Learning Analytics for Computational Thinking (STEM) in Higher Education and K-12

Chair: Prof. Nancy Law, Professor, Faculty of Education, The University of Hong Kong

I will present our research and open source data analytic tools regarding how students learn, especially computational thinking and programming, on Massive Open Online Courses (MOOC) and a blended K-12 course. We use machine learning and data science to study this. In one study, examining how student learning is influenced by active practice, e.g. “doing” exercises, as compared to passively learning by video watching. We also consider scenarios such as how learning designs, prior experience, duration, and course content impact learning behavior and, in turn, how this behavior correlates with an assessment grade. Moreover, we have developed an open source framework for data science for MOOCs, MOOC-Learner-Project (http://mooclearnerproject.csail.mit.edu/). This has components that transform “raw” edX platform clickstream and other behavioral data streams into tables organized according to the MOOCdb data model, support feature engineering, visualization and modeling via machine learning.