NeuroK: A Collaborative e-Learning Platform based on Pedagogical
Principles from Neuroscience
Fernando Calle-Alonso
1
, Agustín Cuenca-Guevara
1
, Daniel de la Mata Lara
1
,
Jesús M. Sánchez-Gómez
2
, Miguel A. Vega-Rodríguez
3
and Carlos J. Pérez Sánchez
4
1
Research & Development Department, ASPgems SL, Spain
2
Catedra ASPgems, University of Extremadura, Spain
3
Department of Technologies of Computers & Communications, University of Extremadura, Spain
4
Department of Mathematics, University of Extremadura, Spain
Keywords: Educational Data Mining, e-Learning, Learning Analytics, Learning Management Systems, Neurodidactics,
Computer-Supported Collaborative Learning, Social Learning, Social Networks.
Abstract: The use of online education platforms has grown extensively and most education centers and companies use
them for their learning programs. Although technology has changed the learning environment, the
pedagogical model has mostly remained the same as it was many years ago. Therefore, another education
paradigm should arrive to online platforms in a generalized way. In this paper, NeuroK is presented as a
new e-Learning platform leveraging the latest technologies and, moreover, implementing new tools that
support pedagogical principles from neuroscience. While most of traditional platforms focus on content,
content management and applying teacher-centred methodologies (everything goes through the teacher),
NeuroK focuses on students, and uses collaborative learning, motivational processes and a “learning by
doing” perspective to achieve a long-term relevant learning. The proposed NeuroK framework describes the
already implemented tools and the new ones to be included in next versions. An active R&D process allows
new methodologies from the fields of Learning Analytics, Data Mining and Social Learning to be proposed
and implemented. The main contribution of this platform is to deploy a significant improvement in the e-
Learning process based on a neurodidactics approach and data analysis research results.
1 INTRODUCTION
During the last decade, there has been significant
changes in the educational field, especially
strengthened by new technological developments.
Tools as e-learning platforms or Learning
Management Systems (LMS) are now common for
educational institutions and companies. Indeed,
NMC Horizon Report (Johnson et al., 2014) pointed
out that online, blended and collaborative learning
will be widely adopted in the short term, and that
learning analytics environments, using predictive
modelling, will be adopted in the mid-term.
The main advances happened around online
training frameworks, enabling people to learn almost
everything remotely and costless. This new concept
of learning is called e-learning. It is defined as a
remote way of learning which facilitates and makes
more flexible the teaching/learning process, adapting
it to the skills, needs and availability of each
different student. E-learning saves time, money,
materials, and resources by keeping everything
online. As technology continues to push e-learning
forward, education and training will become more
popular and reach wider audiences. But it is not only
the technology alone what makes e-learning so
important (Haythornthwaite et al., 2016), but also
the inclusion of innovations from different fields
such as the use of educational gaming and
gamification (De Marcos et al., 2016), big data
techniques (Anshari et al., 2016), collaborative
learning (Masud, 2016), among others.
There are many e-learning platforms allowing
the communication and interaction between
teachers, students and the study contents. They
include different learning systems such as instructor-
led learning, e-books, tests, video tutorials and so
on. And they can be served from web browsers,
mobile apps or desktop applications mainly (Fallon
and Brown, 2016). Also, e-learning platforms can be
550
Calle-Alonso, F., Cuenca-Guevara, A., Lara, D., Sánchez-Gómez, J., Vega-Rodríguez, M. and Sánchez, C.
NeuroK: A Collaborative e-Learning Platform based on Pedagogical Principles from Neuroscience.
DOI: 10.5220/0006378705500555
In Proceedings of the 9th International Conference on Computer Supported Education (CSEDU 2017) - Volume 1, pages 550-555
ISBN: 978-989-758-239-4
Copyright © 2017 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
extended with administrative resources, and these
systems are called Learning Management Systems.
In this paper the interest will be focused on web-
based applications.
Some of the most recognized platforms in this
field are commercial as Blackboard, WebCT,
OSMedia, Saba, eCollege, Fronter, SidWeb, E-
ducativa or Catedr@, among others. Nevertheless,
the market of open source solutions has grown a lot
in the last years and platforms as ATutor, Dokeos,
Claroline, dotLRN, Moodle, Ganesha, ILIAS or
Sakai are as known and useful as the commercial
ones. In some cases, offering even more
functionalities than commercial solutions.
