Learning Tracking Data Analysis
How Privacy Issues Affect Student Perception on e-Learning?
Madeth May
1
, Sébastien Iksal
1
and Claus Alexander Usener
2
1
LUNAM University of Maine, Avenue Olivier Messiaen, 72085 Le Mans, France
2
University of Münster, Leonardo-Campus 3, 48149 Münster, Germany
Keywords: Privacy Issues in E-Learning, Privacy Threat, Tracking System, Tracking Data, Data Analysis.
Abstract: Research works from the past ten years have demonstrated that technologies could in many ways enhance
learning experience. Meanwhile, technologies can also create obstacles to the latter. For instance, using
tracking approaches on the majority of e-learning platforms to monitor learners’ activities raises many
privacy questions. As for learners, knowing that their personal data are being used, even for educational
purposes, they could radically change their perception on e-learning technologies. This paper presents a
study on privacy issues in e-learning, based on both existing research findings and an experiment that we
have conducted with the participation of students from three universities in France and one university in
Germany. The study covers two main aspects. First, it outlines various tracking approaches in e-learning.
Second, it analyzes how the participants perceive the use of their tracking data and the related privacy
issues. The major contribution of this paper is the awareness-raising of privacy concerns, which are often
overlooked by researchers and e-learning content providers.
1 INTRODUCTION
In 2011, when we first presented a study on security
and privacy issues in e-learning (May and George,
2011a), we pointed out the lack of data protection
measures from learning content providers. We also
discussed how users relied on trust when accessing
online learning platforms, and how technical
solutions still had their limitation in terms of privacy
protection. Since then, we continue to expand our
research scope by focusing more on users and their
perception on the use of their personal data in
various educational settings. The study presented in
this paper combines existing research findings with
the empirical data acquired throughout the
experiment that we have conducted with the
participation of students from three universities in
France and one university in Germany.
The purpose of this paper is to share scientific
findings based on field studies and empirical data.
Our research team has no intention to make any
claim regarding how to definitely solve privacy
matters that one might encounter accordingly to a
variety of factors, including institution’s policies and
regulations, learning contexts and cultural points of
view. Nonetheless, we hope that the discussion made
in this paper could raise awareness of the issues in
question, which are often neglected in the research
efforts that involve user tracking and data analysis.
This paper is structured as follows. The second
section provides an overview of our research work
that emphasizes on an explicit tracking approach to
efficiently monitor users’ activities on e-learning
platforms. A general discussion on user tracking
approach is made in the third section. It is based on a
number of related works, which help us gain a
broader perspective on what causes privacy
concerns. The fourth section is dedicated to a
presentation of our experiment. Data analysis and
commenting on results are made in the same section.
In the last section, we draw a conclusion and
highlight future work.
2 RESEARCH CONTEXT
2.1 User Tracking Approach to
Enhance e-Learning Experiences
E-learning has been evolving rapidly, from purely
Web-based to mobile and ubiquitous learning
experiences, thus providing even more personalized
154
May, M., Iksal, S. and Usener, C.
Learning Tracking Data Analysis - How Privacy Issues Affect Student Perception on e-Learning?.
In Proceedings of the 8th International Conference on Computer Supported Education (CSEDU 2016) - Volume 1, pages 154-161
ISBN: 978-989-758-179-3
Copyright
c
2016 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
solutions that better suit each individual needs. For
that reason, institutions, teachers and learners
seemingly embrace e-learning (Popovici and
Mironov, 2015) and consider it among the most
innovative learning mediums (Gaur et al., 2015). In
fact, within the past ten years, we witness a strong
ongoing growth of research interests in e-learning
(Scott and Vanoirbeek, 2007) and an emergence of
technologies that better support user interaction
(Gamage et al., 2014) and content sharing (Lau et
al., 2013).
