ISILA Piloting curated xAPI dataset

This dataset was created as part of the ISILA project and brings together learning activity data from multiple Learning Management Systems (LMSs)—including Canvas, Moodle, and LAMS—with complementary data collected through surveys and other external tools. All data have been transformed into the Experience API (xAPI) format in an effort to enable interoperability and unified analysis across heterogeneous sources. The dataset illustrates both the potential and the practical challenges of applying a common standard, such as xAPI, to integrate learning data. Although xAPI provides a shared structure for representing learning experiences, differences in how LMS platforms generate, structure, and semantically encode events lead to inconsistencies that require extensive preprocessing, mapping, and interpretation. Variations in logging granularity, event naming conventions, user identifiers, and contextual metadata highlight the complexity of achieving true interoperability, even when a standard is in place. In addition to LMS-derived traces, the dataset incorporates weekly survey responses and data from auxiliary tools, offering a richer, multimodal view of learner activity and experience. The integration of these sources required alignment across differing temporal resolutions, data schemas, and levels of abstraction. This dataset is intended to support research on learning analytics, data interoperability, and educational data standardization. It provides a realistic example of the challenges involved in merging multi-platform educational data and may serve as a benchmark for developing methods in data harmonization, semantic alignment, and cross-system analytics. Data description The file isila_all.csv contains the activities in xAPI format with the additional fields University and Course for ease of use. Below is the number of events per university and course. University Course Number of students Number of events BMU Distributed Systems 55 3942 BMU Object Oriented Programming 71 7268 BMU Web programming 79 2668 SU Digital Design and Multimedia 115 25882 SU Human-computer Interaction 126 25826 UEF Data Management Systems 60 20412 UEF Social Network Analysis 78 8385 ULe Computer Animation 81 39638 ULe Computer Architecture 99 42340 UiB Basic course in statistics 14 392 UiB Fantastic data 88 7192 The file isila_weekly.csv contains the data aggregated per week, including the following fields University: institution offering the course Course: course identifier/name Week: normalized week index (starting from 1) actor.id: anonymized learner identifier Concise SRL survey measures: the following variables represent weekly average scores derived from the Concise SRL survey. Efficient: average score for perceived efficiency TaskValue: average score for perceived task value Monitoring: average score for monitoring strategies Goals: average score for goal setting Effort: average score for effort regulation Environment: average score for environmental structuring Help: average score for help-seeking behavior Social: average score for social learning aspects TimeMgmt: average score for time management Motivation: average score for motivation Anxiety: average score for anxiety Enjoyment: average score for enjoyment Feedback: average score for feedback perception Metacognition: average score for metacognitive strategies Total_events: total number of recorded events in the week Days_per_week: number of distinct active days within the week Total_sessions: number of identified sessions (sessions are defined using a 15-minute inactivity threshold, i.e., 900 seconds) Mean_Sess_Len: average session duration Total_time: total time spent across all sessions in the week Potential uses The dataset shared can be used to explore a wide range of research questions in learning analytics, educational data mining, and technology-enhanced learning. A unique feature of this dataset is that it combines behavioral traces from multiple learning platforms with self-reported measures. As such, it enables analyses that link observable activity patterns with learners’ perceptions, strategies, and affective states. The multi-platform nature of the dataset makes it suitable for studying interoperability and data integration challenges. Researchers can use it to investigate how differences in data structures, event semantics, and logging practices across LMSs impact downstream analyses, and to develop or evaluate methods for data harmonization and standardization using xAPI.