USING LEARNING TRACKING DATA TO SUPPORT
STUDENTS’ SELF-MONITORING
Madeth May and Sébastien George
University of Lyon, LIESP Laboratory of INSA-Lyon, 21 Jean Cappelle, Villeurbanne F-69621, France
Keywords: e-Learning, Tracking data, Data analysis and visualization, Data indicator, Computer-Mediated
Communication, Self-monitoring.
Abstract: This paper presents TrAVis (Tracking Data Analysis and Visualization Tools), a Web-based system,
implemented to assist the participants in the learning process in analyzing the tracking data of Computer-
Mediated Communication activities. While most of the existing systems in the same genre are exclusively
dedicated to the teachers and only a few are accessible by students, TrAVis is objectively designed and built
for both teachers and students. TrAVis is a technological solution that enables the students to monitor in
real-time the individual or group activity. It is also considered a “reflective tool” that helps students analyze
their own activities in relation to those of others. This paper focuses on (a) the visualization of students’
tracking data to enhance self-monitoring process and (b) the experiment we have conducted in an authentic
learning situation. It also discusses the feedbacks we received from the students regarding their perception
on the usability and utility of TrAVis.
1 INTRODUCTION
Researches in E-learning are involved in improving
learning environments by the use of technology.
They cover countless topics that place equal
emphasis on all three elements: technologies,
learning, and improvements in learning (Scott &
Vanoirbeek, 2007). As we progress, we witness a
big change of research interests in E-learning toward
the learning process and the participants. More
attention has been paid to the improvement of
technologies that better support participation and
interactivity (Manson, 2007). Those technologies
include Computer Mediated Communication (CMC)
tools, which are employed to extend the content and
interaction of a class because of their advantage in
providing users with a great variety of ways to
communicate between them. In fact, in a learning
context, communication has undoubtedly always
been an important part of the learning process.
Whilst it usually creates opportunities for learning to
take place, it also enables the sharing of information,
the confrontation of ideas and thoughts which
contribute to learning (Pearson & Sessler 1991;
Metallinos 1992; Ford & Wolvin 1993; Allen et al.
1999). More evidence to back up such argument can
be found in the research effort of Morreale &
Osborn (2000) along with a thorough study of nearly
one hundred articles, which emphasizes the
importance of communications in various contexts,
from the contemporary life to the specific learning
situations.
In distance learning, communications are made
on CMC tools and can be called as Computer-
Mediated Communication activities (CMC activity
in short). Making CMC activity is not only to
increase interaction between student and teacher, or
interaction among students, but also to compensate
the lack of face-to-face interaction. Berge & Collins
(1995) pointed out that CMC tool is recognized as
an essential element in distance learning and is
strongly recommended for both teachers and
students. Many research evidences proved that using
CMC tools allows the participants to achieve a better
learning performance (Chou & Liu, 2005),
overcome many traditional barriers of distance and
times (Bromme et al., 2005) and gaining flexibility
in learning (Dutton et al., 2002). However, if we
take a closer look at the use of CMC tools in
distance learning, CMC tool alone does not always
enable the participants to fully control their activities
the way they do in a traditional face-to-face learning
situation. As a matter of fact, the interactions
between the participants are not person to person,
but computer-mediated and online, which makes it
46
May M. and George S..
USING LEARNING TRACKING DATA TO SUPPORT STUDENTS’ SELF-MONITORING.
DOI: 10.5220/0003307500460055
In Proceedings of the 3rd International Conference on Computer Supported Education (CSEDU-2011), pages 46-55
ISBN: 978-989-8425-49-2
Copyright
c
 2011 SCITEPRESS (Science and Technology Publications, Lda.)

difficult, for example, for the teachers to supervise
the students’ activities. As for the students, they
could easily encounter difficulties regarding self-
monitoring if CMC tool is the only support they
have for conducting their learning activities. This is
due to the fact that CMC tools, from a technological
standpoint, were not originally built to allow
students to keep track of their own activity or to self-
monitor.
Acknowledging the practical issues related to
using CMC tools, we put our research efforts into
providing technical solutions to assist the
participants in the learning process, particularly the
students in terms of self-monitoring and self-
assessment.
This paper presents a part of our research work
that places an emphasis on TrAVis, which is
designed and developed for real-time analysis and
visualization of users’ tracking data on Web-based
CMC tools (i.e. discussion forums, chat, blog, wiki,
etc.). It is structured as follows. The second section
gives an overview of our research and discusses
some related works. The third section is dedicated to
a presentation of the technical aspects of TrAVis.