In order to make e-learning more efficient in
terms of student monitoring, other research efforts
like (Corbi, 2014) focus more on user tracking
approach that consists of collecting data of users and
of their activities throughout the learning
environment. By doing so, the teachers are able to
follow, for instance, the activities being undertaken
by the students and to observe their behaviors on
learning resource consumption (Gómez-Aguilar et
al., 2015). As for the students, having records of
their own activities allows them to keep track of
their individual progress, their interactions among
other students as well as their achievements during
the learning session (Qahmash, 2013).
Acknowledging the contributing factor of “user
tracking approach” to high quality teaching and
learning guidance in e-learning as pointed out by
(Jermann et al., 2001), researchers and learning
content providers choose to integrate systematically
a tracking system in their educational settings. To
back up this claim, a more recent study of (Alowayr
and Badii, 2014) reviews a variety of learning
platforms that make use of students’ tracking data
for different purposes, among which are student
assessment and evaluation. Further evidence on how
tracking approaches are broadly used to help
enhance e-learning can be found in the research
work of (Burdescu and Mihăescu, 2010; Jeske et al.,
2014; Mehra, 2015).
Implementing a user tracking system in
e-learning has been done in numerous ways as
identified by (Corbi, 2014; Kim et al., 2008;
Popescu and Cioiu, 2012; Pozzi et al., 2007). With
the progress being made in regard to the tracking
mechanism, the technique of collecting data has
become more sophisticated and powerful while
being effortless to be deployed in an existing e-
learning environment. In the meantime, such
progress has increased privacy questions, which lead
to a situation where privacy protection is becoming
crucial for users, researchers and e-learning content
providers.
2.2 Tracking Data Analysis
Our research team works on Technology Enhance
Learning (TEL) and studies numerous research
questions related to “Modeling the Observation of
Usage Tracks and their Analysis” (Choquet and
Iksal, 2007). A part of our research work involves
user tracking on e-learning platforms where
Computer-Mediated Communication tools (i.e.,
forums, chat, newsgroups, etc.) are widely used to
foster learning activities and to compensate the lack
of face-to-face interactions among the participants
(Berge and Collins, 1995). We proposed an explicit
tracking approach to assist the conceptual design and
the implementation of a tracking system for a variety
of Web-based communication tools (May et al.,
2008). The proposed approach was built upon a
tracking mechanism that simultaneously collects
fine-grained data from both client and server sides.
The technical aspects of the tracking approach in
question can be found in (May et al., 2010a).
Another part of our research effort focuses on
tracking data exploitation where research challenges
were studied in (May et al., 2011). Our goal is to
make use of the tracking data in order to help the
participants in exploring their past and ongoing
activities on a learning platform. With the technical
support of UTL (Usage Tracking Language) (Iksal,
2011) in terms of data analysis and visualization, the
participants could not only examine their activities,
but also make an assessment of their effectiveness
and achievements.
The need for data analysis and visualization tools
can be briefly explained as follows. With the current
support of e-learning that prioritizes content sharing
and user communication (Gamage et al., 2014; Lau
et al., 2013), the participants are compelled to
neglect some fundamental facets of online learning,
such as self-monitoring. Not to mention the online
interaction between the participants that makes the
supervision task very laborious for the teachers. Last
but not least, while the students usually encounter
difficulties in getting feedback on their own
activities, the teachers, on the other hand, are often
constrained by the lack of technical assistance to
conduct a proper analysis on the students’ tracking
data.
Having studied these issues, we addressed the
importance of technical support in tracking data
analysis and visualization in order to enhance
e-learning experiences for both the teachers and the
students. Therefore, we have designed and
developed TrAVis (Tracking Data Analysis and
Visualization tool) for the teachers who are in need
Learning Tracking Data Analysis - How Privacy Issues Affect Student Perception on e-Learning?
155
of supervising students’ activities (May et al.,
2010b), and also for the students who seek to self-
monitor during their learning sessions (May and
George, 2011b).
To illustrate how tracking data analysis could
contribute to e-learning enhancement in terms of
student monitoring, we give an example of a “data
indicator” representing a group activity on a
discussion forum below. For the sake of
comprehension, the “data indicator” refers to a piece
of information, extracted from a set of tracking data.
