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Learning is Not a Spectator Sport: Doing is Better than Watching for Learning from a MOOC


Abstract and Figures

The printing press long ago and the computer today have made widespread access to information possible. Learning theorists have suggested, however, that mere information is a poor way to learn. Instead, more effective learning comes through doing. While the most popularized element of today's MOOCs are the video lectures, many MOOCs also include interactive activities that can afford learning by doing. This paper explores the learning benefits of the use of informational assets (e.g., videos and text) in MOOCs, versus the learning by doing opportunities that interactive activities provide. We find that students doing more activities learn more than students watching more videos or reading more pages. We estimate the learning benefit from extra doing (1 SD increase) to be more than six times that of extra watching or reading. Our data, from a psychology MOOC, is correlational in character, however we employ causal inference mechanisms to lend support for the claim that the associations we find are causal.
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Learning is Not a Spectator Sport: Doing is Better than
Watching for Learning from a MOOC
Kenneth R. Koedinger
Carnegie Mellon University
5000 Forbes Avenue
Elizabeth A. McLaughlin
Carnegie Mellon University
5000 Forbes Avenue
Jihee Kim
Carnegie Mellon University
5000 Forbes Avenue
Julianna Zhuxin Jia
Carnegie Mellon University
5000 Forbes Avenue
Norman L. Bier
Carnegie Mellon University
5000 Forbes Avenue
The printing press long ago and the computer today have
made widespread access to information possible.
Learning theorists have suggested, however, that mere
information is a poor way to learn. Instead, more effective
learning comes through doing. While the most
popularized element of today's MOOCs are the video
lectures, many MOOCs also include interactive activities
that can afford learning by doing. This paper explores the
learning benefits of the use of informational assets (e.g.,
videos and text) in MOOCs, versus the learning by doing
opportunities that interactive activities provide. We find
that students doing more activities learn more than
students watching more videos or reading more pages.
We estimate the learning benefit from extra doing (1 SD
increase) to be more than six times that of extra watching
or reading. Our data, from a psychology MOOC, is
correlational in character, however we employ causal
inference mechanisms to lend support for the claim that
the associations we find are causal.
Keywords: Learning by doing; MOOCs; learning
prediction; course effectiveness; Open Education; OER
ACM Classification Keywords K.3.1
The use of online learning resources to provide and
support instruction is on the rise and spectacularly so [5].
Further, there is a growing recognition and interest in the
opportunity to apply and contribute to the learning
sciences by conducting education research through online
learning environments [29, 16]. While there are notable
success stories of online courses that were shown to be
more effective than traditional instruction [e.g., 20], the
more typical situation is that, at best, online courses
achieve the same outcomes at lower cost [e.g., 6, 9].
Perhaps more importantly, we do not know enough about
what features of online courses are most important for
student learning.
The prototypical feature of a Massive Open Online
Course (MOOC) is lecture videos, but many MOOCs also
include activities such as questions for students to answer
or problems for them to solve, in some cases with
immediate online feedback. What is more important for
student learning? Is it the information students get from
watching lecture videos, the practice and feedback they
get from online questions or problems, or some
combination. The idea of scaling the best lecturers for
open access is a compelling feature of MOOCs.
However, in contrast to the passive form of learning
characterized by watching video lectures or reading text, a
diversity of learning theorists have recommended more
active learning by doing [e.g., 3, 11, 28]. Many argue for
learning by doing as it focuses on authentic activities that
are more representative of knowledge use in the real
world. More fundamentally, learning by doing is
important because most of human expertise involves tacit
knowledge of the cues and conditions for deciding when,
where, and what knowledge to bring to bear in complex
situations [36]. In this view, there appears to be no verbal
shortcut to acquiring expertise. It is gained by observing
examples (or “models”), attempting to engage in expert
activities with feedback and as-needed instruction
(“scaffolding”), and having that support be adaptive to
advancing proficiency (“fading”) [cf., 10].
Intelligent tutoring systems provide such support through
adaptive feedback and hints during learning by doing [1,
2, 34]. When well designed, they yield significant
learning gains [e.g., 24, 27, 35]. “Well-designed” means
designed using theory of learning and instruction [cf., 14,
26], data-driven methods including cognitive task analysis
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ACM 978-1-4503-3411-2/15/03.
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[8, 17], and classroom design research iterations [4, 19].
Interactive activities in Carnegie Mellon University’s
Open Learning Initiative courses attempt to mimic some
of the behavior of intelligent tutors and, importantly,
follow these good design practices [21, 31].
In the past few years, much attention has been paid to the
demonstrated potential of Massive Open Online Courses
(MOOC) to scale access to educational resources [25].
While the exact definition and role of MOOCs continues
to be debated, much of the popular dialogue surrounds the
three major MOOC platforms – Coursera, Udacity and
EdX – who describe MOOCs as online courses with open,
unlimited enrollment [23]. Though specific activities
vary from course to course, video-based lectures and
student discussion forums typically form the core of the
MOOC instructional experience. We call this the “lecture
model”. The Open Learning Initiative, which has offered
online learning environments since 2002, takes a different
approach focusing on rich and interactive learn-by-doing
activities, aligned with student-centered learning
outcomes, and designed around science-based learner
models. We call this the “learn-by-doing model.” Both
models offer rich datasets, though with different focuses
and capturing different kinds of learner interactions.
