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Initial Trends in Enrolment and Completion of
Massive Open Online Courses
Katy Jordan
The Open University, UK
Abstract
The past two years have seen rapid development of massive open online courses
(MOOCs) with the rise of a number of MOOC platforms. The scale of enrolment and
participation in the earliest mainstream MOOC courses has garnered a good deal of
media attention. However, data about how the enrolment and completion figures have
changed since the early courses is not consistently released. This paper seeks to draw
together the data that has found its way into the public domain in order to explore
factors affecting enrolment and completion. The average MOOC course is found to
enroll around 43,000 students, 6.5% of whom complete the course. Enrolment numbers
are decreasing over time and are positively correlated with course length. Completion
rates are consistent across time, university rank, and total enrolment, but negatively
correlated with course length. This study provides a more detailed view of trends in
enrolment and completion than was available previously, and a more accurate view of
how the MOOC field is developing.
Keywords: MOOCs; higher education; massive open online courses; online education;
distance learning
Initial Trends in Enrolment and Completion of Massive Open Online Courses
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Introduction
In the past two years, massive open online courses (MOOCs) have entered the
mainstream via the establishment of several high-profile MOOC platforms (primarily
Coursera, EdX, and Udacity), offering free courses from a range of elite universities and
receiving a great deal of media attention (Daniel, 2012). 2012 has been referred to as
‘the year of the MOOC’ (Pappano, 2012; Siemens, 2012), and some herald this as a
significant event in shaping the future of higher education, envisioning a future where
MOOCs offer full degrees and ‘bricks and mortar’ institutions decline (Thrun, cited in
Leckart, 2012).
There are clearly great potential individual and societal benefits to providing university-
level education free of some of the traditional barriers to participation in elite education,
such as cost and academic background. However, it is not clear the extent to which
MOOCs provide these benefits in practice. MOOCs may favour those who are already
educationally privileged; Daphne Koller of Coursera has stated that the majority of their
students are already educated to at least undergraduate degree level, with 42.8%
holding a bachelor’s degree, and a further 36.7% and 5.4% holding master’s and
doctoral degrees (Koller & Ng, 2013). A further study of Coursera students enrolled in
courses provided by the University of Pennsylvania indicates a greater dominance of
highly educated students, 83.0% of respondents being graduates and 44.2% being
educated at the postgraduate level (Emanuel, 2012). The author concludes that MOOCs
are failing in their goal to reach disadvantaged students who would not ordinarily have
access to educational opportunities (Emanuel, 2013). In order to succeed in a MOOC
environment, higher digital literacy may be required of students (Yuan & Powell, 2013),
potentially exacerbating pre-existing digital divides. In theory MOOCs remove
geographical location as a boundary to access, although a lack of internet access may
prevent this from being realized in practice (Guzdial, 2013).
Although smallerscale, connectivist MOOCs have existed for several years, the
development of largerscale MOOCs offered by elite institutions has propelled MOOCs
into the mainstream. The earliest and perhaps most highly cited example is the Stanford
AI class, which attracted 160,000 students (20,000 of whom completed the course)
when it ran in autumn 2011 (Rodriguez, 2012). However, while this example is often
used, it is unlikely to be representative of how the field is developing. A survey
undertaken by The Chronicle of Higher Education in February 2013 suggested that the
average MOOC enrolment is 33,000 students, with an average of 7.5% completing the
course (Kolowich, 2013). Detailed studies of particular courses have emphasized that
those who enroll upon courses have a wide variety of motivations for doing so (Breslow
et al., 2013; Koller, Ng, Do, & Chen, 2013); however motivation does not predict
whether a student will complete a course (Breslow et al., 2013). In examining
completion and engagement with courses, studies have focused upon characterizing
types of learners (Kizilcec, Piech, & Schneider, 2013; Koller et al., 2013). Limitations of
these studies are that they focus upon a small number of early MOOCs, and ascribe
Initial Trends in Enrolment and Completion of Massive Open Online Courses
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course completion primarily to student choice and motivation. There is a gap in the
research literature here about what could be learnt about characteristics of courses
themselves and their effect upon enrolment and completion, which this study sought to
explore.
