A Case Study on How Greek Teachers Make Use of Big Data Analytics in K12 Education Big Data Analytics can help teachers to make better and informed decisions. Several recent articles in the field of technology enhanced learning concern this potential, yet little is known about how teachers actually make use of Big Data Analytics in their school to support themselves and their students. To compensate for this gap, this paper focuses on the actual uses of Big Data Analytics by active schoolteachers. Thirty teachers who live in Greece participated in survey about their usage of (a) Big Data analytics and (b) online learning environments which capture student data. The data were analysed using mixed methods. Main findings reveal that the schoolteachers are storing and actively using student data as well as Big Data which involve the support of the teachinglearning process. Also, it became clear that teachers use Big Data Analytics for two main distinctively different purposes: to cover teachinglearning aspects and to complete administrative tasks. Finally, it emerged that a small number of teachers is archiving digital multimedia. Consequently, a need arises for appropriate analytics and relevant privacy frameworks. Other practical implications of the findings of this work touch upon the design of teachers xe2x80x98development programs in Big Data and their analytics.

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.