AN APPLICATION OF THE STUDENT RELATIONSHIP
MANAGEMENT CONCEPT
Maria Beatriz Piedade
School of Technology and Management, Polytechnic Institute of Leiria,2411-901 Leiria, Portugal
Maribel Yasmina Santos
Information Systems Department, Algoritmi Research Centre,University of Minho, 4800-058 Guimarães, Portugal
Keywords: Business Intelligence, Customer Relationship Management, Data Mining, Data Warehouse, OLAP, Student
Relationship Management.
Abstract: It is largely accepted that a way to promote the students’ success is by implementing processes that allow
the students closely monitoring, the evaluation of their success and the approximation to their day-by-day
activities. However, the implementation of these processes does not take place in many Higher Education
Institutions due to the lack of appropriates institutional practices and an adequate technological
infrastructure able to support these practices. In order to overcome these conceptual and technological
limitations, this paper presents the Student Relationship Management System (SRM System). The SRM
System supports the SRM concept and the SRM practice, also here presented, and it is implemented using
the technological infrastructure that supports the Business Intelligence (BI) systems. The SRM system was
used in an application case (in a real context) to obtain knowledge about the students and their academic
behaviour. Such information is fundamental to support the decision-making associated with the teaching-
learning process. All the obtained results are also presented and analysed in this paper.
1 INTRODUCTION
One of the measures that it is largely accepted as a
way to promote the students’ success in Portuguese
Higher Education context is by implementing
mechanisms that allow the students closely
monitoring, the evaluation of their success and the
approximation to their day-by-day academic
activities (Pereira, Motta and Vaz, 2006). Several
actions have been undertaken in order to improve
the success of the day-by-day academic activities,
like for example, tutoring programs (Pile and
Gonçalves, 2007). However, some of the tasks
involved are not compatible with the work overload
of the teaching staff, the large number of students in
academic years with high failure rates and the lack
of institutional practices and technological support
to monitor the students’ academic activities. In order
to overcome these conceptual and technological
limitations, it was proposed a system designated by
SRM System - Student Relationship Management
System. The system supports the SRM concept and
the SRM practice that are defined in this paper.
Underlying to the SRM concept is the success
promotion, through the establishment of a close
relationship between the institution and the student.
The objective is the continuous monitoring of the
students’ academic activities, allowing the
identification and anticipation of problematic
academic situations related with the students’
failure. Knowing such situations, adequate
actions/decisions can be taken to handle them,
supporting an adequate and effective institution-
student relationship.
This paper is organized as follow: Section 1
presents the motivation for this work, justifying the
SRM system proposal. Section 2 includes an
overview about the principles behind the proposed
SRM concept and practice and the adopted
methodology to validate these concepts. Section 3
describes the structural options, which supports the
SRM system architecture proposal and the issues
related with the SRM system implementation.
Section 4 presents an application case and the results
360
Piedade M. and Santos M. (2009).
AN APPLICATION OF THE STUDENT RELATIONSHIP MANAGEMENT CONCEPT.
In Proceedings of the 11th International Conference on Enterprise Information Systems - Artificial Intelligence and Decision Support Systems, pages
360-367
DOI: 10.5220/0002002003600367
Copyright
c
 SciTePress

obtained through data analysis using OLAP and data
mining tools. Section 5 includes some remarks about
the SRM system advantages and the work already
undertaken. Future work is also addressed in this
section.
2 CONCEPTUAL FRAMEWORK
The SRM System was inspired in the principles
underlying the CRM - Customer Relationship
Management Systems, which are used in business
environments to support, develop, maintain and
manage relationships between organizations and
their customers. These systems help to translate
customer information into customer knowledge. The
customer knowledge is obtained from all the
information and business transactions between the
organization and customers. Supported on this
customer knowledge, the organization defines and
implements the activities and organizational
practices that allow developing and maintaining an
adequate relationship with customers. Values like
reliance, fidelity, loyalty and durability must be
present, developed and maintained in this practice.
The quality of the relationships is a key factor, since
it should result into a competitive advantage of the
organization over its customers, and must also result
in a value for its customers.