Some examples of tracking data analysis and
visualization are given in the fourth section to
demonstrate how students can use TrAVis to
monitor their own activities and those of others.
The experiment of TrAVis in an authentic learning
situation is presented in the fifth section along with a
discussion on the feedbacks we received from the
students.
2 RESEARCH CONTEXT
AND RELATED WORKS
2.1 Tracking Students’ Activities
Online learning through CMC requires a significant
investment of resources, and involves considerable
effort from various participants. For instance, the
teachers usually seek for effective pedagogical
concepts and strategies to foster the learning process
via CMC tools. The students, on the other hand,
often request for technical supports to enhance their
learning experiences, such as being able to observe
their own activities and to analyze them. But instead,
the supports they obtain still strongly rely upon their
teacher’s commitment and are usually constrained
by other factors related to distance and time.
Meanwhile, with the current support of CMC tools
that are often limited to communication means, the
students are compelled to neglect some important
facets of learning, such as self-monitoring when they
are in a distance learning situation. It is needless to
remind how crucial self-monitoring is to the students
in increasing more appropriate behaviours in the
classroom, boosting completion of homework
assignments, improving both academic performance
and social skills, and reducing disruptive behaviours
(Hallahan & Kauffman, 2000).
Having studied these issues, we addressed the
importance of tracking CMC in learning situations
for the benefits of tracking data to online tutoring
and learning enhancements. An explicit tracking
approach has been proposed for the implementation
of tracking systems for a great variety of CMC tools.
It focuses on a tracking mechanism capable of
observing different types of user action and
interaction on CMC tools. We discussed in detail
this research effort in May et al. (2008). Later, we
continue our research by focusing on exploiting the
collected data to design “data indicators” that
support students in terms of gaining awareness and
making assessment of their learning activities,
outcomes, effectiveness, etc.
For the sake of comprehension, data indicators
refer to a piece of information, generally presented
in a graphical form and may feature the process of
the considered “cognitive system” learning activity,
the characteristics or the quality of the interaction
being performed on a technology-based learning
environment (Dimitracopoulou, 2005). Obtaining
data indicators is a complex process. It involves
many phases, among which the design of each data
indicator at the conceptual level. Later in this paper,
we discuss about the data indicators that serve for
self-monitoring, how we design and visualize them.
2.2 Visualizing and Analyzing
Communication Activities
Data indicator gives considerable assistance to the
participants in the learning process. It provides
means of abstracting, synthesizing, inferring and
viewing the information that it features. Found in the
following research works, there are three main types
of assistance: awareness, assessment and evaluation.
Each of the three types is correlated to the nature of
data indicator and the system that computes it.
ARGUNAUT (Groot et al., 2007) is an
awareness tool that provides data indicators of
online discussions between users (i.e. students and
teachers). Its main objective is to support the
teachers in their endeavor to increase the quality of
synchronous discussion in collaborative learning
situations. The original indicators of users’
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47

discussion were first seen in the research work of
Gerosa et al. (2004). They displayed the links of
discussions in a tree form, giving an awareness of
the discussion dept and how users interacted among
each other. Later, we have seen iHelp (Brooks et al.,
2006), another awareness tool that aims to improve
the user collaboration throughout their
communication activities. For example, iHelp assists
the teacher in supervising the communication
process between students.
Regarding assessment tools, they are dedicated
to the analysis of various aspects of a
communication activity. As seen in Donath (2002)
and Shaul (2007), to assess the productivity of a
student in a group discussion, the teacher can
analyze the participation level of that student (e.g.
number of messages posted or replied in a
discussion forum). In the same context, Gibbs et al.
(2006) suggested a tool that offers means to analyze
the temporal and spatial dimensions of students’
discussions. The proposed data indicators are
illustrated in a form of activity map, allowing
teachers to observe and assess communication
characteristics such as the degree of participations of
a student.
Beside gaining awareness and making
assessment of student activities, evaluating students
is also needed. From a teacher standpoint, the
evaluation, in the context of CMC, can be carried
out based on the communications made among the
students and the results of the communications.
Mazza & Dimitrova (2003) suggested CourseVis to
the teachers who wish to visualize the social aspect
of student discussion. Not too far from CourseVis in
terms of information visualization, DIAS
(Discussion Interaction Analysis System) of Bratitsis
& Dimitracopoulou (2005) is a Web-based system
that supports the teachers in analyzing students’
interactions on a discussion forum. Data indictors
computed by DIAS mainly serve for the evaluation
of the social dimension of each student. Last but not
least, GISMO, is a Graphical Interactive Student
Monitoring tool, developed by Mazza & Botturi
(2007). GISMO visualizes behavioural and social
data of student’s activities on a discussion forum.
