Assisting School Units Management with Data Mining
Techniques and GIS Visualization
John Garofalakis, Antonios Maritsas and Flora Oikonomou
Computer Engineering and Informatics Department, Patra’s University, University Campus, Patra, Greece
Keywords: Educational Data Mining, Geographic Information Systems, Visualization, Decision Support, Clustering,
Classification, Epidemy Spread.
Abstract: Educational Data Mining (EDM) has emerged as an interdisciplinary research area that applies Data Mining
(DM) techniques to educational data in order to discover novel and potentially useful information. On the
other hand, Geographic Information Systems (GIS) are ones designed to manage spatial data and related
attributes and can be used for assisting decision support. This paper proposes an innovative use of DM and
visualization GIS techniques for decision support in planning and management of Greek public education
focused on high risk groups such as young children. The developed application clusters school units with
similar features, such as students’ and teachers’ absences, and represents them on a map, enabling user to
make decisions being aware of geographical information. Afterwards, based on real data stored during
epidemic spread periods, such as the H1N1 flu pandemic during 2009, the application predicts whether a
school should be opened or closed considering students’ and teachers’ absences of a specific time interval.
1 INTRODUCTION
Educational knowledge representation has emerged
as a burgeoning new area in the research landscape.
The need to unambiguously describe knowledge
deriving from educational data has given rise to the
development of methods for exploring unique types
of data which come from educational context.
Increasingly, the use of educational software and
state databases of student information has created
huge depositories of data. Moreover, the use of
Internet in education resulted in the context of e-
learning in which large amounts of information are
ubiquitously available and boundlessly generated
(Castro et al., 2007). Educational Data Mining
(EDM) is the application of Data Mining (DM)
techniques to educational data, and so, its objective is
to analyze these type of data in order to resolve
educational research issues (Barnes et al., 2009). The
techniques which EDM uses convert raw data
deriving from educational systems into usefull
information which could possibly have an impact on
educational research and practice.
On the other hand, nowadays people’s daily
habits, from the common ones to the most complex,
are most of the times interwoven with the concept of
space. Practically, all the decisions made in
government, business or scientific level are
influenced or determined by geographical
characteristics. Decisions are made after evaluating
several data which are characterised as information
and are space connected (Kapageridis, 2006).
A Geographic Information System (GIS) is an
information technology which stores, analyses, and
displays both spatial and non-spatial data (Parker,
1988). Or, as Cowen states (Cowen, 1988), a GIS is a
decision support system involving the integration of
spatially referenced data in a problem-solving
environment. Nowadays, there is an emerging interest
for these systems which are designed to manage
spatial data and related attributes and are also used for
decision support.
This paper describes an application of data mining
and decision support in the educational field within a
project called GeoMapping. The goal of GeoMapping
is to assist the knowledge management by enabling
user to make decisions being aware of geographical
information. The application analyzes data from
Greek school units of primary and secondary
education and it geographically represents them. It
uses data mining techniques in order to cluster school
units with similar features, such as students’ and
teachers’ absences, and represents them on a map.
Garofalakis, J., Maritsas, A. and Oikonomou, F.
Assisting School Units Management with Data Mining Techniques and GIS Visualization.
DOI: 10.5220/0006317603310338
In Proceedings of the 9th International Conference on Computer Supported Education (CSEDU 2017) - Volume 1, pages 331-338
ISBN: 978-989-758-239-4
Copyright © 2017 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
331

Afterwards, based on absences’ data stored from
school year 2009-2010 due to H1N1 virus, the
application predicts, whether a school should be
opened or closed.
The paper is organized as follows. Section 2
presents background information concerning specific
needs leading to the decision to implement the
application. Section 3 presents the application. The
results of applying data mining and decision support
techniques, and the visualization of the results, are
presented in Section 4. Section 5 concludes by
summarizing the application’s results and by
presenting plans for future work.