Generally computed in graphical representations, a
data indicator features the process of the considered
“cognitive system” learning activity, the
characteristics or the quality of the interaction being
performed in a learning environment.
Figure 1: An example of data indicator that analyzes the
participation level of two groups of students.
Figure 1 gives an example of a visualization of
two groups of students on three forums that have the
same structure, dedicated to group collaboration.
Each radar graph, filled in with a distinct color,
provides a quick perception of the forum and its
access frequency, number of active participants,
threads, messages, and files, etc. Hence, the teacher
can make use of these indicators to analyze the
interactions among the students. On top of that, the
given quantitative information allows the teachers to
evaluate the collaboration level of each group. For
instance, Figure 1 shows that group A has more
intense interactions than group B in almost the three
forums. Such indications can be used to (i) compare
the participation rates of both groups during the
collaborative task or (ii) to evaluate the productivity
rates of one group in relation to another, according
to the number of created threads and shared files.
More data indicators and their analysis can be found
in (May et al., 2011).
To sum up, data indicators computed from
e-learning tracking data provide means of
awareness, assessment and evaluation of a learning
situation. However, obtaining data indicators
involves a complex procedure that starts with a
tracking technique. Indeed, the latter is crucial to the
whole data gathering process and always has an
impact on the production of quality and substantial
data indicators. Consequently, most tracking
techniques that are robust and efficient in collecting
users’ data are at the same time very intrusive
(Corbi, 2014; Popescu and Cioiu, 2012). To gain a
broader perspective on how tracking approaches
could cause privacy issues, the study in the
following section covers a number of research works
with examples of how tracking data are used.
3 RELATED WORKS
3.1 Understanding the Correlation
between User Tracking and Privacy
Issues
Using data indicators in the analysis process enables
one to synthesize, infer and interpret the information
that it features. While it gives considerable
assistance to the teachers in the tasks of monitoring
online learning, it also creates major drawbacks for
students. For example, some students who are
cautious about privacy matters would become
apprehensive of being traced and of having no
control over how their personal data are being
exploited. To better understand this phenomenon,
we studied a number of research works where data
indicators computed from e-learning tracking data
are being exploited by the teachers to gain
awareness, to make an assessment and to evaluate
students’ activities.
The first is Argunaut (Groot et al., 2007), which
is an awareness tool, built to keep track of online
interactions. It provides data indicators of
collaborative learning activities, thus allowing the
Group A
Group B
CSEDU 2016 - 8th International Conference on Computer Supported Education
156
teachers to examine the behavioral aspects of each
individual student during a collaborative task. iHelp
(Brooks et al., 2006) is another awareness tool that
shares the same characteristics as Argunaut. iHelp
assists the teacher in supervising the communication
process among the students. Besides gaining
awareness, making an assessment of students’
activities is also compulsory. For that matter, (Shaul,
2007) suggested an analysis of various aspects of
individual or group activities (e.g. social
interactions). On the other hand, (Gibbs et al., 2006)
proposed a platform that offers means to analyze the
temporal and spatial dimensions of students’
interactions.
Regarding the evaluation tools that provide data
indicators on students’ activities and their outcomes,
(Mazza and Dimitrova, 2003) introduced CourseVis
to teachers who wish to study and evaluate the social
aspect of each student in a learning session. Not too
far from CourseVis in terms of data analysis,
GISMO, a Graphical Interactive Student Monitoring
tool (Mazza and Botturi, 2007), proposes another
way to visualize behavioral and social data of
students’ activities. Its objective is to help teachers
evaluate the involvement of the students during the
course activities on a learning platform (e.g.
Moodle).
For a more complex learning environment like
MOOC, (Coffrin et al., 2014) made used of data
indicators to classify student types and to analyze
students’ engagement and performance throughout
their learning process. Last but not least, Glass
(Gradient's Learning Analytics System), a Web-
based platform by (Leony et al., 2012), offers the
possibility to keep track of students’ activities and to
evaluate their performance in a given learning
context.
