A review of using multilevel modeling in e-learning research

Hung Ming Lin, Jiun Yu Wu, Jyh Chong Liang*, Yuan Hsuan Lee, Pin Chi Huang, Oi Man Kwok, Chin Chung Tsai

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


Improving e-learning involves various levels of supports. Accordingly, researchers usually adopt complex research designs with a multilevel structure or repeated measurements to capture a heuristic view of learners’ perceptions, comprehension, and behavior in e-learning settings. A total of 76 studies with Hierarchical Linear Modeling (HLM) as a multilevel modeling technique in 13 major e-learning journals from January 2000 to September 2022, published in the Web of Science, were reviewed. We assessed the applications of the following key criteria: reasons for using HLM, data characteristics, sample characteristics, model characteristics, variables used in the research, software use, and main technology used in the research. The results revealed that two-level models and random-intercept models are mostly used in multilevel model building. Moreover, most e-learning studies included two-level random intercept models with “students” as sampling units of analysis in Level 1, and “cognitive learning” (i.e., examination score, learning achievement) as the dependent variable in Level 1. Based on our review results, we provide suggestions and potential applications of using multilevel modeling in e-learning studies.

Original languageEnglish
Article number104762
JournalComputers and Education
Publication statusPublished - 2023 Jun


  • HLM
  • Hierarchical linear modeling
  • Multilevel modeling
  • Repeated measures
  • e-learning

ASJC Scopus subject areas

  • General Computer Science
  • Education


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