It is a common practice to issue a summary of a learner's learning achievements in form of a transcript or certificate. However, detailed information on the depth of learning and how learning or teachings were conducted is not present in the transcript of scores. This work presents the first practical implementation of a new platform for keeping track of learning achievements beyond transcripts and certificates. This is achieved by maintaining digital hashes of learning activities and managing access rights through the use of smart contracts on the blockchain. The blockchain of learning logs (BOLL) is a platform that enable learners to move their learning records from one institution to another in a secure and verifiable format. This primarily solves the cold-start problem faced by learning data analytic platforms when trying to offer personalized experience to new learners. BOLL enables existing learning data analytic platforms to access the learning logs from other institutions with the permission of the learners and/or institution who originally have ownership of the logs. The main contribution of this paper is to investigate how learning records could be connected across institutions using BOLL. We present an overview of how the implementation has been carried out, discuss resource requirements, and compare the advantages BOLL has over other similar tools.

Early prediction systems have already been applied successfully in various educational contexts. In this study, we investigated developing an early prediction system in the context of eBook-based teaching-learning and used students' eBook reading data to develop an early warning system for students at-risk of academic failure-students whose academic performance is low. To determine the best performing model and optimum time for possible interventions we created prediction models by using 13 prediction algorithms with the data from different weeks of the course. We also tested effects of data transformation on prediction models. 10-fold cross-validation was used for all prediction models. Accuracy and Kappa metrics were used to compare the performance of the models. Our results revealed that in a sixteen-week long course all models reached their highest performance with the data from the 15th week. On the other hand, starting from the 3rd week, the models classified low and high performing students with an accuracy of over 79%. In terms of algorithms, Random Forest (RF) outperformed other algorithms when raw data were used, however, with the transformed data J48 algorithm performed better. When categorical data were used, Naive Bayes (NB) outperformed other algorithms. Results also indicated that models with transformed data performed lower than the models created using categorical data. However, models with categorical data showed similar performance with models with raw data. The implications of the results presented in this research were also discussed with respect to the field of Learning Analytics.

No abstract

No abstract