Learning Tracking Data Analysis

How Privacy Issues Affect Student Perception on e-Learning?

Madeth May

, Sébastien Iksal

and Claus Alexander Usener

LUNAM University of Maine, Avenue Olivier Messiaen, 72085 Le Mans, France

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

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

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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

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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

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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

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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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