2 BACKGROUND
Since the late 1990s government agencies around the
world have embraced the digital revolution and
placed a wide range of materials on the Web
including publications, databases and actual online
government services (West, 2002). The construction
and management of e-government systems is
becoming an essential element of modern public
administration (Torres et al., 2005) in order to
enhance the access to and delivery of government
information and service to citizens. Moreover, e-
government facilitates the decision making process of
national interest matters.
Prospective monitoring of public health is a
crucial element of the strategies used for the control
of diseases at a national level. The effective collection
and analysis of data feels necessity for specialized
methods and tools (What is Epidemiology All About,
1999; Burkom et al., 2005) which Information and
Communication Technologies (ICT) achieve it
effectively.
Back in 2009, the wide fast spread of the H1N1
virus was a specific case where ICTs assisted
authorities at their effort to take the needed measures
in order to minimize the virus’s evolution. Several
applications collected data concerning factors
directly or indirectly related with H1N1 spread. One
of these applications was the Absences System (part
of the Survey System) of the Greek Ministry of
Education (Garofalakis et al., 2011).
The latter system recorded daily absences from
school units in Greece of primary and secondary
education. Studies have shown that children are prone
to viruses and the monitoring of this age group can
provide useful information about a virus’s spread in a
country. The aforementioned application assisted the
Greek Ministries of Education and Health on their
effort to epidemiologically monitor H1N1 evolution
in Greece and compensated to extract conclusions for
taking public health measures in the country.
However, the application neither provided any
integration with a web GIS adaptive interface for
better visualization of the stored data nor performed
any data mining on the data in order to draw helpful
conclusions. Public health care is a knowledge
intensive domain in which neither data accumulation
nor data analysis can be lucrative without using
knowledge about both the problem domain and the
data analysis process. This indicates the usefulness of
integrating decision support techniques with data
mining in order to create effective decision support
models. Furthermore, the use of visualization
techniques facilitates the knowledge management
and improves the whole decision support process.
Information visualization uses graphic techniques
to help people understand and analyse data (Mazza,
2009). Visual representations and interaction
techniques take advantage of human eye’s broad
bandwidth pathway into the mind to allow users to
see, explore, and understand large amounts of
information at once (Romero and Ventura, 2010). The
objective of data visualization is to highlight useful
information and support decision making (Romero
and Ventura, 2010).
There are several systems which attempt to
visualize educational data. For example, SAMOS
system (Juan et al., 2009) monitors students’ and
groups’ activities in collaborative e-learning by using
spreadsheets on data contained in server log files. Or
GISMO (Mazza and Milani, 2004) generates
graphical representations of its data which can be
explored by course instructors to examine various
aspects of distance students.
Thus, the presented application uses the data
stored at the Absences System and by applying
descriptive and predictive data mining methods in
combination with visualization techniques, achieves
to improve the management of data and knowledge.
This goal is approached in two phases: first, there is a
data analysis with data mining techniques and second
the results of data mining are evaluated in order to
facilitate decision support. In the second phase, the
geographical representation of the data is the one
which enriches the decision support process.
3 THE SYSTEM
The GeoMapping system is a web application and it
is accessed after authentication through a browser.
The authenticated users belong into two levels with
different privileges. The first level consists of
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administrators, who can access all the stored data and
all the application’s menus, whereas the second level
consists of simple users, who can access the stored
data of their area of interest and have limited access
to application’s menus. The application is
personalized based on the user’s level. In the
following paragraphs, there is an extent description of
the structure of the application. In order to better
understand the system, there will be a presentation of
the Absences system upon which the application has
been based for data retrieval. Afterwards, there will
be an analysis of the presented system.
3.1 Data Retrieval System
The Absences System (Garofalakis et al., 2011) was
part of the Survey System (Garofalakis et al., 2007)
which was used to record data about students in
school units and teachers employed either in school
units or in administration offices. The whole
application was integrated in the existing Resources
Management System of the Greek Ministry of
Education and it operated over the Greek School
Network (Greek School Network, 2016) utilizing its
access and its security policies. The Absences system
started on October 2009, leading to important results
assisting in real time the relevant Greek Authorities
responsible to deal with the H1N1 spread. The latter
system was used until January 2014 when it was
replaced by myschool system (myschool, 2016).