2.1 CRM vs. SRM
The CRM Systems main functionalities allow to
automate, to support and to manage processes and
activities dedicated to the customers, as marketing
activities (customer prospects, customer
segmentation, planning and managing marketing
campaigns and contacts management); sales
activities (automatic sales management, sales
support services, orders management, and analytical
sales) and services (call-centers management, help-
desks management, product configuration), among
many others (Payne, 2006). There are also
technological solutions designated by “CRM Systems
in Higher Education”. In such cases, the “customer”
is understood as the “student” (prospective, current
and alumni), as an “institution member” (teachers
and administrative staff) and as an “external
institution member” (parents, suppliers,
organizations and other higher education
institutions). Each one of these “customers” could
interact with one or more different functional areas.
These solutions are mainly dedicated to support,
automate and manage processes related with the
“current students” in the academic area (students’
management information, courses and lessons
management, admissions management, enrolment
and registration management) and other areas related
with the available services (communications,
marketing, financial aids, accommodation). A close
attention is also dedicated to the “prospective” and
to the “alumni” students, through personalized
communications, directed advertising or marketing
activities, which main role is to attract prospective
students and to improve the amount of donations
from alumni and other donors (the refereed above is
not applied in many higher education contexts) or to
advertise new courses, like pos-graduate courses, to
turn back these students to the institution. For an
“institution member” these technological solutions
allows to optimize and to facilitate the internal
interactions and communications, the access to the
several available services, including the access to
fundamental information, related with the students
or other administrative services. For an “external
institution member” allows to enhance and to
automate the interactions with the institution using
different contact channels (personnel, phone, email,
mobile, web) (Grant and Anderson, 2002),
(Fayerman, 2002). The different technological
solutions available in market are aligned with the
practices enforced in a higher education context and
with the reality of each institution in particular.
The “Student Relationship Management”
designation was already used in higher education
contexts, but only with a technological/commercial
role. The main difference, looking at the “CRM
Systems in Higher Education”, is related with the
designation of customer, representing the student,
but the supported activities and the underlying
principles are similar.
2.2 SRM Concepts
This paper presents a new definition of the Student
Relationship Management concept and the related
technological support. This definition is focused on
the closely monitoring of the students’ academic
activities, with the main purpose to promote the
students’ academic success. To be possible, the
institution needs to define a SRM strategy and a
SRM initiative. The SRM concept, the SRM practice,
the SRM system, the SRM strategy and the SRM
initiative are thus fundamental and now described.
The SRM concept is a process based on the students’
acquired knowledge, whose main purpose is to keep
an effective students-institution relationship through
the closely monitoring of the students’ academic
AN APPLICATION OF THE STUDENT RELATIONSHIP MANAGEMENT CONCEPT
361

activities, having in mind that there exists a strong
correlation between the closely monitoring of the
student’s activities and his/her scholar success
promotion. The SRM practice is a set of activities,
defined by the institution, which should guarantee a
customized contact with the students and an
effective, adequate and closely monitoring of their
academic performance. The SRM system is the
technological infrastructure that supports the SRM
concept and that makes possible the implementation
of the SRM strategy and practice in the institution.
The SRM strategy is the strategy defined by the
institution, to set up the main activities associated by
the SRM concept and practice. The strategy must be
aligned with the institution vision, mission and aims,
and must also commit with it all the involved actors
(teachers, students, directors, among others). The
strategy must include the activities to be developed
in this scope, as well the several participants and
related actions. To take a SRM initiative, the
institution should define: (i) A SRM strategy that
commits the institution with the SRM practice; (ii)
The set of activities that are included in the SRM
practice, and the specific actions that must be
carried out by the several participants of this
practice. The SRM practice includes, for example:
the identification of the performance indicators and
the behaviour patterns that characterize the different
situations that will be supervised; the students’
monitoring process definition and the related
activities and actions to be executed by each
participant; (iii) The implementation of the SRM
practice in the institution, adequately supported by
the SRM system; (iv) The validation of the SRM
practice, using the obtained results, and, if
necessary, the redefinition of the practice.