Thus far, our primary observation regarding the
existing tools is that most of them aim to better
support the teachers in exploiting students’ tracking
data. To do so, they explore every possible piece of
information related to the student activity in order to
accordingly generate significant data indicators on
the latter. The intrusive characteristics of each tool
allow, on the one hand, a pertinent analysis on
students’ activities, but causes major privacy
concerns on the other hand (Bandara et al., 2014).
While some recent research efforts like (Esposito,
2012) and (Ivanova et al., 2015) took into account
the need of users in controlling how their personal
data are being used, only a few are accessible by the
students. This is due to their restricted user rights
from a technical standpoint, as well as their roles in
the learning process. As a result, the students always
comply with the regulations of a learning platform
and put their trust on the latter (Juan Carlos Roca et
al., 2009). Consequently, the privacy concerns
remain to be addressed as the students are not
always in the position to determine what data to
share and who to share with.
Another observation is relevant to the strong
focus of the previous works, placed on the efficiency
of the tracking approach and the data indicators. The
privacy concerns seem to be overlooked even though
they have a direct impact on student behavior in
e-learning as studied by (Brown, 2008). Sharing the
same concerns as (Kanuka and Anderson, 2007), our
research team attempts to study how privacy issues
perceived by the students could make changes to the
behavioral aspects of their activities. For instance,
the confidentiality and anonymity can alter student
engagement and performance as an individual or a
group. Without necessary protection measures,
students are becoming too afraid of being exposed to
what meant to help them learn in the first place as
found in the studies of (Bandara et al., 2014).
3.2 Identifying the Privacy Concerns
According to (Klobucar et al., 2007), privacy
concerns are mainly caused by the use of
technologies. As a matter of fact, with the growth of
new platforms, new learning opportunities can be
created along with new problems. The participants
usually require technical knowledge of how
technologies work in order to understand the privacy
levels and threats. Nevertheless, the lack of
information and technical support in that matter
causes the most privacy concerns.
Learning service and content providers also bear
the responsibility of intensifying the privacy issues.
The students frequently ask the question of how
their personal data are stored and protected by the
learning content providers. As found in the study of
(Anwar and Greer, 2006), the students are primarily
concerned with trust assessment of learning
environments they are using, and with the protection
of their sensitive data.
The study of (Kanuka and Anderson, 2007)
pointed out that privacy issues are also related to the
participants’ consent, data ownership, confidentiality
and anonymity. The students expressed their
concerns regarding how information collected
throughout the learning process would be kept
secure and private. The confidentiality is part of the
privacy protection that refers to students’ rights to
control the access of their tracking data as well as
other information about them. Regarding the
Learning Tracking Data Analysis - How Privacy Issues Affect Student Perception on e-Learning?
157
anonymity, most students are unaware of their right
to request a removal of any characteristics (i.e.,
name, address, affiliated institution, geographical
area, etc.) that would allow them to be identified.
Our findings reveal that most students regret not
being part of the decision making on what
information to be collected, what to be used, and for
what purpose. Despite the compromise they have to
make when consuming resources on an e-learning
platform, students expect to have a choice to accept
to be traced, to deny the use of their data or to limit
access to some users.
Privacy issues also concern the security threats
of technologies we are using. Indeed, students are
exposed to a risk of data and identity theft. Such
issues make students doubt the confidentiality and
data protection measures proposed by their affiliated
institutions or learning content providers. Research
evidence can be found in the study of (Ben Arfa
Rabai et al., 2012).
Our study also takes an interest in mobile
e-learning where security and privacy threats remain
challenging despite technological progress made on
ubiquitous learning. This is due to the fact that
granting access to mobile devices on learning
contents opens doors to security threats that have not
been taken into account by the learning service
providers. The diversity of mobile devices and their
security protection measures are varied in
accordance with the operating system, the
application used and the users’ own measures to
protect their privacy. Research data from the study
of (Yong, 2011) cover the privacy preservation for
mobile e-learning. Yong pointed out the security
threats regarding ubiquitous learning and the privacy
preservation techniques for the students.