Six-figure enrolment statistics have generated a good deal of interest in MOOCs in the
higher education sector, and are frequently conflated with active participation or
completion. However, the earliest courses are the most frequently cited examples and
may not be representative of how the phenomenon is developing, and the extent to
which enrolment numbers are indicative of completion has not been explored
comprehensively. These issues are obscured to an extent by a lack of consistent data
being made open to those outside of the MOOC platforms. For example, the Coursera
data export policy gives individual institutions control over the data that is released
about courses (Coursera, 2012), and in practice the extent of data sharing is highly
variable and ad hoc.
Now, over 18 months on from the advent of the large MOOC platforms, this paper seeks
to synthesise the data that has found its way into the public domain in order to address
some of the very basic questions associated with MOOCs. How massive is ‘massive’ in
this context? Completion rates are reputedly low, but how low? From the available data,
can we learn anything about factors which might affect enrolment numbers and
completion rates?
Methods
The approach taken here drew together a variety of different publicly available sources
of data online to aggregate information about enrolment and completion for as many
MOOCs as possible. Information about enrolment numbers and completion rates were
gathered from publicly available sources on the Internet. Given the media attention
which MOOCs have garnered, and their ‘massive’ nature, there is a good deal of publicly
available information to be found online, including news stories, university reports,
conference presentations, and MOOC student bloggers. Issues of reliability associated
with using this data are addressed below.
The list of completed MOOCs maintained at Class Central1 was used as a starting point
for the inquiry. Completed courses from Coursera, EdX, and Udacity were identified for
inclusion in the study, while other individual MOOCs and platforms were excluded. This
criteria was used because (i) Coursera, EdX, and Udacity are the platforms which have
received the greatest media focus and have fuelled the global interest in MOOCs, (ii) the
platforms account for the vast majority of MOOCs to date, and (iii) the platforms reflect
the higher education sector more broadly, offering courses presented from ‘bricks and
1 http://www.class-central.com/#pastlist
Initial Trends in Enrolment and Completion of Massive Open Online Courses
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mortar’ institutions through the platforms. At the time of writing (22nd July 2013), this
list comprised 279 courses (including courses which have run multiple times).
Enrolment and completion figures were selected as the data to be collected for the
courses, as these are the metrics which are most commonly available. Completion in this
sense was defined as the percentages of students who had satisfied the courses’ criteria
in order to gain a certificate. The exact activities required to achieve this vary according
to course. Where possible, data was also recorded about the number of ‘active users’ in
courses. Information about the number of active users was available for 33 courses,
although some did not provide any definition of the term. Those courses who did define
active users characterized them as students who actively engaged with the course
material to some extent (as opposed to those who enrolled but did not use the course at
all). For example, this includes having logged in to a course, attempted a quiz, or viewed
at least one video. Data was also collected about the date a course began, the course
length in weeks, and university ranking (using the Times Higher Education World
Rankings; THE, 2013) in order to explore whether these factors affect enrolment and
completion.
The enrolment and completion data was collected in two ways: via internet searches and
crowdsourcing information from students who participated in courses, by appealing via
social media. Students contributed data which had been shared with them by the course
instructor to the author’s blog (Jordan, 2013). This yielded information about
enrolment numbers for a total of 91 courses (32.6% of total potential sample), and
completion for 42 courses (15.1% of total). For transparency, the sources used for all
data items are included here. Details of courses for which only enrolment data was
available are shown in Table 1; details of courses for which completion data was found
are shown in Table 2.
