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news, articles, multimedia content, and even tourist
attractions (Smirnov et al., 2016). Recommenda-
tions can be made based on what people with simi-
lar profiles might like, the so-called collaborative fil-
tering (Sarwar et al., 2001), or based on user his-
tory, namely, content-based filtering (Van Meteren
and Van Someren, 2000). Content-based filtering is
based on finding content similar to what the user has
been consuming and then recommending new content
to them, while collaborative filtering recommends
content that has been consumed by other users with
a similar profile. In an educational context, recom-
mendation systems can be applied to e-learning tasks
for recommending resources such as articles, books,
podcasts, or videos to students with the aim to tutor
them and make them achieve better performance.
3 METHODOLOGY
In this research, we propose a methodology that al-
lows teachers to monitor the progress of students in a
course, identifying potential problem situations early
on, so that it is possible to offer differentiated treat-
ment to students who need it. Thus, we expect to
minimize problems such as demotivation, failure, and
student dropout, increasing the success rate of the
teaching-learning process as a whole.
We assume the course will be mediated by a
Learning Management System (LMS), in face-to-face
or distance education settings. LMSs often generate a
wealth of data about their use by students and teach-
ers that, if well explored, can become a valuable tool
for tracking student achievement.
In Figure 1, we present a general scheme of how
we believe the proposed methodology can intercon-
nect the different actors and entities of the teaching-
learning process. The LMS is the intermediary ele-
ment and teachers are responsible for providing con-
tent, tasks, quizzes, questionnaires and various ac-
tivities, which will be read, watched and solved by
students during a course. In this process of inter-
action of students and teachers with the LMS, us-
age statistics are generated, such as number of con-
tent views, comments made by students, percentage
of successes/failures for a task, number of attempts,
compliance with deadlines, among many other as-
pects. Those statistics are computed globally, by class
and individualized by student.
This Data Collection phase will generate educa-
tional datasets obtained from LMS use, which will
serve as input to an Educational Data Mining phase
where machine learning models will be used to pre-
maturely identify students who are likely to fail a
course, are candidates to drop out, disoriented as to
what content to study or which tasks to solve, among
other problems. Data mining will also enable stu-
dents to be classified into learning profiles so that they
can receive customized service. Finally, based on the
previous classification, we come to an Intervention
phase, in which semi-automatic preventive and cor-
rective actions will be triggered by the system, and
validated by the teacher, in order to rescue a student
with problems and increase their likelihood of com-
pleting the course successfully.
One point to consider is that students are individu-
als with different characteristics, interests, and goals,
so a given content or assignment that seems attractive
or simple to one person can be boring or complicated
to someone else. Therefore, it is important to iden-
tify the Learning Profile of the students enrolled in a
course, for example, by means of a questionnaire ap-
plied to each student when registering in the LMS.
Thus, content that is likely to be more attractive
to the profile into which a given student fits will be
recommended. Besides, if that student has any issues
during the course, he may receive corrective actions
that are more appropriate to his profile. We believe
that adjusting the content and tailoring corrective ac-
tions to each student’s learning profile or style are
essential, as it allows, in a context of such diverse
and numerous classes, to provide more personal, cus-
tomized treatment, as if the students had the teacher
by their side all the time. We even intend to adapt the
system interface to a student’s profile, thus seeking to
provide a differentiated and potentially more motivat-
ing user experience.
This approach does not intend to dismiss the fig-
ure of the teacher as a mediator and facilitator of the
teaching-learning process, quite the opposite. The
master will have even more tools at hand, stemming
from the methodology and products to be developed,
which will allow him to conduct the course more ef-
fectively, acting globally and also individually.
Another important point is that students’ profile,
initially collected from the application of question-
naires, can and should be adjusted throughout their
history in a given course or LMS, thus inferring new
behaviors and preferences that the students them-
selves not even perceived as their own characteristics
until then. We consider this adequacy of the content
and corrective actions to the student profile as an im-
portant contribution to be achieved in this research.
Although current technology allows the massification
of educational content, reaching an increasing num-
ber of students, it fails when considering that every-
one learns and feels content the same way, what can
often contribute to failure.
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