The application of student interaction data is a promising field for blended learning (BL), which combines conventional face-to-face and online learning activities. However, the application of online learning technologies in BL settings is particularly challenging for students with lower self-regulatory abilities. In this study, a personalized learning analytics (LA) intervention approach that incorporates ebook and recommendation systems is proposed. The proposed approach provides students with actionable feedback regarding personalized remedial actions as the intervention to help them to strategically engage in the use of the ebook system and avoid academic failure when engaged in BL. A quasi-experiment was conducted to examine two classes of an undergraduate course that implemented a conventional BL model. The experimental group comprised 45 students from one class who learned using the proposed approach and received personalized intervention, whereas the control group comprised 42 students from the other class who learned using the conventional BL approach without receiving personalized intervention. The experimental results indicated that the proposed approach can improve students’ learning achievements and behavioral engagement in BL. The findings provide pedagogical insights into the application of LA intervention with actionable feedback in BL environments.

学生交互数据的应用是混合学习（BL）的一个有前途的领域，它结合了传统的面对面和在线学习活动。然而，在线学习技术在 BL 环境中的应用对于自我调节能力较低的学生来说尤其具有挑战性。在这项研究中，提出了一种结合电子书和推荐系统的个性化学习分析 (LA) 干预方法。所提出的方法为学生提供关于个性化补救措施的可操作反馈作为干预措施，以帮助他们战略性地参与电子书系统的使用并避免在参与 BL 时学业失败。进行了一项准实验，以检查实施传统 BL 模型的本科课程的两个班级。实验组由一个班级的 45 名学生组成，他们使用所提出的方法学习并接受了个性化干预，而对照组由另一个班级的 42 名学生组成，他们使用传统的 BL 方法学习而没有接受个性化干预。实验结果表明，所提出的方法可以提高学生在 BL 中的学习成绩和行为参与度。这些发现为 LA 干预的应用提供了教学见解，并在 BL 环境中提供了可操作的反馈。实验结果表明，所提出的方法可以提高学生在 BL 中的学习成绩和行为参与度。这些发现为 LA 干预的应用提供了教学见解，并在 BL 环境中提供了可操作的反馈。实验结果表明，所提出的方法可以提高学生在 BL 中的学习成绩和行为参与度。这些发现为 LA 干预的应用提供了教学见解，并在 BL 环境中提供了可操作的反馈。