Data & Research

The HarvardX Research Committee is charged with the coordination and support of HarvardX research.

We will prioritize research projects that have the potential to:

  1. Inform the design of HarvardX courses / modules
  2. Improve residential education
  3. Answer key questions about how people learn
  4. Inform an understanding of who the learners are and their experiences
  5. Generate findings that are potentially generalizable
  6. Align with the priorities of HarvardX and University leadership

Recent Development (selected)

  • Publication of a series of HarvardX Working Papers
  • An NSF-funded grant with MIT that analyzes student participation, persistence, performance in open online courses;
  • A HarvardX Research Registry for the coordination and support of ongoing and proposed research in HarvardX courses; and
  • A HarvardX Data Repository that stores HarvardX course data for secure secondary analysis by registered researchers.

Priorities for 2014-2015

  1. Develop the next generation of assessment instruments and technologies to better measure student learning
  2. From micro-levels (design elements in learning objects) to macro-levels (course structure), experiment with instructional strategies to refine teaching and learning
  3. Enrich our understanding of student experiences and study practices—especially those not captured by the edX platform—through surveys, interviews, and remote observations
  4. Scale-up the science of motivation by using edX as a platform for interventions developed in smaller-scale psychology and behavioral economics labs

Broad Questions HarvardX Hopes to Answer

Motivation

  • Why do students take HarvardX courses?
  • What rationales do they provide when asked open-ended questions about motivations and aspirations?
  • How do they rank competing alternatives that we propose? How do these rationales predict course activity, course persistence, and student learning outcomes?
  • Are there ways to experimentally manipulate student declarations of commitment in order to produce better (or worse) outcomes in regards to student activity, course persistence, and student learning?

Commitment

  • What is the range and distribution of student commitment levels among students registering for a HarvardX course?
  • How does commitment level at registration predict course activity, course persistence, and student learning outcomes?
  • Are there ways to experimentally manipulate student declarations of commitment in order to produce better (or worse) outcomes in regards to student activity, course persistence, and student learning?

Background

  • What are the backgrounds of our students in terms of geographic location, socioeconomic status, education level, and previous experience with online learning and pervious experience with the course topic?
  • How do those background and experience predict course activity, course persistence, and student learning outcomes?

Learning Conditions

  • What are the circumstances under which people are taking these courses?
  • What times of day do they expect to work?
  • What technologies do they expect to expect to have access to?
  • Do they plan to work in public or private spaces?
  • How likely are they to encounter access difficulties?
  • Do they have plans for addressing potential difficulties in access?
  • How do learning conditions predict student activity, persistence, or learning outcomes?
  • Experimentally, does being asked these questions in advance of a course change student activity, persistence, and learning outcomes?