Meta-analysis reveals the effectiveness evaluation of blended learning models across different academic disciplines

Feng  Shi; Yuqing  Wu; Rufeng  He

doi:10.59400/fes2501

Meta-analysis reveals the effectiveness evaluation of blended learning models across different academic disciplines

Feng Shi School of Economics and Management, Nanning Normal University, Nanning 530001, China
Yuqing Wu School of Economics and Management, Nanning Normal University, Nanning 530001, China
Rufeng He School of Economics and Management, Nanning Normal University, Nanning 530001, China

Article ID: 2501

DOI: https://doi.org/10.59400/fes2501

Keywords: blended learning; learning outcomes; meta-analysis

Abstract

With the rapid development of modern electronic information technology, blended learning has gradually gained attention and acceptance among universities and individuals. Most studies indicate that, due to the combination of teacher guidance and active student participation in blended learning models, their effectiveness often surpasses that of traditional, single-learning methods. However, different disciplines possess unique characteristics, and there is currently a paucity of research exploring the effectiveness of blended learning across various academic fields. This study aims to evaluate the effectiveness of blended learning models in different academic disciplines using meta-analysis methods, through a comprehensive quantitative synthesis of 18 relevant experimental and quasi-experimental studies. The results reveal that blended learning demonstrates a moderately high level of effectiveness in the humanities (g = 0.795, P < 0.01) and an exceptionally high level of effectiveness in science and engineering disciplines (g = 1.017, P < 0.01). Lastly, this study offers suggestions on effectively implementing blended learning, considering three aspects: discipline characteristics, learning resources, and evaluation criteria.

Published

2025-04-16

How to Cite

Shi, F., Wu, Y., & He, R. (2025). Meta-analysis reveals the effectiveness evaluation of blended learning models across different academic disciplines. Forum for Education Studies, 3(2), 2501. https://doi.org/10.59400/fes2501

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Vol. 3 No. 2 (2025)

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Article

This work is licensed under a Creative Commons Attribution 4.0 International License.

References

[1]Procter C. Blended learning in practice. Available online: http://www.ece.salford.ac.uk/proceedings/2003 (accessed on 1 January 2025).

[2]Bonk CJ, Graham CR, Cross J, & Moore MG. The Handbook of Blended Learning: Global Perspectives, Local Designs. Pfeiffer & Company; 2005.

[3]Al‐Qahtani AAY, Higgins SE. Effects of traditional, blended and e‐learning on students’ achievement in higher education. Journal of Computer Assisted Learning. 2012; 29(3): 220-234. doi: 10.1111/j.1365-2729.2012.00490.x

[4]Setiyawan A, Achmadi TA, Anggoro AB. The Effect of Blended Learning to the Students Learning Achievements in Department of Mechanical Engineering. In: Proceedings of1st Vocational Education International Conference (VEIC 2019);30 April 2019; Semarang, Indonesia. pp. 162-166.

[5]Zhang W, Zhu C. Blended Learning as a Good Practice in ESL Courses Compared to F2F Learning and Online Learning. International Journal of Mobile and Blended Learning. 2020; 12(1): 64-81. doi: 10.4018/ijmbl.2020010105

[6]Warman LAD. The effect of Google classroom in Blended Learning on university students’ English ability. J-SHMIC: Journal of English for Academic. 2021;8(1): 12-23.doi: 10.25299/jshmic.2021.vol8(1).6216

[7]Olitsky NH, Cosgrove SB. The effect of blended courses on student learning: Evidence from introductory economics courses. International Review of Economics Education. 2014; 15: 17-31. doi: 10.1016/j.iree.2013.10.009

[8]Means B, Toyama Y, Murphy R, et al. Evaluation of evidence-based practices in online learning: A meta-analysis and review of online learning studies. Available online: https://www.ed.gov/sites/ed/files/rschstat/eval/tech/evidence-based-practices/finalreport.pdf (accessed on 1 January 2025).