Its objective is to help the teachers evaluate the
involvement of the students in the communication
process during the course activities on a learning
platform (e.g. Moodle).
Our primary observation regarding the existing
tools is that most of them are exclusively dedicated
to the teachers. Only a few are accessible by the
students. Moreover, students are usually allowed
minimal access to the tools due to their restricted
user rights, as well as their roles in the learning
process. As a result, students always receive less
support in visualizing and analyzing their CMC
activities. This is not to mention that most of the
existing tools were not intentionally built to enable
students to perform self-monitoring.
Other observation is relevant to the assistance of
the data indicators proposed by each tool. It is worth
mentioning that a communication activity consists of
a variety of user interactions and contents exchanged
over a CMC tool. Therefore, it needs to be described
with adequate and pertinent information to help
students identify the level of interactions of their
communication activities. At this point, we are
suggesting that self-monitoring starts with acquiring
an awareness of the activities being carried out at
different levels, recognized as individual or group
activities (discussed further in section 3). Indeed, it
would make more sense for the students to observe,
assess or evaluate their own activities in relation to
others.
The study on the existing tools leads us to
propose TrAVis, which is objectively designed not
only for the teachers but also for the students. Plus,
TrAVis is distinguished from the existing tools by
its capacity in computing substantial data indicators,
allowing users to efficiently self-monitor and
analyze both the process and the product of an
activity (i.e. how an activity is carried out and what
the output is.)
3 TRAVIS DESIGN
AND DEVELOPMENT
3.1 Overview
TrAVis is a technological solution that enables users
to directly access to the tracking data repository via
a Graphical User Interface, to compute the data
indicators, and to visualize them in different visual
forms and scales. To support student self-
monitoring, TrAVis offers three tools to monitor in
real time the ongoing communication activities.
More particularly, TrAVis is also a “reflective tool”
or in other words, a guide giving students an insight
on their interactions with others, thus allowing them
to make assessment of several aspects of both
individual and group activities (e.g. social,
cognitive, behavioural aspects). For instance,
TrAVis allows the students to acquire an overview
of their personal learning progress, their
participation rate in social interactions, or other
statistical data from their communication activities.
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The development of TrAVis is guided by a
number of rules that lead to an achievement of good
quality engineering process in relation to the
architecture design and the evolution of TrAVis for
further usage. While TrAVis is an independent
platform, designed to be applied to a wide range of
CMC tools, it is also built upon multi-component
architecture. The significant advantage of such
architecture is that each component can be
individually modified. Moreover, it is practical for
us to be able to add extra features or new functions
to improve a particular component without having to
alter the whole architecture of TrAVis. Further
information regarding TrAVis’ architecture can be
found in May et al. (2010).
For the reasons of flexibility and accessibility,
TrAVis is developed with Web-based technologies.
Our main objective is to share TrAVis with the E-
learning community, a part of which, regularly seeks
additional technological solutions to enhance
tracking data analysis and visualization practices. On
top of that, TrAVis is carefully designed to be
accessible by non-computer specialists who often
request for technical support in using a particular
tool to make use of tracking data. Thanks to Web-
based technologies, the interface of TrAVis is
flexible, allowing users with limited technical skills
to easily manipulate it. For example, users have
choices between manually filling the visualization
parameters and selecting them from a preset list.
Figure 2 shows a screenshot of a visualization tool,
among others, currently available to TrAVis users.
3.2 TrAVis Design Approach
Having adopted a mixture of iterative and
participative approaches in the design of TrAVis,
several changes have been made to the latter during
the progress of our research. Each version of TrAVis
was built to improve the data indicators at a
conceptual level, their graphical forms and their
visualization techniques.
In respect of the conceptual level of data
indicators, we refer to the research efforts of
Mangenot (2008) that focused on different levels of
user interactions during CMC activities in learning
situation. More precisely, Mangenot distinguished
the four levels of interaction – aggregation,
discussion, cooperation and collaboration, which
reflect the form or the modality of a communication
activity. For instance, while the aggregation level
refers to the activities of an individual user, the
collaboration level refers to the collaborative
activities of a small group of users.
Our main objective is to propose different sets of
data indicators to support the visualization and
analysis of each level of interaction. Accordingly,
we identify at first the significant information
describing the latter and how it is a represented in a
visual form easily interpretable by the users.
