Log data (i.e., event data documenting individuals’ interactions with a digital environment) have become an important source of information in empirical educational research (Becker et al., 2022; Goldhammer et al., 2021). They provide fine-grained insights into learners’ behavior and performance-related processes, thus extending beyond what can be captured with traditional questionnaire or test score data. However, log data also pose specific challenges for data sharing and reuse. Their high volume, technical nature, and strong context dependence make it difficult to implement the FAIR principles (Wilkinson et al., 2016), in particular with respect to reusability. Consequently, log data remain underutilized in secondary analyses despite their high scientific value and reuse potential (Becker et al., 2022). One of the central challenges in making log data reusable is the provision of high-quality documentation. Log data typically cannot be interpreted without extensive background knowledge, much of which is held implicitly by the primary researchers. This concerns not only the structure of the log data itself but also the digital system that generated them. The meaning of a log event, such as a mouse click, can only be understood in relation to the technical elements of the system, including which interface elements can be interacted with and what is displayed on the screen at the time of the interaction. Codebooks and purely textual descriptions are therefore often insufficient to convey this information. In addition, log data typically undergo a multi-step processing procedure to derive interpretable indicators of learners’ behavior and performance-related processes (Goldhammer et al., 2021). This procedure includes data preprocessing, the extraction of low-level features, and the modeling of process indicators (Kroehne & Goldhammer, 2025). Without transparent documentation of these steps, the interpretability, reproducibility, and scientific reuse of the data are severely limited. Taken together, these challenges highlight the need to develop high-quality log data documentation strategies. Our presentation highlights these challenges and illustrates practical approaches to addressing them. Our examples draw on documentation strategies from large-scale assessments such as PIAAC (OECD, 2013), PISA (OECD, 2016), and TIMSS (Martin et al., 2020), as well as our own experience documenting log data from digital learning environments. For instance, we compare different methods for documenting the relationship between log data and the underlying digital environment, ranging from static documentation formats to log data replays, and discuss their respective strengths and limitations. The presentation concludes with concrete recommendations and proposed minimum requirements for documenting log data.

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