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Table 1: Data Drawn from Online Sources for Courses for which Enrolment Numbers
Only were Available
Course
Institution
Enrolled
Start date
Length
(weeks)
Platform
Source
Introduction to
Databases
Stanford
University
60000
2011-10-01
9
Coursera
Widom, 2012
Human-Computer
Interaction
Stanford
University
29105
2012-05-28
5
Coursera
Lugton, 2012
Introduction to
Sociology
Princeton
University
40000
2012-06-11
7
Coursera
Lewin, 2012a
Introduction to
Finance
University of
Michigan
125000
2012-07-23
15
Coursera
Masolova,
2013
Algorithms, Part I
Princeton
University
65000
2012-08-12
6
Coursera
Princeton
University,
2012
Introduction to
Sustainability
University of
Illinois at
Urbana-
Champaign
32000
2012-08-27
8
Coursera
Rushakoff,
2012
Securing Digital
Democracy
University of
Michigan
14000
2012-09-03
5
Coursera
University of
Michigan,
2012
Statistics One
Princeton
University
96000
2012-09-03
12
Coursera
Bialik, 2013
Modern &
Contemporary
American Poetry
University of
Pennsylvania
36000
2012-09-10
10
Coursera
Unger, 2013
Introduction to
Mathematical
Thinking
Stanford
University
57592
2012-09-17
10
Coursera
Devlin, 2012
A History of the
World since 1300
Princeton
University
83000
2012-09-17
12
Coursera
Cervini, 2012
Organizational
Analysis
Stanford
University
81000
2012-09-24
10
Coursera
Hawkins,
2013
An Introduction to
Interactive
Programming in
Python
Rice
University
54000
2012-10-15
Coursera
Weinzimmer,
2012
The Modern
World: Global
History since 1760
University of
Virginia
40000
2013-01-14
15
Coursera
Kapsidelis,
2013
Microeconomics
for Managers
University of
California,
Irvine
37000
2013-01-21
10
Coursera
Heussner,
2013
Fundamentals of
Human Nutrition
University of
Florida
45000
2013-01-22
Coursera
Nelson, 2013
Data Analysis
Johns
Hopkins
University
102000
2013-01-22
8
Coursera
Jordan, 2013
Initial Trends in Enrolment and Completion of Massive Open Online Courses
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138
Course
Institution
Enrolled
Start date
Length
(weeks)
Platform
Source
Principles of Public
Health
University of
California,
Irvine
15000
2013-01-28
5
Coursera
Florida Public
Health
Training
Center, 2013
Introduction to
Digital Sound
Design
Emory
University
45000
2013-01-28
4
Coursera
Williams,
2013
Nutrition for
Health Promotion
and Disease
Prevention
University of
California, San
Francisco
50000
2013-01-28
6
Coursera
Ferraro, 2013
Grow to Greatness:
Smart Growth for
Private Businesses,
PartI
University of
Virginia
71000
2013-01-28
5
Coursera
University of
Virginia, 2013
Developing
Innovative Ideas
for New Companies
University of
Maryland,
College Park
85000
2013-01-28
6
Coursera
Welsh &
Dragusin,
2013
The Modern and
the Postmodern
Wesleyan
University
30000
2013-02-04
14
Coursera
Roth, 2013
Clinical Problem
Solving
University of
California, San
Francisco
28000
2013-02-11
6
Coursera
Harder, 2013
Aboriginal
Worldviews and
Education
University of
Toronto
23000
2013-02-25
4
Coursera
Stauffer, 2013
Introduction to
Music Production
Berklee
College of
Music
50000
2013-03-01
6
Coursera
Clark, 2013
Songwriting
Berklee
College of
Music
65590
2013-03-01
6
Coursera
Pattison, 2013
Sustainable
Agricultural Land
Management
University of
Florida
13000
2013-03-04
9
Coursera
Nelson, 2013
How Things Work
1
University of
Virginia
20000
2013-03-04
Coursera
Burnette,
2012
Leading Strategic
Innovation in
Organizations
Vanderbilt
University
33000
2013-03-05
8
Coursera
Furman
University,
2013
Economic issues,
Food & You
University of
Florida
16000
2013-03-18
10
Coursera
Nelson, 2013
Global sustainable
energy: past,
