Student engagement refers to students’ involvement in their learning activities as a product of both individual and class influences. There is considerable literature on student engagement; however, more research is needed with regards to factors that explain student engagement in the setting of a smart classroom learning environment (SCLE). SCLE is defined as a physical classroom that integrates enhanced technologies such as 4G technology, interactive whiteboards, and mobile devices to increase the instructors' ability and facilitate students' learning beyond traditional classrooms’ possibilities (Alelaiwi et al., 2015).

With regards to positively affecting student engagement, investigating classroom process quality, i.e., the interactional patterns between teachers and students, is considered important because these patterns have been found to influence students’ learning outcomes (Mashburn et al., 2008). Instructional quality has usually been studied by using three global dimensions (i.e., cognitive activation, supportive climate, and classroom management; Klieme, Pauli, & Reusser, 2009). From a conceptual point of view, classroom process quality refers to variables that are located at the class level. However, previous studies have provided strong indications that individual student ratings of instructional quality can be viewed as construct-specific measures consisting of shared perceptions at the class level and non-shared perceptions at the individual level (Wagner, Göllner, Helmke, Trautwein, & Luedtke, 2013). To date, few studies have investigated classroom process quality in the SCLE at the student level and the class level simultaneously.

Undoubtedly, the teacher has a role in creating supportive conditions in the SCLE. However, whether teachers try to create such conditions and how they go about trying to do so is likely to depend on their beliefs of the SCLE (Chand, Deshmukh, & Shukla, 2020).  Many studies support the idea that teacher beliefs are related to students’ perceptions of cognitive activation, classroom management, supportive climate, and technology use (see e.g., Burić & Kim, 2020). Besides, studies confirm the positive effect of classroom process quality on student engagement. For instance, Decristan et al. (2015) found that the aggregated elementary school student ratings of cognitive activation, supportive climate, and classroom management positively affected students’ conceptual understanding. Moreover, a proper investigation of the benefits of a learning environment to student engagement requires careful consideration of individual and group background characteristics. Lastly, given evidence supporting the association between teacher beliefs and student engagement (van Uden, Ritzen, & Pieters, 2014), classroom process quality is crucial because it can function as a  connection between teacher beliefs to student engagement.

Upon reviewing relevant literature, the current study is designed to fulfill the gaps in previous research by investigating the relations among teacher beliefs, classroom process quality, and student engagement in the context of the smart classroom learning environment.  Specifically, we attempt to answer the following research questions:

RQ1. At the classroom level, which variables (i.e., teacher beliefs, teacher and class background variables) explain differences between students’ shared perception of classroom process quality in the SCLE?

RQ2. At the classroom level (i.e., teacher beliefs, teacher and class background variables) and student level (i.e., students’ shared and non-shared perception of classroom process quality, student demographic variables), which variables explain differences between student engagement in the SCLE?

RQ3. Is there an indirect effect of teacher beliefs, and teacher and class background variables on student engagement in the SCLE through students’ shared perception of classroom process quality?

As a context for the study, smart classrooms were selected where each student owns one mobile device (here: tablets). Participants were teachers and their students in the smart classrooms. We developed a student questionnaire to measure student engagement and classroom process quality as observed by the students. A teacher questionnaire was developed to measure teacher beliefs. Both questionnaires contained questions about background variables such as gender and age. The final sample included 1825 students and their teachers of 38 smart classrooms in secondary schools in China. The number of participating students per class ranged from 16 to 85 (M = 48.026, SD = 16.511). Teacher beliefs of SCLE were measured by adapting the Chinese version of the Preference Instrument of Smart Classroom Learning Environments (PI-SCLE) (MacLeod, Yang, Zhu, & Li, 2018). Instructional quality was measured using the scales cognitive activation (CA), connectedness (CN) from PI-SCLE, and classroom management (CM) from the Teaching Quality Scales (Fauth, Decristan, Rieser, Klieme, & Büttner, 2014). Nine items of the use of technology (TECH), including dimensions of digital devices and digital educational resources, were self-developed. Student engagement was measured by an adaptation of the scale of Student Engagement (Jang, Kim, & Reeve, 2012). Teachers’ and students’ gender was dummy coded (0= female, 1= male). After initial analyses, CM was deleted from the model because its high correlation with CN. To answer RQ1 and RQ2, we employed multilevel regression analysis. In the first stage, to determine the possible difference in student engagement outcomes across classes, we conducted an unconditional, two-level regression analysis: students at Level 1, classroom at Level 2. The results showed that the between-class variance (ICC) for student engagement was 0.065. In the second stage, the full model with student-level and classroom-level variables and covariates was conducted. Except for the covariates at both levels, student-level variables contained students’ individual perceptions of CA_S, CN_S, TECH_S, and classroom-level variables contained teacher ratings of teacher beliefs, and students’ aggregated perceptions of CA_C, CN_C, and TECH_C. Finally, to answer RQ3, we performed multilevel mediation analysis. We treated teacher beliefs, teacher and class background variables as independent variables, students’ aggregated perception of classroom process quality (i.e., CA_C, CN_C, TECH_C) as mediators, and student engagement as the dependent variable. Both independent variables and mediators were Level-2 variables, while the dependent variable was a Level-1 variable.

