Design of Learning History Retention Framework using Blockchain Infrastructure to Ensure Reliability of Learning Logs
Authors: Togawa Satoshi , Kondo Akiko , Kanenishi Kazuhide 
Published: January 2024

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.Changes in the world situation can impede stable system operation and cause a loss of learning logs. As of May 2023, about 20% of Ukrainian territory is occupied by Russia and the war is still going on. Conflict and war destroy many buildings, infrastructure, and communication networks. Climate change is causing increasingly severe disasters. In Japan, special heavy rainfall warnings are issued every year, mainly in western Japan. Special warnings are intended to be issued when there is a threat of a once-in-a-decade catastrophe. Matthew Rodell et al. suggest that more frequent, more severe, and more extensive droughts and floods will occur if global warming continues.The severity of a disaster, whether man-made or natural, can cause more damage than the availability of the cloud service that serves as the system operation infrastructure. Catastrophic damage to the system operation infrastructure will cause a loss in the generated learning history. The loss of the learning history causes a loss of continuity in the learning history, and consequently, the reliability of the learning history is compromised. We must ensure that the learning history of learners, which cannot be recovered once it is lost, is stored and maintained even in multi-hazard situations.In this study, we designed a learning history retention framework based on blockchain technology to ensure the reliability of learning history. In order to store and retain the learning history, we apply the decentralized autonomous blockchain technology to enable the detection of learning history inconsistencies in the LRS. A framework for ensuring the availability of the learning history was studied against the factors that cause the loss of the learning history, regardless of whether it is caused by human or natural disasters. In this paper, we describe the design of a blockchain mechanism for learning history retention, and describe a learning history retention mechanism that is linked to an existing LMS. We also describe the design and effectiveness of a prototype system implemented for validation.