Contextualising the LearnerCentric MOOCs Model for Effective Blending of FlippedClassroom Method in Engineering Physics Course This study focuses on contextualising the LearnerCentric MOOCs (LCM) model for an effective blending of flippedclassroom strategy alongside the inclass traditional lecturing in an undergraduate engineering physics course. The research design employed in this study is a single group, pretest, posttest analysis. The main research problem being addressed in this study is does the flipped contents when integrated with LCM based structured learning activities lead to an improved engagement of students, thereby increasing the conceptual understanding. First, the students were primed for one full semester to get adapted with the flipped classroom method as well as the LCM activity formats. Further, few key interventions were prompted such as giving incentives, grouping, counselling of low engaged and performers, support materials, NPTel based resources, etc across the first few modules, with an aim to improve the outofclass engagement. Our results showed that the engagement and learning effectiveness showed considerable improvement upon priming following the key interventions. Further, the students showed positive perception of learning, engagement and usefulness towards this blended technique. Few recommendations are provided for the other teachers who would like to adopt the contextualize improvisation of the LCM model for their flipped teachinglearning practices.

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.