IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i9p5619-d810051.html
   My bibliography  Save this article

Self-Regulation, Teaching Presence, and Social Presence: Predictors of Students’ Learning Engagement and Persistence in Blended Synchronous Learning

Author

Listed:
  • Qiuju Zhong

    (School of Education Science, Nanjing Normal University, Nanjing 210097, China)

  • Ying Wang

    (Faculty of Education, The Chinese University of Hong Kong, Hong Kong 999077, China)

  • Wu Lv

    (School of Preschool Education, Jiangsu Second Normal University, Nanjing 210013, China)

  • Jie Xu

    (School of Education Science, Nanjing Normal University, Nanjing 210097, China)

  • Yichun Zhang

    (School of Education Science, Nanjing Normal University, Nanjing 210097, China)

Abstract

Blended synchronous learning (BSL) is becoming increasingly widely implemented in many higher education institutions due to its accessibility and flexibility. However, little research has been conducted to explore students’ engagement and persistence and their possible predictors in such a learning mode. The purpose of this study was to investigate how to facilitate students’ engagement and persistence in BSL. In detail, this study used structural equation modeling to explore the relationships among specific predictors (self-regulation, teaching presence, and social presence), learning engagement, and learning persistence in BSL. We recruited 319 students who were enrolled in BSL at a Chinese university. The online survey was administered to gather data on the variables of this study. The results demonstrated that self-regulation, teaching presence, and social presence were positively associated with learning engagement. Self-regulation and learning engagement were positively associated with learning persistence. Moreover, learning engagement mediated the relationships between self-regulation, teaching presence, social presence, and learning persistence. This study suggests that self-regulation, teaching presence, and social presence are significant predictors for student learning engagement and persistence in BSL.

Suggested Citation

  • Qiuju Zhong & Ying Wang & Wu Lv & Jie Xu & Yichun Zhang, 2022. "Self-Regulation, Teaching Presence, and Social Presence: Predictors of Students’ Learning Engagement and Persistence in Blended Synchronous Learning," Sustainability, MDPI, vol. 14(9), pages 1-18, May.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:9:p:5619-:d:810051
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/9/5619/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/14/9/5619/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yeh, Wei-Chang, 2021. "A quick BAT for evaluating the reliability of binary-state networks," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    2. Lydia Kyei-Blankson & Francis Godwyll & Mohamed A. Nur-Awaleh, 2014. "Innovative blended delivery and learning: exploring student choice, experience, and level of satisfaction in a hyflex course," International Journal of Innovation and Learning, Inderscience Enterprises Ltd, vol. 16(3), pages 243-252.
    3. Mahdi Mohammed Alamri, 2022. "Investigating Students’ Adoption of MOOCs during COVID-19 Pandemic: Students’ Academic Self-Efficacy, Learning Engagement, and Learning Persistence," Sustainability, MDPI, vol. 14(2), pages 1-15, January.
    4. Raphael M. Guillory & Mimi Wolverton, 2008. "It's about Family: Native American Student Persistence in Higher Education," The Journal of Higher Education, Taylor & Francis Journals, vol. 79(1), pages 58-87, January.
    5. Saleh Alhazbi & Mahmood A. Hasan, 2021. "The Role of Self-Regulation in Remote Emergency Learning: Comparing Synchronous and Asynchronous Online Learning," Sustainability, MDPI, vol. 13(19), pages 1-12, October.
    6. Pilhyoun Yoon & Junghoon Leem, 2021. "The Influence of Social Presence in Online Classes Using Virtual Conferencing: Relationships between Group Cohesion, Group Efficacy, and Academic Performance," Sustainability, MDPI, vol. 13(4), pages 1-19, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Muharman Lubis & Muhammad Azani Hasibuan & Rachmadita Andreswari, 2022. "Satisfaction Measurement in the Blended Learning System of the University: The Literacy Mediated-Discourses (LM-D) Framework," Sustainability, MDPI, vol. 14(19), pages 1-29, October.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ardvin Kester S. Ong & Jelline C. Cuales & Jose Pablo F. Custodio & Eisley Yuanne J. Gumasing & Paula Norlene A. Pascual & Ma. Janice J. Gumasing, 2023. "Investigating Preceding Determinants Affecting Primary School Students Online Learning Experience Utilizing Deep Learning Neural Network," Sustainability, MDPI, vol. 15(4), pages 1-24, February.
    2. Yeh, Wei-Chang & Tan, Shi-Yi & Forghani-elahabad, Majid & Khadiri, Mohamed El & Jiang, Yunzhi & Lin, Chen-Shiun, 2022. "New binary-addition tree algorithm for the all-multiterminal binary-state network reliability problem," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    3. Permata Nur Miftahur Rizki & Indria Handoko & Purba Purnama & Didi Rustam, 2022. "Promoting Self-Regulated Learning for Students in Underdeveloped Areas: The Case of Indonesia Nationwide Online-Learning Program," Sustainability, MDPI, vol. 14(7), pages 1-24, March.
    4. Yeh, Wei-Chang & Tan, Shi-Yi & Zhu, Wenbo & Huang, Chia-Ling & Yang, Guang-yi, 2022. "Novel binary addition tree algorithm (BAT) for calculating the direct lower-bound of the highly reliable binary-state network reliability," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    5. Chen, Liwei & Cheng, Chunchun & Dui, Hongyan & Xing, Liudong, 2022. "Maintenance cost-based importance analysis under different maintenance strategies," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    6. Davila-Frias, Alex & Yodo, Nita & Le, Trung & Yadav, Om Prakash, 2023. "A deep neural network and Bayesian method based framework for all-terminal network reliability estimation considering degradation," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    7. Yeh, Wei-Chang & Zhu, Wenbo & Tan, Shi-Yi & Wang, Gai-Ge & Yeh, Yuan-Hui, 2022. "Novel general active reliability redundancy allocation problems and algorithm," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    8. Cui, Hongjun & Wang, Fei & Ma, Xinwei & Zhu, Minqing, 2022. "A novel fixed-node unconnected subgraph method for calculating the reliability of binary-state networks," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    9. Koel Roychowdhury & Radhika Bhanja & Sushmita Biswas, 2022. "Mapping the research landscape of Covid-19 from social sciences perspective: a bibliometric analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(8), pages 4547-4568, August.
    10. Yeh, Wei-Chang, 2022. "Novel direct algorithm for computing simultaneous all-level reliability of multistate flow networks," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    11. Xiaoqi Wang & Lianghong Hui & Xin Jiang & Yuhan Chen, 2022. "Online English Learning Engagement among Digital Natives: The Mediating Role of Self-Regulation," Sustainability, MDPI, vol. 14(23), pages 1-21, November.
    12. Yeh, Wei-Chang & Du, Chia-Ming & Tan, Shi-Yi & Forghani-elahabad, Majid, 2023. "Application of LSTM based on the BAT-MCS for binary-state network approximated time-dependent reliability problems," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    13. Yeh, Wei-Chang, 2022. "Novel self-adaptive Monte Carlo simulation based on binary-addition-tree algorithm for binary-state network reliability approximation," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    14. Chan, Jianpeng & Papaioannou, Iason & Straub, Daniel, 2022. "An adaptive subset simulation algorithm for system reliability analysis with discontinuous limit states," Reliability Engineering and System Safety, Elsevier, vol. 225(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:9:p:5619-:d:810051. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.