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Relationships between Teaching Presence, Connected Classroom Climate, and Deep Learning within the Rotational Synchronous Teaching Model

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Abstract

The rotational synchronous teaching (RST) model has attracted attention as it can increase the teaching presence and connected classroom climate in multiple synchronous classroom learning environments. This paper presents an investigation of the effects of the relationships between college students’ perception of teaching presence, connected classroom climate, and deep learning in RST. A total of 264 valid data sets were collected from 288 first-year college students. Structural equation modeling was employed, showing that teaching presence and connected classroom climate were both positively related to students’ deep learning. In addition, an indirect effect was identified between teaching presence and deep learning through connected classroom climate. Further analysis showed that the facilitating discourse dimension of teaching presence had a direct effect on the reflective learning dimension of deep learning and connected classroom climate. Moreover, the assessment dimension of teaching presence had a direct effect on higher-order learning and integrated learning dimensions of deep learning and connected classroom climate. Employing connected classroom climate as a mediator, (1) partially mediated the relationships between facilitating discourse and reflective learning, as well as assessment and higher-order learning, and (2) fully mediated the relationships between facilitating discourse and higher-order learning. These findings have practical implications for educators, which can be used to enhance teaching presence and connected classroom climate thus promoting students’ deep learning within the RST model of instruction.

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Data Availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Abbreviations

MSSC:

Multiple synchronous smart classroom learning environments

F2F:

Face-to-face

RST:

Rotational synchronous teaching

CCC:

Connected classroom climate

HL:

Higher-order learning

IL:

Integrated learning

RL:

Reflective learning

DE:

Design and organization

FD:

Facilitating discourse

DI:

Direct instruction

AS:

Assessment

CMV:

Common method variance

CR:

Composite reliability

AVE:

Average variance extracted

χ2 :

Chi-square

df:

Degree of freedom

GFI:

Goodness of fit index

RMSEA:

Root mean square error of approximation

CFI:

Comparative fit index

TLI:

Tucker-Lewis index

SRMR:

Standardized root mean square residual

CI:

Confidence interval

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Acknowledgements

This work was partly supported by the Fundamental Research Funds for the Central University (Cultivation project of excellent doctoral thesis) (2019YBZZ011).

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Authors and Affiliations

  1. Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, China

    Di Gong, Harrison Hao Yang & Di Wu

  2. School of Education, State University of New York at Oswego, Oswego, NY, USA

    Harrison Hao Yang

  3. School of Mathematics and Statistics, Central China Normal University, Wuhan, China

    Jinjun Dai

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Gong, D., Yang, H.H., Wu, D. et al. Relationships between Teaching Presence, Connected Classroom Climate, and Deep Learning within the Rotational Synchronous Teaching Model. Educ Inf Technol 28, 1715–1733 (2023). https://doi.org/10.1007/s10639-022-11207-0

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