MoodleBased Learning: Motivating and Demotivating Factors Over the past 10 years, a lot of universities worldwide have designed different online courses available to their students. The emergence of online courses makes Higher Education a more flexible and elastic concept. The present paper documents a qualitative study that examines motivating and demotivating factors in using Moodle for English language learning in higher schools. The survey encompasses data collected from 137 students and 20 teachers in a higher technical school (Russia) with a focus on studentsxe2x80x99 and teachersxe2x80x99 perception of Moodlebased learning. Analysis of the obtained data reveals a set of factors concerning pedagogical, technical, management approaches towards Moodle implementation. The results suggest further development of positive experiences and minimizing demotivating factors in online learning environment.

Exploring Important Motivation Drivers for Completion of Massive Open Online Courses by Bayesian Hierarchical Logistic Regression Model Massive Open Online Courses (MOOCs) have provided over 9 thousand highquality online courses to 81 million learners. MOOCs have brought about a revolution in education by eliminating the geographic and time constraints of traditional classrooms. It is also wellknown that high dropout rates come with such massive user numbers. The retention rate of MOOCs may differ according to the various kinds of motivations that influence learners to finish courses. Few studies have taken into account the effect of user motivations on MOOCsu0027 retention rates. This research involved comparisons between the effects of different variables on motivation as well as administrative variables in MOOCs by using Bayesian hierarchical logistic regression models. A Bayesian model allows explanatory variables that are not necessarily independent and treats missing values in variables within its method. This research also uses data from many course disciplines (instead of courses from the same discipline) and builds models for general data. The results show that hierarchical models performed better than nonhierarchical models, identifying the combinations of administrative independent variables and motivations that can contribute to the improvement of future architecture of MOOCs. Such improvements may help motivate targeting learners to actively engage in related courses on promotion.