Regarding RQ1, teacher beliefs was not significantly related to all aspects of students’ shared perception of classroom process quality. Male teachers showed significantly lower CA_C (B = -0.156, β = -0.344, p = 0.017), and teachers with higher degree was associated with higher CA_C (B =0.321, β = 0.405, p < 0.001), CN_C (B = 0.160, β = 0.244, p = 0.001) and TECH_C (B = 0.393, β = 0.379, p < 0.001). Teachers teaching in higher grades performed higher TECH_C (B = 0.123, β = 0.311, p = 0.017) than those not. Regarding RQ2, CA_S (B = 0.313, β = 0.384, p < 0.001), CN_S (B = 0.362, β = 0.370, p < 0.001), TECH_S (B = 0. 047, β = 0. 065, p = 0.001) and student gender (B = 0.043, β = 0.039, p = 0.019) were positively related to student engagement. At the classroom level, students’ shared perceptions of CN_C (B = 0.693, β = 0.782, p < 0.001), TECH_C (B = 0.141, β = 0.250, p = 0.252), Teacher degree (B = 0.102, β = 0.175, p < 0.001) and teaching year (B = 0.033, β = 0.177, p = 0.014) were significantly related to student engagement, but teacher beliefs and CA_C were not. Regarding RQ3, the results indicated that only the indirect effect of teachers’ degree level on students’ engagement through CN_C (B= 0.111, p = 0.003), and the indirect effect of degree level on student engagement through TECH_C (B= 0.055, p = 0.043) were statistically significant. This study attempted to shed light on factors explaining student engagement in the SCLE, which has not been explored in the literature. Our findings show the classroom process quality is vital for student engagement, and this study proposes strategies for improving student engagement.

Alelaiwi, A., Alghamdi, A., Shorfuzzaman, M., Rawashdeh, M., Hossain, M. S., & Muhammad, G. (2015). Enhanced engineering education using smart class environment. Computers in Human behavior, 51, 852-856. Burić, I., & Kim, L. E. (2020). Teacher self-efficacy, instructional quality, and student motivational beliefs: An analysis using multilevel structural equation modeling. Learning and Instruction, 66, 101302. Chand, V. S., Deshmukh, K. S., & Shukla, A. (2020). Why does technology integration fail? Teacher beliefs and content developer assumptions in an Indian initiative. Educational Technology Research and Development, 68, 2753-2774. Decristan, J., Klieme, E., Kunter, M., Hochweber, J., Büttner, G., Fauth, B., ... & Hardy, I. (2015). Embedded formative assessment and classroom process quality: How do they interact in promoting science understanding?. American Educational Research Journal, 52(6), 1133-1159. Fauth, B., Decristan, J., Rieser, S., Klieme, E., & Büttner, G. (2014). Student ratings of teaching quality in primary school: Dimensions and prediction of student outcomes. Learning and Instruction, 29, 1-9. Jang, H., Kim, E. J., & Reeve, J. (2012). Longitudinal test of self-determination theory's motivation mediation model in a naturally occurring classroom context. Journal of Educational psychology, 104(4), 1175. Klieme, E., Pauli, C., & Reusser, K. (2009). The Pythagoras study: Investigating effects of teaching and learning in swiss and German mathematics classroom. In T. Janik, & T. Seidel (Eds.). The power of video studies in investigating teaching and learning in the classroom (pp. 137–160). Münster, Germany: Waxmann. MacLeod, J., Yang, H. H., Zhu, S., & Li, Y. (2018). Understanding students’ preferences toward the smart classroom learning environment: Development and validation of an instrument. Computers & Education, 122, 80-91. Mashburn, A. J., Pianta, R. C., Hamre, B. K., Downer, J. T., Barbarin, O. A., Bryant, D., . . . & Howes, C. (2008). Measures of classroom quality in prekindergarten and children’s development of academic, language, and social skills. Child Development, 79(3), 732–749. van Uden, J. M., Ritzen, H., & Pieters, J. M. (2014). Engaging students: The role of teacher beliefs and interpersonal teacher behavior in fostering student engagement in vocational education. Teaching and Teacher Education, 37, 21-32. Wagner, W., Göllner, R., Helmke, A., Trautwein, U., & Luedtke, O. (2013). Construct validity of student perceptions of instructional quality is high, but not perfect: Dimensionality and generalizability of domain-independent assessments. Learning and Instruction, 28, 1-11.