Segmenting learners in online learning environments
Sinan Aydin, Aylin Ozturk
Affiliation: Anadolu University, Open Education Faculty
Country: Turkey
e-Mail: snaydin@anadolu.edu.tr; aylin_ozturk@anadolu.edu.tr
Abstract
Segmentation means dividing explained entities into homogeneous sub-groups by benefiting from the
similar features. Segmentation is generally used in marketing. It is used education area in particular to
higher education. Segmentation studies may contribute to rendering customized services to similar
learners, specification of the students that may drop-out, formation of special communication method
to each learner group, enhancing the sense of belonging and satisfaction of the learner. The main aim
of this study is to analyze types of segmentation, use of segmentation in learning environments, its
benefits and studies conducted in the literature. It is thought that segmentations of the learners would
contribute significantly for improvement of the services and customizations, to take academic,
administrative and corporate decisions basing on the learner for the Open Universities which have
students from different socio-economic regions, income groups, age and professions.
Keywords: Segmentation, e-Learning, Learner characteristics, Learner types, Need-based approach,
Service quality.
1
Introduction
In an educational process of 21st century, learning and learner concepts have become prominent
issues with the changes and transformations in learning with no time and place limitations, lifelong
learning needs, the flexibility and diversification of the content. Along with these changes, open and
distance learning has become a learner-centered education rather than being technology-based
education. In addition, contents, designs and offered services have been started to be improved in
accordance with learners’ abilities and preferences. Tailor made adaptive environment prepared to
the characteristics of the learner comes forward rather than one-size-fits-all approach in e-learning
environment.
Open and distance learning institutions, whose preference in the global market have been increasing
day by day in direct proportion of their number of learners and data. Advances in communication and
information technologies have made it possible to monitor and quantify the learning activity of
individuals and have created a number of data sets in this sense. Discovering significant and
unexplored information, and improvement and development of them has gained importance by using
modern analytical tools such as data mining, learning analytics, data analysis on the data. In an open
and distance learning system, it is primarily necessary to be acquainted with the learners and know
the characteristics of the students to provide need-based services, to apply learner-centered approach,
to increase the quality of services and to establish effective communication and interaction
environment. In this context, segmentation is one of the methods to be applicable towards to getting
to know learners.
Segmentation means dividing the data into homogenous sub-groups with respect to their similar
characteristics. Segmentation is generally used in marketing. It is used education area in particular to
higher education. The concept of segmentation which was first developed by Smith in 1956, was used
mostly in marketing field to create customer profiles, to provide better services to customer, and to
develop more effective marketing strategies and strategic plans. Segmentation studies may contribute
to rendering customized services to similar learners, specification of the students that may drop-out,
formation of special communication method to each learner group, enhancing the sense of belonging
and satisfaction of the learner.
The objectives of this study are to recommend services, approaches and access methods appropriate
to each group, and in this way to make it easier for the institutions to take administrative, academic
and support service related decisions, and to support initiatives for differentiation and designing of
services with respect to the characteristics of these groups. It is thought that this study will help
determining the needs of individuals more clearly in the open and distance learning environments in
which the personal learning experiences are increasingly getting important and developing new
services in this term.
Literature Review
There are many studies in the marketing area with regard to segmentation in the literature. Especially
in the higher education-level studies in education, segmentation has been used. Despite the studies
on the examination of the learners in the open and distance learning environments, the learner
typology and the modeling of the learners, not many segmentation studies have been performed.
Cluster analysis, multi-dimensional scaling, discriminant analysis, classification, decision trees and
artificial neural networks have been mostly used in the segmentation studies. Generally, the
demographic data have been used in the studies, but psychographic, behavioral and personality traits
of learners should also be identified to determine the characteristics of learners. Performing
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segmentation through accurate data in the open and distance learning institutions serving to learners
of several different characteristics will also contribute an increased service quality as well as increased
satisfaction and sense of belonging.
Goodnow (1982) performed a benefit segmentation based on the learner motivation on university
level. As a result of the study, five different segments were obtained which had different motivational
orientations. The findings of the study were used to define the target markets and plan program offers
according to the needs and interests of the learners in the selected segments.
In the quality-based segmentation study performed by Woo (1998) on distance learners, a measuring
tool was developed to determine the quality perceptions of the learners and focus group interviews
were made. As a result of this study in which three different segments were set through cluster
analysis, it was emphasized that, in the distance learning institutions, different segments need to be
presented with custom services instead of treating all learners as a homogenous group.
In the study by Rogers, Finley and Kline (2001), a segmentation based on the learner needs was
performed to have a better understanding of individual differences of undergraduate learners. In this
study, the learner segments and needs were first determined through a qualitative approach, and then