2.3 Concepts Validation Methodology
The proposal of the concepts was based in a first
phase on the experience of the authors of this paper
and, on a second phase, in a research study. In the
last one, it was followed an interpretative research
methodology, which recognizes the subjective
importance of the observations, and also the
subjectivity associated with the events narrative and
related results (Myers, 2007). To validate the
proposed concept and related practice it was adopted
a methodology which included the realization of a
set of interviews. The objective was to involve other
participants in the concept definition and in the
identification of the several activities included in the
SRM practice (the concept and the activities were
previously defined by the authors of this paper, but
the interviews allowed their validation and
complementation). The interviews were guided by a
script, prepared beforehand, but including open
question (semi-structured interview). The selected
interviewers, and in order to complement the initial
definitions, were teachers with institutional
responsibilities (courses’ directors, institutions’
directors/presidents, council’ members). Each
interview was recorded and later transcribed. The
interviews analysis process was done following the
Grounded Theory principles (Hansen and Kautz,
2005) and was supported by a Computer Assisted
Qualitative Data Analysis Software (Budding and
Cools, 2007), concretely the NVivo software. The
interview questions included topics like: academic
success/failure, activities to promote the academic
success; student-institution relationship; practices to
maintain an effective student-institution relationship;
monitoring and supervising processes (also
including participants and related activities);
indicators and behaviour patterns; main activities
that can be supported by a technological platform.
The interviews analyses supported the definition of
the SRM concept, the set of activities that integrates
the SRM practice and the SRM System main
functionalities (Piedade and Santos, 2008).
3 SRM SYSTEM
As it was previously mentioned, nowadays there no
exists an adequate technological support to the SRM
concept and practice, like they are understood and
conceptually defined in this paper. Besides the
conceptual formulation, this paper also proposes the
technological infrastructure, the SRM System that
supports the SRM concept and makes possible the
SRM practice implementation.
3.1 Structural Framework
To implement the SRM practice it is crucial to have
appropriate, consistent and complete information
about the students. From an effectively information
analysis must result knowledge about the students
and their academic behaviour. Based on this
knowledge a set of actions over the student or the
students (previously defined in the SRM practice)
should be executed. For the implementation of the
activities included in the SRM practice, it is
necessary: (i) That the information about the
students (which is normally distributed by different
data sources) be stored in an appropriate data
repository, maintaining a single view of the students;
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(ii) The information be analyzed, using the
appropriate data analysis tools, to obtain knowledge
about a specific student or a group of students and
the associated academic behaviours; (iii) That a set
of adequate actions be automatically carried out,
over a student or a group of students, when a
specific situation, event or behaviour is detected;
(iv) That the impact of all the actions that took place
over the student/students be assessed and, if
necessary, be redefined.
3.2 Proposed Architecture
The structural issues related with the data repository
and the data analysis tools suggest that the SRM
System must be implemented using the concepts and
the technological infrastructure that traditionally
support the Business Intelligence Systems (Negash
and Gray, 2003). The SRM System architecture
includes four main components: the Data
Acquisition and Storage component; the Data
Analysis component; the Interaction component and
the Assessment component (Figure 1).
The Data Acquisition and Storage component is
responsible for storing the students’ data in a data
warehouse, which structure was modelled for this
purpose. The students’ data exist in different data
sources. All the data is stored in the data warehouse
after the ETL (Extraction, Transformation and
Loading) process. The Data Analysis component is
responsible for obtaining knowledge about the
student/students. The stored data is analyzed using
appropriate data analysis tools, allowing patterns
identification. The obtained knowledge is stored in
an adequate data repository (knowledge database).
The data analysis tools allows: statistical analysis,
querying, reporting, analysing data under different
perspectives and views (using OLAP tools),
identification of students’ profiles and behavior’s
patterns (through the identification of patterns and
trends that exists in data using data mining
techniques). The Interaction component is
responsible for maintaining an adequate and
effective relationship with the students, using the
obtained knowledge. The system must allow the
definition and the automatic execution of adequate
actions over the student/students. These are
practicable using technological tool that allows the
interaction and communication between the different
users, as well as automatically execute personalized
actions. The Assessment component is responsible
for the assessment of all the actions carried out and
their impact, by monitoring the students’ academic
behaviour - verifying different rates (assiduity,
marks, among others). The using tools allow the
monitoring of different indicators, statistical
analyses and reporting.
