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On Using Learning Analytics to Track the Activity of Interactive MOOC Videos


Abstract and Figures

It is widely known that interaction, as well as communication, are very important parts of successful online courses. These features are considered crucial because they help to improve students’ attention in a very significant way. In this publication, the authors present an innovative application, which adds different forms of interactivity to learning videos within MOOCs such as multiple-choice questions or the possibility to communicate with the teacher. Furthermore, Learning Analytics using exploratory examination and visualizations have been applied to unveil learners’ patterns and behaviors as well as investigate the effectiveness of the application. Based upon the quantitative and qualitative observations, our study determined common practices behind dropping out using videos indicator and suggested enhancements to increase the performance of the application as well as learners’ attention.
Content may be subject to copyright.
On Using Learning Analytics to Track the
Activity of Interactive MOOC Videos
Josef Wachtler, Mohammad Khalil, Behnam Taraghi, and Martin Ebner
Educational Technology
Graz University of Technology
unzgrabenstraße 35A - 8010 Graz - Austria
Abstract It is widely known that interaction, as well as communication,
are very important parts of successful online courses. These features are
considered crucial because they help to improve students’ attention in a
very significant way. In this publication, the authors present an innova-
tive application, which adds different forms of interactivity to learning
videos within MOOCs such as multiple-choice questions or the possibil-
ity to communicate with the teacher. Furthermore, Learning Analytics
using exploratory examination and visualizations have been applied to
unveil learners’ patterns and behaviors as well as investigate the effec-
tiveness of the application. Based upon the quantitative and qualitative
observations, our study determined common practices behind dropping
out using videos indicator and suggested enhancements to increase the
performance of the application as well as learners’ attention.
1 Introduction
It is a common knowledge that interaction, as well as the communication, are
very important influencing factors of students’ attention. This indicates that
different possibilities of interaction should be offered at a MOOC 1in all possible
directions. So it is vital to offer some communication channels like e-mail or a
discussion forum, and in addition it is suggested that a form of interaction with
the content of the course itself is available [9]. [1]
The attention is considered as the most crucial resource for human learning
[5]. Due to that, it is from high importance to understand and to analyze this
factor. The results of such an analysis should be used to further improve the
different methods of attention enhancing [6]. Moreover, learning analytics plays a
major factor into enhancing learning environments components such as the video
indicator of MOOCs and finally acts into reflecting and benchmarking the whole
learning process [7]. In this publication, the usage of a web-based information
system which provides the possibility to enrich the videos of a MOOC with
different forms of interactivity (see Section 3) is presented. This paper covers an
experiment on a MOOC named Making - Creative, digital creating with children2
1short for Massive Open Online Course
2 en/maker.html (last accessed Jannuary
Copyright © 2016 for the individual papers by the papers' authors. Copying permitted only for private and academic purposes.
This volume is published and copyrighted by its editors.
SE@VBL 2016 workshop at LAK’16, April 26, 2016, Edinburgh, Scotland
and is attended by both, school-teachers as well as people who educate children
in non-school settings. It is scheduled in seven weeks with at least one video
per week. A detailed analysis of the activity of the attendees at the videos is
presented by Section 4.
Finally, this work aims to show how learning analytics could be done to
monitor the activity of the students within videos of a MOOC. In other words,
the research goal of this publication could be summarized to ”using an interactive
video platform to support students’ attention and to analyze their participation”.
2 Related work
In comparison to the approach shown by Section 3there are other services pro-
viding similar features.
First, there is the possibility to use the built-in features of Youtube3(e.g. text
annotations or polls) itself. However, the different methods of analysis are very
limited. A further tool is Zaption4which provides various forms of interactive
content for videos (e.g. multiple-choice questions) at planned positions as well
as a rich set of analysis possibilities. Unfortunately, it shows the position of the
interactions in the timeline of the video. This means that the users are able
to jump from interaction to interaction without really watching the video. In
comparison to that, a tool named EdTed5also offers the possibility to enrich a
video with questions. However, the questions are not bound to a position in the
video and furthermore, they could be accessed every time during the video.
