Can the utilization of technology-enhanced learning spaces lead to improved learning outcomes? A meta-analysis based on 39 experimental and quasi-experimental studies

• Published in: Interactive learning environments Vol. 33; no. 4; pp. 3052 - 3072
• Main Authors: Hu, Liangliang, Zhang, Wenlan, Lin, Pandeng
• Format: Journal Article
• Language: English
• Published: Routledge 21.04.2025
• Subjects: effect size; learning outcomes; meta-analysis; moderator variables; Technology-enhanced learning space
• ISSN: 1049-4820; 1744-5191
• DOI: 10.1080/10494820.2024.2436943

In recent years, the promotion and application of technology-enhanced learning spaces in teaching have garnered significant attention, but the effectiveness of their application remains controversial. This is the first meta-analysis to examine the effectiveness of technology-enhanced learning spaces on student learning outcomes. The study integrates data from 39 experimental and quasi-experimental studies. The results show that technology-enhanced learning spaces have a significantly positive effect on student learning outcomes (SMD=0.62). Further analysis of moderator variables such as educational stage, subject nature, knowledge type, sample size, experimental duration, teaching methods, and technical type number revealed that sample size and teaching methods significantly moderated the effects of technology-enhanced learning spaces. Based on the results of the meta-analysis, several recommendations for the application of technology-enhanced learning spaces are proposed: (1) choose learning spaces based on the type of knowledge; (2) strengthen emotional care for students in technology-enhanced environments; (3) control class size; and (4) integrate technology with pedagogy. However, the study has some limitations, including a relatively small sample size, lack of consideration of artificial intelligence technologies, and uneven research distribution. Future studies should incorporate a larger and more balanced set of research, and include more moderator variables for analysis.