Satisfaction in Higher Education: A Multi-Dimensional and Fuzzy Approach

Francesca Gagliardi, Laura Neri, Edmira Shabu and Aurora Hoxha

Introduction

The dynamics of higher education in the western Balkan region remain on the margins of studies on contemporary higher education. Regional higher education has never been the subject of systemic research, and both data and prior studies are lacking.

In Albania, institutions of higher education (HEIs) are both public and private. The first university there was founded in 1957, and until 1990 it was the only university, though some new institutes or branches were established in this period.

Reforms outlined in ‘On Higher Education and Scientific Research in Higher Education Institutions in the Republic of Albania’ were approved in 2015. These reforms aim to create a sustainable mechanism of quality assurance at HEIs that align with European standards. This framework measures and evaluates the performance of everyone involved in academic life based on those standards. They are intended to better integrate teaching with scientific research and to ensure that the system of higher education is oriented towards the needs of the labour market.

Against this background, the Graduates Advancement and Development of University Capacities in Albania (GRADUA) project in Albania aims to construct an innovative IT-based platform for tracking graduates and their employability with the support of policymaking and reform in higher education. Unlike the current experience, which involves databases and job intermediaries, this platform is an integrated system with a model based on an online database of Albanian graduates that matches supply and demand and is easily replicable across universities, creating concrete synergy between universities and the labour market by providing unified and innovative recruitment tools.

Our analysis of the GRADUA project database and the quantitative indicators built employ a fuzzy methodology. The goal of the analysis is to evaluate student satisfaction with the system of higher education in Albania. Student satisfaction is evaluated based on a multi-dimensional approach, using fuzzy indicators. For this purpose, we identified a set of variables in a GRADUA survey of students grouped into three dimensions.

The remainder of this chapter is organised as follows. The literature on student satisfaction is reviewed in the second section. The empirical strategy is presented in the third section. The dataset and variables used in the empirical analysis are described in the forth section. In the fifth section, the main results are provided. Finally, the last section concludes the analysis.

Literature Review on Student Satisfaction With University Education

The literature on customer satisfaction is based on ideas such as perceived value, quality of service, experience, expectations and consequently service evaluation (Joseph et al., 2015). Indeed, satisfaction is the state in which an individual feels that an experience meets his/her expectations (Lagrosen et al., 2014). Therefore, satisfaction is a perception of pleasurable fulfilment of a service (Oliver, 1997). In the university education context, students are the customers, and student satisfaction is the only sign of the performance of quality of service for suppliers of higher education (Parameswaran and Glowacka, 2015).

Student satisfaction is a multi-dimensional concept which is influenced by different factors. Several explanations exist for the dimensions of student satisfaction.

Student satisfaction in higher education is affected mainly by two groups of factors: personal and institutional factors (Marzo-Navarro et al., 2005; Appleton-Knapp and Krentler, 2006). The personal factors include age, gender, employment status, a student’s grade point average (GPA) and the preferred teaching system, whereas the institutional factors are the quality of instruction, the timeliness of feedback, clarity of the expected results and the teaching style. Wilkins and Balakrishnan (2013) identified the following determinants of student satisfaction: teacher quality, quality of physical facilities and the use of technology. Other researchers list the quality of classrooms, the quality of feedback, the teacher-student relationship, interaction with fellow students, course content, learning equipment available, library facilities and learning materials (Garcia-Aracil, 2009; Kuh and Hu, 2001). Teaching ability, university status and prestige, independence, flexible curriculum, student growth and development, student centrality, campus climate and institutional effectiveness have also been identified as significant factors in student satisfaction in higher education (Palacio et al., 2002). Finally, Walker-Marshall and Hudson (1999) claim that the student’s GPA is the most influential factor in that person’s satisfaction.

