Influence of mobile technology and smart classroom environment on learning engagement

Abstract

To expand the application of mobile technology and smart classroom environment in education, and explore their influence on learning engagement, this paper, based on the Situated Cognition Theory, took 296 sophomores from six universities in China as the research objects and investigated the relationship between mobile technology, smart classroom environment, and learning engagement. The findings show that: (1) in the mobile technology and smart classroom environment, the frequency of learning by mobile devices in class, the frequency of learning by mobile devices after class, the duration of learning by mobile devices in class, the duration of learning by mobile devices after class, the hardware environment, software resources, and technology acceptance have positive effects on learning engagement; (2) mobile technology and smart classroom environment can better mobilize learners’ interest and initiative in learning and increase learners’ engagement in learning; (3) mobile technology and smart classroom environment can greatly improve college learners’ behavioural engagement, emotional engagement, and cognitive engagement respectively. This study helps turn the learners’ external learning needs into their internal learning motivation, thus enhancing their learning engagement.

Keywords

Mobile technology smart classroom environment learning engagement Situated Cognition Theory intercultural communication

1. Introduction

Mobile technology and smart classroom environment have brought unprecedented changes and opportunities to teaching through individual portraits, visual recognition, speech recognition, and artificial intelligence. Mobile technology is a wireless device based on wireless communication, comprised of two-way communication devices, computing devices, and network technology, including laptops, tablets, pads, smartphones, and Bluetooth. The time of “Internet +” has dramatically advanced the ever-increasing and rapid advancement of mobile technology. The extensive employment of mobile technology in teaching has profoundly altered traditional teaching methods and promoted the transformation of teaching strategies and learning strategies, from computer-assisted to mobile-assisted ones. Mobile technology has made information communication and interaction between teaching and learning more convenient and efficient. Using mobile technology, teachers can grasp learners’ learning in real-time and give timely feedback, significantly improving teachers’ teaching effect and learners’ learning engagement and learning impact.

Under informatization 2.0, smart classroom is an important place to implement smart education. Smart classroom, also called intelligent classroom, is a high-end form of multimedia and internet classrooms based on traditional classrooms, which integrates big data, the Internet of Things, cloud computing, and artificial intelligence, including tangible physical and intangible digital space. The smart classroom is presented through various intelligent equipment-assisted teaching contents, with the core of in-depth teaching interaction to improve teaching and learning effects. A smart classroom possesses a smart learning environment with the ultimate goal of cultivating learners’ autonomous learning and inquiry ability. Smart classrooms have richer resources, tools and technologies, which can optimize the display of teaching contents, help acquire learning resources, and foster communication in the classroom. Smart classrooms offer support to teaching activities of different types, and the equipment is more flexible, which can enhance the various forms of group collaborative learning. Moreover, smart classrooms fully embody the teaching concept of teachers being the guide and learners being the centre of learning.

Learning engagement has always been one of the hot issues in educational circles. Learning engagement is a critical factor that closely connects learners with the curriculum. Also, learning engagement directly affects learners’ learning output and learning effect. Learning engagement and learning results have a strong correlation [1]. Engagement is critical in second language learning [2, 3]. Learning engagement is an essential index to evaluate the quality of online foreign language learning or flipped learning, which receives increasing attention from language teaching researchers [4, 5, 6].

From the above, scholars have explored the influence of mobile technology concerning language acquisition as well as language teaching, paid attention to how smart classroom environment influences language acquisition and language teaching, and done many researches on learning engagement, including the concept, the dimension of composition, measurement, the interrelation between learning engagement and technology use, and the influence of learning engagement on learning effect. However, the influence of mobile technology on learning engagement has not received enough attention, and less research of smart classroom environment on the impact of learning engagement is made, lacking theoretical guidance and related empirical research.

Given the above research shortcomings, based on the Situated Cognition Theory, this study takes 296 sophomores from 6 universities in China as the research objects to investigate the influence of mobile technology and smart classroom environment on learners’ learning engagement. Specifically, this paper will solve the following two research questions: which factors of mobile technology and smart classroom environment will affect learning engagement? How do these factors influence learners’ learning engagement? The findings can help teachers take advantage of the benefits of mobile technology and smart classroom environment, facilitate learners to turn external learning needs into internal motivation for learning, and enhance learners’ learning engagement under mobile technology and smart classroom. The findings can also enrich and expand the existing learning input theory. Besides, the findings will also have significant reference value for developers, managers, and users of education platforms based on mobile technology and smart classroom environment. The research is also beneficial to the application and promotion of mobile technology and smart classroom environment in education.