Massive Open Online Courses (MOOCS) have
also experienced a great improvement with
platforms as Udacity, Coursera, Udemy, edX,
Ecaths, Wiziq or Edmodo. But this kind of platforms
are more limited than the general ones mentioned
before. They are focused only on video-based
lessons and tests/exercises, with the purpose to
obtain badges demonstrating some kind of
knowledge.
Analyzing the market for all these applications, it
can be highlighted that more than 90% of the US
higher education institutions are using LMS’s (De
Smet et al., 2016). But OECD reports that
universities use LMS for administrative and
communicative purposes, instead of offering new
pedagogical ways of teaching (Dalsgaard, 2006). So,
there is still a lot to research to improve learning
processes, experiences and students’ satisfaction.
One of the main problems for online education is
the attrition and dropout rate of students. Many
researchers are focusing on this subject (Monteiro et
al., 2016). The issue is that students motivation fall
down after the first two weeks and they leave the
course. Also, the completion rates are too small,
especially for MOOCs where the completion rate
almost always falls between 2% and 5%, and in few
occasions exceeds 10% (Jordan, 2015). Moreover,
the information learnt by the students is forgotten in
a short period of time (Bacon & Stewart, 2006).
The issue underneath all the advances in
education is that technology has changed a lot, but
the way of teaching remains the same. Platforms
such as Moodle, Blackboard or even MOOCs have
just displaced traditional education from classrooms
to websites, but without changing the way of
teaching knowledge.
Neurodidactics (Anastasia 2016; Sabitzer, 2011),
also called brain-based teaching, tries to overcome
classical education with some new tools, and starting
to teach from a different point of view. “Knowledge
cannot be transferred, it must be newly created in
the brain of each student” (Roth, 2004). According
to this, neurodidactics proposes a learner centred
education. It avoids just giving materials to study,
instead of that, it offers examples, associations and
linking information to build knowledge. The reason
is that just giving new information, the long term
memory is not activated. The more often
associations and links are used and practiced, the
better the knowledge will be acquired (Westerhoff,
2010). The magic recipe to learn with the long term
memory is motivation (Di Ges
ù & Seminara, 2012;
Rivas, 2009), which gives the students the incentive
to learn and the ability to concentrate (Hermann,
2009 and Harandi, 2015). There are several ways to
achieve this goal, for example, using gamification,
learning by doing, flipped classroom, cooperative
learning, small group learning, peer tutoring, and the
use of real life challenges, among others (Hattie,
2009).
Most of the current e-learning platforms don’t
implement any of these tools to manage motivation
as a keystone of the education, nor follow the
neurodidactics perspective. Therefore, there is a
research opportunity in the e-learning field. The
overall objective of this paper is to address on-line
education from the perspective of the new trends in
e-learning and the principles of the neurodidactics
provided by the proposed new platform NeuroK
(https://neurok.es/en). This could provide an
important step in changing traditional online
learning paradigm, allowing to learn in a
collaborative and cooperative way by means of an
attractive and motivating social network
environment. In addition to the already implemented
tools, there are many others that will be incorporated
in the next versions. These tools are based on new
research lines in the field of Learning Analytics and
Social Learning that match the neurodidactics
approach.
2 NeuroK FRAMEWORK
After many successful experiences in software
development, ASPgems started collaborating with
some Universities trying to develop an innovative e-
learning solution. NeuroK platform was built based
on the latest methodological advances of
neurodidactics (Sousa, 2014; Edelenbosch et al.
2015). As other social networks, it pays special
attention to debate, new opinions and engaging
features as notifications, hashtags or mentions. All
NeuroK: A Collaborative e-Learning Platform based on Pedagogical Principles from Neuroscience
551
of them encouraging to learn by motivation (Waelti
et al. 2001).
NeuroK focuses on the student, not on the
content or the teacher like traditional learning
paradigms do. The students interact and collaborate
to build the information they will learn. They
choose, analyze, compare materials, and argue about
the proposed learning units. They also peer review
the activities of their classmates, and give opinions
about them. The teachers are not expendables, as
other online platforms intend. They are not eligible
for expense reduction. In NeuroK, the teacher has a
fundamental role as a “learning guru”, providing the
course criterion, the main guidelines to follow,
managing the information provided by the students,
and personally supporting them. He tries to achieve
the goals of the course, following the path the
students set up, and redirecting according to his
experience.