Aim of the Absences System was to assist the
epidemiology monitoring of H1N1 epidemic
evolution in Greece, in the whole country and per
region leading to the extraction of important
conclusions for taking precautionary measures
against the flu (e.g. temporary school closures). The
system required the daily record of students’ and
teachers’ absences and the school’s state (opened or
closed). If the school was reported as closed the user
had to declare the reason for its closure (due to flu
symptoms or holiday or other reason). Furthermore,
if a school unit had a lot of students’ absences then
the school would not function for a few days in order
to prevent the spread of the flu virus.
The system assisted the Greek Ministries of
Education and Health on their effort to support the
epidemiological monitoring of H1N1 evolution in
Greece. Although it provided some statistic results
concerning absences data, neither performed any data
mining techniques nor geographic representation of
the closed school units. However, the data collected
from this system constituted the data retrieval system
for GeoMapping application in which data mining
techniques, geographical representation and
prediction techniques were performed in order to
assist decision making process.
3.2 The GeoMapping Application
The GeoMapping system constitutes an extension of
the Absences system since it retrieves data from the
latter and applies several data mining techniques to it
for drawing conclusions concerning the
epidemiological monitoring of viruses.
As a web application, the system requires user
authentication. There are two user levels,
administrators and simple users, and the application
is personalized based on their level. Administrators
can access all stored data and menus whereas simple
users have limited access to menus and can access
only data related to their area of interest. Moreover,
administrators perform account management to all
user accounts thus enabling them to change their data,
their user level or their status (active/inactive)
allowing them to deactivate their account.
Apart from the aforementioned functionalities,
the application data mines the absences’ data entered
by school units, depicts results on a map and can
predict a school unit’s state (open/closed) based on
similar school units’ state. The school units we are
mainly interested in are those which are thought of as
high risk units (i.e. school units with the biggest
percent of absences for a specific characteristic).
Those high risk units are considered as the best
candidates for viruses’ spread. The preceding
functionalities of the application will be described in
the subsequent paragraphs.
3.2.1 Geocoder
The objective of this application was the usage of data
mining techniques in an educational context and the
representation on a map of the resulted information.
The data used in this application was retrieved by the
Absences system. Nevertheless, the school unit’s
addresses in the Absences system did not contain any
geographic coordinates and in order to illustrate data
mined information on a map, some correlation had to
be performed on school units’ addresses with
geographic coordinates. This correlation was
implemented with the usage of a geocoder subsystem
which converted the necessary data for all school
units stored in the application (Figure 1).
Geocoding is usually synonymous with address
matching (Drummond, 1995; Vine et al., 1998;
Bonner, et al., 2003) highlighting its prevalent use of
transforming postal addresses into geographic
representations. In this application geocoding is
Assisting School Units Management with Data Mining Techniques and GIS Visualization
333

performed by using google maps api. The api on the
one hand allows the geocoding service usage by using
an HTTP request but on the other hand there are some
limitations. The api can serve up to 2500 free
requests/day with a maximum of 50 requests/sec.
Thereby a geocoder subsystem was developed in
which a timer delay process maximized api’s usage
and allowed more requests per second. When the
geocoding process is complete, the geocoder returns
a JSON array with the geographic coordinates of the
postal address which are stored in our system’s
database. Those geographic representations are then
used in order to place markers on a map and depict
the conclusions drawn from data mining process.
Figure 1: Geocoding operation.
3.2.2 Data Mining
The data mining process from the absences’ data is
performed by using predictive and descriptive
algorithms. The predictive data mining algorithms
result in models that can be used for prediction and
classification whereas descriptive data mining
algorithms result in finding interesting patterns in
data, like associations, clusters and subgroups.
In GeoMapping application, we mainly used
descriptive data mining methods and combined them
with google maps’ visualization and decision support
techniques to improve the management of data and
knowledge at the Absences system and subsequently
at the Greek Ministries of Education and Health. In
order to achieve the latter, we firstly analysed the
available absences’ data with data mining techniques
and secondly, we used the results of data mining as
input for the decision support techniques.
