Exploring massive open online courses to predict how students acquire knowledge
Researchers find it difficult to get inside students’ learning processes to show how students learn and determine what most enhances their learning. But with the advent of massive open online courses (MOOCs), researchers have an unprecedented opportunity to analyze student learning more deeply and accurately by studying complex data sets regarding student enrollment, demographics and achievement.
This project explores the opportunities and challenges that MOOCs generate for research.
The researchers involved in this project analyzed two MOOCs, one in business and one in history. They tracked the numbers and percentages of students initially enrolled in the course who took and/or passed each quiz or exam and who received a final statement indicating they had passed all course requirements.
The project has already produced exciting results. For instance, students who watch lectures or read class forums between their first and last attempts at exams improve more than those who do not. Across quizzes and courses, there is a consistent negative correlation between procrastination and achievement.
Using IP addresses to geolocate students, the team found evidence that students from the United States procrastinate significantly less than students elsewhere.
Paul Diver is a PhD student in the Department of Statistics.
Ignacio Martinez is a PhD student in the Department of Economics. He studies complementarities between MOOCs and brick-and-mortar courses.