An opportunity emerged recently to compare the
instructional features of these two different models in
terms of how variations in student use of them impacts the
learning outcomes they achieve. In 2013, Georgia
Institute of Technology and Carnegie Mellon University
(CMU) collaborated to incorporate elements of CMU’s
Open Learning Initiative (OLI) “Introduction to
Psychology” learning environment into Tech’s
Introduction to Psychology as a Science MOOC. Taught
via the Coursera platform, OLI materials were available
as part of the larger course, in addition to lectures, quizzes
and other Coursera-based activities. This paper explores
the impact of the use of the OLI elements on learning, in
comparison to use of MOOC elements (alone or in
association with the OLI materials). As part of this
exploration, we examine which OLI features are most
associated with learning and if we can infer causal
relationships between these features. In addition, we
analyze the potential for predicting the course dropout
using student performance in OLI activities and survey-
based demographic information.
The key research questions we pursue are:
1. What factors determine whether or not students
stay in the course or dropout?
2. Do students who use OLI features learn more
than those using the MOOC only?
3. What variations in course feature use (watching
videos, reading text, or doing activities) are most
associated with learning? And can we infer
causal relationships?
A key goal is to provide evidence relevant to alternative
hypotheses about what makes a course effective. The
“lecture model” suggests that students’ primary source for
learning is through listening to lectures. The “learn-by-
doing model” suggests that students’ primary source for
learning comes from answering questions and solving
problems with feedback. Of course, it may be that both
sources are critical.
In considering the features available in the course, we
divide components into two broad categories:
passive/declarative information and active/interactive
activities. Students learn from passive/declarative
information by reading, watching or studying; these
features include video lectures, lecture slides and other
expository materials (text). Active/interactive features by
definition require students to be more active, and include
quizzes, exams, discussion forum participation and
interactive activities that provide targeted feedback and
hints. Although engagement with the full range of
learning materials assigned was encouraged, the final
grade in the course was awarded based upon a
combination of quiz scores, final exam score and two
written assignments. In addition, the course contained
two additional features intended to support research rather
than learning: a pre/post test and student background
survey. Neither of these elements was factored into
students’ grades.
Introduction to Psychology as a Science was designed as
a 12-week introductory survey course, as is often taught
during the first year of college. For each week of class,
the course targeted a major topic area (e.g. Memory,
Sense and Perception, Abnormal Behavior, Brain
Structures and the Nervous System); these topics were
broken into 3-4 sub-topics, each supported by a pre-
recorded video lecture (10-15 minutes, with
downloadable slides) and included assigned modules and
learning outcomes in the OLI learning environment. A
high-stakes quiz assessed students against these outcomes
at the end of each week.
The Coursera MOOC platform provided general course
structure (registration, syllabus, etc), video lectures and
slides, discussion forums, writing assignments, quizzes
(with questions drawn from OLI item banks, see Figure
1b for an example), and a final exam (with questions
created by the instructor, see Figure 1c for an
example). The background survey was also administered
via this platform, which focused on demographic
information (gender, age, education, occupation) as well
as some questions to assess learner intent and opinion.
The OLI Learning Environment was embedded in the
Coursera platform using Learning Tools Interoperability
(LTI) for a seamless learner experience. The
corresponding OLI modules included a variety of
L@S 2015 • Learning
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expository content (text, examples, images, and video
clips) and a large number of interactive activities.
Broadly, these activities serve two purposes. “Learn By
Doing” activities, intended to support student outcome
achievement, provide feedback targeted to diagnose
misconceptions and robust hints to support students. In
Figure 1a, we show a screenshot of a Learn by Doing
activity from the unit on Personality covered in week 9 of
the course. “Did I Get This” activities provide a self-
comprehension check for students. They are introduced at
points when students are expected to have achieved
mastery and do not provide hints, though they do offer
feedback [31].
Figure 1. (a) Screen shot of a Learn By Doing OLI activity
from the unit on Personality© OLI. (b) Corresponding quiz
question © OLI. (c) Related final exam question © Dr.
Anderson Smith, GA Institute of Technology.
It is important to point out that using data from natural
student use of MOOCs adds uncertainty in making
inferences about causal relationships as compared to
using data from experimental designs. This uncertainty is
further increased by the large attrition or dropout that is
typical in MOOCs. The sample of students involved in
any particular analysis is determined by student
participation and effects that might be attributed to other
factors (e.g., course features) might instead be so-called
“selection effects”, that is, effects of sampling differences
based on the choices or selections that students make.
Nevertheless, there is a real opportunity to use the large
and naturally-occurring data that comes from MOOCs to
provide initial, if not confirming, evidence of factors of
potential importance for course participation and learning
Table 1 shows different subsets of students as indicated
by different forms of participation in the course. We refer
to it in describing how samples were selected to address
our research questions.
Our first research question is: What factors determine
whether or not students stay in the course or dropout?