Assessment
Interaction
Data Analysis
Data Aquisition and
Storage
External sources
Analysis Tools
Marks
ETL(Extraction, Transformation, Loading)
Academic
activities
e-learning
Data Warehouse
Data
Knowledge
Actions
Presences
Data Mining Statistical Visualization Reporting Querying
Technological Tools
Communication
Personalization
Actions Definitions
Interaction
Knowledge
Tutor Teacher
Course Director
Actions Execution
Students Group
Student
Monitoring and assessment
Monitoring Assessment
Knowledge database
...
...
Technological Tools
Statistical Analysis Visualization Reports
OLAP
Tutor
Teacher
...
Student
Course Director
Student
Figure 1: The SRM system’s architecture.
3.3 Implementation
The SRM System has been implemented mainly by
Business Intelligence tools and development tools.
For reasons that are subjacent to the development
context, the choices were the integrated environment
supported by Microsoft tools, namely the SQL
Server Business Intelligence Development Studio,
and the Visual Studio .NET. The first includes a set
of integrated components (Database Engine,
Integration Services, Analysis Services, Reporting
Services and Notification Services) for developing
and managing Business Intelligence solutions. The
Database Engine component provides support for
relational and multidimensional databases (in this
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particular case support the data warehouse and the
knowledge database implementation and
maintenance); the Integration Services component
supports the ETL process; the Analysis Services
component supports the data warehouse
exploration/analysis (through OLAP and data mining
tools); the Reporting Services component supports
the reports design, management and delivering; and
the Notification Services component supports users
notification processes (sending personal messages,
when some event happens) (Mundy, Thornthwaite
and Kimball, 2006). The Visual Studio.NET supports
the web application development, once it is expected
that the SRM System results in an application fully
integrated in the web environment.
4 APPLICATION CASE
To demonstrate the SRM concept relevance, its
applicability in a real context was carried out.
4.1 Application Case Domain
This application case was carried out in a Portuguese
Higher Education Institution, namely in a graduate
course of the computer science field. Each course
integrates a set of units, or signatures. The selected
unit includes 139 students and runs through a
presential component and an e-learning component.
The presential component activities include, among
others: curricular subject explanation, exercises
development, projects implementation, laboratory
experiences; tests and exams assessment. The e-
learning component activities include, among
others: access to general information about the
course unit, access to detailed information of the
curricular topics and lessons, projects guidelines,
exercises, homework, quizzes and a discussion
forum.
4.2 Data Collection, Acquisition
and Storage
The available data, about each student and his/her
involvement in the teaching-learning process, was
(i) provided by the institutional information system
and include students’ personal information and unit
information; (ii) obtained by the teacher
(information related with the presence in classes, the
developed activities and the corresponding
assessments); (iii) provided by the e-learning system
(information related with the consulted materials,
web logs, analysed exercises).
The analysis of all the available data allowed the
identification of the data subset that was considered
in the application case. The subset considered
crucial to the analysis includes:
(i) Personal Information: student_number (student
number identification), situation (worker/full-time
student), registration_year (student registration
year) and information about the units in which the
student is registered. In a normal situation, a student
that has a registration year with the values 2 or 3
means that the student is at the first time in the unit,
values 4 and 5 means that the students are repeating
the unit. In order to maintain the student’s privacy,
all the information that allows his/her identification
is ignored or codified.
(ii) Unit or Signature Information: id_unit (unit
identification), designation (unit name), year
(curricular year), semester (curricular semester),
course (associated course).
(iii) Presences in unit classes’ information:
assiduity_rate (presential classes’ assiduity rate
related to each student, the percentage value) and
Assiduity type information: idAssiType (assiduity
type identification), description (assiduity type
description), range (related range). In this particular
application case were used the descriptions:
VeryLow (0% - 25%), Low (> 25% - 50%),
Acceptable (> 50% - 75%), High (> 75% - 90%)
and VeryHigh (> 90% - 100%).
(iv) Assessment Information: id_assessmentActivity
(assessment activity identification), description
(activity description), weight (weight in the final
mark), mandatory (obligatorily indication, yes/no)
and relating information about the marks obtained
by each student. The mark scale comprises values
among 0 to 20. To represent some specific
situations, it was used a negative value; -2 for the
students that are not allowed
to do the next exams,
due to the lack of a positive project classification or
because he/she missed most of the presential classes;
-1 for the students that fail the evaluation, due to the
lack of a positive written test classification (but, they
could pass in next exams). Values greater or equal
then 10 represent that the students have a positive
mark. In a qualitative scale, mark values between 10
and 13 correspond to Satisfactory, between 14 and
16 correspond to Good, and between 17 and 20
correspond to Very Good.
