Using Learning Analytics to Build a Learning Support Program for Distance Learning in Nursing Japanu0027s nurses have seen an increase in distance learning opportunities, but there is little information on the current situation or associated outcomes. This makes it difficult to appropriately implement measures that support distance learning. This study uses learning analytics based on distance learning logs and learner information to build a learning support program that is suitable for learners in the field of nursing. Our findings show that frequency of logins to a distancelearning course for nurses was related to course completion, as was login frequency to an orientation course and after three months from the course start. These results have implications for implementing support. For instance, educators will check the learneru0027s learning status and provide suitable support as advice and metering. Additionally, these supports were applicable not only nurses but also all health professionals who receive training through distance learning.

DataDriven Validation of Pedagogical Model  A Case of Blended LCM Model Learning Analytics has been successful in supporting learners and instructors with large and finegrained datasets. However, its impact on practice and pedagogical theory has been limited. One of the solutions to this problem is to apply learning analytics to support evidencebased education. In this paper, we analyse the implementation of Learningcentric MOOCs (LCM), a pedagogical model, which was adopted in a blended classroom setting for an undergraduate Physics course. Extracting learning logs from the elearning platform, we applied Gaussian Mixture model to classify students and built a logistic regression model of the student performance. Our analysis demonstrated that 1) Videos (called LeD in LCM model) and Discussion Forum (called LxI) were positively related to exam scores and 2) studentu0027s access pattern of learning materials also affected exam scores. Our study is an example where learning analytics explores the dynamics of the LCM model for a blendedlearning context and provide evidence from studentsu0027 learning data. We discuss how such analysis is integrated into our technologyenhanced and evidencebased education and learning (TEEL) platform and its implications.