27720 students registered in the Coursera MOOC
Psychology course while 1154 students completed it (see
Table 1). We are interested in what indicators or features
may predict dropouts throughout the course, and we use
quiz and final exam participation as estimates of student
dropout. For example, if a student has a score for quiz 4
but none of the remaining quizzes or the final, we
consider that student to have dropped out after quiz 4. We
are interested in factors that predict future dropouts. In
addition to whether students used the OLI material or not,
we also included quiz participation and quiz score in a
logistic regression model to predict final exam
Our second research question is: Do students who use
OLI learn more than students who only use the MOOC
materials? MOOC+OLI students (N=9075) are those who
registered to use the OLI materials. MOOC-only students
(N=18,645) did not (see Table 1). To address the
question, we did a quasi-experimental comparison of
learning outcomes between the MOOC+OLI students who
took the final (N=939) with the MOOC-only students who
took the final (N=215).
Our third research question is: What variations in course
feature use (watching videos, reading text, or doing
activities) are most associated with learning? And can we
infer causal relationships? In the results section, we
describe an exploratory data analysis to identify
relationships between usage of these features (garnered
from the log data [15]) and our two measures of learning,
quizzes scores and final exam score. To frame that
analysis, we present some global data on feature usage.
Of all MOOC registrants, 14,264 (51.4% of total) started
to watch at least one lecture video. Of the 9075 students
(32.7% of total) registered for OLI material study, 84.5%
(7683 students) accessed at least one page of OLI
readings and visited or revisited an average of 69 pages
with a maximum of 1942 pages (variable pageview). On
average, 33 unique pages were viewed with a maximum
of 192 unique pages. Of the 9075 OLI registered students,
62.3% (5658 students) started at least one interactive
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Percent total
Average Score
or Feature
All students 27720 100%
Pre-test 12218 44.89% 6.9/20
Quizzes 23731 8.7% 7.1 /10
Final 1154 4.2% 25.6/ 40
MOOC only 18645 67.3% (100%)
Pre-test 4872 17.6% (39.9%) 5.8/20
Quizzes 496 1.8% (4.1%) 6.3/10
Final 215 1% (1%) 22.8/40
OLI registered 9075 32.7% (100%)
Pre-test 7346 26.5% (80.9%) 8.6/20
Quizzes 1876 6.8% (20.7%) 7.5/10
Final 939 3.4% 26.3/40
video 902 (96.1%) 164.4/4460
pages 939 (100%) 296.8/1942
activities 939 (100%) 387.2/695
pages 939 (100%) 182/1759
Table 1. Student participation in assessments and in course
features; average score or usage.
16912 students took quiz 1 and 1136 students took quiz 11. 2374
is the average across all 11 quizzes.
activity with an average of 127 and a maximum of 695
activities started (variable activities_started). Table 1
shows these same activity use statistics for the subset of
OLI registered students who took the final exam (939
Because activities sit on pages and students must go to
those pages to do the activities, we created a new measure
to represent pages accessed beyond those needed to get to
a page. A scatterplot between pageview and
activities_started indicated a lower bound on pages seen
for a given number of activities and the bound is
reasonably estimated at the maximum of 695 activities,
where no one did this many activities in fewer than 206
pageviews. We used this ratio of about 3.4 activities per
page to subtract out the pages due to activity access and
computed a new variable, non_activities_pageview, that is
arguably a more pure measure of the variation in the
reading students did.
After the exploratory data analysis, the results present a
search for potential causal relationships, including
estimates of the strength of these relationships, by using a
causal inference system called Tetrad [32]. Tetrad is a
program that creates, estimates, tests, predicts with, and
searches for causal and statistical models. Tetrad has a
distinct suite of model discovery search algorithms that
include, for example, an ability to search when there may
be unobserved confounders of measured variables, to
search for models of latent structure, to search for linear
feedback models, and to calculate predictions of the
effects of interventions or experiments based on a model1.
We used Tetrad to infer the causal relationships between
pretest score, course feature use variables (watching
videos, reading pages, doing activities), quiz scores, and
final exam score. To aid causal model search, we
specified some extra time-based constraints, particularly
that leader measures could not use earlier ones (i.e., the
final scores cannot cause quiz scores, quiz scores cannot
cause feature use, and feature use cannot cause pre-test
score). We used Tetrad’s PCD algorithm for causal
model search and we normalized the data (using a
correlation matrix input to PCD) so that resulting model
coefficients could be better compared and interpreted.
Predicting Course DropOut and Completion
From the total number of registered students (27,720),
only 4% take the final exam. Of the 9075 students who
registered for the OLI materials, only 10% took the final
exam. Such high attrition rates in MOOC courses are not
uncommon [12] as many students register for reasons
other than completing the course (e.g., determine their
interest in the material, check their knowledge on the
topic, etc.). But, we were interested in examining when
students dropout (i.e., is there a crucial point in the course
for students to not continue to participate) and what
factors might contribute to dropping out.
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Figure 2. The participation rate in quizzes among three
groups of students, registered in MOOC (N = 27720), also
registered in OLI (N = 9075), and also taking the final exam
(N = 939).
Figure 2 shows different participation rates among three
student groups: 1) the 27720 students who registered for
the course, 2) the 9075 students who registered to use the
OLI materials, and 3) the 939 of those MOOC+OLI
students who took the final. Quiz participation decreases
over time both for all MOOC students and for the
MOOC+OLI subset. The rate of quiz participation is
consistently higher for MOOC+OLI students than for
MOOC students in general, about twice as high.
