Research & Pedagogy

Working with our xConsortium of university partners, edX is empowering research on pedagogy or learning about learning.

The online environment provides a powerful platform to conduct experiments, exploring how students learn and how faculty can best teach using a variety of novel tools and techniques.

Fundamental questions include:

  • What motivates students to learn and persist?
  • What helps students retain knowledge?
  • What are the best ways to teach complex ideas?
  • How can we assess what students have learned?
  • What is best taught in person vs. online?

By carefully assessing course data, from mouse clicks to time spent on tasks, to evaluating how students respond to various assessments, researchers hope to shed light on how learners access information and master materials, with the ultimate aim of improving course outcomes.

We are not only expanding access to knowledge, but developing best practices to enhance the student experience and improve teaching and learning both on campus and online.

Below you will find a sampling of research papers authored by our xConsortium partners:

Teaching Electronic Circuits Online: Lessons from MITx’s 6.002x on edX

IEEE 19 May 2013 By Piotr F. Mitros, Khurram K. Afridi , Gerald J. Sussman, Chris J. Terman, Jacob K. White , Lyla Fischer and Anant Agarwal .
IEEE

A New Approach to Developing Interactive Software Modules through Graduate Education

arXiv.org 8 Aug 2013 By Nathan E. Sanders, Chris Faesi, Alyssa A. Goodman (Harvard University)
The authors discuss a set of fifteen new interactive, educational, online software modules developed by Harvard University graduate students to demonstrate various concepts related to astronomy and physics. Their achievement demonstrates that online software tools for education and outreach on specialized topics can be produced while simultaneously fulfilling project-based learning objectives. Read more about A New Approach to Developing Interactive Software Modules through Graduate Education
arXiv.org

Changing “Course”: Reconceptualizing Educational Variables for Massive Open Online Courses

TLL 1 Sep 2013 By Jennifer DeBoer, Andrew D. Ho, Glenda S. Stump, Lori Breslow
Increases in the amount and richness of available educational data require researchers to expand the ways in which they define familiar variables. The authors illustrate that the advent of massive open online courses (MOOCs) is such juncture. Read more about Changing “Course”: Reconceptualizing Educational Variables for Massive Open Online Courses
TLL

Development of a Framework to Classify MOOC Discussion Forum Posts: Methodology and Challenges

TLL 1 Oct 2013 By Glenda S. Stump, Jennifer DeBoer, Jonathan Whittinghill, Lori Breslow
The purpose of this paper is to describe the methodology used to confront one of the challenges associated with analyzing discussion forum data from the inaugural edX MOOC, "Circuits and Electronics." The authors detail the development and testing of a framework to classify large amounts of MOOC data into a manageable number of categories so that further analysis con be conducted in targeted areas of interest. Read more about Development of a Framework to Classify MOOC Discussion Forum Posts: Methodology and Challenges
TLL

MOOCdb: Developing Data Standards for MOOC Datascience

International Artificial Intelligence in Education Society 1 Jul 2013 By Kalyan Veeramachaneeni, Zachary A. Pardos, Una-May O'Reilly
In this position paper, the authors advocate harmonizing and unifying disparate “raw” data formats by establishing an open-ended standard data description to be adopted by the entire education science MOOC oriented community. The concept requires a schema and an encompassing standard which avoid any assumption of data sharing. It needs to support a means of sharing how the data is extracted, conditioned and analyzed. Read more about MOOCdb: Developing Data Standards for MOOC Datascience
International Artificial Intelligence in Education Society

Adapting Bayesian Knowledge Tracing to a Massive Open Online Course in edX

Educational Data Mining 1 Jul 2013 By Zachary A. Pardos, Yoav Bergner, Daniel T. Seaton, David E. Pritchard
In this paper the authors show how existing learner modeling techniques based on Bayesian Knowledge Tracing can be adapted to the inaugural course, 6.002x: Circuits and Electronics, on the edX MOOC platform. They identify three distinct challenges to modeling MOOC data and provide predictive evaluations of the respective modeling approach to each challenge. Read more about Adapting Bayesian Knowledge Tracing to a Massive Open Online Course in edX
Educational Data Mining

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