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The importance of self-regulation in a MOOC has been extensively discussed in research studies that provide evidence about the significant relationship between self-regulated learning and success in an e-learning environment. Learners with high self-regulated learning are more independent in regulating their learning and have a greater probability of success in their online courses. This study identifies factors that influence self-regulated learning and determines relationships between these factors and self-regulated learning. A conceptual model is proposed for combining success factors for self-regulated learning in a MOOC environment. A research instrument based on the model was designed and administered to six hundred and twenty-two MOOC students enrolled in five universities. Relationships between relevant factors and selfregulated learning were examined using a Partial Least Squares Structural Equation Modeling (PLS-SEM) technique, and the statistical findings revealed that three factors-service quality, attitude, and course quality-influence self-regulated learning in a MOOC.

Intelligent tutoring and personalization are considered as the two most important factors in the research of learning systems and environments. An effective tool that can be used to improve problem-solving ability is an Intelligent Tutoring System which is capable of mimicking a human tutor's actions in implementing a one-to-one personalized and adaptive teaching. In this paper, a novel Flowchart-based Intelligent Tutoring System (FITS) is proposed benefiting from Bayesian networks for the process of decision making so as to aid students in problem-solving activities and learning computer programming. FITS not only takes full advantage of Bayesian networks, but also benefits from a multi-agent system using an automatic text-to-flowchart conversion approach for engaging novice programmers in flowchart development with the aim of improving their problem-solving skills. In the end, in order to investigate the efficacy of FITS in problem-solving ability acquisition, a quasi-experimental design was adopted by this research. According to the results, students in the FITS group experienced better improvement in their problem-solving abilities than those in the control group. Moreover, with regard to the improvement of a user's problem-solving ability, FITS has shown to be considerably effective for students with different levels of prior knowledge, especially for those with a lower level of prior knowledge.

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Massive Open Online Courses (MOOC) is a new phenomenon in online learning that has aroused increasing interest by researchers as a significant contribution to improving educational system quality and openness. The purpose of this paper is to compile and analyze MOOC research that has been published between 2012 and 2016. A systematic analysis technique was employed and Template Analysis (TA) approach was used for mapping MOOC research into three dimensions in accordance with the Biggs 3P model. First dimension is Presage, include the following factors: Learners' characteristics with sub-factors (learner demographics, learner motivation, and interactivity) and instructor. Second, Process, including factors of pedagogy, pattern of engagement, instructional design, assessment, credit, plagiarism, sustainability, and learning analytics. Third dimension is Product, including factors of student dropout rate and MOOC quality. This classification is aimed at providing a comprehensive overview for readers interested in MOOCs who seek to understand the critical success factors influencing MOOC success.