Determinants Affecting Learner’s Behaviour in Music Education
Applying Information Technology
Jing Li
1
, Chi-Hui Wu
2
, Tung-Jung Lin
2
and Reed-Joe Chang
2
1
Music and Film College, Tainjin Normal University, Binshui West Rd., Tianjin, China
2
Ph.D. Program in Management, Da-Yeh University, University Rd., Chunghua, Taiwan
Keywords: Applying Information Technology for Music Teaching, Self-Directed Learning, Music Learning Motivation,
Online Learning Attitudes, Learning Engagement, Fuzzy Delphi Method, ISM with Fuzzy MICMAC.
Abstract: This article is to investigate the learner’s behaviour in music teaching applying information technology
using ISM with Fuzzy and MICMAC approach. Since learner’s behaviour features multiple characteristics
which are complicated and interact with each other, this article makes clear the relationships within
characteristics of learning behaviour and provides education institutions with instruction on teaching
strategies for music teaching applying information technology on activating learners’ learning behaviour.
This research shows that music teaching applying information technology affects behaviour relating to
learners’ online learning attitudes, music learning motivation, and learning engagement. Among them, the
self-directed learning factor p most critical to the learning behaviour.
1 INTRODUCTION
It has been decades applying information technology
for teaching over the world. However, the music
teaching is a subject of learning-by-doing, one
information technology for all music teaching is not
enough, but a multiple and integrated system with
different teaching contents and methods is needed.
A learner’s behaviour is a concept of multiple
dimensions. To understand the relationships
beteween complicated behaviours on the music
teaching applying information technology, this
article aims at music teaching applying information
technology for the universities in the China Tianjin
and Beijing and Taichung Chunghua in Taiwan to
construct a framework through questionaire of
Fuzzy Delphi method to analyze the causal effects
between the dimensions and criteria of learner’s
behavior by ISM with Fuzzy MICMAC.
2 LITERATURE REVIEW
2.1 Applying Information Technology
to Music Teaching
It is an innovative concept and method integrating
IT into teaching and education. Teachers are
applying IT to developing multiple and innovative
teaching activities and ability of IT application.
(Chang and Wang, 2008; Wang, 2010).
This article is to describe the integration of IT
with music teaching comprising music theory, music
composition creation, music composition recording,
music performance, musical instrument teaching,
music appreciation, and music research (Lee, 2003;
Tseng, 2009), and the corresponding online music
teaching systems and courses.
2.2 Self-directed Learning
It is requested that learners’ online courses have a
tendency toward self-active learning and motivation.
Guglielmino (1977) and Driscoll (1994) proposed
that self-directed learning implies an independent
and continual behaviour affecting the learning
motivation, (Mount et al., 2005; Gendron, 2006;
Chen and Liang, 2009).
424
Li, J., Wu, C., Lin, T. and Chang, R.
Determinants Affecting Learner’s Behaviour in Music Education Applying Information Technology.
DOI: 10.5220/0007734404240431
In Proceedings of the 11th International Conference on Computer Supported Education (CSEDU 2019), pages 424-431
ISBN: 978-989-758-367-4
Copyright
c
 2019 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

Knowles (1975) proposed that a self-directed
learning is a process that learners can actively
recognize learning requirements and evaluate
learning results. Therefore, this study emphasizes
self-directed learning is an ability that learners can
actively learn, and make plans.
Based on self-directed learning scale,
Guglielmino (1977), and self-directed learning
(Oddi, 1986; Liang, 2008; Chen and Liang, 2009),
self-directed was divided into five dimensions: Self-
learning, continuous learning, efficiency learning,
independent learning, self-understanding, planning
learning, loving learning.
2.3 Online Learning Attitudes
Learning attitudes is constituted interacting with the
environments, accordingly, resulting in the
complexity of factors affecting learner’s attitude in
learning (Huang, 2003).
Online education is rapidly becoming an
important method of instructional delivery for
various educational contexts (Ku and Lohr, 2003).
Computers and the Internet designed for educational
purposes have fundamentally changed school
education, especially in universities (Liaw and
Huang, 2011). The attitudes of learners have been
very positive and supportive toward online
instruction (Chang, 2000). Online learning provides
different learners with multiple teaching
environments through smart phones when doing
computer-aided learning, that might spawn various
problems and attitudes. Rainer and Miller (1996)
research points out that the main factors affecting
using computer is learners’ attitude. As a result, how
to establish a positive attitude and computer
operating skills is crucial to learners’ learning
effectiveness. Hignite (1990) proposed that the
attitudes for computers means general senses when a
person or people using computers.
In addition, this study also studies the music
learning course contents for learners using computer
and network facility when teaching engages in IT.
For that, below are referenced as Computer Attitude
Scale (Loyd and Gressard, 1984), the Online
Tutoring Attitudes Scale (Graff, 2003), related
research of online learning attitudes (Okwumabua et
al., 2010), for constructing dimensions in online
learning attitudes.
