Attrition in online learning is generally higher than in traditional settings, especially in large-scale online learning environments. A systematic analysis of individual differences in attrition and performance in 20 massive open online courses (N > 67, 000) revealed a geographic achievement gap and a gender achievement gap. Online learners in Africa, Asia, and Latin America scored substantially lower grades and were only half as likely to persist than those in Europe, Oceania, and Northern America. Women also exhibited lower persistence and performance than men. Yet more persistent learners were only marginally more satisfied with their achievement. The primary obstacle for most learners was finding time for the course, which was partly related to low levels of volitional control. Self-ascribed successful learners reported higher levels of goal striving, growth mindset, and feelings of social belonging than unsuccessful ones. Insights into why learners leave online courses inform models of attrition and targeted interventions to support learners achieve their goals.
ClassX is an interactive online lecture viewing system developed at Stanford University. Unlike existing solutions that restrict the user to watch only a pre-defined view, ClassX allows interactive pan/tilt/zoom while watching the video. The interactive video streaming paradigm avoids sending the entire field-of-view in the recorded high resolution, thus reducing the required data rate. To alleviate the navigation burden on the part of the online viewer, ClassX offers automatic tracking of the lecturer. ClassX also employs slide recognition technology, which allows automatic synchronization of digital presentation slides with those appearing in the lecture video. This paper presents a design overview of the ClassX system and the evaluation results of a 3-month pilot deployment at Stanford University. The results demonstrate that our system is a low-cost, efficient and pragmatic solution to interactive online lecture viewing.
Massive Open Online Courses (MOOCs) have opened new educational possibilities for learners around the world. Numerous providers have emerged, which usually have different targets (geographical, topics or language), but most of the research and spotlight has been concentrated on the global providers and studies with limited generalizability. In this work we apply a multi-platform approach generating a joint and comparable analysis with data from millions of learners and more than ten MOOC providers that have partnered to conduct this study. This allows us to generate learning analytics trends at a macro level across various MOOC providers towards understanding which MOOC trends are globally universal and which of them are context-dependent. The analysis reports preliminary results on the differences and similarities of trends based on the country of origin, level of education, gender and age of their learners across global and regional MOOC providers. This study exemplifies the potential of macro learning analytics in MOOCs to understand the ecosystem and inform the whole community, while calling for more large scale studies in learning analytics through partnerships among researchers and institutions.
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