Factors affecting deep learning of <scp>EFL</scp> students in higher vocational colleges under small private online courses‐based settings: A grounded theory approach
Abstract Background The introduction of Small Private Online Courses (SPOCs) in English as a Foreign Language (EFL) instruction at Higher Vocational Colleges (HVCs) signifies a shift in education. Understanding the factors that affect deep learning in this SPOC context is crucial for improving educational outcomes. Objectives By employing grounded theory, we seek to explore the key factors that shape deep learning experiences for students in SPOC learning environments at HVCs and clarify the interrelationships among these influencing factors. Methods Semi‐structured interviews were conducted with 18 EFL students and 4 teachers and NVivo 11 software was utilised to aid in the qualitative analysis of the collected data. Through a rigorous three‐tier coding analysis, an “environment‐person‐mediation ‐behaviour” (EPMB) model was constructed, aiming to clarify the mechanisms that influence deep learning among EFL students in HVCs. Results Our findings reveal that intrinsic motivation and cognitive abilities are crucial for deep learning among EFL students in HVCs. Blended learning settings, English curriculum satisfaction, and English teachers' teaching methods serve as situational influencing factors. These factors are interconnected, mediating positive or negative effects on deep learning through various intermediaries like continuity, attraction, guidance, motivation, and regulation. Implications The findings offer pedagogical insights for HVC stakeholders, enabling them to enhance students' deep learning experiences.
Authors
- Xia Chen (ORCID: https://orcid.org/0000-0003-2153-1639)
- Changhong Peng (ORCID: https://orcid.org/0000-0002-3963-5906)
- Liping Jiang (ORCID: https://orcid.org/0000-0002-1785-7225)
- Menglei Lv
- Mengmeng Cheng
Institutions
- Shanghai University of Finance and Economics (CN)
- Guangzhou Academy of Special Equipment Inspection and Testing (CN)
- Guangzhou Chemistry (China) (CN)
- Guangdong Polytechnic Normal University (CN)
- Institute of Software (CN)
- Software Engineering Institute of Guangzhou
- UCSI University (MY)
- Zhejiang University of Finance and Economics (CN)
Publication Details
- Journal
- Journal of Computer Assisted Learning
- Published
- 2024-08-25
- DOI
- https://doi.org/10.1111/jcal.13060
- Citations
- 28
- Primary Topic
- Experimental Learning in Engineering
- Type
- article
- Field-Weighted Citation Impact
- 8.95