To summarize, security and privacy levels differ
in various learning environments and depend on
types of learning activities being conducted by the
participants. In practice, it is not always
straightforward or simple to promise absolute
privacy, confidentiality and anonymity when using
open e-learning environments. However, identifying
clearly the privacy levels and their relative
protection measures allows us to set rules and
policies in terms of student tracking.
4 CASE STUDY
4.1 Set-up and Participants
On top of the study we made on existing research
findings, we have conducted a semi-controlled
experiment where TrAVis, the tool mentioned
earlier in section 2.2, was used to analyze and
visualize tracking data collected on a Moodle
learning platform. Our main goal is to acquire an
overview of student perception on privacy issues
when their personal data are being used in an
authentic learning situation. Our clear intention is to
consult the students who are naturally concerned
about their data in an actual practice setting instead
of interviewing some random students. As a matter
of fact, every student who participated in our
experiment uses online learning platforms on a
regular basis. A total of 178 students from three
universities in France and one university in Germany
participated in our study.
4.2 Procedure
The participants were assigned the task of using
Moodle to organize their group activities. They were
also encouraged to use a discussion forum, already
integrated into Moodle, to perform their
communication activities. Depending on their
affiliated university, the participants had between
one and two weeks to complete the assigned task.
They were then asked to use TrAVis to analyze their
personal data gathered during the period of the
experiment. We also provided technical assistance to
the participants in choosing tracking data to analyze
and in visualizing data indicators on their past
activities.
At the end of the experiment session, the
participants were solicited to answer a questionnaire,
which emphasizes on two main points: (i) their
perception of privacy issues in e-learning, and (ii)
their request for privacy protection measures. Other
aspects of the questionnaire will be studied in our
future work.
4.3 Experimental Data
Having acknowledged the necessity of protecting the
participants’ personal data used in our study, we
choose to discuss in this paper the findings from a
general perspective. Experimental data will be
presented without distinguishing the groups of
participants, their respective university, and
academic background.
The most significant data from the questionnaire
that reflect how students perceive the privacy issues
in e-learning are illustrated in figure 2. What gets
our attention the most is the belief of the participants
that an anti-virus or an anti-spyware would help
them overcome the privacy issues in
CSEDU 2016 - 8th International Conference on Computer Supported Education
158
e-learning. Indeed, 34% of the participants claimed
to have a good protection system that prevents their
personal data from being collected. Such
misunderstanding is a big part due to the lack of
technical knowledge on how a tracking mechanism
works. It is also because the privacy issues are very
confusing for the participants. For instance, the most
frequently asked question during our experiment
was whether or not they were tracked when using a
browser in incognito or private mode to access their
online learning environment. In fact, most
participants do not have a clear perception on the
tracking process and its correlation to the privacy
concerns.
Figure 2: Student perception on privacy issues
in e-learning.
Although the majority of the participants seem to
figure out the most common security aspects in
e-learning technologies, they still have difficulties
identifying the related privacy threats. As confirmed
the data from the fourth and fifth rows in Figure 2,
only 21% claimed to have knowledge of the tracking
process and considered it without harm to their
private data. Respectively, 29% admitted that they
have neither privacy nor security preoccupation in
e-learning. On the contrary, over 78% disagreed
with the previous claims and felt unsafe when using
e-learning regardless the tracking process being
deployed or not.
Figure 2 also reveals interesting data that
supports our hypothesis regarding the impact of
user-tracking approach on user behavior in
e-learning. 68% of the participants expressed their
fears towards a learning environment with an
integrated tracking system. The participants
recognized that the latter had sometimes affected
how they perform certain types of activities. For
example, they suggest limiting private activity or to
reduce public intervention like on a discussion
forum, so that they would leave the least of their
traces possible on an open e-learning environment.