In order to detect similarities between school units
with similar absences, two different clustering
methods were used: partitional clustering method and
density-based clustering. For the first method K-
Means algorithm was selected whereas for the second
method we used DBSCAN algorithm. Apart from the
clustering method, the user in our application had to
select the appropriate sample in which data mining
would be performed. The samples were the students’
age, the students’ absences percentage, the teachers’
absences percentage and the school unit’s absences
percentage. At the end, we compared the results
provided by those algorithms and we found that the
conclusion was the same regardless of the data
mining algorithm.
Figure 2: Using K-Means algorithm to cluster school units
based on students’ absences percent on cold winter day.
An illustration of the clusters, generated by data
mining methods is given in Figure 2. In this example,
K-Means algorithm is selected in order to cluster the
school units during a cold winter day (20/11/2009)
when it is expected to have a great number of
absences. The sample for the K-Means algorithm is
the students’ absences percent and the number of the
desired clusters is 7. It should be mentioned that after
several trials on our dataset we found that for our
context the optimal number of clusters is 5 or 7
clusters of data. Apart from the above, we also choose
a date in which the clustering will be performed since
we have a daily recording of absences. As a result, the
K-Means algorithm returns 7 clusters of school units.
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Cluster 0, for example, means that 1143 school units
were found in which students’ absences percent
reached 37.5%. In this example, we can see that on
20/11/2009 the majority of school units (3390 units)
had a small number of students’ absences while a
handful of school units’ (238) had an absences
percent close to 94%. Those school units with the
largest percent of absences are the ones we are mainly
interested in and this kind of information is stored in
our database in order to use it later for visualizing the
high risk units. The value of 94% of students’
absences constitutes our threshold value which is
used in our decision support part of our application.
If the data mining process is performed on a hotter
spring day (i.e. 15/04/2010) then the expected number
of absences will be significantly smaller compared to
those of a cold winter day. In Figure 3 we execute the
K-Means algorithm for students’ absences on
15/04/2010. In this example, we can see that the
majority of school units’ (6259 units) had a small
number of absences and a minor number of units (8
units) had a students’ absences percent close to 98%.
This insignificant number of school units will not be
taken into account for high risk schools with a
possible virus spread.
Figure 3: Using K-Means algorithm to cluster school units
based on students’ absences percent on hot spring day.
3.2.3 Decision Support
By using the results from data mining algorithms,
which are basically those school units with the
greatest percent of absences for a specific sample, the
application creates a selection threshold. This
threshold will help to distinguish the high risk units.
It has to be mentioned that in order to create the
threshold, the application takes into account not only
students’ but also teachers’ absences.
Afterwards, several queries are performed to the
application’s database in order to discover those
school units with absences’ percent close to the
threshold. The database queries have as a parameter
the threshold value for students’ and teachers’
absences that was calculated in the previous data
mining step. With the utilization of the threshold
value it was made possible to discover the high risk
school units for a specific day from the total school
units which are recording absences on a daily basis.
Subsequently, GIS data can be used to visualize
those high risk school units in Greece. Instead of raw
data visualization, presented by markers of school
units, we implemented some processes that allow
markers to depict information concerning the high
risk school units. That information regards the school
unit’s name and description.
This leads to the development of a map, which
enables the visualization of areas of Greece with high
risk school units.
Figure 4 Visualizing high risk school units during a winter
day (22/01/2010).
In the example depicted in Figure 4, the decision
maker can see that in the area of Thessaloniki a lot of
school units had a high number of students’ and
teachers’ absences.
The validation of the acquired results was
juxtaposed with the results provided by the “Greek
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335

center of monitoring and prevention of diseases –
KEELPNO” (KEELPNO, 2010). KEELPNO
provided weekly statistics which depicted the H1N1
virus spread to all regions of Greece. Thus, by
examining the graph in Figure 5 which shows the
students’ absences per region in Greece, it can be seen
that GeoMapping application correctly shows a
cluster of high risk school units in the area of
Thessaloniki during winter and more specifically on
22/01/2010 (North Greece region, brown colored line
on Figure 5).