Methodology

In the international literature, student satisfaction is considered a multidimensional concept. From a methodological perspective, it can be treated in a manner similar to those used for concepts of poverty (Betti and Verma,

2008) or educational mismatch (Betti et al., 2011). All these concepts are very complex issues in which many reasons converge, and a definition based on a univariate measure simplifies the phenomenon too much. Moreover, defining the concept by dividing it into two distinct groups (poor vs. not poor or matching vs. mismatching, satisfied vs. not satisfied in our study) can create a misleading impression, as a dichotomous indicator cannot take into account the different degrees of this phenomenon (i.e. of satisfaction) that might be experienced.

In this chapter, we use a multi-dimensional approach for defining student satisfaction and, at the same time, fuzzy-set theory (Zadeh, 1965; Dubois and Prade, 1980) to create fuzzy indicators that overcome the division of satisfaction into two exclusive groups.

In describing a given phenomenon, the fuzzy set approach (Zadeh, 1965) treats measures as a matter of degree, replacing a simple dichotomy (0,1) (i.e. poverty/non-poverty, satisfied/non-satisfied) into which individuals or households are divided in the traditional approach. In the fuzzy approach, individuals who are subject to a phenomenon experience it in varying degrees; that is, everyone has a certain degree of propensity to experience a phenomenon, defined in the range [0,1].

Betti et al. (2015) proposed a step-by-step procedure for constructing a fuzzy multi-dimensional index, as follows. First, the items to be included in the index or indices, which should be those that are the most meaningful and useful, need to be identified. Then, for each item, a quantitative indicator is defined in terms of the range [0,1]: the set of indicators defined for the set of items, are used in a preliminary exploratory factor analysis to identify underlined unobserved ‘dimensions’. By ‘dimension’, they mean a distinct group of items that describe the phenomenon under analysis, ideally independent of the other dimensions, and which should describe a facet of it. After this exploratory factor analysis, they rearrange some items in the dimensions identified, to create more meaningful groups: a confirmatory factor analysis is necessary to test the goodness of fit of the final grouping. The weights assigned to each item are determined within each dimension; they are based on two elements: the dispersion of the item (prevalence weights) and the correlation weights, which show the correlation with other items in the same dimension (Betti and Verma, 2008). The score assigned within each dimension b to each student i (sih), is calculated as a weighted mean of items in this dimension, whereas the ‘overall score’ of each student i (st) is defined based on the simple average:

thus, giving the same importance to all dimensions, each represents a different facet of the phenomenon. The positive results achieved from applying this methodology to different fields demonstrate its applicability and robustness (see e.g. Aassve et al., 2007; Betti et al., 2011, 2016; Belhadj, 2015; Betti, 2017).

The Dataset

Our empirical analysis is based on the GRADUA project database. The project aims to set up an innovative IT-based platform for graduates in Albania in order to track their employability with the support of policymaking. The platform has a twofold scope: enhance university-enterprise cooperation and the employability of graduates using data aggregation, strengthening both the effectiveness/efficiency of the higher education system with IT-based practices and methodology.

To collect qualitative data, the working group organised some dissemination activities with the university representatives who were responsible for informing students about the platform and a questionnaire that they had to complete, in order to be registered on this platform. The survey’s purpose was to collect data on students’ university experience and their future prospects according to a set of variables that emerged from six online surveys of students who were going to graduate in the academic year 2019. Specifically, the questionnaire consists of the following sections: 1. Personal information, 2. Education and training, 3. Information on the course that graduate is completing, 4. Evaluation of the course that graduate is completing, 5. Information about the graduate’s family and 6. Future intentions and prospects.

The survey database contains the full set of responses from 2,635 graduating students at Albanian universities. In particular, data come from nine universities: University of Tirana, Polytechnic University of Tirana, Agricultural University of Tirana, University of Arts, University of Medicine, European University of Tirana, Polis University, Catholic University Our Lady of Good Counsel and Albanian University.

The goal of this chapter is to construct the fuzzy multi-dimensional indicators of student satisfaction with the degree programme they are concluding and their university experience. For this purpose, we identified a set of variables in the questionnaire that could be grouped into three dimensions, based on their satisfaction with the course, the teachers and the university’s facilities. In Figure 19.1, we summarise the variables identified for each dimension.

