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

Institutions

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
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Factors affecting deep learning of EFL students in higher vocational colleges under small private online courses‐based settings: A grounded theory approach

Xia Chen, Changhong Peng, Liping Jiang, Menglei Lv et al.
28 citations
Journal of Computer Assisted Learning
Experimental Learning in Engineering
8.95
article

Factors affecting deep learning of EFL students in higher vocational colleges under small private online courses‐based settings: A grounded theory approach

Xia Chen, Changhong Peng, Liping Jiang, Menglei Lv, Mengmeng Cheng
article en
28 citations

Abstract

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.

Journal of Computer Assisted LearningVol. 40(6)
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)
Quality Education
Openalex Percentile: Top 2%
Experimental Learning in Engineering
8.95
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.