Student’s Preferences for Lecturers with Conjoint Analysis

R. Warti

, A. Murtadlo

, R. Amalia

, V.Y Sundara

, K. Anwar

Department of Mathematics Education, UIN Sulthan Thaha Saifuddin, Jl. Jambi-Ma.Bulian Km.16, Jambi, Indonesia

Department of Islamic Education, UIN Sulthan Thaha Saifuddin, Jl. Jambi-Ma.Bulian Km.16, Jambi, Indonesia

Keywords: Conjoint Analysis, Lecturers, Student’s Preferences.

Abstract: Lecturer is one of the most important factors in determining the quality of education and college graduates.

This study aims to determine the factors of student’s preference on the quality on lecturers at the Faculty of

Education and Teacher Training Sulthan Thaha Saifuddin Jambi State Islamic University. The sample in

this study is all active students in Faculty of Education and Teacher Training chosen by using proportionate

stratified random sampling method. The data were collected by using questionnaires containing seven

factors which were analyzed using conjoint analysis with full-profile approach. The result shows that the

factors which become the preference of the students toward the lecturers are interactive, innovative,

applicable, relaxed, motivated, timely and objective in assessing. Based on the relative importance value of

the factors obtained in sequence, it results lecturer’s personalities, assessment, discipline, learning method,

material mastery, communication style and motivating ability.

1 INTRODUCTION

Lecturer is one of the most important

components in a system of education in higher

education. Lecturer is based as professionals with

the main task of transforming, developing, and

disseminating knowledge, technology and art

through education, research and community service.

The lecturer is a professional person who aims to

implement the national education system and realize

the goals of national education, namely the

development of potential students to be human

beings who believe and fear the God, have noble

character, healthy, knowledgeable, capable, creative,

independent, and become citizens who democratic

and responsible (UUD RI No 14 tahun 2005). In

their daily lives, sometimes there is a difference

between the ideal character of lecturer and what is

encountered by students in the campus.

The ideal characteristics of the lecturer in the

educational process are: 1) Always showing a

personality that is character, fearful of God and

proud of his identity as a college lecturer, 2)

Mastering in depth the fields of science that

becomes his expertise and able to actualize in

modern life and always oriented to the future, 3)

Have extensive and deep scientific and

intellectualism insight, adequate professionalism,

and the right methodology, 4) Demonstrate

discipline, diligence, responsibility, integrity,

critical, innovative, dynamic, open, respect the

opinions of others, oriented to productivity, and

noble and believes that work is worship, 5) Soul and

at the same time apply as educators and mentors,

have honesty, trustworthy, friendly, communicative,

attention to science, have responsibility for the

progress and success of students, emphasize problem

solving, and make students have moral behavior, 6)

Be proactive and feel ownership and responsibility

for the success and progress of the institution where

he works, 7) Oriented towards the future, always

aware of improve personal knowledge and quality

and upholding the lecturer’s code of ethics and

complying with all provisions regarding obligations

as lecturers, 8) Avoid corruption, collusion and

nepotism, 9) Upholds ukhuwah and togetherness,

wisdom, full dedication and worship, and is able to

make itself a role model for students in behavior and

especially in the fields of taught disciplines, as well

as an examples and role models for the whole

community, 10) Must always try to be able to give a

contributing of knowledge that is beneficial to

humanity (Alba, 2011).

Tarbiyah and Teacher Training Faculty are the

largest faculty in the State Islamic University of

Sulthan Thaha Saifuddin Jambi which consist of

several departments and have more than one

hundred lecturers. The results of interviews with

several students about lecturers showed that each

Warti, R., Murtadlo, A., Amalia, R., Sundara, V. and Anwar, K.

Student’s Preferences for Lecturers with Conjoint Analysis.

