We present results from meta-analyses of 68 experimental studies on digital learning prompts, examining what features constitute an effective prompt for learning achievement. First, a systematic review reveals the different features of prompts used across various studies. Second, a quantitative meta-analysis using a random-effects model shows that digital prompts significantly enhance learning achievement (d = .394), though adjustment for publication bias yielded a more conservative estimate (d = .220). Their effectiveness is largely moderated by prompt design features. Based on meta-regression models, we find the highest efficacy for action-based prompts (rule-based AI) (d = .465), prompts designed for specific learner groups (d = .513), and combinations of generic and directed prompts (d = .571). Regional differences were pronounced, with studies from East Asia showing substantially larger effects than European settings. The effectiveness of learning prompts is further moderated by the learning domain and target group. Our findings reveal that prompts are not a universal solution but require thoughtful implementation. We recommend implementing action-based prompts triggered by learner behavior, using log data to tailor prompts to expertise levels. Designers should keep prompts concise to minimize cognitive load. We advise combining generic and directed prompts based on learning goals. Finally, it is essential to ensure learners are familiar with prompt use. These evidence-based guidelines can help optimize digital learning prompts to support diverse learner needs.

中文翻译：

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数字学习中通过提示搭建脚手架：学习成绩有效性的系统评价和荟萃分析

我们展示了对 68 项关于数字学习提示的实验研究的荟萃分析结果，研究了哪些特征构成了学习成就的有效提示。首先，系统评价揭示了各种研究中使用的提示的不同特征。其次，使用随机效应模型的定量荟萃分析表明，数字提示显着提高了学习成绩 （d = .394），尽管对出版偏倚的调整产生了更保守的估计 （d = .220）。它们的有效性在很大程度上受到提示设计特征的调节。基于元回归模型，我们发现基于作的提示（基于规则的 AI） （d = .465）、为特定学习者组设计的提示 （d = .513） 以及通用和定向提示的组合 （d = .571） 的效率最高。地区差异明显，东亚的研究显示其影响远大于欧洲环境。学习提示的有效性进一步受到学习领域和目标群体的调节。我们的研究结果表明，提示不是一个通用的解决方案，但需要深思熟虑的实施。我们建议实施由学习者行为触发的基于作的提示，使用日志数据根据专业知识水平定制提示。设计人员应保持提示简洁，以最大程度地减少认知负荷。我们建议根据学习目标将通用提示和定向提示组合在一起。最后，必须确保学习者熟悉及时使用。这些循证指南可以帮助优化数字学习提示，以支持不同的学习者需求。