Figure 5: Students’ absences per region in Greece (until 4
th
week of 2010) provided by KEELPNO.
3.2.4 Prediction
Apart from the clustering algorithms which were used
in order to discover school units with similar absences
results, the application implemented a predictive data
mining algorithm for proposing the status of a school
unit (whether should remain opened or closed). The
classification method which was chosen in order to
predict a school unit’s state used a training set of
absences data retrieved by the Absences system for
the first three months of its usage. The algorithm that
performed the classification was the ID3 decision tree
algorithm.
The ID3 algorithm was selected due to its
advantages. It can create a decision tree with the
smallest number of steps, it examines the whole
database and the choices of one level benefit the other
levels.
Figure 6: Classification algorithm example.
In our application, the user enters some input data for
classification and the system responds whether a
school unit should be opened or closed. The input
data are the students’ age, the students’ absences
percent, the teachers’ absences percent and the total
absences percent. For example, the application’s user
wants to see whether a primary school unit with some
students’ absences should remain open or closed for
precautionary measures. In Figure 6 we can see that
the system’s response is that the school unit should
close for precautionary measures if the age group is 8
years old, the students’ absences reach 33%, the
teachers’ absences reach 10% whereas the whole
population absences reach 5%. This kind of
information is extremely helpful since it can predict a
school unit’s state and facilitate the decision making
process in order to take precautionary measures (i.e.
vaccination to the areas where high students’
absences are monitored).
4 FUTURE WORK
The developed application, as any other application,
can be further improved driven by results’ accuracy
and user friendliness. Therefore, future extensions
could be the use of other clustering and classification
algorithms with more advanced features or the more
thorough inspection of the clustering and
classification results with evaluation metrics for
ensuring the validity and the accuracy of the returned
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data. Another improvement could be the maps’
enrichment in order to include thematic maps for
depicting for example demographic data such as
population density.
5 CONCLUSIONS
The use of data mining and decision support
techniques, including GIS visualization, can lead to
better results in decision making, can improve the
effectiveness of developed applications and enables
interfering with new types of problems that have not
been addressed before.
In the GeoMapping system, a web application was
implemented in which data mining techniques are
applied to school units of primary and secondary
education in Greece and the results of those
techniques are geographically represented on a map.
Aim of GeoMapping system is to offer a data
mining model which performs two operations.
Firstly, the clustering methods group similar school
units together based on their students’ and teachers’
absences. Afterwards, a map representation takes
place allowing the user to make a decision taking into
account the geographic information. The knowledge
of this information is extremely critical in epidemical
spread periods when a disease outbreak to a large
number of people in a given population within a short
period of time necessitates governmental decision
making and measures taking. The second operation is
related with the prediction of a school unit’s state
(open/closed) based on stored data. This operation
can be used by school units’ principals or education
executives who are directly in charged with taking
decisions for unit’s state.
The data used in GeoMapping application was
retrieved from the Absences system and is related
with all school units in Greece. This characteristic
reinforces the completeness and the validity of the
returned results.
It has to be mentioned that the results taken from
the clustering operation are accurate enough as it can
be seen from the comparison made with the official
data which was given by KEELPNO for that period
of time (school year 2009-2010) (Figure 5). More
specifically, the official data showed that many
school units of primary education were closed in
North Greece region. This comes to an agreement
with the results provided by GeoMapping (Figure 4)
in which a high risk school units’ cluster is depicted
in Thessaloniki, North Greece region.
Conclusively, the innovation of this application
compared to others is the combination of data mining
methods in an educational context and the results’
GIS visualization on a map. When critical situations
appear, such as a virus outbreak, the help provided by
such a data mining system is valuable since it
facilitates the decision making process.
REFERENCES
(2016). Retrieved from Greek School Network:
www.sch.gr.
(2016). Retrieved from myschool: myschool.sch.gr.
Barnes, T., Desmarais, M., Romero, C., & Ventura, S.
(2009). Educational Data Mining 2009. 2nd
International Conference on Educational Data Mining,
Proceedings. Cordoba, Spain.
