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Understanding Persistence in MOOCs (Massive Open Online Courses):
Descriptive & Experimental Evidence
Rachel Baker, Brent Evans, Erica Greenberg and Thomas Dee
take a MOOC for the human capital production and the
consumption value (non-financial private gains) taking the
MOOC provides. Alstadsaeter and Sieverstsen (2009)
provide a literature review about the consumption value
of higher education, and of particular relevance in
MOOCS are the potential to build cultural capital and the
satisfaction of learning new things.
We believe that students weigh the human capital and
consumption value benefits of pursuing an online course
against its costs. Because there is no charge to take a
MOOC, the costs are limited to psychic costs associated
with exerting effort in the course and time costs associated
with watching lectures and completing assignments.
Students will continue to complete the course week by
week as long as their perceived benefits outweigh the
costs. This perspective enables us to identify the potential
reasons for a lack of course persistence. If many students
cease participating at the same time, we can examine the
video lectures and assignments at that time to observe
whether increased length or difficulty of the material may
have contributed to dropping out of the course. Likewise,
if many students from the same type of area cease participating,
we can begin to assess the relationship between
students’ background characteristics and persistence.
We also incorporate lessons from behavioral economics
into our experiment. The combination of demonstrated
student interest (high numbers of registered students)
coupled with a lack of follow-through (large dropout
rates) suggests that the use of choice architecture in
the design of MOOCs may provide a way to promote
the human-capital acquisition of MOOC students. The
second part of this study presents the results of a field
experiment examining the validity of this conjecture. We
focus on a pre-commitment device that asks students to
schedule when they will watch the first course video of
the week and report that time to the instructor. Previous
work in the field has shown that deadlines imposed by
the students themselves do improve performance (Ariely
& Wertenbroch, 2002). Our study tests whether this
phenomenon holds in a more diverse set of students in a
MOOC. If such strategies are found to be effective, this
very low cost strategy could be widely implemented to improve
persistence across online courses.
Data & Methods
The first part of this study presents descriptive evidence
on MOOC students and participation and persistence in
several MOOCs. We use unique student-level administrative
data from dozens of courses across a range of disciplines
all fielded on one widely used MOOC platform,
Coursera. This detailed micro data about individual student
participation enables us to track students in every
component of the course including when they watched
the lecture, what they posted on forums, and when they
complete assignments. This level of data provides an exceptional
capability to understand the learning process in
higher education at the student level.
The administrative data is available for all courses we
study, but it is complemented by pre-course survey data
for the one course in which we conducted the experiment.
In the survey, students volunteered why they were taking
the course and how they intended to approach the course
(the course offered three tracks, audit, quantitative, and
qualitative, which had different assignments).
In order to explore the geographic and demographic
characteristics of students enrolling in MOOCs, and to examine
the relationship between these characteristics and
course persistence, we make use of student IP addresses.
These identifiers are available for roughly 80 percent of
the students participating in each MOOC. We convert
these IP addresses to latitude and longitude coordinates,
and map these coordinates using ArcGIS 10.1. We are
able to provide a global picture of MOOC participation
for all courses included in this study. For students located
in about 30 countries (including France, Brazil, Switzerland,
India, Mexico, Vietnam and the United States)
we overlay their IP address locations onto geographic
and tabular Census data available from international databases
like IPUMS. These data allow us to describe the
demographic and socioeconomic characteristics of areas
in which course participation occurs, and to make inferences
about the types of students who enroll in and complete
MOOCs. Finally, we make use of international institutional
data on brick-and-mortar institutions of higher
education. We map these institutions alongside student
IP addresses and ask whether MOOC students would be
able to access in-class instruction in the absence of online
offerings.
We also use IP addresses to determine students’ time
zones. With these data, combined with micro-level course
data, we can examine patterns of activity for MOOC students
in great detail. Ours is the first study that analyzes
at what times of day MOOC students are most active for
all students enrolled in a range of classes.
We investigate persistence patterns in MOOCs using
quantitative methods. By capturing individual observations
on when (and if) students watch lectures, complete
assignments, and use the discussion forums, we can longitudinally
measure students’ changes in course participation
patterns and performance. To measure persistence
patterns, we build on Kizilcec, Piech, and Schneider’s
(2013) work which aggregates student participation at
the weekly level. By using variables for day of week and
week of course, as well as indicators for course features,
we identify if dropouts cluster in specific weeks, if participation
varies throughout the week, and if certain course
features (such as email communication) are associated
with increased or decreased student activity.
Research Track |
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