present and future
University of
Florida
18000
2013-03-24
15
Coursera
Nelson, 2013
Initial Trends in Enrolment and Completion of Massive Open Online Courses
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139
Course
Institution
Enrolled
Start date
Length
(weeks)
Platform
Source
Science,
Technology, and
Society in China I:
Basic Concepts
The Hong
Kong
University of
Science and
Technology
17000
2013-04-04
3
Coursera
Sharma, 2013
Introduction to
Improvisation
Berklee
College of
Music
39000
2013-04-29
5
Coursera
Burton, 2013
Grow to Greatness:
Smart Growth for
Private Businesses,
Part II
University of
Virginia
71000
2013-04-29
4
Coursera
University of
Virginia, 2013
TechniCity
Ohio State
University
16000
2013-05-04
4
Coursera
Campbell,
2013
Nutrition, Health,
and Lifestyle:
Issues and Insights
Vanderbilt
University
66000
2013-05-06
6
Coursera
Moran, 2013
History of Rock,
Part One
University of
Rochester
30000
2013-05-13
7
Coursera
Rivard, 2013
First-Year
Composition 2.0
Georgia
Institute of
Technology
17000
2013-05-27
8
Coursera
Head, 2013
Creative
Programming for
Digital Media &
Mobile Apps
University of
London
International
Programmes
70000
2013-06-03
6
Coursera
Gillies, 2013
Growing Old
Around the Globe
University of
Pennsylvania
4500
2013-06-10
6
Coursera
Posey, 2013
Initial Trends in Enrolment and Completion of Massive Open Online Courses
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Table 2: Data Drawn from Online Sources in Relation to MOOC Enrolment, Number of
Active Users, and Completion Rates
Course
Institution
Enrolled
Active
Completed
Start date
Length
Platform
Source
Introduction
to Machine
Learning
Stanford
University
104000
41600
13000
2011-10-01
10
Coursera
McKenna,
2012
Introduction
to Artificial
Intelligence
Stanford
University
160000
80000
20000
2011-10-01
10
Udacity
Schmoller,
2012
6.002x -
Circuits and
Electronics
Massachusetts
Institute of
Technology
154763
7157
2012-03-05
14
MITx
Lewin,
2012b
Software
Engineering
for SaaS
University of
California,
Berkeley
50000
3500
2012-05-18
5
Coursera
Meyer, 2012
Listening to
World Music
University of
Pennsylvania
36295
22018
2191
2012-07-23
7
Coursera
Jordan,
2013
Internet
History,
Technology,
and Security
University of
Michigan
46000
11640
4595
2012-07-23
13
Coursera
Severance,
2012
Gamification
University of
Pennsylvania
81600
49776
8280
2012-08-27
6
Coursera
Werbach,
2012
6.002x:
Circuits and
Electronics
Massachusetts
Institute of
Technology
46000
6000
3008
2012-09-05
14
EdX
Chu, 2013
Functional
Programming
Principles in
Scala
École
Polytechnique
Fédérale de
Lausanne
50000
9593
2012-09-18
7
Coursera
Miller &
Odersky,
2012
Social
Network
Analysis
University of
Michigan
61285
25151
1410
2012-09-24
8
Coursera
Jordan,
2012
Bioelectricity:
A Quantitative
Approach
Duke
University
12000
7761
313
2012-09-24
9
Coursera
Belanger &
Thornton,
2013
Greek and
Roman
Mythology
University of
Pennsylvania
55000
2500
2012-09-24
10
Coursera
Jordan,
2013
An
Introduction
to Operations
Management
University of
Pennsylvania
87000
58000
4000
2012-09-24
8
Coursera
Barber,
2013
Mathematical
Biostatistics
Bootcamp
Johns
Hopkins
University
15930
8380
740
2012-09-24
7
Coursera
Anderson,
2012
Computing for
Data Analysis
Johns
Hopkins
University
50899
27900
2012-09-24
4
Coursera
Simply
Statistics,
2012
Initial Trends in Enrolment and Completion of Massive Open Online Courses
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141
Course
Institution
Enrolled
Active
Completed
Start date
Length
Platform
Source
Learn to
Program: The
Fundamentals
University of
Toronto
38502
8243
2012-09-24
7
Coursera
St.