DOI: 10.5220/0008524204910495

In Proceedings of the International Conference on Mathematics and Islam (ICMIs 2018), pages 491-495

ISBN: 978-989-758-407-7

491

student had different preferences about the lecturers

who had taught them so far. Fatrisia as Islamic

Education Department said “there are lecturers who

are disciplined, diligent, assignments and judgments

are clear, skilled in teaching, responsible for their

duties and having authority. But there is lecturer

who rarely for come, give a lot of assignments and

bad assessment”. Wahyu from Islamic Education

management Department said, “there are lecturers

who are disciplined and teach well, but there are also

who are lazy to come and teach”. Based on the

diversity of opinions, it is necessary to evaluate

which aims to determine the student’s preferences in

lecturers at Tarbiyah and Teacher Training Faculty

of State Islamic University of Sulthan Thaha

Saifuddin Jambi. One method that can be used to

examine and analyze preferences is conjoint

analysis.

Conjoin analysis is a multivariate analysis that

can be used to obtain a combination or composition

of factors in the form of attributes of a new or old

product or service that is most preferred by

consumers so that consumer’s preferences for a

product or service can be known. In this study

conjoint analysis is used to determine student’s

preferences for lecturers at Tarbiyah and Teacher

Training Faculty which are expected to increase

learning motivation on students.

The previous researches related to student

preferences in the field of education were discussed

by several researchers. Cynthia (2013) use conjoint

analysis to determine students' perception of

lecturers. Fitri (2011) use conjoint analysis to

examine student preferences for the quality of

lecturers. Arief (2017) use conjoint analysis to

examine student preferences towards lecturers. Rini

(2017) use conjoint analysis to determine student

preferences for mathematics statistics courses. Based

on these studies, researchers are interested to

determine the factors of student's preference for

lecturers using conjoint analysis. The purpose of this

research was to determine both student’s preference

and relative importance value among the factors of

student’s preference for lecturer at Tarbiyah and

Teacher Training Faculty of State Islamic University

of Sulthan Thaha Saifuddin Jambi.

2 METHODS

This research was conducted at Tarbiyah and

Teacher Training Faculty of State Islamic University

of Sulthan Thaha Saifuddin Jambi. This research

was conducted in March and April 2018. The data in

this study are primary data obtained from the

distribution of questionnaires that have been given to

active students of 2015, 2016 and 2017 at Tarbiyah

and Teacher Training Faculty of State Islamic

University of Sulthan Thaha Saifuddin Jambi. The

sample in this study was 2830 students at Tarbiyah

and Teacher Training Faculty who were selected

using proportionate stratified random sampling. This

sampling technique is carried out by the requirement

that the population consists of levels, layers, strata,

or sub-groups (Tukiran, 2014). Using the Slovin

formula obtained a sample size of 351 students

which will then be divided in proportion based on

the number of students in each department and class.

The collected data will be analyzed using conjoint

analysis.

2.1 Determination of Attributes and

Levels

From some literature, theory and research

obtained several lecturer criteria that were assessed

by students when teaching in the classroom. The

lecturer criteria are the attributes used in the study.

These attributes are communication style, learning

method, material mastery, lecturer’s personalities,

motivation ability, discipline and assessment. Each

attribute has two choice criteria called level which

are usually equal levels or choices. The attributes

and level of attributes in this study can be seen in

Table 1.

Table 1: The attributes and level of attributes

Attributes

Level of Attributes

Communication Style

1. Communicative

2. Interactive

Learning Method

1. Conventional

2. Innovative

Material Mastery

1. Theoretical

2. Applicative

Lecturer’s Personalities

1. Relax

2. Serious

Motivation Ability

1. Motivator

2. Inspiratory

Discipline

1. Timely

2. Flexible

Assessment

1. Objective

2. Subjective

2.2 Designing a Combination of Stimuli

The design of the combination of attribute levels

(stimuli) that have been used in this researched was

compiled based on full profile approach. Because

there are seven attributes, the cards formed are 2

= 2 × 2 × 2 × 2 × 2 × 2 × 2 =128. Because stimuli

are formed too much and will make it difficult for

ICMIs 2018 - International Conference on Mathematics and Islam

492

respondents to score, the stimuli are reduced by

using factorial designs called orthogonal arrays,

which make it possible to estimate all main effects.

With minimum stimuli = Number of Levels -

Number of Attributes + 1, the stimuli that have been

evaluated by respondents are 14-7 + 1 = 8 stimuli.