2. Theoretical background

2.1 Application of mobile technology and smart classroom environment in education

Using three experienced second language teachers as examples, Van Pragg and Sanchez (2015) investigated mobile technology use [7]. Hoang et al. (2022) explored the benefits of virtual reality (VR) to promote spoken fluency of English as Foreign Language (EFL) learners and their understanding and cognition of applying VR mobile technology to EFL learning [8]. Eilola and Lilja (2021) investigated the individual cognitive artifact feature of the smartphone in second language use and learning by using multimodal conversation analysis. Gao and Shen (2020) reported the results concerning the learning strategies or methods of a group of Chinese English as foreign language (EFL) learners within a mobile and technological environment [9]. Klimova (2018) pointed out that mobile technologies can produce beneficial consequences concerning EFL learning and stimulate students’ learning motivation [10].

Lu et al. (2021) examined the interconnection between the vital components influencing learners’ learning and their thinking skills based on a smart classroom environment [11]. Yu et al. (2022) explored the distinctions of communicating behaviours and engagement in a smart class compared with a traditional class in literature. They found that learners engaged more in the smart classroom [12]. Based on the innovation diffusion theory and external pressures, Selim et al. (2020) constructed a model to dig into the major motivations of smart classroom acceptance and sufficiently discussed the factors affecting smart classroom adoption [13]. Petchamé et al. (2021) carried out many qualitative research activities, such as first-day of class observations and user experience interviews on the completion of a task, to achieve learners’ retroaction to promote the engineering programs within a smart classroom context [14].

2.2 Learning engagement and Situated Cognition Theory

Learning engagement primarily includes behavioural engagement, cognitive engagement and emotional engagement, which are interrelated and interactive [15]. Learning engagement is a multi-dimensional construct, which refers to the state of learners’ high attention and participation in the completion of tasks; and besides at the cognitive level, the participation is also reflected at the social, emotional and behavioural levels [16]. Daumiller et al. (2021) looked into the effects of academics’ performance objectives on learning engagement as well as learning achievements in the professional development courses [17]. Balwant (2018) extended the current conceptual researches on learners’ learning engagement by presenting obvious definitions and conceptualizations of learners’ engagement and their disengagement in classrooms [18]. Rashid and Asghar (2016) investigated the acquisition and application of technology to boost academic achievements and foster learners’ engagement in real world [19]. Chi and Wylie (2014) proved that learners will be more engaged with active and interactive learning materials [20]. Li et al. (2022) explored learners’ engagement online and the essential elements concerning teachers. They pointed out that the learners’ cognition and understanding of online learning and learners’ goals to share the learning experience were significantly higher than those learning offline [21].

The situation is considered an essential factor in language acquisition or second language acquisition. The time and space of a learner’s behaviour can affect the learner’s knowledge acquisition in specific time and space. Kim and Hwang (2012) stated that the mobile environment embodies a series of factors that influence the personal environment in the mobile environment [22]. A situation is information that describes the characteristics of an entity. This entity is the interactive process with users, applications, and related people, places, or objects, including users and applications. Situated Cognition Theory holds that an individual’s psychological and mental behaviours and experiences are always in a particular specific situation or context. The situation’s instruction, support, and construction help learners learn knowledge. Learners’ knowledge learning, acquisition, and application are viewed as activities of organization, process, and changeable constructions in situations. The design of learning or teaching tasks should be learner-centered, and the design and plan of learning tasks and activities should consider certain real societal situations. Teaching activities should be designed and arranged through specific dynamic and real situations, to acquire static knowledge in moving situations and combine situations, knowledge acquisition, and learners’ cognition.

Accordingly, the influence of mobile technology and smart classroom environment on learning engagement is of great value for understanding learners’ learning engagement process and enhancing learners’ learning engagement. The research can also provide some suggestions to promote the application of mobile information technology and smart classroom environment in education, thus giving full play to the positive influence of mobile technology and smart classroom environment on learning engagement.