The real-world pendant of interactive learning videos is known as ARS 6,
which enables the lecturer to present questions to students during the lecture in
a standard classroom situation [10] [4]. Based on that, it offers several possibil-
ities of analysis. It is well-known that an ARS has the power to enhance both,
students’ attention and participation [2]. This means that the addition of inter-
activity to learning videos tries to generate similar benefits as those generated
by an ARS.
3 Interactions in Learning Videos
To provide interactive learning videos a web-based information system called
LIVE 7first introduced by [3] is developed. It offers the possibility to embed
different forms of interaction in videos (e.g. multiple-choice questions). As indi-
cated above (see Section 1), the main purpose of these interactions is to support
the attention of the students. The functionalities of LIVE could be categorized
by the tasks of three different types of users.
3 (last accessed Jannuary 2016)
4 (last accessed Jannuary 2016)
5 (last accessed Jannuary 2016)
6short for Audience-Response-System
7short for LIVE Interaction in Virtual learning Environments
The first ones are normal users who could be seen as students. They are
only allowed to watch the videos and to participate to the interactions. Figure 1
shows a screenshot of a playing video which is currently paused and overlaid by
an interaction (1). To resume playing, it is required to respond to the interac-
tion which means that the displayed multiple-choice question has to be answered
in this example. Furthermore, it can be seen that there are some other control
elements on the right side of the videos (2). They could be used to invoke in-
teractions manually. For instance, it is possible to ask a question to the teacher.
Figure 1. A Screenshot of LIVE shows a video interrupted by a multiple-choice ques-
In comparison to that, the users of the second group are equipped with
teacher privileges. They are additionally able to embed interactions in the videos
as well as to view different forms of analysis. To add an interaction, the teacher
has to select its position within the video by using a preview of it or by entering
the position. With a dialog, the teacher can embed multiple-choice questions or
text-based questions in the video and furthermore, it is possible to add an image
to a question. [12]
The analysis consists of several parts. At first, there is a list of all students
who watched the video and for each student in this list it is shown how much of
the videos they watched. In addition, a chart shows the number of users (green)
and views (red) across the timeline of the video (see Figure 2). This chart could
be used to identify the most interesting part of the video. Furthermore, it is
possible to access a detailed analysis of each student. It shows the timeline of
the video and marks each watched part of it with a bar (see Figure 3). If such a
bar is hovered with the mouse-pointer, additional information is displayed. This
consists of the time of the joining and the leaving of this watched timespan in
relative as well as the absolute values. [12]
Figure 2. A Screenshot of the timeline analysis.
Figure 3. A Screenshot of the watched-parts analysis.
In comparison to these forms of analysis related to the watching of the stu-
dents, there is also a detailed statistic about the answers to the embedded ques-
tions. This means that for the multiple-choice questions, the answers of the
students as well as their correctness is displayed. For the text-based questions,
LIVE displays answers of the students and the teacher has to evaluate them
manually because text-based answers are impossible to check automatically. [12]
The third group of users are researchers. They are able to download different
forms of analysis as raw data. This means that they can select a video and obtain
the data as a spreadsheet (CSV 8).
Finally, the following list aims to give a summarizing overview of the features
of LIVE [3] [12]:
only available for registered and authenticated users
different methods of interaction
automatically asked questions and captchas9
asking questions to the lecturer by the learners
asking text-based questions to the attendees live or at pre-defined posi-
multiple-choice questions at pre-defined positions
reporting technical problems
different possibilities of analysis [11]
a detailed logging of the watched time-spans to point out at which time
a user watched which part of the video
8short for Comma-Separated Values
9short for Completely Automated Public Turing Test to Tell Computers and Humans
a calculation of an attention level to measure the attention of the stu-
raw data download for researchers
4 Evaluation
This section presents a very detailed analysis of the videos of the MOOC as
well as of the multiple-choice questions. For that, the data provided by LIVE is
evaluated using visualizations and exploratory analysis.