Another crucial aspect of data indicator design at
the conceptual level is that every single indicator
from the inferior levels can be reused in the superior
levels (cf. figure 1), thus enabling TrAVis to
compute additional indicators.
Aggregation
Discussion
Cooperation
Collaboration
Figure 1: Four suggested levels of data indicators.
For example, every indicator in the aggregation
level is found in the discussion level and the
combination of indicators from both aggregation
and discussion levels are included in the cooperation
level; and so on. We present in section 4.2 the
proposed data indicators with example of
visualization.
Regarding the visualization techniques in
TrAVis, our main goal is to provide users with
flexibility in constructing the visual forms of the
indicators. Hence, we choose to render as many
indicators as possible in a form that varies from
statistical data in tabular format, to synthetic
information in graphical representation. Talking
about flexibility, we also add visualization variables
that allow the transformation of the indicators,
depending on their type, from one visual form to
another. For instance, the “user” variable enables
indicators of an individual user, or multiple users, to
be visualized either separately or together. Plus, an
indicator can also be viewed with a specific date and
time or even within a period of time (i.e. an interval
of time). Having such flexibility in TrAVis is to
provide students not only ease of control in
formation visualization, but more importantly an
efficient way to self-monitor.
4 TRAVIS TO ENHANCE
SELF-MONITORING
In this section, we give some examples of data
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49


Figure 2: A screenshot of a visualization tool of TrAVis.
indicators dedicated to support self-monitoring on a
discussion forum. It should be noted that discussion
forum is a reference CMC tool that has been used in
our case studies and experiment. Nonetheless, our
research covers a variety of CMC tools, both
synchronous and asynchronous.
4.1 Real-time Monitor a Student’s
Activities
One of the three visualization tools of TrAVis is
called “Time Machine” due to the technical capacity
of retrieving the information from the tracking data
repository and computing data indicator on the fly.
This makes it a particularly efficient tool for users
who wish to observe in real-time the ongoing
activities on the discussion forum.
Figure 3 illustrates the view screen of Time
Machine with the list of the activities of a user
(Tdelille). With this view, we can move up and
down the activity list (A) and update it in order to
get the most recent activities performed by
Tdelille. Each activity is represented as a
horizontal bar and in a unique colour. When we
select an activity by clicking on a bar, the detail
information of the activity is displayed at the right
part of the screen (B) with an “extra menu” (C),
allowing us to view other activities that are related to
the current activity.
In figure 3, we are viewing an activity of
Tdelille while reading a message “Outils et
modes de collaboration” in the forum
“Scénario de communication”. From that
view, we choose to display who else read the same
message that Tdelille is reading (D). Each
sphere shown in portion (D) of figure 3 represents an
activity of displaying a message and the diameter of
the sphere is proportional to the time spent by each
user reading the message. The distance between two
Figure 3: List of student’s activities displayed on Time Machine view screen.
A
B
C
D
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spheres represents the time gap between two
different readings. The colour of the sphere indicates
if a message has been displayed, read partially or
entirely. For instance, the green sphere notifies us
that the user read the message by having moved the
vertical scrollbar downward to the end of the page.
Regarding self-monitoring, Time Machine supports
students in navigating among the past and the
current activities with or without a specific time slot.
It can be used as a “User Control Panel” that enables
students to access to other types of indicators. As
shown in figure 4, an indicator that features the
statistical data related to four other different
activities on a discussion forum.
Figure 4: Overview of a student’s activity on a discussion
forum.
Time Machine is useful for students to monitor
very closely their activities and their interactions
with the rest of the group. It gives a quick perception
of their individual ongoing activities, which also
enables time-dependent analysis of several aspects
in relation to their personal progress and
participation in the discussion with other students.
4.2 Analyze Students’ Levels
of Interaction
Earlier in section 2.2, we suggested that self-
monitoring can be carried through an analysis of
different levels of interactions among the students.
In this regard, TrAVis provides tools to compute
data indictors for students to analyze their levels of
interaction from an individual and a group
perspective. Presented in a radar graph, each
indicator can be restricted to one single user (figure
5) or extended to multiple users (figure 6).
Figure 5 gives an example of aggregation data
indicators of a user Tdelille. The five data points
of the radar graph summarize the following
activities: connection frequency, threads started,
messages posted, message replied, and message
quoted.
Figure 5: Data indicator for aggregation level.
As shown in figure 5, data indicators at the
aggregation level reflect the students’ activities
being performed for mutual benefit. They are
commonly used to describe the activities of each
individual student but in the context of pooling the
resources in the discussion group.