3. Research hypotheses and research model

Eubanks et al. (2018) stated that the application of iPads in classrooms can foster teachers’ teaching, and learners’ learning engagement, and promote learners’ learning achievements [23]. The deep integration of technology and teaching can be considered as a collaborative means for promoting learners’ learning motivation and learners’ engagement. Compared with traditional learning, mobile technology can enhance the interest, effectiveness, passion, and efficiency of learning. The frequency and duration of learning and using mobile devices can be taken as direct reflections of learners’ learning engagement. The development of mobile technology can reduce the requirement of studying in a fixed place, provide learners with a flexible learning time and learning arrangement, which helps stimulate learners’ learning enthusiasm, emotional engagement and cognitive engagement, and improve learners’ behavioural engagement and learning efficiency.

Concerning technology and equipment, smart classroom can be regarded as an advanced form of a traditional multimedia classroom, which provides a richer hardware environment, software resources, and technical support for the teaching process. Comparatively speaking, now traditional classrooms gradually cannot satisfy the teaching needs and requirements of smart classrooms, and the learners’ various and individual needs, which greatly influence learning engagement. A smart classroom can help develop and promote learners’ talents and enhance their passion for making more achievements in classroom activities within a changing and appealing learning environment. By using the experiment, and taking the fifth-grade primary school learners as the research objects in smart classrooms and traditional classrooms, respectively, Jo and Lim (2015) analysed the interrelated behaviours between learners and their teachers in different environments and found that smart classrooms had more beneficial effects on teaching and learning than traditional classrooms did [24]. Compared with classrooms in the conventional situation, questionnaires, and interviews, smart classrooms are helpful in motivating learners’ learning passion and curiosity, and the flexibility of smart classrooms helps to promote interactive, cooperative learning or self-learning activities. A lot of inexpensive technologies arranged optimally provide evidence which helps know a learner’s learning location and timetable, identifies learners’ learning activities, offers learners rich learning resources and contents, and enables effective real-time cooperation, resource sharing, and experience sharing between learners and their teachers as well as their peers [25]. By using mobile technology and smart classroom environment, teachers can design corresponding learning tasks for learners at different levels. Learners can complete learning tasks freely and flexibly to improve their behavioural engagement. Based on the above, the research hypotheses are proposed in this study:

H1-1: Concerning MT, the frequency of LMD in class positively influences BE.

H1-2: Concerning MT, the frequency of LMD in class positively influences EE.

H1-3: Concerning MT, the frequency of LMD in class positively influences CE.

H2-1: Concerning MT, the frequency of LMD after class positively influences BE.

H2-2: Concerning MT, the frequency of LMD after class positively influences EE.

H2-3: Concerning MT, the frequency of LMD after class positively influences CE.

H3-1: Concerning MT, the duration of LMD in class positively influences BE.

H3-2: Concerning MT, the duration of LMD in class positively influences EE.

H3-3: Concerning MT, the duration of LMD in class positively influences CE.

H4-1: Concerning MT, the duration of LMD after class positively influences BE.

H4-2: Concerning MT, the duration of LMD after class positively influences EE.

H4-3: Concerning MT, the duration of LMD after class positively influences CE.

H5-1: Concerning SCE, HE has a positive influence on BE.

H5-2: Concerning SCE, HE has a positive influence on EE.

H5-3: Concerning SCE, HE has a positive influence on CE.

H6-1: Concerning SCE, SR has a positive influence on BE.

H6-2: Concerning SCE, SR has a positive influence on EE.

H6-3: Concerning SCE, SR has a positive influence on CE.

H6-1: Concerning SCE, TA has a positive influence on BE.

H6-2: Concerning SCE, TA has a positive influence on EE.

H6-3: Concerning SCE, TA has a positive influence on CE.

Notes

MT: mobile technology

LMD: learning by mobile devices

SCE: smart classroom environment

HE: hardware environment

SR: software resources

TA: technology acceptance

BE: behavioural engagement

EE: emotional engagement

CE: cognitive engagement

With the above research hypotheses, the research model is proposed in Fig. 1.

Figure 1.

Research model.

Concerning mobile technology and smart classroom environment, the author mainly explores the influence of seven factors on the three aspects of learning engagement. Specifically, the seven factors are frequency of learning by mobile devices in class and frequency of learning by mobile devices after class, duration of learning by mobile devices in class, duration of learning by mobile devices after class, hardware environment, software resources, and technology acceptance. The three aspects of learning engagement are behavioural, emotional, and cognitive engagement.