First, the delay of response to the questions provided by LIVE in the MOOC
videos during the seven weeks is demonstrated by two figures. Figure 4visualizes
a box plot. The x-axis records MOOC videos during the period of the course,
while the y-axis shows students’ delay of response in seconds. This period was
limited to 60 seconds. Students are categorized to certified students, who finished
the course successfully and applied for a certificate, and non-certified students. In
this figure, we tried to study the difference in behavior between both categories.
In some of the weeks, certified students took more time to answer the questions
such as in week 4 and week 7. For instance, certified students median in week 4
was 15 seconds, while the median for the non-certified students was 13 seconds.
Furthermore, there was 3 seconds difference in the median between certified and
non-certified students in week 7. Additionally, the median in week 1 and week 5
are typically the same with an insignificant variation between the first and the
third quartiles.
Figure 4. A box plot showing the reaction delays to multiple-choice questions.
In comparison to that, Figure 5visualizes a violin plot. The x-axis indicates
students’ status. This visualization summarizes the students’ status and the
delay of response time to the multiple-choice questions in all of the MOOC
videos. The thickness of the blue violin shape is slightly wider than the red one
in the (8-13) seconds range, which indicates the more time needed to answer
the questions. In addition to that, the non-certified violin shape holds more
outliers attributes than the certified division. It is believed from the previous two
observations, that certified students took less time in answering the questions in
general. This case can be explained as the questions were easy to answer if the
student were paying enough attention to the video lectures.
Figure 5. A violin plot summarizes the reaction delays to multiple-choice questions.
Figure 6displays the timespan division in percentage and the timing of the
first multiple-choice question represented as a vertical dashed line. Using this
visualization, we can infer the relevance timing of the first question to describe
the drop rate during videos. The questions were programmed to pop up after 5%
of any MOOC video. Students may watch the first few seconds and make skips
or drop out after that [13], and this can be seen in the plot where students are
dropping in the early 15% of the videos. To grab the attention of the students
and maintain a wise attrition rate, the multiple-choice questions were intended
to be shown randomly in the high drop rate scope. Further, week 6 was tested
to check the postponed question effect on the retention rate. The data in the
figure also shows that students do not drop out a learning video in the range
between 20%-80%, unless they replay it on that period and spend time on a
particular segment to understand a complex content. The promising outcomes
are seen with a stable attrition rate in the last four weeks when students are
offered an interactive content during the video indoctrinate process.
In Figure 7, the data is displayed in order to trace the video drop ratio of
each second in every video. The x-axis displays the percentage of videos. The
Figure 6. Timespan division in percentage and the timing of the first multiple-choice
Figure 7. Trace of the video drop ratio of each second in every video.
colored points specify the video name and the watchers count. While the black
shadowed points indicate number of views. For instance, it is obvious that the
data in the first three weeks shows more views per user which can be explained
as an initial interest of the first online course weeks. On the other hand, the
views nearly equaled the number of users from week 4 to the last week. Another
interesting observation is the slow drop rate during the videos in all of the weeks
despite the high drop in the last 2-3% of every video. A clarification of such
attitude is due to the closing trailer of every video which most students jump
over it.
Due to the independency of the examined MOOC, each video of this course
does not rely on the previous one. The activity of every video varies in every
week. For this reason, Figure 8shows activity of the total number of stop and
play actions in the MOOC videos. The blue points denote the certified students
while the orange ones denote the non-certified students. In fact, the first three
weeks reflect proper enthusiastic count of actions. We realized that there was a
distinct activity by the non-certified students in week 5. A reasonable clarifica-
tion is because of the interesting topic of that week which was about 3D-Printing.
However, their engagement becomes much less in the last two weeks, as this was
proven in other MOOCs’ videos analysis [8].
Figure 8. The activity of the total number of stops and plays in the MOOC videos.
5 Conclusion
Catching the attention of learners in online videos of MOOCs is an intriguing
argument across learning analytics discussions. With this publication, the usage
of an interactive video platform presenting videos of a MOOC is shown. It points
out the main functionalities of this platform as well as the participation and
the activity of the students. Additionally, we demonstrated an evaluation of
this system in order to examine its performance and describe the behavior of
students. Finally, the results show that the main concept behind latching on the
students’ attention becomes attainable through evaluating the questions’ content
and the interactions timing.
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