Understanding the edX MOOC: How can Circuits and Electronics (6.002x) Help Us Understand the MOOC Learning Experience?

Principal Investigator: 
Project Overview
Background & Purpose: 

In this project, we examine the nature of the first edX online course and its students, first by investigating the relationship between student background (e.g., country of origin) and learning behaviors (e.g., time on task for different course components) and persistence (defined as completion of the course) and achievement (defined as the total number of points earned on the final exam). We further explore the online learning communities and the interactions within them, their relationship to persistence and achievement, and the interaction between online learning and residential learning in the same course. The objective is to identify the types of instructional materials and strategies that optimize learning outcomes for groups of learners who may differ in age, level of preparedness, family or work responsibilities, etc.


Instructional setting: massive open online course (MOOC)
Research setting: MIT, Harvard, edX offices

Research Design: 

The project uses a longitudinal, comparative, and cross-sectional research design and will generate evidence that is descriptive [observational], associative/correlational [quasi-experimental] and causal [quasi-experimental, statistical modeling, difference in differences, survival analysis]. Original data are being collected on participants in MIT's first MOOC using observation [web logs] and survey research [self-completion questionnaire and semi-structured or informal interviews]. Instruments or measures being used include achievement and activity information, discussion board posting from web use of MOOC; student background data from computer log (e.g., IP address) and background survey (e.g., educational experience); interviews with residential experience students.

We will use a multi-level model to estimate the predictive power of student background and learning resources for achievement and course completion. We will then create a hazard model using student-level demographics and longitudinal behaviors to predict dropout from the course. We will use social network analysis to understand the user groups that connected online, and we will use interview methods and propensity score matching to understand the experience of residential MIT students who took the online course and were offered in-person supplemental activities.


The start of data collection for the first edX course was midnight EST on February 13, 2012, and we are utilizing a closing date of June 15 for our data. Findings will be posted as they become available.