Understanding Student Motivation, Behaviors and Perceptions in MOOCs

By Saijing Zheng, Mary Beth Rosson, Patrick C. Shih, John Millar Carroll. Conference on Computer Supported Cooperative Work, 2015

387 citations recorded by the source corpus.

Subfields: Computer Science Applications, Education

View Paper: https://www.semanticscholar.org/paper/4ff82e720481613f4093fc7cc22f95e82d5938a4

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Science Applications

1. René F. Kizilcec
2. Jane Yin-Kim Yau
3. Dirk Ifenthaler

In Education

1. Mar Pérez-SanAgustín
2. Jorge J. Maldonado
3. René F. Kizilcec

Citations in the corpus

• Self-regulated learning strategies predict learner behavior and goal attainment in Massive Open Online Courses (2017, background): “In another study, Zheng et al. (2015) interviewed 18 learners about their experience in MOOCs and the issue of not having enough time also emerged in their analysis.”
• Behavior-Based Grade Prediction for MOOCs Via Time Series Neural Networks (2017, background): “C ONCLUSION , D ISCUSSION , AND F UTURE W ORK In this paper, we used time series neural networks for personalized prediction of students’ average CFA grade in two MOOCs.”
• Virtual Reality and Its Applications in Education: Survey (2019, background): “Many students have problems understanding issues, especially the science courses, because of its technical complexity, a necessity of abstract thinking and the fact that those concepts are not entirely tangible [6,7].”