Petersburg
College,
2013
Introduction
to Genetics
and Evolution
Duke
University
33000
14000
1705
2012-10-10
12
Coursera
Duke
Today, 2012
CS50x:
Introduction
to Computer
Science I
Harvard
University
150349
100953
1388
2012-10-15
24
EdX
Malan, 2013
3.091x:
Introduction
to Solid State
Chemistry
Massachusetts
Institute of
Technology
28512
6000
2082
2012-10-15
12
EdX
Chu, 2013
Computational
Investing, Part
I
Georgia
Institute of
Technology
53205
28199
2554
2012-10-22
9
Coursera
Balch,
2013a
Think Again:
How to
Reason and
Argue
Duke
University
226652
132000
5322
2012-11-26
12
Coursera
Riddle,
2013a
Introduction
to Astronomy
Duke
University
60000
40000
2141
2012-11-27
8
Coursera
Belanger,
2013
Drugs and the
Brain
California
Institute of
Technology
66800
10426
4400
2012-12-01
5
Coursera
Lesiewicz,
2013
Calculus:
Single
Variable
University of
Pennsylvania
47000
7000
2013-01-07
13
Coursera
Unger, 2013
Calculus One
Ohio State
University
35579
24385
2013-01-07
15
Coursera
Evans, 2013
Image and
video
processing:
From Mars to
Hollywood
with a stop at
the hospital
Duke
University
40000
23000
4069
2013-01-14
9
Coursera
Riddle,
2013b
Artificial
Intelligence
Planning
University of
Edinburgh
29894
15546
654
2013-01-28
5
Coursera
University
of
Edinburgh,
2013
E-learning and
Digital
Cultures
University of
Edinburgh
42844
21862
1719
2013-01-28
5
Coursera
University
of
Edinburgh,
2013
Critical
Thinking in
Global
Challenges
University of
Edinburgh
75844
35084
6909
2013-01-28
5
Coursera
University
of
Edinburgh,
2013
Initial Trends in Enrolment and Completion of Massive Open Online Courses
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Course
Institution
Enrolled
Active
Completed
Start date
Length
Platform
Source
Introduction
to Philosophy
University of
Edinburgh
98128
53255
9445
2013-01-28
7
Coursera
University
of
Edinburgh,
2013
Astrobiology
and the Search
for
Extraterrestria
l Life
University of
Edinburgh
39556
20413
7707
2013-01-28
5
Coursera
University
of
Edinburgh,
2013
Equine
Nutrition
University of
Edinburgh
23322
18998
8416
2013-01-28
5
Coursera
University
of
Edinburgh,
2013
Introductory
Organic
Chemistry -
Part 1
University of
Illinois at
Urbana-
Champaign
17400
9000
2013-01-28
8
Coursera
Arnaud,
2013
Stat2.1x:
Introduction
to Statistics:
Descriptive
Statistics
University of
California,
Berkeley
52661
8181
2013-02-20
5
EdX
Adhikari,
2013
Computational
Investing, Part
I
Georgia
Institute of
Technology
25589
15688
1165
2013-02-23
8
Coursera
Balch,
2013b
AIDS
Emory
University
18600
10601
2013-02-25
9
Coursera
Williams,
2013
Introductory
Human
Physiology
Duke
University
33675
1036
2013-02-25
12
Coursera
Zhou, 2013
Pattern-
Oriented
Software
Architectures
for Concurrent
and
Networked
Software
Vanderbilt
University
30979
20180
1643
2013-03-04
8
Coursera
Jordan,
2013
Introduction
to
Mathematical
Thinking
Stanford
University
27930
1950
2013-03-04
10
Coursera
Schmoller,
2013
A Beginner's
Guide to
Irrational
Behavior
Duke
University
142839
82008
3892
2013-03-25
8
Coursera
Jordan,
2013
Gamification
University of
Pennsylvania
66438
34548
5592
2013-04-01
6
Coursera
Werbach,
2013
Medical
Neuroscience
Duke
University
44980
18433
756
2013-04-08
12
Coursera
Novicki,
2013
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Course
Institution
Enrolled
Active
Completed
Start date
Length
Platform
Source
Healthcare
Innovation
and
Entrepreneurs
hip
Duke
University
15596
1520
2013-04-15
6
Coursera
Kenyon,
2013
Mathematical
Biostatistics
Bootcamp
Johns
Hopkins
University
21916
2087
2013-04-16
7
Coursera
Jordan,
2013
Generating the
Wealth of
Nations
University of
Melbourne
28922
12197
500
2013-04-29
10
Coursera
Signsofchao
s blog, 2013
Sports and
Society
Duke
University
19281
6918
1626
2013-04-30
7
Coursera
Anderson,
2013
Introduction
to
International
Criminal Law
Case Western
Reserve
University
21000
1432
2013-05-01
8
Coursera
Farkas,
2013
Inspiring
Leadership
through
Emotional
Intelligence
Case Western
Reserve
University
90000
58000
2013-05-01
8
Coursera
Farkas,
2013
Statistical
Molecular
Thermodynam
ics
University of
