A systematic review of online persistent learning
Methodologies, findings and issues
Bibliographic Data
| ID | 21297777 |
|---|---|
| Authors | Gaojun Shi (0000-0003-2457-2209, College of Education Zhejiang University Hangzhou China), Yan Li (0000-0001-6650-7726, College of Education Zhejiang University Hangzhou China), Rustam Shadiev (0000-0001-5571-1158, College of Education Zhejiang University Hangzhou China, corresponding author) |
| Year | 2026 |
| Volume | 57 |
| Issue | 2 |
| Pages | 395-422 |
| Publication date | 2026-03-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | British Journal of Educational Technology (JOURNAL) |
| Journal identifiers | ISSN: 0007-1013 • E-ISSN: 1467-8535 |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1111/bjet.70036 |
| OpenAlex | W4417422738 |
| Language | EN |
| References cited | 74 |
Online persistent learning is a significant and growing field of study. Despite the numerous published studies on this topic, there is a notable lack of comprehensive reviews that analyse these studies, particularly in terms of their methodologies, findings and the issues reported by researchers. This study aims to systematically review twenty‐seven papers on online persistent learning, evaluating them across nine dimensions. It offers a synthesized conceptualization of OPL that clarifies its scope, variability and core components and identifies key patterns related to learning environments, research designs, pedagogical strategies and technological applications. The results show that most studies focus on higher education students, employ short‐ to medium‐term course durations and rely heavily on quantitative methods such as log data analysis. Assessment, engagement, interaction and discussion are frequent strategies to support online persistent learning, whereas the use of tools remains limited. Based on these results, this study highlights two key gaps. First, AI‐supported learning activities remain limited, making it challenging to sustain learner persistence. Second, there is a lack of mixed‐method and longitudinal research designs necessary to examine the dynamic nature of OPL. For researchers, this underscores the need for more integrative and longitudinal investigations to capture the nuanced processes underlying persistence. For practitioners, the findings suggest that persistence is not merely a function of platform or content design but is closely tied to how learning activities are supported and personalized. Practitioner notes What is already known about this topic Persistence is an important characteristic for online learning, as it effectively enhances the efficiency of online learning. Existing studies have explored the factors affecting online persistent learning, and some reviews have comprehensively analysed these factors. Previous reviews focus only on category factors, yet more important details involved in online persistent learning, such as learning activities, are often overlooked. What this paper adds More granular evidence is added to explore the factors affecting online persistent learning, such as duration and time unit. Support strategy, learning activity and tools are included in the coding to examine their effectiveness on online persistent learning. Issues with online persistent learning reported in existing studies are identified, such as the inability to maintain persistence, the inability to predict persistence and the inability to assess persistence. Implications for practice and/or policy Despite being a widely focused topic, some gaps in online persistent learning still need to be addressed, such as the lack of investigations on micro‐persistence. Nine key dimensions of online persistent learning are identified for future study to offer valuable insights for researchers and educators. It is essential for future research to expand the range of research variables and to collect longitudinal mixed data to better understand online persistent learning
Conceptualization · Educational technology · Field (mathematics) · Focus (optics) · Function (biology) · Key (lock) · Longitudinal data · Online learning · Persistence (discontinuity) · Innovative Teaching and Learning Methods · Innovative Teaching Methods · Online Learning and Analytics
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| Citation velocity | historical |
|---|---|
| Highly cited | No |