Adaptive intelligent educational systems are gaining popularity, offering personalized learning experiences to students based on their individual needs and styles. One crucial feature of such systems is real-time personalized feedback. However, identifying real-time learning processes impacting student performance remains challenging due to data volume constraints. Current research often relies on labor-intensive human observation, which is time-consuming and not scalable. To efficiently collect real-time data, an observation tool is essential. Qualitative/Mixed Method research explores participant experiences in education, social science, and healthcare, utilizing methods like focus groups and observations. However, these methods can be labor-intensive, particularly in maintaining observation time intervals. Existing tools lack comprehensive support for education-focused focus groups and observations. To address these issues, this paper introduces the Data Logging and Organizational Tool (DLOT), a flexible tool designed for qualitative studies with human observers. DLOT offers customizable time intervals, cross-platform compatibility, and data saving and sharing options. The tool empowers observers to log timestamped data and is available on GitHub. The DLOT was validated through two studies. The first study predicted students' affective states using real-time annotations collected via DLOT, observing 30 students in each class. The second study created multimodal datasets in a computer-enabled learning environment, observing 38 students individually. A successful usability test was conducted, offering a potential solution to challenges in real-time learning process identification and labor-intensive qualitative research observation.

翻译：自适应智能教育系统日益普及，能够根据学生的个体需求和风格提供个性化学习体验。此类系统的关键特性之一是实时个性化反馈。然而，由于数据量限制，识别影响学生表现的实时学习过程仍具挑战性。当前研究通常依赖劳动密集型的人工观察，既耗时又难以扩展。为高效收集实时数据，观察工具必不可少。定性/混合方法研究通过焦点小组和观察等方法，探索教育、社会科学及医疗领域中的参与者体验。但此类方法可能劳动密集，尤其在维护观察时间间隔方面存在困难。现有工具缺乏对教育领域焦点小组和观察的全面支持。为解决这些问题，本文提出数据记录与组织工具（DLOT），这是一款专为人类观察者设计的灵活定性研究工具。DLOT提供可定制的时间间隔、跨平台兼容性以及数据保存与共享选项。该工具支持观察者记录带时间戳的数据，并已在GitHub上开源。DLOT通过两项研究得到验证：第一项研究利用DLOT收集的实时标注预测学生情感状态，每班观察30名学生；第二项研究在计算机辅助学习环境中创建多模态数据集，单独观察38名学生。成功开展的可用性测试为实时学习过程识别及劳动密集型定性研究观察中的挑战提供了潜在解决方案。