25-08-2017, 09:32 PM
A Decision Support System to improve e-Learning Environments
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ABSTRACT
Nowadays, due to the lack of face-to-face contact, distance course
instructors have real difficulties knowing who their students are,
how their students behave in the virtual course, what difficulties
they find, what probability they have of passing the subject, in
short, they need to have feedback which helps them to improve
the learning-teaching process. Although most Learning Content
Management Systems (LCMS) offer a reporting tool, in general,
these do not show a clear vision of each student’s academic
progression. In this work, we propose a decision making system
which helps instructors to answer these and other questions using
data mining techniques applied to data from LCMSs databases.
The goal of this system is that instructors do not require data
mining knowledge, they only need to request a pattern or model,
interpret the result and take the educational actions which they
consider necessary.
INTRODUCTION
In recent years, more and more, universities and educational
centers offer the possibility of enrolling in their degrees and
masters in a semi-presential or completely virtual (online) way in
order to facilitate the lifelong learning and to make this
compatible with other activities. In general, they use e-learning
platforms such as learning content management systems (LCMS),
intelligent tutorial systems, adaptative and intelligent web-based
systems, etc. to support the learning and teaching process.
A lot has been written and said about guidelines for designing
virtual courses [3][5][25] and more and more instructors follow
them with the aim of increasing the pass rate. But, even if the
course is well-designed, it may be not suitably adapted to
students’ learning styles [10][14] or perhaps students feel underattended
or lost in the hyperspace of the course and they require
extra motivation [6].
Instructors unfortunately have very few tools to monitor and track
the student activity in the platform and so be able to detect and
solve these problems. These systems offer some reporting tools
that, in general, show raw data (nº of accesses, time spent in the
course, nº of message read, etc.) in a tabular format. As a
consequence of this, getting a clear vision of each student or
group academic progression during the course is difficult and time
consuming for instructors [8].
RELATED WORK
Educational data mining (EDM) is an emergent discipline
concerned with developing methods for exploring the unique
types of data that come from the educational context [22].
In short, EDM is the application of the data mining techniques in
the area of education, with the aim of obtaining a better
comprehension of the students’ learning process and of how they
participate in it, in order to improve the quality of the educational
system.
Data mining techniques are extensively used in other fields such
as business, marketing, bioinformatics, science and so on, but the
specific characteristics of data from e-learning environments make
their application particular. One of these characteristics is the fact
that it is difficult, or even impossible, to compare different
methods or measures a posteriori and decide which is the best
[16]. Take the example of building a system to transform handwritten
documents into printed documents. This system has to
discover the printed letters behind the hand-written ones. It is
possible to try several sets of measures or parameters and
experiment what works best. Such an experimentation phase is
difficult in the educational field because the data is very dynamic
and can vary a lot among samples (different course design,
students with different skills, different methods of assessment,
different resources used, etc.). This reduces the amount of data
available to mine, only that corresponding to the students enrolled
in the course. Furthermore, as a consequence of not using more
data than that stored in the database of the e-learning platform,
data mining models lack context information. That means that we
will obtain a model but it will surely not be the best. We would
obtain more accurate patterns if we knew more about course
details, had background knowledge of the students or their interest
in the course and so on (this information could be obtained from
surveys, for example). One advantage is that data sets are usually
very clean, i.e., the values are correct, so that few pre-processing
tasks are required.
ARCHITECTURE
The proposed system with the aim of being generic and usable for
different e-learning platforms is designed based on a modular
architecture as can be observed in Figure 1. This will have at least
the following modules:
• A module to read and gather data from the e-learning
platform, to carry out the pre-processing tasks related to
the application of data mining algorithms and to store
this data in the data warehouse database (this module
gathers ETL processes and the Data Staging Area)
• A module which wraps the data mining algorithms
(Data Mining Module)
• A user-friendly interface oriented towards the analysis
of results.
Three open source data mining software packages, RapidMiner
[17], Weka [30] and Keel [2] will be mainly used and tested for
our proposal. We have chosen these tools because they are opensource,
their algorithms can either be applied directly to a dataset
from their own interface or used in your own Java code and all of
them contain tools for data pre-processing, classification,
regression, clustering, association rules and visualization.
Furthermore, RapidMiner is currently the leading open-source
data mining solution according to KDnuggets Data Mining
Software Usage polls in 2009 [21]. Although RapidMiner
incorporates most of the Weka algorithms, it still contains some
algorithms, especially in the area of descriptor selection, which
are not available in other software. Finally Keel will be used to
build models using evolutionary algorithms which are not
gathered in the other tools. With regard to commercial tools, BI
SQL Server 2005 will be used due to the fact that our data
warehouse is developed with it.
CONCLUSIONS
Adding intelligence to e-learning platforms means giving tools the
ability to understand and profit from data (experience).
Consequently, in this paper we present the proposal of a decision
making system which helps distance instructors to know who their
students are, how they work, how they use the virtual course,
where they find the problems and so on, and in this way,
instructors can act as soon as they detect any difficulty, for
example, proposing new tasks, re-organizating the content-pages,
adding new information, opening discussions and so on.
Likewise we propose some questions that, in our opinion, are of
interest to teaching staff and show how the answers are very
useful for improving the learning and teaching process. These
answers are obtained by means of data mining techniques. Lastly
we also suggest a modular architecture for its implementation.
This work presents two main challenges: firstly, to determine the
input variables, the technique and the parameters with which to
execute the algorithms to answer the teachers’ questions
appropriately; and secondly, to define a graphical interface which
allows instructors to interpret the results easily.