Seriousness to take the extra step to register in OLI
appears not to be enough to explain greater MOOC+OLI
participation rates. If we further restrict the sample to
those who completed the first quiz (another indicator of
early seriousness), we still find the MOOC+OLI students
are more likely to take quiz 11 (18.5%) than the MOOC-
only students (14%). The biggest drop in participation
comes between quiz 1 and 2 with 43% of students
dropping overall (39% of MOOC+OLI and 50% of
MOOC only). Perhaps not surprisingly, the quiz
participation of the MOOC+OLI students who attended
the final exam (N = 939) is quite high with 98% taking
quiz 1 and 95% taking quiz 11.
We also explored whether quizzes’ participation and/or
quizzes’ scores can predict final exam participation. We
used a logistic regression model with final exam
participation as the outcome variable. The predictor
variables were Pretest participation and Pretest score and
22 others for participation in each of the 11 quizzes and
for scores on each of the 11 quizzes. Table 2 shows the
coefficients, standard errors and P values for all predictors
that are highly significant, at the p<0.01 level.
Participations in quizzes later in the course (i.e., quizzes 7
and 11) are, perhaps not surprisingly, good predictors of
final exam participation. Additionally, there is an
indication that how well students are doing in the course
may also be predictive. A student’s score on quiz 1, above
and beyond having merely taken it (and taken other
quizzes), is associated with higher final exam
participation. Given this is the first quiz, this result may
suggest that students that are either underprepared for the
course or are not engaging sufficiently with the first unit
materials to learn from them are unlikely to continue
through to the end of the course and take the final exam.
Factors Estimate Std. Error Pr(>|z|)
(Intercept) -6.34020 0.19564 < 2e-16 ***
Quiz 1 Score 0.24434 0.07698 0.0015 **
Took Quiz 7 or Not 2.95023 0.71933 4.11e-05 ***
Took Quiz 11 or Not 4.95794 0.69094 7.20e-13 ***
Table 2. Significant factors in a logistic regression model
predicting final exam participation.
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 AIC: 1469
Comparing Learning Outcomes of MOOC-only and
MOOC+OLI Students
The first measure of comparison was students’ final exam
scores. MOOC-only students (N=215) had an average
final exam score of 56.9% and the MOOC+OLI students
(N=939) averaged 65.7%. This difference is highly
significant based on a t-test (p < .001), however, applying
a t-test directly is not appropriate because the group score
distributions are not normally distributed. They are
skewed toward higher scores and include a number of low
outliers. We employ a simple transformation (a cubic) of
the measure (final_score3) to produce normal
distributions. Applying a t-test to the transformed data
once again yields a highly significant difference (p <
As mentioned above, the difference in these self-selected
groups may be a consequence of features of the students
rather than of the OLI instruction. Students who
registered to use the OLI materials may simply be better
students. One way to test for (but not completely
eliminate) the possibility of such a selection effect is to
build a statistical model using all the information we have
about student characteristics. We can then test whether a
difference based on OLI use still remains after accounting
for these other characteristics. In particular, we created a
linear regression with final exam score as the outcome
variable and, in addition to instructional group
(MOOC+OLI vs. MOOC-only), we included six other
student characteristic variables: pretest score, Quiz 1
score, occupation, age, education and gender. Because not
all students answered the survey, our sample is now
reduced to 551 students, 251 in MOOC+OLI and 301 in
MOOC-only. Table 3 shows only the significant
coefficients in the model and we see that Quiz 1 score and
education make significant independent contributions to
the final exam score and, importantly, the increase due to
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OLI use remains. None of the other variables (pretest
score, occupation, age, or gender) make a significant
independent contribution to the final exam score.
Estimate Std Error P-value
(Intercept) 16.90 3.32 5.21e-07***
OLI use 1.43 0.55 0.009 **
Quiz 1 score 1.06 0.11 <2e-16 ***
Education = PhD 3.96 1.84 0.032 *
Table 3. Significant factors in a linear regression predicting
final exam score.
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05
The model parameters indicate that students with a PhD
(N=41) get 3.96 more questions (out of 40) correct on
average than students in other educational groups, that
every extra point on the pre-test yields 1.06 more
questions correct, and that use of OLI (at least registering)
produces 1.43 more questions correct. Just because
students register to use OLI, does not guarantee that they
do. And, similarly, just because students are in the MOOC
does not mean they take advantage of the features it
provides, such as watching the lecture videos. The next
section investigates the log data to explore the impact of
estimated student use of such features.
Variations in Course Feature Use Predict Differences
in Learning Outcomes
Exploratory Data Analysis: Doing, not Watching, Better
Predicts Learning. As a method of exploratory data
analysis, we simply performed a median split on each of
our metrics of instructional feature use -- videos played,
pages accessed (beyond activities), and activities started.
