Learning analytics has been actively pursued in the field of learning assistance field. The spread of LMSs such as Moodle and Canvas LMS has realized the acquisition of a large amount of learning history. Furthermore, an educational assistance system does not consist of only a single LMS. Multiple educational assistance services generate their own learning histories.

A Learning Record Store (LRS) exists to collect and analyze these learning logs in an integrated manner. OpenLRW and Learning Locker exist as LRS implementations. In order to avoid losing the large number of learning records generated by LMSs, key value stores (KVS) such as MongoDB are generally used for data persistence in LRSs. For architectural reasons, KVS allows duplicate registration of stored data. Therefore, unlike SQL-based databases, KVS persistence does not guarantee consistency. If consistency is not maintained in the persistence of learning history, it is difficult to ensure the reliability of the stored learning history. If the stored learning history is not reliable, the reliability of the analysis results using learning analytics cannot be ensured.

These LRSs are operated by public cloud services such as Amazon Web Services (AWS) and Google Cloud (GCP), etc. A region in a public cloud such as AWS generally consists of multiple availability zones (AZs). However, in order to minimize latency, it is important to minimize the number of AZs. However, from the perspective of minimizing latency, it is necessary to minimize the area where all AZs are located within a region. All AZs are located within 100 km.

Large-scale conflicts and global environmental changes can disrupt the stable operation of systems, regardless of whether they are cloud services or on-premises systems. This also applies to learning support systems such as LMS. These external factors can potentially cause damage or loss of learning logs. The conflict in Ukraine, which began in February 2022, continues even after three years. The Copernicus Climate Change Service (C3S) announced that the global average temperature in January 2025 was 13.23 degrees Celsius, marking the highest January temperature on record. Large-scale heavy rainfall disasters and floods are now being observed worldwide. Since learners' learning histories cannot be recovered once lost, it is essential to ensure their preservation and maintenance even under multi-disaster conditions.

In this study, we are developing a framework for retaining learning histories using closed blockchain technology in order to counteract learning history loss and ensure reliability. We have implemented a learning history retention mechanism using blockchain technology to ensure the retention and authenticity of learning histories. This allows us to attempt to detect inconsistencies within the LRS. In this paper, we describe the overview of the designed learning history retention framework and explain the mechanism for retaining learning history in conjunction with existing LMSs. Additionally, we describe the implementation and demonstration of the prototype system developed to validate the framework's effectiveness, thereby clearly demonstrating its efficacy.

Keywords: e-learning, learning record, closed blockchain infrastructure, disaster recovery.