The NeuroK environment is a social network. It
gives the teachers the tools to teach by doing. The
knowledge is built by all the participants of the
course in a continuous way, thanks to their
comments, favorites, reviews, documents sharing,
cooperative problem solving and much more. This
kind of social learning boosts their motivation and
makes easier reaching a long term learning.
The main features of NeuroK are oriented to
serve the three C’s: Communication (to share the
knowledge), Community (to connect students and
build groups) and Cooperation (to propose ideas and
solve problems with cooperative learning).
NeuroK is a cloud platform always accessible
from any device. It is very easy to use, and the
learning curve is very soft, according to the students’
opinions who have already tested it. It supports from
small groups to a large number of students. Many
different kinds of documents can be published from
Youtube, Vimeo, Drive, Dropbox, or local materials
from students and teachers.
Every course starts on the home page (see Fig.
1). The central space is devoted to the social activity,
the same as Facebook or Twitter do, denoting the
importance given to collaboration and interaction
among students. The students follow the teacher and
other students who they think are especially
relevant. The social interactions appearing in the
central timeline can be filtered including everything,
just the people they are following, or just the
teacher. This collaborative way of creating contents
by publishing documents, debating, solving
problems and peer based rating, strictly follows the
neurodidactics principles, and it is very attractive
and motivating to the students (Elsenbaumer, 2013).
They see NeuroK as another social network, easy to
use, engaging, offering new materials and comments
all the time, and challenging their skills to solve
problems. Students are intended to learn with
motivation, like if they were playing.
Figure 1: NeuroK home page.
The main actions to take in NeuroK are accessed
from the home page. At the top, the one-on-one
contact and the notification buttons can be found.
Just below, there is a small summary about the user
participation on the course. In the central panel the
timeline shows the latest publications, but the user
can also filter by only his contributions, favourites
(marked before), or the noticeboard
(communications from the platform or the teacher).
The student can directly read, comment, recommend
or mark as favorite any publication. It is very easy
and intuitive to interact giving opinions, rating
others comments, or even peer reviewing activities
from other classmates.
In the left panel the access to Learning Units can
be found. It includes the proposed contents to
generate a debate, Learning Activities and practical
questions and problems about the contents of each
learning unit. Everything to “learn by doing” both in
an individual or collaborative way.
Figure 2: Global statistics.
Some other important options in the left panel are
Library (with contents shared by the teacher), Travel
CSEDU 2017 - 9th International Conference on Computer Supported Education
552
to the past (if the student leaves the course for some
time, then he can restart from that point and follow
the evolution of the posts, comments, etc), Statistics
(more detailed global and individual statistics) and
Video call (allowing a group of students to contact
by video conference).
The global statistics table shows the participation
level of the students in a color scale (Fig. 2). It gives
the teacher quick information about the development
of the course in general and lets him know which
students are at risk of abandonment. With the
individual statistics (Fig. 3), the teacher can observe
the students at risk, and make some engaging actions
to try to motivate them to continue participating in
the course. Some actions to re-engage students can
be performed based on these data. Some actions can
be automated (with rules e.g., sending an automatic
e-mail) and others can be done directly by the
teacher.
The objective of these descriptive measures is to
offer all the information available to the teacher, so
he/she follows the students, motivates them,
customizes the contents, and redirects the courses.
As we mentioned before, dropping out is one of the
main concerns for e-learning. To reduce the dropout
rate, some new measures are being developed, as the
use of percentiles, the estimation of probabilities of
abandonment or the evaluation of a social index to
detect influencers. Other new tools have also been
researched and/or developed at Cátedra ASPgems
(University of Extremadura), i.e.: word clouds,
multi-text summarization and social graphs (see
Section 3). They will be used to evaluate students,
identify possible dropouts, and to direct the course in
case that data show the learning units are deviating
from their objectives.
Besides, other features such as gamification have
already been implemented. The students earn points
with the actions they do in the platform. Then, a
ranking by points is shown at the global statistics.
This motivates them to earn more points
participating in the course. It could be possible to
expand this feature to include badges.
In conclusion, NeuroK not only has implemented
tools supporting neurodidactics theories, but also it
obtains very interesting information by using
learning analytics to follow and evaluate students in
real-time. With the integration of new educational
tools, a robust e-learning platform is built, leaving
behind the traditional ones just focused on contents.