A student is a “watcher” if they played more than a
median number of videos, a “reader” if they accessed
more than a median number of pages, and a “doer” if they
started more than a median number of activities. Figure
3a shows that the most frequent combinations are the
extremes, either being on the low half for all three, a non-
watcher-non-reader-non-doer shown in the leftmost bar
(N=201) or being on the high half for all three a watcher-
reader-doer shown in the leftmost bar (N=184). The next
most frequent combinations are reader-doers (N=120),
who are using the OLI features, and watchers (N=105),
who are using the distinctive MOOC feature, video
Which type of student appears to learn the most? Figure
3b shows the results for the total quiz score and indicates
that the doers do well on the quizzes (score of about 94
points) even without being on the high half of reading or
watching (the red bars are equally high). (Note, however,
that the doers tend to read and watch more than the non-
doers in the matched reading and watching groups.)
Those in the lower half of doing (the non-doers) do not
perform as well on the quizzes, but those who are on
either the high half of reading or watching do better (80
points) than those low on both (62 points). In other words,
doing the activities may be sufficient to do well on the
quizzes, but if you do not do the activities, you will be
better off at least reading or watching.
Figure 3. (a) Most students are either on the low half of
watching, reading, and doing (the first blue Neither column
for Non-doers) or high on all three (the last red
Read&watcher column for Doers), but many (>70) are
present in the other 6 combinations. (b) Doers consistently
do better in the total quiz score and next best is either
watching or reading. (c) Final exams appear to show greater
sensitivity to reading and watching further boosting the
benefits of doing, but doing is still the clear dominant factor.
Figure 3c indicates that, as for quiz score, a higher final
exam score is more typical of those on the higher half of
doing (about 28 points) and next best is either reading or
watching (about 26 points) with low on all being the
worst (23 points). In contrast with the quizzes, there is at
least a hint in the final exam score showing that simply
doing (26.7) is further enhanced by watching (27.4),
reading (28.2), and both (28.7).
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Quiz Final
Poor Excellent Poor Excellent
High watcher only 46% 5% 38% 15%
High doer only 13% 42% 13% 16%
High both 2% 52% 0% 31%
Table 4. Percent of students of different extreme types
(highest quartile) that do poorly or excellently on the quizzes
and final.
Table 4 summarizes an analysis of the extremes that starts
by splitting each of the watching, doing, quiz, and final
variables into four equal groups of students and focuses
on the lowest and highest quarters of these groups. The
first row in Table 4 illustrates how watching lots of the
videos but doing few activities leaves one quite likely to
do poorly on quizzes (46% of such students) or on the
final (38%) and quite unlikely to do an excellent job on
quizzes (5%) and final (15%). The second row shows how
simply doing a lot even without much watching of video
lectures avoids poor performance on quizzes and the final
(only 13% for both) and enhances excellent performance
on quizzes (42%). However, a lot of doing alone does not
facilitate excellent performance on the final (16%). As
seen in the last column of the third row, reaching
excellent levels of transfer to performance on the final is
one place where it appears that extensive video watching
is beneficial, but only for those who are also extensively
engaged in the learning by doing activities (31% as
opposed to 15% and 16% for those who are only high
watchers or high doers alone).
Why might high video watching along with high doing
aid excellent performance on the final, whereas lots of
doing without much video watching appears sufficient for
excellent performance on the quizzes? One possible
reason is that the final was created by the MOOC
professor and may have had items on it that are not
covered in the OLI material, but only in the lecture
videos. Learning by doing activities may generally better
support learning, but if certain items on the final involved
concepts or skills that were not required in those activities
but that were presented in the videos, then students that
watched lots of video lectures are more likely to get those
items correct. Alternatively, we find that replacing “high
watcher” with “high reader” in Table 4 produces similar
results, that high doing is enough to increase chances of
excellent quiz performance but it takes combining high
reading along with high doing to increase chances of
excellent quiz performance. In other words, it may not be
the video lecture per se, but the declarative content
present in video or reading, that appears important, on top
of lots of learning by doing, for excellent performance on
the final exam.
Is Course Feature Use Causally Related with Student
Outcomes? As introduced in the Methods, we used
Tetrad, a tool for causal inference, to evaluate whether
associations between key variables, pre-test, course
features (doing, reading, and watching), quiz total, and
final are potentially causal. Figure 4 shows the causal
model that a Tetrad search identified. The model is a
good one in that a chi square (df=7) test shows its
predictions are not statistically different from the data (p
= 0.39)2.
The model indicates direct causal impacts from all course
features, doing activities, reading materials, and watching
videos, to a higher total quiz score. The most influential
impact comes from doing activities, with a normalized
coefficient of 0.44 (a 1 standard deviation increase in
doing activities produces 0.44 sd increase in quiz score).
The strength of this relationship is more than six times the
impact of watching video or reading pages (both with
coefficients of about .065) and more than three times the
combined impact of watching and reading.
Figure 4. Tetrad inference of causal relationships between
pretest score, course features (doing activities, watching
videos, reading pages), total quizzes score, and final exam.
Looking at other features of the model, we see that a
higher pretest score directly causes a higher quiz score
(coefficient = 0.25). Thus, effects of course feature use
are adjusted for and above and beyond this influence.