Minnesota
10000
5000
2013-05-20
8
Coursera
Friedrich,
2013
Introduction
to Systems
Biology
Icahn School
of Medicine at
Mount Sinai
26,915
15392
2013-06-03
6
Coursera
Course site
at Coursera
Data analysis was conducted using linear regression carried out with Minitab statistical
software. Linear regression was chosen as the approach to analysis because at this stage
the aim of the research was exploratory, to identify potential trends rather than being
explanatory and seeking to fit a model. This would be a valuable goal for follow-up
research particularly if more consistent data became available for MOOCs more broadly.
Linear regression analyses were carried out individually according to different factors of
interest rather than as a single multiple regression due to issues of data consistency and
availability; that is, data is not available for every field in Tables 1 and 2 for every course,
so n varies according to different tests (see Results and Analysis section). Rather than
discarding courses for which the full spectrum of data was not available and in order to
gain the greatest insight possible into the different factors, a series of individual
regression analyses were carried out.
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Limitations
There are a number of limitations which must be borne in mind with the approach
taken by this study, including issues of validity of data and reliability of the research
instruments used.
In terms of validity, it should be noted that the accuracy of figures varies according to
sources, with some institutions releasing highly accurate figures and others (particularly
when releasing enrolment data through the press) are rounded figures. This reflects the
fact that MOOC courses do not consistently release this information into the public
domain, and most of the courses that would have been eligible for inclusion (67.4%)
have not released any data. Of the institutions or instructors choosing to make data
available, bias may be introduced according to their motivations for publicizing this
information, which are unknown. There is also a degree of trust involved in the
information provided by student informants via the blog.
It should be emphasized that the study sought to be exploratory in nature, identifying
trends of interest in the data as a starting point for further research but not seeking to
explain or model the phenomenon. Reliability of the approach is less contentious as the
data have been collected via several rounds of internet searches during the data
collection period (February 13th to July 22nd 2013) and shown in full in Tables 1 and 2
should others wish to reproduce the tests or carry out alternative analyses. By collating
data ‘in the open’ at the author’s blog (Jordan, 2013), this offered a platform for others
(including course leaders) to scrutinize the data and provide more accurate figures in
some cases.
Results and Analysis
Trends in Total Enrolment Figures
Total enrolment numbers draws upon the data in both Tables 1 and 2, which comprises
a total of 91 courses (excluding three courses which are missing total enrolment figures).
Total enrolment figures range from 4,500 to 226,652 students, with a median value of
42,844. The data does not exhibit a normal distribution (Figure 1); six-figure
enrolments are not representative of the ‘typical’ MOOC. Total enrolments are shown
plotted against the date each course began in Figure 2. This demonstrates a negative
correlation, with enrolment numbers decreasing over time.
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20000016000012000080000400000
30
25
20
15
10
5
0
Total number of students enrolled
Frequency
Figure 1. Histogram of total enrolment numbers for the sampled courses (n = 91).
2013-07-01
2013-04-01
2013
-01-01
2012-10-01
2012-07-01
2012-04-01
2012-01-01
2011-10-01
250000
200000
150000
100000
50000
0
Date course began
Total number of students enrolled
Figure 2. Scatterplot of total enrolment numbers plotted against course start date for
the sampled courses (n = 91).
