The increasing prevalence of micro-learning, driven by the "information explosion" and individuals' preference for consuming bite-sized knowledge, has highlighted the need for effective learning mechanisms. Micro-learning, characterized by short, focused learning units, is used across formal, non-formal, and informal settings. However, the effectiveness and experience of micro-learning, particularly in non-formal or informal settings, can be hampered by a lack of interaction and potential disruptions. This study addressed the gap in research examining the impact of online learning communities on micro-learning outcomes and learner experiences in non-formal contexts. It aimed to explore the integration of learning communities within micro-learning strategies to optimize knowledge acquisition and the overall learning experience. The central research question focused on how online learning communities affect learners' knowledge acquisition and learning experience in micro-learning settings. The study's importance lies in addressing the challenges of non-formal micro-learning and offering evidence-based insights for enhancing its effectiveness through community-based learning designs.

Existing research emphasizes the benefits of micro-learning in reducing cognitive load and enhancing learning satisfaction across various settings, including K-12, higher education, and corporate training. Studies have shown positive outcomes when microlearning is used as supplementary material in traditional classrooms, incorporated into corporate training as microtraining, or used in non-formal and informal settings to support spontaneous learning. However, research evaluating the impact of peer interaction within learning communities on micro-learning outcomes and learners' perceptions remains limited. While some advocate for leveraging online communities to enhance informal learning through microlearning, comprehensive strategies for their integration remain scarce. Existing literature suggests that the transformation of a learner from a passive consumer of content to an active producer, fostered by social interaction and content creation, can enhance motivation and learning outcomes. However, the challenges of information overload and the diverse backgrounds of learners within online communities have prompted this research to assess the impact of online learning communities on both knowledge acquisition and learning experience within a microlearning context.

This study employed a mixed-methods sequential explanatory design, combining a randomized control trial (RCT) with qualitative data from semi-structured interviews. One hundred participants, recruited online, were randomly assigned to either an experimental group (with a learning community on WeChat) or a control group (without a community). After eliminating 20 participants due to attrition, data analysis was conducted on 80 participants (40 in each group). A pre-test and post-test on interview research methods, comprising 20 multiple-choice questions, measured knowledge acquisition. Pre- and post-course surveys collected data on demographics, learning habits, satisfaction, mental effort, and preference for community learning. The experimental group also completed a sense of community scale. Semi-structured interviews (n=10) explored learning experiences further. Quantitative data were analyzed using paired sample t-tests, one-way ANCOVAs, and one-way ANOVAs. Qualitative data were analyzed thematically. The learning materials consisted of an eight-module micro-learning course on conducting research interviews (20 videos, 3-10 min each) designed according to Gagné's learning theory. Participants received daily learning materials via WeChat, with the experimental group also receiving community messages. The course lasted 20 days. The study's instruments included a knowledge test (with good internal consistency—pre-test KR-20=0.69, post-test KR-20=0.73), a satisfaction scale (α = 0.85), a mental effort scale, a sense of community scale (α = 0.84), and a measure of preference for community learning. The study carefully considered and addressed potential attrition bias.

The paired sample t-test revealed significant improvement in post-test scores across both groups, indicating the overall effectiveness of the micro-learning course. However, one-way ANCOVA, controlling for pre-test scores, showed no significant difference in post-test scores between the experimental and control groups, indicating that community participation did not significantly enhance knowledge acquisition. Interview data reinforced this finding, highlighting that learning behaviors like note-taking and repeated review were stronger predictors of knowledge gain than community participation. While there was no significant difference in mental effort between groups, learners in the control group reported slightly higher mental effort. A significant difference emerged in community learning preferences: the control group (without a community) expressed a stronger desire to learn in a community compared to the experimental group. There was no significant difference in learning satisfaction between the groups. However, qualitative data revealed that excessive community messages caused distress for some learners in the experimental group, leading to muted notifications and missed information. Conversely, some experimental group learners appreciated the community for peer interaction and alternative perspectives, which helped them fill knowledge gaps. Analysis of the sense of community scale in the experimental group revealed high overall scores (mean 51.95), suggesting positive attitudes towards the community for the actively engaged group members. The results point to high levels of learner satisfaction with the micro-learning course irrespective of community participation, likely related to the self-directed and flexible nature of the learning materials.

The findings suggest that while micro-learning itself is effective in enhancing knowledge of interview research methods, the addition of an online learning community does not necessarily lead to significantly better knowledge acquisition. This challenges the assumption that community participation automatically improves learning outcomes in all micro-learning contexts. The study emphasizes the importance of learner engagement and active learning strategies (note-taking, review) as key factors driving knowledge retention. The desire for community participation from the control group highlights a potential benefit of incorporating community features in future micro-learning designs, but the study's results caution against simply adding a community without addressing potential issues such as information overload and managing learner engagement. The mixed results regarding community engagement suggest that carefully designed strategies are needed to optimize community features in micro-learning environments. The qualitative data provides rich insights into the diverse experiences within the online learning community, highlighting the need for more nuanced approaches in future micro-learning designs. Learners' active participation in reviewing course material, rather than mere passive community membership, appears to be a more robust predictor of knowledge gain. Thus, micro-learning course designers should focus on instructional design to enhance engagement and knowledge retention.

This study offers valuable insights into the effectiveness of online learning communities in micro-learning. While micro-learning is effective in boosting knowledge, community participation alone does not guarantee superior knowledge acquisition. Learner engagement and active learning strategies, including note-taking and content review, are key predictors of knowledge gain. The findings underscore the need for careful consideration of community design to prevent information overload and promote active participation. Future research should explore strategies to optimize community features, potentially including smaller, more focused groups and strategies to encourage active engagement, to maximize the benefits of community-based micro-learning.

The study's limitations include a relatively small sample size (80 participants after attrition), and the potential for attrition bias despite efforts to address this. Furthermore, the study did not directly measure learning behaviors like note-taking and content review in the surveys, relying instead on interview data. Finally, the post-test was administered immediately after the course, limiting the assessment of long-term knowledge retention. Future research should address these limitations by increasing sample size, incorporating measures of learning behaviors into surveys, and assessing knowledge retention over a longer period.