Bonner, M. R., Han, D., Nie, J., Rogerson, P., Vena, J. E.,
& Freudenheim, J. L. (2003). Positional accuracy of
geocoded addresses in epidemiologic research.
Epidemiology, 14(4), 408-411.
Burkom, H. S., Murphy, S., Coberly, J., & Hurt-Mullen, K.
(2005, August 26). Public Health Monitoring Tools for
Multiple Data Streams. Retrieved from CDC : Centers
for Disease Control and Prevention: http://www.
cdc.gov/Mmwr/preview/mmwrhtml/su5401a11.htm.
Castro, F., Vellido, A., Nebot, A., & Mugica, F. (2007).
Applying Data Mining Techniques to e-Learning
Problems (Studies in Computational Intelligence ed.,
Vol. 62). (T. T. Jain, Ed.) Springer-Verlag.
Cowen, J. D. (1988). GIS versus CAD versus DBMS: what
are the differences? (Photogrammetric Engineering and
Remote Sensing ed., Vol. 54).
Drummond, W. J. (1995). Address matching: GIS
technology for mapping human activity patterns.
Journal of the American Planning Association, 61(2),
240-251.
Garofalakis, J., Koskeris, A., & Vopi, A. (2007). An E-
Government Application for Integrated, Multi-level
Management of Large Scale resources of the Greek
Primary and Secondary Education. 7th European
Conference on e-Government (ECEG 2007). The
Netherlands.
Garofalakis, J., Koskeris, A., Michail, A.-T., Boufardea, E.,
& Oikonomou, F. (2011). An Information System to
Collect and Analyze Data From Educational Units
During Epidemy Spread Periods. 11th European
Conference on e-Government (ECEG 2011). Ljubljana,
Slovenia.
Juan, A., Daradoumis, T., Faulin, J., & Xhafa, F. (2009).
SAMOS: a model for monitoring students' and groups'
activities in collaborative e-learning. International
Journal of Learning Technology, 4(1-2), 53-72.
Kapageridis, I. (2006). Introduction to Geographic
Information Systems, Theory Notes.
KEELPNO. (2010, February 03). Weekly Epidemiological
Report of Flu Virus, February 3rd 2010. Retrieved 12
16, 2016, from Hellenic Center for Disease Control &
Prevention:
http://www.keelpno.gr/Portals/0/Αρχεία/Γρίπη και
Assisting School Units Management with Data Mining Techniques and GIS Visualization
337

Εποχική γρίπη/2003-2010/2009-2010/gripi_ebdo
_20100203.pdf.
Mazza, R. (2009). Introduction to Information
Visualization. Springer.
Mazza, R., & Milani, C. (2004). GISMO: a Graphical
Interactive Student Monitoring Tool for Course
Management Systems. T.E.L.’04 Technology Enhanced
Learning ’04 International Conference, (pp. 1-8).
Milan.
Parker, D. H. (1988). The Unique Qualities of a Geographic
Information System: A Commentary (Photogrammetric
Engineering and Remote Sensing ed., Vol. 54).
Romero, C., & Ventura, S. (2010, December). Educational
Data Mining: A Review of the State of the Art. IEEE
Transactions on Systems Man and Cybernetics Part C
(Applications and Reviews), 40(6), 601-618.
Torres, L., Pina, V., & Acerete, B. (2005). EGovernment
developments on delivering public services among EU
cities (Government Information Quarterly ed., Vol. 22).
(G.-G. J. Ramon, Z. Jing, & P.-C. Gabriel, Eds.)
Elsevier.
Vine, M. F., Degnan, D., & Hanchett, C. (1998).
Geographic information systems: their use in
environmental epidemiologic research. Journal of
Environmental Health, 61, 7-16.
West, D. M. (2002, September). Global E-Government.
Retrieved from Inside Politics: http://
www.insidepolitics.org/egovt02int.html.
What is Epidemiology All About. (1999). American
Journal of Public Health Devotes August Issue to
Epidemiology and Statistics.
CSEDU 2017 - 9th International Conference on Computer Supported Education
338