(v) E-learning Information: id_action (e-learning
action identification), action (e-learning action),
description (action description) and relating
information about the number of accesses (actions)
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undertaken by each student. It was considered that 0
to 35 accesses corresponds to a few number of
accesses, 36 to 57 corresponds to an expressive
number of accesses and values greater than 57
corresponds to many accesses (accordingly with the
values distribution). After the identification of the
relevant data, it was (i) designed the data warehouse
model (a multidimensional data model), which
follows the constellation schema represented in
Figure 2.
Figure 2: Data warehouse model.
(ii) Implemented the data warehouse, through the
dimensions and facts tables’ creation. This process is
supported by the Database Engine component. (iii)
Loaded the data warehouse. The loading process
follows the steps of the ETL process, in which the
relevant data is extracted from the source databases.
After the extraction, the data is cleaned (when errors
in data are detected) and transformed in order to
accomplish the format of the target system, the data
warehouse system. The last step is associated with
the loading process. These processes were supported
by the Integration Services component through the
development of several packages.
4.3 Data Analysis
To explore the data warehouse it was used OLAP
and data mining techniques.
4.3.1 OLAP
The OLAP analysis main purpose is to understand
the unit students’ behaviour and the influence of the
adopted teaching-learning experiences. To analyse
the correlation between the classes’ presences, the
accesses to the e-learning platform and the students’
evaluation, a cube was created. The cube integrates
the classes students’ presence (represented by the
AssiduityRate fact), the assiduity type description
(represented by the Description attribute with the
values VeryLow, Low, Acceptable, High, VeryHigh),
the accesses to the e-learning platform (represented
by the Actions fact) and the final results (represented
by the Mark fact) of the final test evaluation
(represented by the id_assessmentActivity attribute
with value 500, in the Filter Expression). Figure 3
and 4 represents an extract of the cube data.
Figure 3: Extract of students data grouped by Very Low
and Low assiduity rate.
Figure 4: Extract of students data grouped by High and
Very High assiduity rate.
From the analysis of Figure 3, it could be seen that a
great number of students with a VeryLow and a Low
assiduity rates fail the evaluation (mark value -1) or
are not allowed to do the final exams evaluation
(mark value -2). There are only four students (IDs
601, 2010, 7013 and 704), with these characteristics,
that have a positive mark, passing the unit. These
students are repeating the unit (since they have the
value 5 in the attribute Registration Year), fact that
could explain their final mark. Another fact is their
expressive number of unit interactions through the e-
learning platform. Having in attention that these
students are worker_students, it could be concluded
that the e-learning platform is a good complement to
the presential classes, particularly for this kind of
AN APPLICATION OF THE STUDENT RELATIONSHIP MANAGEMENT CONCEPT
365

students. In addition, it could be seen in Figure 4,
that a very expressive number of students with High
or VeryHigh assiduity rates have a positive mark and
that many of these students have Good marks. There
is only a student (ID 6012), on these conditions, that
fail (mark value -1).
4.3.2 Data Mining
The data mining analysis main purpose is to identify
the profile of the students in order to decrease the
unit failure rate. For that, it was implemented a
classification task (Han and Kamber, 2001) to
identify a model that classifies the students’
evaluation profile, including the values that are
associated with the students’ failure (Not Allowed),
the students with failure risk (Fail) and the students
with success (Pass).
The knowledge discovery process follows the
traditional Knowledge Discovery in Databases
(KDD) steps (Data Selection, Data Treatment, Data
Pre-Processing, Data Mining and Results
Interpretation). The classification task requires
finding a model that describes the predictable
attribute (in this case the Mark attribute) as a
function of the input attributes (in this case, the
registration_year, situation, assiduity_rate and
actions attributes). To carry out the classification
task it was selected the decision tree algorithm.
Based on the patterns (in this case a set of rules)
represented by the decision tree, it can be possible to
identify the students’ evaluation profile. The
obtained model (Figure 5) integrates a set of rules
(in a tree form). From the analysis of the model, it
was selected the following set of rules that explicitly
describes the patterns:
(i) Not Allowed:
1. IF assiduity_rate = ’VeryLow’ and
Accesses < 35 THEN mark= ’NotAllowed’;
2. IF assiduity_rate = ’VeryLow’ and
Accesses >= 35 and registration_year=
’second’ then mark=’NotAllowed’.