Empirical Results

The fuzzy methodology described in below is applied to the variables regarding the GRADUA database (see Figure 19.1). For this purpose, only observations with complete information are used, yielding a sample for analysis made up of 2,480 students.

The three dimensions of satisfaction

Figure 19.1 The three dimensions of satisfaction.

For each of the three dimensions, we calculated a score as well as an overall score on general satisfaction, in which s is the score of overall satisfaction concerning all aspects of the degree programme, s, is the score for satisfaction with the courses, s2 is the score for satisfaction with the teachers and s3 is the score for satisfaction with the facilities. These scores range between 0 and 1, where 1 means completely satisfied and 0 means totally unsatisfied. Table 19.1 reports a descriptive analysis of overall satisfaction in terms of these four scores.

Students are particularly satisfied with the courses (the average value of s, is about 0.72) and with their teachers (the average value of s, is about 0.72), but they are slightly less satisfied with the facilities (the mean of s3 is about 0.67). Overall, they are satisfied: the average degree of overall student satisfaction (s) is about 0.70.

The coefficient of variation for all scores is quite low, meaning that the students’ answers show strong convergence; however, the scores on satisfaction with teachers and facilities are a bit more heterogeneous than the others; thus s2 and s3 are the most informative in the empirical analysis.

The scores on the three dimensions and overall student satisfaction (s) are cross analysed with the GRADUA survey question on ‘general satisfaction’. The respondents could choose a value from among four Likert-scale items, from 1 (not satisfied at all) to 4 (completely satisfied). Table 19.2 and Figure 19.2 show that, as the Likert-scale value increases, so does the mean value of all the scores. This is additional proof of the robustness of the measures obtained with multi-dimensional analysis.

For Likert-scale values between 2 and 4, the score means are similar and constant. This is not the case for the lowest value for general satisfaction

Table 19.1 Mean and coefficient of variation (%) of the satisfaction measures

n

Mean

Coeff. of variation

s

2480

0.699

30.10

si

2480

0.717

29.97

s2

2480

0.707

37.01

s3

2480

0.672

35.98

Table 19.2 Satisfaction scores by ‘general satisfaction’ with the degree programme

General satisfaction

5

S2

1

0.384

0.583

0.297

0.272

2

0.401

0.465

0.353

0.387

3

0.596

0.630

0.580

0.578

4

0.823

0.822

0.860

0.789

Likert scale: 1 (not satisfied at all) to 4 (completely satisfied)

Satisfaction scores by ‘general satisfaction’ with the degree programme

Figure 19.2 Satisfaction scores by ‘general satisfaction’ with the degree programme.

Table 19.3 Satisfaction scores by university

University

s

s,

s2

University of Tirana

0.567

0.610

0.545

0.545

Polytechnic University of Tirana

0.597

0.626

0.614

0.552

Agricultural University of Tirana

0.704

0.735

0.718

0.658

University of Arts

0.645

0.584

0.710

0.643

University of Medicine

0.598

0.630

0.591

0.572

European University of Tirana

0.781

0.765

0.773

0.803

Polis University

0.868

0.853

0.858

0.894

Catholic University Our Lady of Good Counsel

0.810

0.757

0.833

0.841

Albanian University

0.835

0.834

0.866

0.804

(‘not satisfied at all’): students who are not satisfied at all with the degree programme are not satisfied with teachers and facilities, but are much more satisfied with the courses. Students who are completely satisfied with the degree programme have the highest level of satisfaction with teachers.

The four multi-dimensional satisfaction scores are disaggregated by university in Table 19.3 and Figure 19.3. Based on the results, the scores are much higher at private universities than public universities. Polis University has the best performance (overall satisfaction score of about 0.87), followed by Albanian University (overall satisfaction score of about 0.84). Both universities are private and have a very high degree of satisfaction with all aspects of the degree programme. Among the public universities, the Agricultural University of Tirana received the highest scores. Public universities appear to perform very poorly in satisfaction with the facilities,

Satisfaction scores

Figure 19.3 Satisfaction scores.

which reflects the low level of investment and financing for facilities. The University of Tirana has the lowest degree of satisfaction overall and with teachers and facilities.