The data indicators at the discussion level refer
to quantitative information regarding user interaction
(e.g. number of messages posted in a discussion
forum) and to the content exchanged throughout the
communication activities (e.g. message content,
document, etc.). In practice, analyzing the discussion
indicators leads to an identification of the level of
social interaction and the activeness of each student
in the group. For example, from figure 6, the number
of forums a student participated could reflect the
interest of the student in making discussions in the
forums, which belong to other groups or are
dedicated to other discussion topics. Meanwhile, the
number of messages a student read and posted in the
forum could reveal how active the student was in
interacting with others.
More interestingly, the visualization of users’
communication activities is not limited to a single
activity, a single user or a single group of users.
Data indicators at the cooperation level feature
group activity being carried out to reach a common
goal. They are most useful for students to identify
the part of their contribution comparing to the rest of
the group.
Data indicators at the collaboration level focus
on the product of group activity within a defined
time span. Due to this condition, the analysis of
collaboration level of students’ activities is time-
dependent and usually realized from a group of
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51

Figure 6: Data indicator for discussion level.
users’ perspective. Figure 7 gives an example of
collaboration level of two groups of students on
three forums that have the same structure, dedicated
to the group discussion to perform the same
collaborative task. Each radar graph, filled in with a
distinct colour, gives a quick perception of the forum
and its access frequency, number of threads, files,
messages, etc. In practice, the analysis of
interactions among group discussion leads to an
evaluation of various aspects of the collaboration
level of each group. For instance, figure 7 shows that
group A has more intense interaction than group
B in almost the three forums. Thus, it 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 messages posted or files
created and shared.
Thus far, we would like to emphasize that
TrAVis offers means to the students to self-monitor
by visualizing data indicators of CMC activities in
different manners. However, the interpretation of
each data indicator is still reliant on each student’s
personal point of view and analysis objective.
GroupA
GroupB
Figure 7: Data indicator for collaboration level.
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5 TRAVIS IN AN AUTHENTIC
LEARNING SITUATION
5.1 Experiment Set-up
In order evaluate TrAVis as well as to study the
impact of TrAVis in an authentic learning situation,
we have conducted an experiment with both teachers
and students. A total of 13 students and 3 teachers of
FFL (French as Foreign Language) from Stendhal
University of Grenoble III participated in the
experiment, which lasted for three months. It is
worth mentioning that FFL is a two-years-
professional-master course to train students to be
tutors, specialized in French language teaching.
With the support of Moodle and a discussion
forum, the students undertook several learning tasks
by making group discussions. Throughout the
experiment, TrAVis has been used by the teachers to
monitor the interactions between the students. The
students, on the other hand, used TrAVis to visualize
their progress in the group activities and to compare
their profiles with the rest of their group members.
5.2 Discussion on Students’ Feedbacks
At the end of the experiment, we used a
questionnaire to study the feedbacks of the
participants. Only 7 out of 13 students have
responded to the questionnaire despite several
invitations. While statistical data from the
questionnaire are interesting, students’ comments
are the most significant and beneficial to the
qualitative analysis of the usability and utility of
TrAVis.
5.2.1 Usability of TrAVis
Despite the fact that most of the students are non-
specialist computer users, they find TrAVis easy to
use, as mentioned in the comments below:
“The tool is very handy. I can instinctively use it
from the beginning and without a user’s guide”.
This comment is very interesting as it points out
that even without a user’s guide, the student could
easily operate TrAVis. From a developer standpoint,
it is important for us to ensure TrAVis the most
practical to the users so that they will not have to
spend time exploring how to use it, but start to
exploit immediately, its functionalities and services.
Some other students expressed in a few words to
give their appreciation on TrAVis interface design.
As found in the two following quotes:
“I found the interface very cheerful and
colourful. There are many buttons but they all
seem to be necessary to enable the information
searching at a specific point.”
“The interface is very pleasant to use.”
Besides the good evaluation scores and positive
comments on using TrAVis, some minor difficulties
were also stated as follows:
“Even if I appreciated the interface, and gave it
a good score, it took me a while to understand
the functionality of the project (TrAVis)”.
“At the beginning, I had a little difficulty
understanding the purpose of each tool.”
In spite of having created a technical guide to
assist the users in manoeuvring different tools of
TrAVis, we recognize that some practical issues
might eventually occur when users get to experience
TrAVis for the first time. From the experiment, we
found out that the difficulties students had were
commonly not related to using TrAVis, but to the
unfamiliarity of the graphical data indicators and the
associated analysis concepts.