The second analysis we made on the data from
the questionnaire focuses on privacy protection
measures as seen by the participants, to help them
get beyond privacy concerns. Table 1 shows the
most demanding features regarding personal data
protection, consent agreement, anonymous use of
learning services, ethics legislation, and awareness
raising. On a scale of 0 to 5, the participants
expressed the least and the most important privacy
protection measures.
Table 1: Most requested features in terms of privacy and
personal data protection.
Importance level
0 1 2 3 4 5
Awareness
raising
0% 2% 15% 15% 33%
35%
Avoidance of
personal data
2% 5% 10% 16% 18%
49%
Data protection 6% 11% 15% 22% 18%
28%
Anonymous
access
10% 7% 18% 13%
33%
19%
Consent
agreement
0% 3% 12% 33% 14%
38%
Ethics
legislation
19%
29%
14% 13% 13% 12%
Interesting information can be retrieved from
Table 1. Examples include “avoidance of personal
data” and “consent agreement”, which are both
strongly relevant to privacy concerns in e-learning.
In fact, consent is one of the keystones of privacy
research practices in e-learning. Somehow, we were
surprised to learn that most of the participants had
never been reached out by anyone to sign a consent
form. Yet, they have been regularly using Moodle,
and their tracking data have been exploited in both
educational and research settings. The data from
Table 1 also shows that the participants consider
“user data protection” and “anonymity” among the
most important privacy provisions. As for “personal
data protection”, the participants requested to be
informed of the tracking process. According to the
participants, being aware of the latter is one of the
key facts to reduce privacy concerns.
If taken a closer look at how the participants
perceive tracking approach in e-learning, while 49%
of them claim that user tracking as a big threat, only
12% believe that ethics legislation could help them
control the visibility and the use of their sensitive
data. Interestingly, we have found similar results in
our previous study (May and George, 2011a) that
user tracking is not welcome even when users
receive personalized content and assistance in return.
To wrap up, this study enables us to gain a
broader perspective of the most crucial aspects
regarding privacy concerns in e-learning. While we
still need to conduct more analysis on the
experimental data we have acquired, the early
Learning Tracking Data Analysis - How Privacy Issues Affect Student Perception on e-Learning?
159
findings point to the most critical measures to
undertake to keep users informed of the privacy
issues and to help them avoid confronting one. The
study we made thus far also inspires us to explore a
proper solution for our research work, which
implicates user tracking and data analysis.
5 CONCLUSIONS
The research effort we presented in this paper
analyzes existing findings and experimental data
obtained from a case study on privacy issues in
e-learning. While attempting to demonstrate, with
research evidence, the benefits of a user tracking
approach to e-learning enhancements, we also point
out the necessity of gaining an insight on the privacy
concerns that most participants encounter in their
daily learning activities. Therefore, we hope to raise
awareness of researchers, pedagogical teams and
other e-learning practitioners in terms of user
tracking and data analysis.
We also address the lack of guidance for the
participants to acquire a better understanding on
privacy levels and threats. Data from our study
reveal that the participants have a very negative
perception on e-learning technologies when it comes
to privacy and data protection.
We recognize that avoidance of personal data is
the most requested privacy provision, enabling
students to anonymously access to e-learning
platforms. However, we should also point out that a
learning application aims at assisting students and so
they cannot act in full anonymity. For that reason,
participants in our study were always informed of
the tracking process and given the right to control
access to their data. On top of that, we always have a
clear policy regarding the use of student tracking
data in research and instructional purposes. For
instance, the consent agreement is compulsory for
the students and only authorized and anonymous
data are used in our publications.
Our future work will focus on a more in-depth
analysis of the current experimental data to explore
other aspects like ethics in e-learning. We are also
attempting to quantify and qualify the impact of the
privacy issues on the behavioral, social and
cognitive aspects of online learning. To do so,
research colleagues from France, Germany and
Greece are collaborating on an experiment to study
the evolvement of privacy questions and their
associated threats by taking into account both ethical
and cultural points of view.
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