在线持续学习的系统回顾:方法、发现和问题

IF 8.1 1区 教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH
Gaojun Shi, Yan Li, Rustam Shadiev
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引用次数: 0

摘要

在线持续学习是一个重要且不断发展的研究领域。尽管关于这一主题的研究已经发表了许多,但明显缺乏对这些研究进行全面分析的综述,特别是在研究方法、发现和研究人员报告的问题方面。本研究旨在系统回顾27篇关于在线持续学习的论文,并从九个维度对其进行评估。它提供了OPL的综合概念化,阐明了其范围、可变性和核心组成部分,并确定了与学习环境、研究设计、教学策略和技术应用相关的关键模式。结果表明,大多数研究集中在高等教育学生,采用中短期课程,并严重依赖于对数数据分析等定量方法。评估、参与、互动和讨论是支持在线持续学习的常用策略,而工具的使用仍然有限。基于这些结果,本研究突出了两个关键差距。首先,人工智能支持的学习活动仍然有限,这使得保持学习者的持久性具有挑战性。其次,缺乏必要的混合方法和纵向研究设计来检验OPL的动态性质。对于研究人员来说,这强调了需要更多的综合和纵向调查来捕捉持久性的微妙过程。对于实践者来说,研究结果表明,持久性不仅仅是平台或内容设计的功能,而且与如何支持和个性化学习活动密切相关。持久性是在线学习的一个重要特征,因为它有效地提高了在线学习的效率。已有研究探讨了影响在线持续学习的因素,一些综述对这些因素进行了综合分析。以前的评论只关注类别因素,而在线持续学习中更重要的细节,如学习活动,往往被忽视。本文添加了更多的细粒度证据来探索影响在线持续学习的因素,如持续时间和时间单位。编码中包括支持策略、学习活动和工具,以检验其对在线持续学习的有效性。在现有的研究中发现了在线持续学习的问题,如无法保持持久性,无法预测持久性和无法评估持久性。对实践和/或政策的影响尽管在线持续学习是一个广泛关注的话题,但仍然需要解决一些差距,例如缺乏对微观持续学习的调查。为未来的研究确定了在线持续学习的九个关键维度,为研究人员和教育工作者提供有价值的见解。扩大研究变量的范围,收集纵向混合数据,以更好地理解在线持续学习,对未来的研究至关重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A systematic review of online persistent learning: Methodologies, findings and issues

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.
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来源期刊
British Journal of Educational Technology
British Journal of Educational Technology EDUCATION & EDUCATIONAL RESEARCH-
CiteScore
15.60
自引率
4.50%
发文量
111
期刊介绍: BJET is a primary source for academics and professionals in the fields of digital educational and training technology throughout the world. The Journal is published by Wiley on behalf of The British Educational Research Association (BERA). It publishes theoretical perspectives, methodological developments and high quality empirical research that demonstrate whether and how applications of instructional/educational technology systems, networks, tools and resources lead to improvements in formal and non-formal education at all levels, from early years through to higher, technical and vocational education, professional development and corporate training.
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