Design of a Kimball-Based Data Warehouse for E-Learning Monitoring and Accreditation Reporting

E-learning platforms record course delivery, learning materials, assessment activities, and student interactions, but many higher education institutions still prepare e-learning monitoring and accreditation reporting by querying operational databases manually. This practice may limit historical analysis, indicator consistency, and reporting reuse under flexible academic service conditions. This study proposes a domain-specific Kimball-based dimensional data warehouse model for accreditation-oriented e-learning reporting. The method analyzes reporting requirements, characterizes generic LMS data sources, prepares a staging and ETL workflow, defines slowly changing dimension policies, declares formal fact grains, constructs star schemas, and evaluates reporting scenarios. Three business processes are modeled: teaching delivery, assessment records, and activity logs. The result is a reporting-oriented schema consisting of three fact tables and eleven dimension tables, supported by representative pseudo-DDL, an example accreditation monitoring query, and a limited proof-of-concept evaluation using synthetic LMS-like records. The evaluation indicates that the reference schema represents selected structured indicators for active course monitoring, learning material availability, assessment evidence, and student participation. It also reports limited ETL and query feasibility metrics and provides dashboard prototype mapping with review criteria, while full accreditation evidence remains partially covered because qualitative documents require integration with document management metadata.

Keywords: data warehouse, dimensional modeling, Kimball approach, e-learning, accreditation reporting, learning analytics, flexible academic services