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Moreno-Marcos, Pedro Manuel; Alario-Hoyos, Carlos; Munoz-Merino, Pedro J.; Kloos, Carlos Delgado – IEEE Transactions on Learning Technologies, 2019
This paper surveys the state of the art on prediction in MOOCs through a systematic literature review (SLR). The main objectives are: first, to identify the characteristics of the MOOCs used for prediction, second, to describe the prediction outcomes, third, to classify the prediction features, fourth, to determine the techniques used to predict…
Descriptors: Prediction, Large Group Instruction, Online Courses, Educational Research
Gitinabard, Niki; Xu, Yiqiao; Heckman, Sarah; Barnes, Tiffany; Lynch, Collin F. – IEEE Transactions on Learning Technologies, 2019
Blended courses that mix in-person instruction with online platforms are increasingly common in secondary education. These platforms record a rich amount of data on students' study habits and social interactions. Prior research has shown that these metrics are correlated with students performance in face-to-face classes. However, predictive models…
Descriptors: Blended Learning, Educational Technology, Technology Uses in Education, Prediction
Sunar, Ayse Saliha; White, Su; Abdullah, Nor Aniza; Davis, Hugh C. – IEEE Transactions on Learning Technologies, 2017
In 2015, 35 million learners participated online in 4,200 MOOCs organized by over 500 universities. Learning designers orchestrate MOOC content to engage learners at scale and retain interest by carefully mixing videos, lectures, readings, quizzes, and discussions. Universally, far fewer people actually participate in MOOCs than originally sign up…
Descriptors: Online Courses, Large Group Instruction, Interaction, Learner Engagement