4. Research method and process

4.1 Questionnaire design

The questionnaire method was adopted to collect the data. To guarantee the reliability and validity of the questionnaire, the existing literature, both in China and abroad, were taken as references, and appropriate adjustments were made according to the learning characteristics of mobile technology and smart classroom environment in China.

The questionnaires used to collect data in this study consisted of three parts: Mobile Technology Usage Scale, Smart Classroom Environment Usage Scale, and Learning Engagement Scale, including seven potential variables. Among them, the Mobile Technology Usage Scale, inspired Elhai et al.’s Smartphone Use Frequency Scale [26], Konok et al.’s Mobile Usage Scale [27], and Rosen et al.’s Media and Technology Usage and Attitudes Scale [28], contained four variables, namely, the frequency of learning by mobile devices in class, the frequency of learning by mobile devices after class, the duration of learning by mobile devices in class, and the duration of learning by mobile devices after class, with 12 items. The Smart Classroom Environment Usage Scale was inspired by the Preference Instrument of Smart Classroom Learning Environments [29] by Macleod et al. in 2018. The Smart Classroom Environment Usage Scale in this paper included three variables, namely, hardware environment, software resources, and technology acceptance, with nine items. The Learning Engagement Scale was inspired by the learning engagement questionnaire [30] compiled by Elmaadaway in 2018, which included three variables, namely, behavioural engagement, emotional engagement, and cognitive engagement, with 10 items, 8 items, and 7 items respectively. The Learning Engagement Scale in this paper was also motivated by the Behavioural Engagement Scale and the Emotional Engagement Scale [31] by Skinner et al. in 2009, and Greene’s Cognitive Engagement Scale [32] in 2015.

Before the formal investigation, 39 undergraduates were pre-investigated to guarantee the reliability and validity of the scale to meet the requirements. Using Likert’s 5-point scoring method, the questionnaire was divided into five levels: very inconsistent, inconsistent, difficult to determine, consistent, and very consistent, and assigned to 1, 2, 3, 4, and 5 points, respectively. According to the pre-investigation results, the scales were moderately adjusted to be more suitable for the national conditions in China.

4.2 Data collection

296 sophomores from 6 universities in China were taken as the research subjects and investigated by questionnaires. All these research subjects were learning by integrating mobile technology into the smart classroom. Smart classrooms were technically combined with cloud computing, big data, and mobile communication technologies. Smart classrooms were also equipped with an electronic interactive whiteboard, microphones, cloud storage, and other hardware facilities, and with digital learning platforms such as correction networks and teaching materials, Rain Classroom, and U Campus based on mobile terminals. Learners all shared mobile intelligent technology devices such as laptops, tablets, PADs, and smartphones in learning. Researchers distributed questionnaires through the QQ and WeChat groups in the form of online questionnaires in April 2022. According to the survey, 319 questionnaires were distributed, and 302 questionnaires were collected, with a relatively high recovery rate of 94.67%. Six invalid questionnaires (if the answering time was less than 1 minute or the scores of all the items were the same) were excluded. Finally, 296 valid questionnaires were collected, representing good representativeness.

4.3 Reliability and validity analysis

The reliability and convergent validity of the variables were verified, and the analyses of factors of validation were shown in Table 1. The Cronbach Alpha coefficients of the ten factors were all higher than 0.75, and the average variance extraction of each factor was above 0.50, indicating that the scale’s reliability and convergent validity were very good.

Table 1
Reliability and convergent validity analysis

Variables	Cronbach’s Alpha	CR	AVE
A	0.915	0.822	0.707
B	0.827	0.833	0.562
C	0.873	0.849	0.626
D	0.915	0.906	0.724
E	0.892	0.874	0.695
F	0.834	0.826	0.619
G	0.918	0.819	0.699
H	0.856	0.824	0.705
I	0.902	0.837	0.522
J	0.875	0.902	0.713

Notes: A: Frequency of learning by mobile device in class; B: Frequency of learning by mobile device after class; C: Duration of learning by mobile device in class; D: Duration of learning by mobile device after class; E: Hardware environment; F: Software resources; G: Technology acceptance; H: Behavioural engagement; I: Emotional engagement; J: Cognitive engagement; CR: Composite reliability; AVE: Average variance extraction.