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A regression analysis was carried out, prior to which the data was subject to a Box-Cox
transformation as the residuals do not follow a normal distribution. Regression analysis
showed that date significantly predicted total enrolment figures at the 95% significance
level by the following formula: ln(Enrolled) = 104.249 - 0.00226915*StartDate (R2 =
0.1719, p < 0.001). The relationship is a negative correlation, indicating that as time has
progressed, enrolment figures have decreased. The relationship is relatively weak (time
as a factor accounts for 17.2% of the variance observed, as R2 is a measure of the fraction
of variance explained by the model; Grafen & Hails, 2002), although the sample is
sufficiently large that this is statistically significant (critical R2 values decrease according
to sample size, with an n of 91 being relatively large; Siegel, 2011). This highlights that a
focus upon figures from early courses is misleading and not representative of how the
field is developing.
The relationship between course length and total enrolments was also considered, and
found to demonstrate a positive correlation between course length and total enrolment
(Figure 3).
2520151050
250000
200000
150000
100000
50000
0
Course length (weeks)
Total number of students enrolled
Figure 3. Scatterplot of total enrolment numbers plotted against course length for the
sampled courses (n = 87).
Following a Box-Cox transformation, regression analysis showed that course length
significantly predicted (at the 95% significance level) total enrolment figures by the
following formula: ln(Enrolled) = 10.2248 + 0.0491206*Length (R2 = 0.0545, p =
0.029). The correlation between the variables is positive, indicating courses that are
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longer attract a greater number of enrolments. The relationship is relatively weak,
accounting for 5.5% of the variance observed, although the sample size is sufficiently
large for this to be a statistically significant relationship. This positive correlation may
suggest that prospective MOOC students prefer more substantial courses (however, see
also the relationship between course length and completion rates).
In addition, the relationship between university ranking and enrolment figures was
considered, although it was not found to be significant at the 95% level.
Trends in Completion Rates
Completion rates were calculated as the percentage of students (out of the total
enrolment for each course) who satisfied the criteria to gain a certificate for the course.
This information was available for 39 courses in the sample. Completion rates range
from 0.9% to 36.1%, with a median value of 6.5% (Figure 4). The data is skewed, so the
higher completion rates are not representative, with completion rates of 5% being
typical.
35302520151050
20
15
10
5
0
Percentage of total enrollment to complete course
Frequency
Figure 4. Histogram of completion rates for the sampled courses (n = 39).
As the residuals were not normally distributed, a Box-Cox transformation was again
carried out before conducting regression analysis. No significant relationships were
found between completion rate and date, university ranking, or the total number of
students enrolled. Completion rates remained consistent across these factors. A
significant negative correlation was found however between completion rate and course
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length, shown in Figure 5. Regression analysis showed that course length significantly
predicted completion rate by the following formula: ln(PercentTotalCompleted) =
2.64802 - 0.100461*CourseLength (R2 = 0.2373, p = 0.002). The correlation in this case
is negative, indicating that a lower proportion of students complete longer courses.
Course length accounts for 23.4% of the variance observed, and the correlation is
significant at the 95% significance level.
252015105
40
30
20
10
0
Course length (weeks)
Percentage of total enrollment to complete course
Figure 5. Scatterplot of completion rate plotted against course length for the sampled
courses (n = 39).
While considering completion rate as the percentage of the total enrolment that
complete the course is the type of data that is most readily available, a criticism of this
characterization is that many students may enroll without even starting the course, and
that completion rates would be better characterized as the proportion of active students
who complete. This level of information is available for a subset of the sampled courses
(39 courses with a number of active students and total enrolment; 33 courses with data
about the proportion of active students who complete).
The number of active students is remarkably consistent as a proportion of the total
enrolment of the course (with approximately 50% of the total enrolment becoming
active students). This is shown graphically in Figure 6. Regression analysis showed that
total enrolment significantly predicted the number of active students by the following
formula: Active = 0.543336*Enrolled (R2 = 0.9556, p < 0.001). The correlation is strong
(accounting for 95.6% of the variance) and positive, showing a consistent relationship
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between total enrolment and the percentage who become active students (being
approximately 54% of those who enroll).
