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.

Explaining Chinese university studentsxe2x80x99 continuance learning intention in the MOOC setting: A modified expectation confirmation model perspective Abstract   To gain more insight into the issue of high dropout rate in MOOC learning, this study aims at exploring the factors underlying the continuance intention to learn in the Massive Open Online Course (MOOC) setting. By modifying and extending the Expectation Confirmation Model (ECM), the authors propose a research model that includes cognitive and affective variables, captures reflections of the past and expectations for the future and takes into account both intrinsic and extrinsic motives in the model construction to explain learnersu0027 intention to persist in learning a MOOC. The proposed model was tested with data from Chinese university students. The results show that the proposed model can explain 48% of continuance intention. The new variables (attitude and curiosity) added to the ECM were all found to be significant in explaining continuance intention. This study deepens our understanding of the development of learnersu0027 continuance intention in the MOOC setting in the following aspects: (a) although the personal trait, curiosity, was found to predict subsequent continuance intention, attitude played a considerably dominant role. In addition to respecting individual differences, practitioners can devise appropriate interventions to change attitudes and influence learnersu0027 retention in MOOCs; (b) the strong link between confirmation and both satisfaction and attitude suggests that MOOC instructors or designers must be prudent in advertising the courses to avoid exaggerating their benefits and the systemu0027s affordances.