Table 2

Discriminant validity analysis

	A	B	C	D	E	F	G	H	I	J
A	0.803
B	0.237	0.819
C	0.459	0.337	0.787
D	0.409	0.417	0.436	0.798
E	0.395	0.259	0.324	0.245	0.806
F	0.297	0.179	0.132	0.337	0.325	0.798
G	0.446	0.242	0.223	0.466	0.159	0.491	0.804
H	0.408	0.166	0.415	0.249	0.226	0.387	0.657	0.836
I	0.389	0.253	0.294	0.424	0.313	0.342	0.533	0.161	0.792
J	0.411	0.319	0.307	0.361	0.279	0.403	0.396	0.295	0.369	0.811

According to Table 2, the evaluation method of discriminant validity was to compare the square root of the mean-variance sampling amount and the correlation coefficient between variables. The AVE square root of the ten factors shown in the diagonal numbers in the table were all higher than their correlation coefficient. Therefore, this scale had good discriminant validity.

4.4 Hypothesis testing

Table 3 showed that the standardized path coefficients and their significance were calculated by the Amos analysis tool. Model verification results indicated that the seven hypotheses were all supported and were significant ( $p<$ 0.01).

Table 3
Path testing and standardized path coefficients

Hypotheses	Path relationship between potential variables	Estimate	Standard error	$P$	Standardized path coefficients	Results of significance testing
H1-1	A $\to$ H	0.243	0.014	0.007	0.243	Support
H1-2	A $\to$ I	0.145	0.017	0.003	0.434	Support
H1-3	A $\to$ J	0.352	0.056	0.005	0.319	Support
H2-1	B $\to$ H	0.257	0.039	0.004	0.361	Support
H2-2	B $\to$ I	0.164	0.026	0.004	0.439	Support
H2-3	B $\to$ J	0.153	0.012	0.002	0.347	Support
H3-1	C $\to$ H	0.276	0.022	0.003	0.326	Support
H3-2	C $\to$ I	0.158	0.024	0.005	0.245	Support
H3-3	C $\to$ J	0.247	0.019	0.004	0.416	Support
H4-1	D $\to$ H	0.159	0.047	0.002	0.372	Support
H4-2	D $\to$ I	0.262	0.027	0.006	0.424	Support
H4-3	D $\to$ J	0.301	0.018	0.007	0.305	Support
H5-1	E $\to$ H	0.225	0.021	0.003	0.289	Support
H5-2	E $\to$ I	0.148	0.015	0.005	0.317	Support
H5-3	E $\to$ J	0.322	0.037	0.004	0.422	Support
H6-1	F $\to$ H	0.304	0.036	0.005	0.294	Support
H6-2	F $\to$ I	0.149	0.024	0.003	0.411	Support
H6-3	F $\to$ J	1.296	0.013	0.002	0.395	Support
H7-1	G $\to$ H	1.384	0.021	0.002	0.287	Support
H7-2	G $\to$ I	1.169	0.017	0.005	0.318	Support
H7-3	G $\to$ J	1.252	0.029	0.003	0.226	Support

Table 4

Influence of mobile technology and smart classroom environment on learning engagement

Factors	B	C	D	E	F	G	H	I	J
A	0.45 ${}^{**}$
B	0.29 ${}^{**}$	0.27 ${}^{**}$
C	0.16 ${}^{**}$	0.22 ${}^{**}$	0.25 ${}^{**}$
D	0.29 ${}^{**}$	0.11 ${}^{**}$	0.13 ${}^{**}$	0.47 ${}^{**}$
E	0.29 ${}^{**}$	0.28 ${}^{**}$	0.17 ${}^{**}$	0.39 ${}^{**}$	0.45 ${}^{**}$
F	0.26 ${}^{**}$	0.25 ${}^{**}$	0.27 ${}^{**}$	0.46 ${}^{**}$	0.48 ${}^{**}$	0.49 ${}^{**}$
G	0.14 ${}^{**}$	0.18 ${}^{**}$	0.19 ${}^{**}$	0.41 ${}^{**}$	0.46 ${}^{**}$	0.42 ${}^{**}$	0.41 ${}^{**}$
H	0.27 ${}^{**}$	0.22 ${}^{**}$	0.18 ${}^{**}$	0.37 ${}^{**}$	0.41 ${}^{**}$	0.47 ${}^{**}$	0.37 ${}^{**}$	0.35 ${}^{**}$
I	0.19 ${}^{**}$	0.26 ${}^{**}$	0.29 ${}^{**}$	0.39 ${}^{**}$	0.44 ${}^{**}$	0.39 ${}^{**}$	0.47 ${}^{**}$	0.28 ${}^{**}$	0.39 ${}^{**}$

Note: ${}^{**}P<$ 0.01 two-tailed test.