250000200000150000100000500000
140000
120000
100000
80000
60000
40000
20000
0
Total number of students enrolled
Number of active students
Figure 6. Scatterplot of number of active students plotted against total enrolment for
the sampled courses (n = 39).
When calculating completion rate as the percentage of active students who complete the
course, completion rates range from 1.4% to 50.1%, with a median value of 9.8% (Figure
7). While completion rates as a percentage of active students span a wider range than
completion rates as a percentage of total enrolments, there remains a strong skew
towards lower values. The differences here would be worthwhile to explore in further
detail to explore features of course design that may account for the wider variation
observed.
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483624120
14
12
10
8
6
4
2
0
Percentage of active students who complete course
Frequency
Figure 7. Histogram of completion rates as a proportion of active students for the
sampled courses (n = 39).
No significant relationships were found between completion rate as a proportion of
active users and date, university ranking, total enrolment, or (in contrast to completion
rate as a percentage of total enrolment) course length. This may suggest that enrolled
students may be put off starting longer courses, but this is less of an issue for those who
do become actively engaged in the course.
Conclusions
The findings here demonstrate changes in the field since the concept of MOOCs entered
the mainstream and the inception of the major MOOC platforms. It is misleading to
invoke early enrolment and completion figures as representative of the phenomenon;
six-figure enrolments are atypical, with the median average enrolment being 42,844
students, and decreasing over time as the number of courses available continues to
increase. Although this is lower than the earliest examples, it emphasizes that it is
inappropriate to compare completion rates of MOOCs to those in traditional bricks-and-
mortar institution-based courses.
The majority of courses have been found to have completion rates of less than 10% of
those who enroll, with a median average of 6.5%. The definition of completion rate used
here is the percentage of enrolled students who satisfied the courses’ criteria in order to
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earn a certificate, and this definition was used because it is the type of information that
is most frequently available. There are potentially many ways in which MOOC students
may participate in and benefit from courses without completing the assessments. The
wider range of completion rates (while still remaining quite low overall, with a median
of 10%) observed when defining completion as a percentage of active learners in courses
is interesting and warrants further work to better understand the reasons why those
who become engaged initially do or do not complete courses.
This is not to say, however, that completion rates should be ignored entirely. Looking at
completion rates is a starting point for better understanding the reasons behind them,
and how courses could be improved for both students and course leaders. For example,
the relationship between enrolments, completion, and course length is an interesting
issue for MOOC course design, balancing the higher enrolments with the lower
completion rates of longer courses. Figures about how many students achieved
certificates obscure how many students attempted to gain a certificate but did not meet
the criteria. Given that MOOCs are offered free of educational prerequisites, striving to
improve teaching on courses so that students who wish to complete are assisted in doing
so is an important pedagogical issue. The extent of understanding that can be gained
outside of running a MOOC will continue to be constrained however as long as the
release of detailed data about courses is limited.
This study has only considered relationships between enrolment and completion and a
small number of general factors for which data is available publicly; various other
factors would be worthwhile to explore. For example, it would be useful to look at in
terms of the underlying pedagogy, whether differences emerged based on how
transmissive (so-called ‘xMOOCs’) or connectivist (‘cMOOCs’) courses are. The impact
of different assessment types, being necessarily linked to the criteria for achieving a
certificate of completion, would also be a worthwhile area to consider in further detail.
Along with the studies discussed in the introduction which focus upon links between
student demographics or behaviours and completion (Breslow et al., 2013; Kizilcec et
al., 2013; Koller et al., 2013), a limitation of the approach used here is that the data
neglects the student voice. While these approaches can identify trends and patterns,
they are unable to explore in detail the reasons behind the trends observed.
Acknowledgments
The author would like to thank Professor Martin Weller and the two anonymous peer
reviewers for their comments on drafts of this paper. Special thanks to all of the MOOC
students, instructors, and other commentators who contributed data and thoughtful
comments about MOOC completion rates to the authors’ blog.
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