Figure 3: Individual statistics.
NeuroK: A Collaborative e-Learning Platform based on Pedagogical Principles from Neuroscience
553
3 NEW EDUCATIONAL TOOLS
There are some educational tools that will be
considered for inclusion in the NeuroK platform in
next versions. Specifically, one module for natural
language processing and another one for social
network analysis are under implementation or under
a research phase.
The first module comprises two different, but
related functionalities. On the one hand, a word or
tag cloud tool has been implemented (see, e.g.,
Resendes et al. 2015). The objective is to provide a
fast and visual representation of the contents
provided by the students within the different
learning units. After considering all the messages
provided by the students in a certain learning unit,
the instructor will be able to analyze if they are
using the right concepts in their comments. Several
metrics to measure the deviation from the theoretical
situation defined by the instructor have been also
implemented. On the other hand, an automatic multi-
document summarization approach is under
research. Multi-document summarization consists in
the extraction from multiple texts about the same
topic. The problem consists in a multi-objective
optimization to obtain the maximum coverage with
the minimum redundancy (see, e.g., Saleh and
Kadhim, 2016). In this case, the multiple texts would
be the messages of the students in a learning unit
about a concrete topic. This allows the instructor to
analyze both, the comments in a learning unit and
the contribution of each particular student to the
summary. Multi-document summarization is a
current research line with a great potential for this
kind of online platforms. Both the word cloud and
the multi-document summarization tools share some
aspects of the natural language pre-processing step.
The second module is based on social network
analysis applied to NeuroK software. Nowadays,
social network analytics is playing an important role
in online platforms (see, e.g. Buckingham and
Ferguson, 2012). A lot of important information can
be extracted from the relationships among the
members of the social network. Firstly, a good
graphical implementation should be displayed,
followed by a number of metrics. These graph and
metrics should represent the relative importance of
each student in the social network based on his/her
comments, observations or ratings, providing a great
information about the structure of the network and
how the students relate. This would help in the
identification of influencers or key players, who may
be good knowledge brokers. Identification of a set of
key players in a given social network is of great
interest in this context. Up to now, most of the used
algorithms for this task are based on single
objective, however, in this case, it is necessary to
find a set of key players which can perform well
with respect to multiple objectives of interest (see,
e.g., Gunasekara et al., 2015). There is much room
for improvement in this area.
4 CONCLUSIONS AND FUTURE
WORK
NeuroK e-learning framework has been presented. It
is an innovative neurodidactics-based platform.
NeuroK offers teachers the essential tools to follow,
guide and evaluate students and, most important, to
motivate them achieving an effective long-term
learning.
It is not a MOOC or a traditional e-learning
framework focused on contents. NeuroK is, instead,
a social network focused on the students which lets
them learn by doing activities in a collaborative way
and not in a memorizing-based approach. It has
innovative features such as gamification, learning
analytics, peer reviewing, content analysis, social
network interface, travel to the past, and a
collaborative learning perspective, among others.
NeuroK has already been tested in relevant
institutions with users such as Universidad Rey Juan
Carlos (Spain), Universidad Libre de Música de
Guadalajara (Mexico), Niuco, Banco Santander or
Catenon Multinational. More tests, including
learning analytics data analysis, will be driven at the
University of Extremadura. Some experimental
results to try to demonstrate the efficacy of this
approach will be obtained during the next months.
Now, Cátedra ASPgems (University of
Extremadura) leads the R&D actions in the fields of
Learning Analytics, Data Mining and Social
Learning. The future work includes researching and
implementing new methodologies, including Big
Data techniques to analyze massive amounts of data
coming from the platform. The results will allow to
understand what happens during the educational
process and to predict what will occur in the future.
This will give to the teachers a deeper knowledge of
the students behaviour (what they know, what they
need and what they are going to do next), allowing
to act in advance by improving the learning process
and completion rate.
CSEDU 2017 - 9th International Conference on Computer Supported Education
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ACKNOWLEDGEMENTS
This research has been supported by GR15106 and
GR15011 projects (Gobierno de Extremadura,
Spain), Cátedra ASPgems (Universidad de
Extremadura and ASPgems SL, Spain), and
European Regional Development Funds (European
Union).
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