Higher pretest also causes greater activity use, though
weakly (0.08). Within the three course features, more
reading causes both more doing and more watching, with
2 In Tetrad, the null hypothesis of the chi square test is that the
population covariance matrix is equal to the estimated covariance matrix
determined as a function of the free model parameters. The test
essentially asks whether the predictions for associations between
variables that are not linked in the causal graph are different from the
actual associations -- a model is good when the prediction is not
significantly different from the data.
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a larger coefficient for doing (0.39 > 0.12). The higher
influence of reading on doing may reflect the proximity of
these features within OLI pages, whereas the videos are
located in the MOOC proper. Higher quiz scores cause
higher final scores and this relationship is quite strong
(0.65). The final exam was developed by the MOOC
instructor whereas the unit quizzes more directly
correspond with the content of the associated OLI
modules. The strength of the connection suggests that
student-learning improvements as measured by the
quizzes (and highly influenced by doing more activities)
do well transfer to the final exam.
Our results on the limited value of video watching for
learning are particularly interesting given other results
suggesting that video watching behavior is predictive
of dropout [30]. Those who are motivated to watch
lecture videos may well be interested in the course
material and may stick with it, however, that is no
guarantee that those experiences produce any
substantial robust or lasting learning. Consistent with
our results, much work on intelligent tutoring systems
is premised on the power of learning by doing. A pre-
MOOC analysis of course elements found that
“electronic homework as administered by CyberTutor
is the only course element that contributes significantly
to improvement on the final exam” [22]. A recent
analysis of two MOOC data sets was less conclusive
“the wide distribution of demographics and initial skill
in MOOCs challenges us to isolate the habits of
learning and resource use that correlate with learning
for different students” [7]. One potential important
difference between some learning by doing activities in
those MOOCs (e.g., use of a circuit simulator) and the
ones provided by OLI is the availability of fast
feedback and hints in the context of doing. Such
interactive (not merely active) experiences may be
particularly effective for learning.
An immediate opportunity for future work is to evaluate
whether our results generalize to data from a second
offering of the same MOOC that was run in Spring 2014
using the same materials and teaching team. In addition,
during Spring of 2013, these OLI materials were used in a
variety of two-year institutions as part of the OLI
Community College evaluation project [13]. An analysis
of this data offers an opportunity to consider the
generalizability of these results beyond the MOOC
Our analysis to date has not taken advantage of all the
available data. In future work, we would like to also
explore how student involvement in peer-graded writing
assignments and in discussion forums is associated with
learning outcomes and dropout. We can also improve on
our estimates of the amount of watching, reading, and
doing students engage in by not only seeing how often a
video, page, or activity is started, but also estimate the
amount of time they spend on each (though variation in
availability of resource/activity use start and end times
makes doing so harder than it may seem).
If more time were spent doing activities than reading, our
results may simply be a consequence of this extra time.
Preliminary analysis of the log data shows that students,
on average, do more activities than read pages (387 vs.
297, respectively), but spend less overall time doing
activities than reading (21.6 hrs vs. 25.0 hrs,
respectively). In other words, it appears students actually
spend substantially less time per activity (3.4 min) than
reading a page (5.0 min). Given the improvement we see
in our learning outcomes and the exploratory analysis
results, this time analysis further supports the notion that
doing interactive activities has a greater impact on
learning than passive reading. It will be interesting to get
the results of a similar analysis to compare lecture video
watching time.
Although our analysis considers elements of watching,
reading and doing across the entirety of the course, a
more fine-grained analysis is likely desirable and it could
take advantage of the fact that learning materials in OLI
have been carefully mapped to specific learning
objectives and skills [cf., 15].
Going beyond this specific psychology MOOC, we look
to doing and seeing an expansion of this kind of analysis
to other MOOCs, online or blended courses that include
both passive declarative elements and interactive
problem-solving elements.
While many MOOCs do include questions and some
online and offline homework assignments, some have
argued that a key limitation of many online courses is that
they lack sufficiently rich, well-supported activities with
adaptive scaffolding for learning by doing [cf., 33, 18].
Our results support the view that video lectures may add
limited value for student learning and that providing more
interactive activities will better enhance student learning
We thank Dr. Anderson Smith and other members of the
Georgia Institute of Technology and Coursera teams that
were critical to developing and delivering the Psychology
MOOC. We used the 'Psychology MOOC GT' dataset,
accessed via DataShop [15] ( at
863. Support for developing and delivering this course
was provided by the Bill and Melinda Gates
Foundation. Support for data storage and analytics was
provided by LearnLab (NSF grant SBE-0836012).
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March 14–18, 2015, Vancouver, BC, Canada
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... While MOOCs offer opportunities to scale up learning (Pappano, 2012;Koedinger et al., 2015), these opportunities also present a challenge (Bulathwela et al., 2021;McAndrew & Scanlon, 2013). Meaningful learning is more likely to happen when learners are given opportunities to actively engage with the course, the material, and their peers (Deslauriers et al., 2011;Koedinger et al., 2015;Wieman, 2014). ...
... While MOOCs offer opportunities to scale up learning (Pappano, 2012;Koedinger et al., 2015), these opportunities also present a challenge (Bulathwela et al., 2021;McAndrew & Scanlon, 2013). Meaningful learning is more likely to happen when learners are given opportunities to actively engage with the course, the material, and their peers (Deslauriers et al., 2011;Koedinger et al., 2015;Wieman, 2014). Yet, it is challenging to design instruction to fit all and still enable active learning when considering the scale of a MOOC as well as learners' diversity (Brooks et al., 2021;Kizilcec et al., 2017;Roll et al., 2018). ...