(ii) Fail:
1. IF assiduity_rate = ’VeryLow’ and
Accesses >= 35 and registration_year
not= ’second’ THEN mark =’Fail’;
2. IF assiduity_rate= ’Acceptable’
and registration_year= ’second’ THEN
mark=’Fail’.
(iii) Pass:
1. IF assiduity_rate = ’VeryHigh’
and Accesses < 35 or >= 57 THEN mark =
’Pass’;
2. IF assiduity_rate = ’Acceptable’
and registration_year not= ’second’ and
situation =’full-time’ THEN mark=
’Pass’;
3. IF assiduity_rate= ’VeryHigh’ or
’High’ and Accesses >= 35 and < 57 and
registration_year = ’third’ THEN mark=
’Pass’.
Figure 5: Students’ profile tree model.
From the rules analysis, it is verified that the
attributes assiduity_rate and accesses are the
attributes that more influence the students’ profile. A
very low assiduity rate and few interactions with the
e-learning platform, is the typical profile of the
students with failure (Not Allowed). This situation
occurs, in a more emphasized form, when the
students were in the second registration year
independently of the interaction level. The student
profile with failure risk (Fail) is associated to
students in the 3, 4 and 5 registration years and with
lower assiduity rates, but with an expressive
interaction level with the e-learning platform. In the
same situation are the students in the second
registration year with an acceptable assiduity. The
students profile with success (Pass) is associated
with students with higher assiduity rates and that
have either few or many interactions levels. In
particular, the students in the third registration year
and with an expressive interaction level were the
students with more success. In the same situation are
the full-time students in the 3, 4 and 5 registration
years, with an acceptable assiduity rate.
4.4 Interpretation of Results
The OLAP and data mining analyses lead us to
conclude that it is necessary to take special attention
to the students with low classes’ presences and/or
students which do not interact or interact few with
the e-learning platform. In this special case the
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teacher could, for example, send an alert messages
to the students (calling their attention to the
importance of the presential classes/interaction
mechanisms) or the tutor (teacher responsible by the
student) could to see what is happening with the
students in order to support them (all these activities
are included the SRM practice). It is also necessary
to take special attention to the students in the second
year and also with the worker-students. All these
situations must be handled, without neglecting the
students that usually have a success profile. The
previous results allow us to conclude that data
analysis supports the process of obtaining
knowledge about the students and their academic
behaviour. Next steps, in this project include: i) the
integration of other types of information about the
students in the data analysis process ii) adding the
interaction capability to the prototype, following the
set of activities defined in the SRM practice. Those
activities are associated with automatic actions that
are carried out over a student or a set of students
presenting the same behaviour. One of these actions
could be the automatic e-mailing of messages to
students that present little interaction with the course
unit or do not have any interaction at all.
5 CONCLUSION AND FUTURE
WORK
In a Portuguese higher education context, there
exists a strong budget control inside the institutions
and also a strong competitiveness among
institutions. Another characteristic is that persist a
high rate of failure and abandon (mainly in the firsts
graduation years). It is required that the institution
adopts measures that help to invert this trend. It is
also known that the teacher staff (already overloaded
with teaching, researching and managing tasks)
plays an active role in the success promotion. In this
context, we believe that the implementation of the
SRM practice, supported by the proposed SRM
system, create an advantage towards the students’
success promotion, and therefore in the institution
success, once the system facilitates the students’
knowledge acquiring process, the actions/decisions
support and the subsequent interactions with the
students. The development of this project has
occurred in different stages. In the first stage it was
proposed the SRM concepts and its validation. It was
also verified that no adequate technological support
to the SRM concept and practice (such as defined
and understood on the scope of this work) exists.
The second stage was associated with the definition
of the structural framework, which allowed the
definition of the SRM system architecture and its
main functionalities. It was also identified and
defined the technological tools used in the prototype
implementation. At this moment, the SRM system
prototype is under development. After that, it will be
validated through the execution of a set of
demonstration cases in different higher education
institutions.
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