops for multitasking in the classroom impairs comprehension, and multitasking during learning correlates negatively with academic performance. This result can also be explained by another study which found that the characteristics of DNs did not correlate significantly with disengagement in second-language vocabulary learning [33]. It is reasonable that digital natives may not necessarily use an electronic device for learning because not all show high digital literacy [104]. Specifically for digital nativity, OSE was not significantly related.

5.3. Relationship between Online Self-Regulated English Learning and Online Student Engagement

Third, OSEL was found to be critically related to OSE, which revealed that self-regulated learners can direct their attention to learning English online. This outcome is partially associated with a previous study by Doo et al. [105] and Antúnez et al. [106], which revealed that self-regulation was important for predicting learning engagement. Similarly, such a result also appeared in earlier studies which found that self-regulation was a strong predictor of learning outcomes [89,107] and flipped-learning success [105]. Bohlmann and Downer [108] also indicated that preschool students with high levels of self-regulation were more willing to take part in learning activities. This also applies to self-regulated students who are more active in English writing [109]. To sum up, students who demonstrate excellent self-regulated skills online are likely to devote themselves to English learning.

5.4. Mediation of Online Self-Regulated English Learning in the Relationship between Digital Nativity and Online Student Engagement

Though digital nativity is not related to online learning engagement, it can be enhanced through the use of self-regulated strategies in online learning. Self-regulation helps students control themselves and focus despite exposure to the Internet [110,111] and, thus, students who self-regulate are more likely to achieve their academic goals [112]. Previous studies show that the social engagement of language learners, together with self-regulated strategies, contribute to language learning [113,114]. Likewise, self-regulated skills enhance a student’s language competence and maintain their motivation to achieve their learning objectives [115,116,117]. Lee et al. also proved the mediating role of self-regulation between digital literacy and learning outcomes [49]. It can be argued that self-regulated students are more likely to engage in an online course despite boring material or Internet distractions.

6. Conclusions

This study addresses a research gap by providing structural equation modeling for digital nativity, self-regulated learning, and online student engagement in English learning contexts. The outcomes illuminate the importance of self-regulation between digital nativity and online student engagement in English learning environments. This study contributes a beneficial conceptual advancement by integrating digital attributes, and self-regulation into online English learning engagement investigations, and lends empirical evidence to the contention that self-regulation strategies are helpful to digital natives’ online English learning engagement. We also showed that digital natives can cope with difficulties and enhance their efforts in online language-learning contexts with the help of self-regulated learning strategies. To be specific, Chinese college digital learners can apply self-regulated skills when they learn English online.

Therefore, online teachers should not only impart knowledge about the language but also facilitate students with more self-regulated learning strategies. Teachers should also be aware of the digital characteristics in designing online courses so that students’ needs are catered for, and their learning difficulties are addressed in the face of the complexity and dynamics of online environments. Self-regulation can help students utilize technologies for learning in the post-COVID-19 period and is conducive to their sustainable development throughout their lifespan. On the other hand, the progress of students will also contribute to the development of teachers, which is in line with the requirements for the sustainable development of education.

7. Implications

This study investigated the relationship between digital nativity (DN), online self-regulated English learning (OSEL), and online student engagement (OSE) by using structural equation modeling, and the full mediating role of online self-regulation was examined.

Theoretically, the current research relates and contributes to DN research. We found that contemporary students exhibited the prominent characteristics of digital natives. Furthermore, we extended the finding to self-regulation strategies that contribute to student engagement, which was in line with previous studies [39,61]. Finally, we added to the literature on DN by finding that learning engagement was not related to digital experience; however, if digital natives are self-regulated, they can overcome the negative factors that decrease engagement to become more attentive when learning online.

Practically, college students can also easily adapt to online learning and hold an open and inclusive attitude toward computer-supported courses. Teachers should be aware of the DN characteristics of students to mobilize them to complete course-related multitasks through intricate course design and the use of videos and pictures to arouse their interest and direct them to online synchronous courses. Teachers should also make full use of the virtual course discussion community to promote communication and timely feedback and actively carry out online self-regulated learning strategies to teach DNs [118,119]. For example, during class, teachers can make the best use of online interactive technological tools to have online short tests and discussions so that all the students can be involved and have their responses collected in a timely manner. In this case, the teacher can check the attention of students and adjust the teaching process accordingly. Thus, the needs for instant responses from digital natives are also catered for. After class, teachers can encourage students to participate in online academic seminars to address issues in their learning [120]. Teachers can also learn more from their students about the application of educational technology to sustainability.

8. Limitations and Future Work

This study has some limitations which can be tackled in future work. Due to the pandemic, an online survey was used in this study, even though participants in online surveys may not complete surveys as thoroughly as traditional written versions in real life. We considered this and removed some illogical answers. Further research is recommended to apply different data collection methods. At the time of writing, data collection could be conducted online and offline, and comparisons can be made before and after the pandemic. Second, other demographic variables associated with students, such as family background, sex, and age, were not considered. These factors could be included in a future study. Third, the participants in this study came mainly from undergraduates enrolled in universities in the north of China. Therefore, the results may lack generalizability to a certain extent. Hence, research samples from regions with different cultural backgrounds are recommended.