Full-text available
Personalization in education describes instruction that is tailored to learners’ interests, attributes, or background and can be applied in various ways, one of which is through choice. In choice-based personalization, learners choose topics or resources that fit them the most. Personalization may be especially important (and under-used) with diverse learners, such as in a MOOC context. We report the impact of choice-based personalization on activity level, learning gains, and satisfaction in a Climate Science MOOC. The MOOC’s learning assignments had learners choose resources on climate-related issues in either their geographic locale (Personalized group) or in given regions (Generic group). 219 learners completed at least one of the two assignments. Over the entire course, personalization increased learners’ activity (number of course events), self-reported understanding of local issues, and self-reported likelihood to change climate-related habits. We found no differences on assignment completion rate, assignment length, and self-reported time-on-task. These results show that benefits of personalization extend beyond the original task and affect learners’ overall experience. We discuss design and implications of choice-based personalization, as well as opportunities for choice-based personalization at scale.
... Should these educational technology contexts be considered "favorable learning conditions" per Bloom's claim? These contexts are prime examples of a learning-by-doing approach that has been repeatedly advocated in different variations and with substantial experimental support, including "mastery-based learning" (15), "active learning" (16), "testing effect" (17), "formative assessment" (18), and "deliberate practice" (5,19,20). These contexts provide favorable learning conditions not only because of the active learning support, but also because of more particular features of learning interactions each with its own scientific basis. ...
... That up-front lectures and readings seem to produce limited performance accuracy is surprising given the great efforts educators continue to put into producing lectures and texts and given that most learners advocate explicit learning as the best way to learn (45). Books and then recorded lectures have facilitated broader dissemination of knowledge historically, but much emphasis on lecture recording remains today even in online course contexts where interactive practice is feasible and effective (cf., 20). A theoretical postulate consistent with limited accuracy after up-front verbal instruction is that human learning is not simply about the explicit processing, encoding, and retrieval of verbal instruction but as much or more about implicit or nonverbal learning-by-doing in varied practice tasks where interactive feedback is available (12). ...
Full-text available
Leveraging a scientific infrastructure for exploring how students learn, we have developed cognitive and statistical models of skill acquisition and used them to understand fundamental similarities and differences across learners. Our primary question was why do some students learn faster than others? Or, do they? We model data from student performance on groups of tasks that assess the same skill component and that provide follow-up instruction on student errors. Our models estimate, for both students and skills, initial correctness and learning rate, that is, the increase in correctness after each practice opportunity. We applied our models to 1.3 million observations across 27 datasets of student interactions with online practice systems in the context of elementary to college courses in math, science, and language. Despite the availability of up-front verbal instruction, like lectures and readings, students demonstrate modest initial prepractice performance, at about 65% accuracy. Despite being in the same course, students' initial performance varies substantially from about 55% correct for those in the lower half to 75% for those in the upper half. In contrast, and much to our surprise, we found students to be astonishingly similar in estimated learning rate, typically increasing by about 0.1 log odds or 2.5% in accuracy per opportunity. These findings pose a challenge for theories of learning to explain the odd combination of large variation in student initial performance and striking regularity in student learning rate.
... The ability to generate intermediate steps not only enables researchers to investigate model behavior but also new applications. For example, in personalized education and intelligent tutoring systems, these models have the potential to generate detailed, personalized solution steps as feedback to improve stu-dent understanding of the mathematical concepts and resolve misconceptions (Walkington, 2013;Karpicke, 2012;Koedinger et al., 2015). The recent GSM8K (Cobbe et al., 2021) dataset contains MWPs that come with 2 to 8 intermediate steps described in natural language, which provides us a good resource to study step-by-step solution generation. ...
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Full-text available
Solutions to math word problems (MWPs) with step-by-step explanations are valuable, especially in education, to help students better comprehend problem-solving strategies. Most existing approaches only focus on obtaining the final correct answer. A few recent approaches leverage intermediate solution steps to improve final answer correctness but often cannot generate coherent steps with a clear solution strategy. Contrary to existing work, we focus on improving the correctness and coherence of the intermediate solutions steps. We propose a step-by-step planning approach for intermediate solution generation, which strategically plans the generation of the next solution step based on the MWP and the previous solution steps. Our approach first plans the next step by predicting the necessary math operation needed to proceed, given history steps, then generates the next step, token-by-token, by prompting a language model with the predicted math operation. Experiments on the GSM8K dataset demonstrate that our approach improves the accuracy and interpretability of the solution on both automatic metrics and human evaluation.