The variation in the four scores among universities is low, the coefficient of variation is around 0.14 for s, and around 0.16 for s and s2; however, it is 0.19 for s3 (satisfaction with facilities), meaning that this aspect could be positive at some universities and negative at others.

All the scores are disaggregated by the gender of respondents (see Figure 19.4). Differences between male and female students are negligible, but in general satisfaction is higher among male than female respondents in all dimensions of satisfaction and overall.

The degree of satisfaction is also analysed by the student’s place of birth (Figure 19.5). Albania has few foreign students; few university programmes are offered in foreign languages and attended by foreign students. However, the number of returning migrants whose children were born abroad (e.g. Greece) is growing. Based on the results, satisfaction is higher among students born in other countries than among those born in Albania. There may be an explanation: students who are born outside the country, especially those who do not speak Albanian, are expected to select programmes offered in foreign languages, which are given by private universities, which score higher on satisfaction overall.

In the questionnaire, the respondents were also required to mention the country where they attended high school. The degree of satisfaction is also disaggregated by this variable (Figure 19.6). Like students born in other countries, students who attended high school elsewhere (many of whom

Satisfaction scores by gender by university

Figure 19.4 Satisfaction scores by gender by university.

Satisfaction scores by place of birth

Figure 19.5 Satisfaction scores by place of birth.

were very likely to have been born elsewhere) also have a higher level of satisfaction than those who attended high school in Albania. The differences in this variable are larger. The largest gap in the scores are for satisfaction with facilities and with teachers.

The degree of satisfaction is also cross analysed with the degree of education of the students’ parents (Table 19.4 and Figure 19.7), based on the parents’ highest level of education. The parents’ education appears to be

Satisfaction scores by the location (country) of the high school attended

Figure 19.6 Satisfaction scores by the location (country) of the high school attended.

Table 19.4 Satisfaction scores by the highest education level of the students’ parents

s

s,

U

No qualification

0.793

0.809

0.806

0.763

Primary school

0.716

0.672

0.736

0.740

Middle school

0.716

0.734

0.737

0.677

Secondary school

0.690

0.712

0.699

0.658

University degree

0.701

0.717

0.706

0.681

inversely related to the overall satisfaction index. Thus, the higher the level of education of students’ parents is, the lower the degree of overall satisfaction for students is. In particular, respondents whose parents have the lowest education level had the highest level of satisfaction on all three scores and the overall score. Probably students from families with a high level of education have higher expectations, not just with respect to the facilities but also the courses and teachers.

Conclusions

This chapter is based on an assumption that student satisfaction is a multidimensional and fuzzy concept. This perspective aligns with the literature on this topic, which holds that student satisfaction is a complex issue influenced by many factors. The added value of our research is the use of a multi-dimensional approach for defining satisfaction, employing fuzzy-set theory to measure satisfaction according to the degree of membership in a set, instead of dichotomous indicators. Our main findings about student

Satisfaction scores by the highest education level of the students’ parents

Figure 19.7 Satisfaction scores by the highest education level of the students’ parents.

satisfaction regarding the system of higher education in Albania can be summarised as follows.

The use of this methodology in different research fields has had good results, which is also the case in our study of student satisfaction. We find a strong relationship between overall student satisfaction, calculated as a simple average of the satisfaction scores for the three dimensions identified, and the general satisfaction indicator, on the basis of the sample data.

The satisfaction scores for each dimension reveal new evidence on student satisfaction. This issue is crucial in terms of policy implications. It is, of course, important to know that students are more or less satisfied, but it is much more important to understand which aspects (dimensions) of higher education make students feel more or less satisfied. For example, it is important for universities to find out that they have the highest heterogeneity in terms of satisfaction with facilities: this finding should concern the management of the universities with the lowest scores in this dimension.

Future research on this topic could construct an econometric model using the indicators proposed here as dependent variables. Then, the inferences from the model could be used to evaluate the net effect of student characteristics on the different dimensions of satisfaction and general satisfaction.

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