2.3 Data Collection

Data collection is done by distributing

questionnaires to respondents. The questionnaire is

in the form of a stimuli card which contain a

combination of attributes that will be chosen by

respondents using rating of 1 to 5 with the following

conditions:

Score 1 = Very Dislike

Score 2 = Dislike

Score 3 = Quite Likes

Score 4 = likes

Score 5 = Very Likes

2.4 Data Analysis

The data obtained will be analyzed using

descriptive analysis and conjoint analysis procedures

to find out what attributes are the student’s

preferences for lecturers. Interpretation of results is

seen from the total usefulness value and the

important relative values found in the model

produced in conjoint analysis.

3 RESULT AND DISCUSSION

This research uses primary data taken from the

results of questionnaires to active students at

Tarbiyah and Teacher Training Faculty at UIN

Sulthan Thaha Saifuddin Jambi for 2017/2018

academic year. The questionnaire begins with filling

in the student biodata which includes class,

department, gender, GPA, Senior High School,

major in Senior High School, origin of region and

residence. From 351 students as respondents, 66

were students of 2015 class, 121 students in 2016,

and 141 students in 2017.

Based on the department, from 351 samples

there were 87 Islamic Education students, 21 Arabic

Language Education students, 25 Islamic Education

Management students, 45 Madrasah Ibtidaiyah

Education students, 48 English Education students,

16 Physics Education students, 48 Biology

Education students, 42 Mathematics Education

students, and 19 Raudhatul Athfal Education

students.

3.1 Combination of Attributes and

Level of Attributes

This research uses a complete combination

presentation method to display a combination of

attributes with an attribute level (stimuli). In making

stimuli with the complete combination presentation

method, the number of attributes and a small level

can be used a factorial design that uses all available

combinations. If selected p attributes that have two

levels, there will be as much as 2

combination of

attributes and level attributes that must be evaluated

by the respondent. So, the number of stimuli that

occur is 2

= 2 × 2 × 2 × 2 × 2 × 2 × 2 = 128 stimuli.

If the 128 stimuli are used, it will be difficult for

the respondent to score the stimuli. Respondents will

find it difficult to provide consistent answers, other

than that it requires a relatively long time. Then the

combination will be reduced using fractional

factorial design, orthogonal arrays. With this design,

some of the combinations of stimuli are chosen

which really affect the main effect. The stimuli taken

are minimum stimuli = number of levels - number of

attributes + 1 = 14-7 + 1 = 8. So, the stimuli that

respondents evaluated were 8 stimuli. Then the

respondent was asked to give a score on the 8

stimuli with criteria: 1 = very dislike, 2 = dislike, 3 =

quite like, 4 = like, and 5 = very like.

3.2 Research Sample Analysis

In this research, the research method that has

been used was the metric method (score). Data for

conjoint analysis were obtained from the scores

given by respondents to each card in the research

questionnaire. The data that has been obtained were

analyzed individually. The estimation model used

was an additive model. Based on the results of the

individual analysis, of the 351 respondents' data

obtained, there were 340 data that could be analyzed

while 11 data were not; this was because the

respondents gave the same score on all combinations

of attributes and attribute levels (stimuli).

3.3 The Use Value in Each Level of

Attributes

The student’s preferences with conjoined

analysis produced the use values that describe the

student's assessment of each level of attributes with

numbers. The positive value and the biggest shown

the level of attributes that students like, and the

negative value means dislike by students. Usability

Student’s Preferences for Lecturers with Conjoint Analysis

493

values at each attribute level based on student

preferences can be seen in Table 2.