The field of Artificial Intelligence in Education (AIED) cares by supporting the needs of learners with technology, and does so carefully by leveraging a broad set of methodologies to understand learners and instruction. Recent trends in AIED do not always live up to these values, for instance, projects that simply fit data-driven models without quantifying their real world impact. This work discusses opportunities to deepen careful and caring AIED research by developing theories of instructional design using computational models of learning. A narrow set of advances have furthered this effort with simulations of inductive and abductive learning that explain how knowledge can be acquired from experience, initially produce mistakes, and become refined to mastery. In addition to being theoretically grounded, explainable, and empirically aligned with patterns in human data, these systems show practical interactive task learning capabilities that can be leveraged in tools that interactively learn from natural tutoring interactions. These efforts present a dramatically different perspective on machine-learning in AIED than the current trends of data-driven prediction.KeywordsStudent ModelsSimulated LearnersComputational Models of LearningInteractive Task LearningCaring AI
Students use learning analytics systems to make day-to-day learning decisions, but may not understand their potential flaws. This work delves into student understanding of an example learning analytics algorithm, Bayesian Knowledge Tracing (BKT), using Cognitive Task Analysis (CTA) to identify knowledge components (KCs) comprising expert student understanding. We built an interactive explanation to target these KCs and performed a controlled experiment examining how varying the transparency of limitations of BKT impacts understanding and trust. Our results show that, counterintuitively, providing some information on the algorithm’s limitations is not always better than providing no information. The success of the methods from our BKT study suggests avenues for the use of CTA in systematically building evidence-based explanations to increase end user understanding of other complex AI algorithms in learning analytics as well as other domains.KeywordsExplainable artificial intelligence (XAI)Learning analyticsBayesian Knowledge Tracing
Over the last ten years learning analytics (LA) has grown from a hypothetical future into a concrete field of inquiry and a global community of researchers and practitioners. Although the LA space may appear sprawling and complex, there are some clear through-lines that the new student or interested practitioner can use as entry points. Four of these are presented in this chapter, 1. LA as a concern or problem to be solved, 2. LA as an opportunity, 3. LA as field of inquiry and 4. the researchers and practitioners that make up the LA community. These four ways of understanding LA and its associated constructs, technologies, domains and history can hopefully provide a launch pad not only for the other chapters in this handbook but the world of LA in general. A world that, although large, is open to all who hold an interest in data and learning and the complexities that follow from the combination of the two.
As educational data science (EDS) evolves and its related fields continue to advance, it is imperative to employ EDS to solve real-world educational challenges. One such challenge is to research how students learn and study effectively in digital learning environments and apply those findings to better their learning resources. The volume of educational data collected by digital platforms is growing tremendously, so it is a pivotal moment for EDS to be applied with an ethical approach in which the best interests of the learner are kept at the forefront. Learning engineering provides a practice and process to engage in EDS with a student-centered approach. In this work, we exemplify how the learning engineering process (LEP) guided large-scale data analyses to advance learning science (i.e., the doer effect), developed new artificial intelligence (AI)–based learning tools, and scaled both effective learning methods in natural educational contexts and automated data analysis methods—all in the service of students. The examples of analyses in this chapter serve to showcase how EDS—applied as a part of learning engineering—can validate learning science theory and advance the state of the art in learning technology.KeywordsEducational data scienceLearning engineeringCoursewareLearn by doingDoer effectArtificial intelligenceAutomatic question generation
Technical Report
Full-text available
ECAR’s report on e-learning incorporates results from a survey, focus groups, and interviews to provide a description of the current state of e-learning in higher education. In this report are insights into the challenges of e-learning, the concerns about e-learning that remain, the most important factors to consider in selecting e-learning technologies, how accreditors view and approach e-learning, and the specific steps institutions can take to make progress in their e-learning initiatives.
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Editor's Introduction Advanced educational technologies are developing rapidly and online MOOC courses are becoming more prevalent, creating an enthusiasm for the seemingly limitless data-driven possibilities to affect advances in learning and enhance the learning experience. For these possibilities to unfold, the expertise and collaboration of many specialists will be necessary to improve data collection, to foster the development of better predictive models, and to assure models are interpretable and actionable. The big data collected from MOOCs needs to be bigger, not in its height (number of students) but in its width—more meta-data and information on learners' cognitive and self-regulatory states needs to be collected in addition to correctness and completion rates. This more detailed articulation will help open up the black box approach to machine learning models where prediction is the primary goal. Instead, a data-driven learner model approach uses fine grain data that is conceived and developed from cognitive principles to build explanatory models with practical implications to improve student learning.
Online instruction is quickly gaining in importance in U.S. higher education, but little rigorous evidence exists as to its effect on student learning. We measure the effect on learning outcomes of a prototypical interactive learning online statistics course by randomly assigning students on six public university campuses to take the course in a hybrid format (with machine‐guided instruction accompanied by one hour of face‐to‐face instruction each week) or a traditional format (as it is usually offered by their campus, typically with about three hours of face‐to‐face instruction each week). We find that learning outcomes are essentially the same—that students in the hybrid format are not harmed by this mode of instruction in terms of pass rates, final exam scores, and performance on a standardized assessment of statistical literacy. We also conduct speculative cost simulations and find that adopting hybrid models of instruction in large introductory courses has the potential to significantly reduce instructor compensation costs in the long run.
MOOCs have fueled both hope and anxiety about the future of higher education. Our objective in this symposium is to surface and explore some of the open questions which have arisen in the MOOC debates. In this symposium, ten authors examine different aspects of MOOCs and technology to advance learning and learning research.
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