[9]Bernard RM, Borokhovski E, Schmid RF, et al. A meta-analysis of blended learning and technology use in higher education: from the general to the applied. Journal of Computing in Higher Education. 2014; 26(1): 87-122. doi: 10.1007/s12528-013-9077-3

[10]Humaira H, Asbah A. Investigating the Effect of Blended Learning Activity in Teaching Reading at Second Grade Senior High School. Linguistics and Elt Journal. 2019; 6(1): 30. doi: 10.31764/leltj.v11i1.745

[11]Isda ID, Purwati P, Imran I. The Effect of Using Blended Learning Model on Enhancing Students’ Speaking Skills in Senior High Schools. Journal of Languages and Language Teaching. 2021; 9(1): 92. doi: 10.33394/jollt.v9i1.2921

[12]Djiwandono PI. The effect of blended learning on reading abilities, vocabulary mastery, and collaboration among university students. The New English Teacher. 2018; 12(1): 23-23.

[13]Grønlien HK, Christoffersen TE, Ringstad Ø, et al. A blended learning teaching strategy strengthens the nursing students’ performance and self-reported learning outcome achievement in an anatomy, physiology and biochemistry course—A quasi-experimental study. Nurse Education in Practice. 2021; 52: 103046. doi: 10.1016/j.nepr.2021.103046

[14]Katasila P, Poonpon K. The Effects of Blended Learning Instruction on Vocabulary Knowledge of Thai Primary School Students. English Language Teaching. 2022; 15(5): 52. doi: 10.5539/elt.v15n5p52

[15]Ebadi S, Ghuchi KD. Investigating the Effects of Blended Learning Approach on Vocabulary Enhancement from EFL Learners’ Perspectives. Journal on English Language Teaching. 2018; 8(2): 57-68.

[16]Al Bataineh KB, Banikalef A, Albashtawi AH. The Effect of Blended Learning on EFL Students’ Grammar Performance and Attitudes: An Investigation of Moodle. Arab World English Journal. 2019; 10(1): 324-334. doi: 10.24093/awej/vol10no1.27

[17]Haryati H. Investigating the effect of blended learning on students’ grammar achievement. HUMANIKA: JurnalIlmuSosial, Pendidikan, dan Humaniora. 2018; 1(1);58-64.

[18]Huda K. The effect of blended learning model on students’ writing skill. JEET, Journal of English Education and Technology. 2020;1(03): 154-172.

[19]Mezaal AK. The Effect of Blended Teaching Method on Iraqi EFL Students’ Performance in Learning English Language. Turkish Journal of Computer and Mathematics Education (TURCOMAT). 2021;12(7): 2817-2823.

[20]Astarilla L, Warman D. The Effect of WhatsApp in Blended Learning on English as Foreign Language (EFL) Undergraduate Students’ Reading Comprehension. In: Proceedings of Fifth International Seminar on English Language and Teaching (ISELT 2017). pp. 74-77.

[21]Ihbar MZ, & Said NEM. The effects of blended learning on EFL high school students’ vocabulary mastery. International Journal of Innovative Research and Creative Technology. 2019;4(6): 111-116.

[22]Wahyuni S. The effect of blended learning model towards students’ writing ability. J-SHMIC: Journal of English for Academic. 2018;5(2): 97-111.

[23]Laili RN, Nashir M. The Effect of Blended Learning by Using Edmodo in Teaching English for Nursing Students. Indonesian Journal of Curriculum and Educational Technology Studies. 2018; 6(2): 71-76. doi: 10.15294/ijcets.v6i2.26509

[24]Kiviniemi MT. Effects of a blended learning approach on student outcomes in a graduate-level public health course. BMC Medical Education. 2014; 14(1). doi: 10.1186/1472-6920-14-47

[25]Chiang PJ, Lin YW, Tseng CL. The Effect of Blended Learning in Mathematics Course. EURASIA Journal of Mathematics, Science and Technology Education. 2016; 13(3). doi: 10.12973/eurasia.2017.00641a

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