the learner segments were confirmed quantitatively. This process was emphasized as a critical step for
creating the academic programs as well as possible and for the organizations to develop their
strategies.
In the study by Blasco and Saura (2006), the segmentation was performed according to the learner
expectations. CHAID, which is a predictive model, was applied to define the segments and the learners
were separated into three segments. These segments showed the differences between the expected
quality of service and the perceived quality. Based on these data, the quality of service was assessed
and recommendations were made for improvements.
In the study by Hagel and Shaw (2007), learner preferences were investigated in the hybrid study
mode. Cluster analysis was utilized to define the learner segments which have distinctive preferences
through a combination of face-to-face and print-based materials and web-based study modes.
Demographic and situational variables were used to create these segments. The results show that
learners' preferring the combination of face-to-face study and more independent study mode may be
caused by their year levels. In addition, findings indicate that there is a relationship between the
preferences in terms of age, gender and web-and print-based studies.
In the geodemographic segmentation study performed in the 2009 annual report of Open Universities
Australia, it was identified who were the learners, what were the best communication methods and
how the satisfaction levels of current learners could be improved. Moreover, online investments
reached maximum level through a study on how learners investigated the facilities of the Open
Universities Australia on bigger search engines.
In the study by Chen and Hsiao (2009), the marketing segmentation theory was adopted to determine
the basic factors emphasized by learners while choosing school or department, and the learners were
divided into three groups. Based on the results of the research, recommendations were made on
improving the assessment standards with regard to how learners would select schools and the school's
reputation itself, enhancing the inner culture of the school, and creating a unit in charge of learner
recruitment.
A segmentation study was conducted by Schatzel et al. (2011) to identify those who were or not intent
to continue their education among adult learners who dropped out from the higher education. Five
segments were created using the demographic and psychographic variables of the learners. As a result,
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strategies were recommended to achieve segments with the highest possibility of going back to the
higher education.
In the study by Bailey, Barton and Mullen (2014) on the e-Learning, it was found that four of the five
segments had positive impressions about e-Learning. It was determined that similarities and
differences between the segments offered important indicators for predicting and shaping the future
of e-Learning, and by this means, institutions may explore great opportunities for improvement, new
platforms for innovation and their potential of altering the learning ways of next-generation learners.
In the segmentation study was performed by Ladd, Reynolds and Selingo (2014) to understand the
different needs of today's learners, six segments were created based on the motivations of learners
and their expectations from undergraduate education. Explanations were made about the
characteristics of the learners in the segments, the source of their motivations, and sizes of the
segments. It was stated that universities could reach appropriate learners and provide them with the
most accurate services through determining those segments.
Methodology
Cluster analysis, which is a descriptive data analyzing technique, will be utilized to divide the learners
into segments in the study. The aim of the cluster analysis is to minimize the similarity between the
clusters and maximize the similarity within a cluster (Han and Kamber, 2006). The process of
knowledge discovery from data (KDD) was utilized in the study, which was composed of the steps of
data cleaning, data integration, data selection, data transformation, data mining, pattern evaluation
and knowledge presentation (Han, Kamber and Pei, 2012).
Within the scope of the study, the data obtained from 43,106 students who were actively registered
in the Open Education System of Anadolu University in 2014-2015 and completed the Service Rating
Questionnaire was used. In addition, the demographic and geographic features of learners and their
grade point averages were obtained from the databases of the institution. The data obtained from the
questionnaire and the databases were combined on Microsoft SQL Server, and the data were
organized as cleaned, noisy, repetitive, contradictory and empty. After cleaning the starting 50,001
data, 43,106 of them were kept. The types of data were organized according to their features and
optimized for the cluster analysis. The studies of achieving the most appropriate and significant
clusters on the data is continuing.
Segmentation
Segmentation was defined by Smith (1956, p.5) as follows: “Segmentation is based upon developments
on the demand side of the market and represents a rational and more precise adjustment of product
and marketing effort to consumer or user requirements”. Segmentation is a rich field of conceptual
research that address defining and analyzing a market, explaining the types of consumer behaviors,
the suitability of basic variables and the relationship between these all and the administrative tasks
(Allenby et al., 2002). Segmentation can be defined as a powerful marketing tool that allows users to
be divided into homogenous groups, taking users' characteristics, needs, demands, expectations,
preferences and requests into consideration in the widest sense. With segmentation, institutions can
get to know users better, creating accurate user profiles, develop more effective services, strategic
plans, marketing strategies, and communicational channels and explore the new opportunities in the
market.
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There are different classifications for segmentation in the literature; however, segmentation has been
divided into four categories in many studies: geographical, demographical, psychographic and
behavioral (Kotler and Keller, 2012; Kotler and Armstrong, 2001; Huddleston and Ivanova, 2004).
General properties of these categories are shown in Table 1.
Table 1
Types and Properties of Segmentation
Type of
Segmentation
Geographical
Segmentation
Properties