The influence of mobile technology and smart classroom environment on learning engagement was shown in Table 4. From Table 4, within the environment of mobile technology and smart classroom, the frequency of learning by mobile devices in class, the frequency of learning by mobile devices after class, the duration of learning by mobile devices in class, the duration of learning by mobile devices after class, the hardware environment, software resources, and technology acceptance had an obviously positive influence on the behavioural engagement, emotional engagement, and cognitive engagement. The seven internal factors of mobile technology and smart classroom were pairwise correlated with the three factors of learning engagement.

5. Conclusion

Through the questionnaire survey of 296 sophomore undergraduates from 6 universities in China, based on the Situated Cognition Theory, this paper provides solid empirical support for the general situation of college learners’ learning engagement, the factors in mobile technology and smart classroom environment that influence college learners’ learning engagement, and also the relationship between mobile technology and smart classroom environment and learning engagement. The study has enriched the literature on mobile technology, smart classroom teaching and learning engagement. Additionally, mobile technology and smart classroom environment constitute a new teaching environment, which can significantly arouse learners’ interest, initiative and enthusiasm in active and positive learning, boost the information and interest in teaching contents, and enhance learners’ learning engagement. The following conclusions could be drawn:

(1)

In the mobile technology and smart classroom environment, the frequency of learning by mobile devices in class, the frequency of learning by mobile devices after class, the duration of learning by mobile devices in class, the duration of learning by mobile devices after class, the hardware environment, software resources, and technology acceptance have positive effects on learning engagement.

(2)

Mobile technology and smart classroom environment can better mobilize learners’ interest, enthusiasm, and initiative in their learning process, thus enhancing their learning outcomes.

(3)

Mobile technology and smart classroom environment can significantly improve college learners’ behavioural, emotional, and cognitive engagement.

Based on the above conclusions, to better improve the application scope and give full play to the positive effects of mobile technology and smart classroom environment in education, the following suggestions are put forward:

First, mobile technology and smart classroom environment provide learners with rich multi-modal teaching resources. Teachers are suggested to make relevant teaching tasks technical and intelligent, consciously guide and instruct learners, strengthen learners’ understanding and cognition of the related functions of mobile technology and the smart classroom environment, improve the use of mobile technology and smart classroom, make learners more convenient to accept and acquire new knowledge, improve learners’ ability to search information, enhance learners’ autonomous learning ability, encourage learners to study hard and enhance their cognitive engagement.

Second, the mobile technology and smart classroom environment diversify teaching means, methods and strategies. Teachers can use mobile technology and smart classroom environment to encourage learners to communicate or interact with their teachers and peers, learn collaboratively, and find and solve problems through learning tasks, different smart teaching platforms and network connections. Mobile technology and a smart classroom environment are encouraged to help learners overcome behavioural inertia and psychological inertia in learning, thus promoting learners’ behavioural engagement.

Third, mobile technology and smart classroom environment should be used to provide rich functional experience and pleasant emotional experiences, foster learners’ interest, passion, and confidence in learning, promote learners’ willingness to take the initiative to learn, acquire knowledge more consciously and actively, improve their self-efficacy, give learners a sense of accomplishment, be willing to help their peers complete cross-cultural communication activities, help their peers improve learners’ awareness of cross-cultural communication, help their peers solve difficulties in learning, and enhance their emotional engagement.

Footnotes

Acknowledgments

The author acknowledges Heilongjiang Province Higher Education Teaching Reform Project (Grant: SJGY20220329), the 11th China Foreign Language Education Foundation Project (Grant: ZGWYJYJJ11A116), “World Language and Culture Research” Project (Grant: WYZL2022HL0004).

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Influence of mobile technology and smart classroom environment on learning engagement

Abstract

Keywords

1. Introduction

2. Theoretical background

2.1 Application of mobile technology and smart classroom environment in education

2.2 Learning engagement and Situated Cognition Theory

3. Research hypotheses and research model

4.1 Questionnaire design

4.2 Data collection

4.3 Reliability and validity analysis

Table 1 Reliability and convergent validity analysis

Table 3 Path testing and standardized path coefficients

Footnotes

Acknowledgments

References

Table 1
Reliability and convergent validity analysis

Table 3
Path testing and standardized path coefficients