5.2.2 Utility of TrAVis
In terms of evaluating the utility of TrAVis, we
focus on the design approach of the proposed data
indicators and their visual forms. Below, we quote
some comments, reflecting how students perceived
the utility of the data indicators and the impact of
using TrAVis in their online learning practices. It is
important to mention that some of the students have
already been practicing their teaching activities
alongside their FFL training courses. As yet, their
comments, as presented below, describe their
appreciation on TrAVis from both student and
teacher standpoints.
“It allows the teacher to analyze and evaluate
dynamics and practices of his students. Besides
the frequency of connections, the 4 indicators
(aggregation, cooperation, discussion,
collaboration) allow the online tutor to evaluate
the engagement and the learning motivation of
each participant.”
“TrAVis allows visualizing the trajectories of
practices so the tutor can proceed to an
instructional adjustment in a realistic situation.”
“I am positive about the innovative learning
approach that the use of TrAVis may imply… It
gives me a whole new perspective on practice,
learning, identity dynamics and motivation.”
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Such comments drew our attention to one crucial
aspect regarding how the four levels of data
indicators actually help the teachers not only to
identify the different levels of interaction among the
students, but mostly to evaluate the engagement and
the learning motivation of each student. Another
student had been a little more specific on the use of
the data indicators from a student perspective.
“From the perspective of a learner, but also of a
tutor, we can technically observe a group of
learners and their activities on the tool (i.e.
forum) as well as monitor the participation of
each individual. When we manage a group,
taking into account the participation of the group
is difficult, which can be a contributing factor to
the failure of the project. Therefore, a tool like
TrAVis could quickly become interesting and
certainly essential.”
The evaluation of TrAVis also reveals some
issues related to the representation of the data
indicators. As expressed in the following comments,
the visual forms of some data indicators cause some
difficulties in the information interpretation, which
obviously require the participants to spend more
time on the visualization.
“I would say that it is not obvious at first glance.
It took me quite a while, and I need some
practice and concentration to figure out what I
could make use of each functionality.”
“Some graphics, such as indicators for
collaboration, are for me a little difficult to
understand (having said that, I only spent a few
minutes to visualize those indicators and I have
not actually used them).”
To sum up, the evaluation we made on TrAVis
turned out to be very positive. The data from the
questionnaire showed good appreciation of the
students on TrAVis. In fact, the students particularly
appreciated the technical capacity of TrAVis in
computing graphical data indicators with significant
information related to the communication activity.
Additionally, both students and teachers provided us
with significant feedback on the issues related to
TrAVis they encountered during the experiment,
which are most helpful for the improvement of
TrAVis in both technical and practical aspects.
However, they did not provide us with information
to evaluate whether or not the proposed data
indicators reflect the reality of the CMC activity.
6 CONCLUSIONS
The major contribution of the research work
presented in this paper focuses on TrAVis, a
technological solution to enhance self-monitoring
processes in distance learning. The design of data
indicators is another important contribution of the
ongoing research effort. We have proposed different
set of data indicators to increase the ease of use in
analyzing students’ CMC activities. More
importantly, TrAVis and the proposed data
indicators reveal an original concept of analyzing in
real-time the levels of interaction (i.e. aggregation,
discussion, cooperation and collaboration) of an
individual or a group of students during the CMC
activity.
To conclude, comparing to the existing systems,
two distinctive characteristics of TrAVis are the
accessibility and the production of data indicators.
Indeed, TrAVis is not only dedicated to users with
different backgrounds and experiences in using
computerized systems, but also customizable to
users with limited technical skills. Furthermore,
while most systems are only built for the teachers,
TrAVis is objectively designed for both teachers and
students. In regard to self-monitoring, TrAVis
provides a new experience of visualizing tracking
data in multiple visual forms and in different scales.
The experiment we conducted has been a
valuable opportunity for us to put TrAVis into an
authentic learning situation. We were able to
demonstrate to the participants the benefits of using
TrAVis in their actual practices. While the
experiment was considered a success, we also
achieved our main goals – evaluating TrAVis from
the point of view of both teachers and students of
FFL. Our future work places a focus on the
improvement of TrAVis. We are currently working
with other research colleagues from other disciplines
to conduct an experiment in which TrAVis will be
used to analyze more complex users’ interactions.
We are also expecting that the upcoming experiment
will help us explore how TrAVis can really be
beneficial to online teaching and learning
enhancement.
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