Demographical
Segmentation




Psychographic
Segmentation


Variables Used
Geographical properties are one of the oldest basics
for segmentation (Burnett, 2008).
Users are separated into segments according to
different geographical units.
Different regions have different needs, and these
should be taken into account and localizations should
be made while creating marketing strategies and
services.
Regional differences are the most known features to
the institutions to determine user preferences for
products (Burnett, 2008).
For education, geographical segmentation is related to
learner's place of residence. This approach includes
the clarification of messages, differentiated
communication, scholarship services and the
promotion of academic programs (Black, 2009).

Countries

Regions

Provinces

Cities

Neighborhoods

Climate

Geographical
structure

Physical
properties

Population
density
The most common properties used for segmentation
are demographical ones because requests,
preferences and using rates are closely related to
demographical properties (Kotler and Keller, 2012).
Variables are easy to measure.
Motivations and barriers for registration in education
generally vary according to demographical segments
(Black, 2009).
Addressing these differences during marketing
processes may increase the possibility of institutions
to be preferred (Black, 2009).


Age
Size of the
family
Gender
Income
Profession
Educational
level
Religion
Nationality
Social class
This is the most powerful type of segmentation but
also the most difficult to be applied.
Psychographic data can be used to understand users
in a better and more detailed way, and concrete data
could be obtained when used with demographical
data (Kotler and Keller, 2012).









Psychologicalpersonality
characteristics
Socioeconomic
status

Lifestyle
5


Behavioral
Segmentation



The problem in this approach is to access appropriate
information about learner-specific psychographic
properties (Black, 2009).
After creating the psychographic profile, the custom
communication channels and social activities should
be combined with this information (Black, 2009).

Motives

Values
Users are grouped according to the behavioral
similarities for the product.
Behavioral segmentation is related to the learner
purposes within academic context, and more
significant guidance can be provided if the purposes
are known (Black, 2009).
Learner’ behaviors can be monitored via web-based
applications, facilitating analyses.

Information on
product
Attitude
User status
Benefit
expected from
the product
Brand loyalty
Frequency and
amount of
product use





In defining segments, behavioral variables based on user responses can be used as well as
geographical, demographical and psychographic. Segment types can be used both individually and
together. The key concept here is to comprehend the user differences (Kotler and Keller, 2012). An
effective and beneficial segmentation study should be measurable, substantial, accessible,
differentiable, actionable (Kotler and Keller, 2012).
Segmentation in Learning Environments
Education as service requires meeting learner needs and satisfying them (Azarnoush et al., 2013).
Learner needs are the primary focal point in the learning environments, and there has been an effort
to determine learner characteristics in many studies. Studies on grouping learners have been
performed since 1980s. In 2000s when it became important to design learning processes according to
learner characteristics and needs, it can be said that segmentation was used especially by institutions
of higher education.
A segmentation classification used in educational environment is as follows (Black, 2009):
 Student Type Segmentation: Segmentation is performed according to the registration status
such as high school graduate, transfer learners, e-learners, drop-outs and the learner
characteristics. In this segmentation, strategies should be both marketing and process
orientated and marketing messages, images and communication media should represent the
students groups of which attention the institution want to attract.
 Program Segmentation: Marketing messages, information and human interactions are
determined according to the program segments of the institution in this type of segmentation.
This approach requires differentiation of strategies, studies and resources based on the
capacity of institution and learner demands.
 Influencer Segmentation: Instead of dealing with the possible learners directly, elements that
affect the university preferring process such as family, peer group and studying conditions are
focused on within this approach.
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Segmentation provides the decision makers with important information especially for learners to
benefit most in the e-Learning environments. Institutions can detect the opportunities more
effectively when they focus on different segments and their needs and make more precise
arrangements by matching the requests of the target group with the program (Kotler and Fox, 1995).
By this means, the risk could be mitigated during decision-making process (Burnett, 2008). In addition,
institutions can focus their energies on the segments and render service in the most effective way, find
out the needs and opportunities yet to be determined, and design and develop programs, services and
processes to meet special needs of this segments (Rogers, Finley and Kline, 2001).
When the segmentation studies performed in e-Learning environments were examined, it was seen
that difficulties were experienced such as different data types while grouping the educational data,
variables recorded on different scales, and large-scale data (Azarnoush et al., 2013). Another thing that
makes it difficult for grouping the educational data is that most standard cluster methods fall
insufficient in the cases in which there are too many features to be clustered (Azarnoush et al., 2013).
There are too many data to be used to define e-learners especially. It can be said that increasing
number of learners in open and distance learning environments and diversifying characteristics require
studying on large and different types of data. It can also be said that difficulties of segmentation in eLearning environments have been mitigated with the effective use of data mining applications.
Through data warehouses prepared by institutions, analyses and inquiries related to individual learner
needs and responses can be easily made, and useful information on individual tendencies and
segments (Kotler and Keller, 2012).
Conclusion
There have been studies performed on segmentation in institutions of higher education; however, no
comprehensive studies on the subject have been conducted in Open and Distant learning institutions
giving education to large masses. Segmentation studies have been used to individualize and
personalize the services in the field of e-Learning. Choosing which data to use for segmentation is
important in appropriate formation of segments. Data mining methods and algorithms can be used to
perform segmentation for learners in the open and distance learning systems. As well as allowing to
achieve the most accurate segments, using such methods helps a fast process. Segmentation studies
are important in terms of customizing the services, obtaining information used to develop individual
solutions such as determining the most appropriate ways to reach learners and the learning services.
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