Table 2: The use of values

Attributes

Level of Attributes

The use of

value

Communication

Style

Communicative

Interactive

-0,012

0,012

Learning

Method

Conventional

Innovative

-0,074

0,074

Material

Mastery

Theoretical

Applicative

-0,069

0,069

Lecturer’s

Personalities

Relax

Serious

0,219

-0,219

Motivation

Ability

Motivator

Inspiratory

0,032

-0,032

Discipline

Timely

Flexible

0,036

-0,036

Assessment

Objective

Subjective

0,211

-0,211

Constant

3,322

Table 2 show the use value in each attribute. It is

known that in the attributes of communication style,

students prefer interactive rather than

communicative, this is indicated by a positive use

value (0,012). In learning method, students prefer

innovative rather than conventional, this is indicated

by a positive use value (0,074). Attributes of

material mastery, students prefer mastery of

applicative rather than theoretical, this is indicated

by a positive use value (0,069). In the attribute of

lecturer’s personalities, students prefer lecturer who

are relaxed rather than serious lecturers, this is

indicated by a positive use value (0,219). In

motivation ability, students prefer motivator rather

than inspiratory, this is indicated by a positive use

value (0,032). In the discipline attributes, students

prefer on time, this is showed by a positive use value

(0,036). And assessment attributes, students prefer

lecturers who give objective values, this is mean by

a positive use value (0.211).

3.4 Relative Importance Value

Relative importance values are used to determine

which attributes are considered the most important

by respondents to lecturers can be seen in Table 3.

Table 3: Percentage of relative importance value

Attributes

Percentage

Communication Style

11.86

Learning Method

12.63

Material Mastery

12.54

Lecturer’s Personalities

20.65

Motivation Ability

10.66

Discipline

13.28

Assessment

18.37

Table 3 show percentage of relative importance

value of attributes by respondents to lecturers. From

the value of relative importance, it is known that the

most important attributes in sequence are lecturer’s

personalities (20,65%), assessment (18,37%),

discipline (13,28%), learning methods (12,63%),

material mastery (12,54%), communication style

(11,86%), and motivation ability (10,66%).

3.5 Correlation Test

Correlation test was conducted to find out the

relationship between the combination of attributes

and the student's preference for lecturers. Correlation

test results can be seen in Table 3.

Table 4: Correlation test

Statistic Test

Value

Sig.

Pearson’s R

1.000

0.000

Kendall’s tau

1.000

0.000

Based on Table 4, the relationship between the

combination of attributes and student’s preferences

can be seen in the Pearson's R value which shows

the value of 1,000 or the influential student

preference value is 100%. This shows that there is a

very strong level of correlation, supported by a

significant value of 0,000 (below 0,05) which

indicates that the value is significant.

4 CONCLUSIONS

Based on the results of the study it can be

concluded that the factors or attributes that become

student preferences for lectures are interactive,

innovative, applicative, relaxed, motivated, timely

and objective in assessing. Generally, the main thing

that is assessed or seen by students from a lecturer is

personalities.

REFERENCES

Alba, C. 2011. Strategi Peningkatan Mutu Pendidikan di

Perguruan Tinggi. Bandung : ITB.

Lestari, F. C. 2011. Study of Conjoint Analysis in

Reviewing Student’s Preferences on the quality of

STIS’s Lectures. Thesis. Bogor Agricultural

University. Bogor. Indonesia.

ICMIs 2018 - International Conference on Mathematics and Islam

494

Noegroho, C. A. 2013. Application of Conjoint Analysis

to Determine Student’s Preferences for Lecturers

(Case Study: Department of Mathematics, Faculty

Mathematics and Natural Sciences of Pakuan

University). Thesis. Pakuan University. Bogor.

Indonesia.

Taniredja, T., Mustafidah, H. 2014. An Introduction to

Quantitative Research. Bandung: Alfabeta.

Warti, R., Lestari, N., 2017. Student’s Preferences in

Mathematics Statistics using Conjoint Analysis,

Prosiding SIMaNIs. Volume 1. Nomor 1. Pp.414-

418.

Yulianto, A., Hidayat, R., Akhmad, H., 2017. Student’s

Preferences of Industrial Engineering Lectures at

Trunojoyo Madura University with Conjoint

Analysis. Thesis. University of Trunojoyo.

Madura. Indonesia.

Yunikasari, N., Jatipaningrum M. T., 2017. Comparison of

Metric and Non-Metric Responses in Conjoint

Analysis for preferences of lecturer’s qualities.

Journal of Statistika Industri dan Komputasi. Vol

2. No 2. Pp.136-144.

Student’s Preferences for Lecturers with Conjoint Analysis

495