2.4 Music Learning Motivation
Learning motivation is an elementary driving force
motivating a learner to learn (Wu, 2016). It is a
mental experience to activate, maintain learning
activities, and direct a learner toward the learning
objective designated (Chen, 2007).
This study adopts scales of learning motivation,
which is for undergraduate students who take music
courses, that is categorized into six dimensions:
Cognitive interest, self-growth, interpersonal
facilitation, professional advancement, social
conformity, transforming monotonous life (Boshier,
1971; Garder and Lambert, 1972; Lee and Huang,
2007; Chen and Lin, 2018).
2.5 Learning Engagement
Learning engagement of learners is an experience in
a learning process, and presents main elements:
behaviour, cognition and emotion (Fredricks et al.,
2004). It is a critical index reflecting the learning
status of undergraduate students, the degree of
engagement of that will affect knowledge
acquisition and cognitive development (Kuh et al.,
2005; Pascarella and Terenzini, 2005). Glanville and
Wildhagen (2007) indicates that learning
engagement is what learners behave in schooling
and psychological involvement, that can be a useful
concept for obtaining education effectiveness.
Handelsman et al. (2005) emphasizes that, from
the teaching viewpoints. The more the students
engaged, the more willingness they persist to keep,
and that further encourages teaching as well.
Subsequently, paying attention to the learning status
of students can provide teachers with related
information improving teaching, designing, and
planning activity alternatives (Lin and Huang, 2012).
Apart from motion engagement, it needs to take
account the strategy, performance in classes,
interaction between teachers and students for
behaviour engagement in different aspects (Lin and
Huang, 2012; Tsai, 2016). Consequently, this study
categorized learning engagement to five dimensions:
Skills engagement, emotional engagement,
performance engagement, attitudes engagement,
interaction engagement (Handelsman et al., 2005;
Lin and Huang, 2012; Tsai, 2016).
3 ISM METHODOLOGY AND
MODEL DEVELOPMENT
3.1 Research Framework
The research framework construct evaluation
dimensions and criteria by two steps. First, based on
Determinants Affecting Learner’s Behaviour in Music Education Applying Information Technology
425

literature reviews, to synthesize with viewpoints of
scholars summarizing the effects of music teaching
engaged in IT to five dimensions and thirty criteria.
And through Fuzzy Delphi method, we conduct the
group decision making by experts and scholars who
are good at IT-aided music teaching, solve and
construct their consensus to the fuzzy problems
affecting learners’ learning behaviour in music
teaching engaging IT. Important and high criteria
items were selected for interviewing scholars and
experts in IT-aided music teaching. Second, the 40
ISM questionnaires were issued, 20 of them from
scholars in learning behaviour, and other 20 form
university teachers in music teaching with IT.
3.2 Operation Steps of Fuzzy Delphi
Method
This study uses Fuzzy Delphi method to screen out
important items from the dimensions and criteria of
learning behaviour as the steps of Fuzzy Delphi
method (Liang et al., 2010):
Step1: Collect group decisive opinions: Using
semantic variables in questionnaire, the measure
index for the importance of various criteria can be
obtained. This study uses Likert’s 5 scale to evaluate
learning behaviour and adopts the geometric meant
to integrate expert opinions.
Step2: Construct fuzzy triangle: Calculate the fuzzy
triangles of the importance of various criteria. Klir
and Yuan (1995) proposed geometric mean from
general models of arithmetic mean as Fuzzy Delphi
method for calculating group decisive consensus.
Step3: Solve problems by defuzzification: A fuzzy
number is a quantity whose value is imprecise.
Therefore, we must perform defuzzification to find
the best non-fuzzy performance value, BNP.
Step4: Screen the evaluation criteria: For the screen
of evaluation criteria, a threshold value and statistic
judgement standards of expert opinions must be
established (Yeh et al., 2017). By the threshold value,
the optimum criteria can be screen out from multiple
ones, which generally account for 60% to 80% of
the maximum value, that is 70% in this study.
3.3 Questionnaire and Survey Design
By literature reviews and data collection, this study
figures out five dimensions: IT-aided music teaching,
online learning attitudes, motivation to learn music,
and learning engagement, and the corresponding 30
criteria, the Fuzzy Delphi method was applied to the
screening. The operational definitions of 30 criteria
follows: Music theory (M
1
), music composition
creation (M
2
), music composition recording (M
3
),
music performance (M
4
), music instrument teaching
(M
5
), music appreciation (M
6
), music research (M
7
),
self-learning (S
1
), persistent learning (S
2
), efficiency
learning (S
3
), independent learning (S
4
), self-
understanding (S
5
), learning planning (S
6
), loving
learning (S
7
), confidence of computer/smart phones
and networks (O
1
), using networks (O
2
), online
learning (O
3
), using computers/smart phones (O
4
),
loving to use computer/smart phones (O
5
), cognition
of interests (L
1
), self-growth (L
2
), social
relationships (L
3
), job progress (L
4
), expectation of
others (L
5
), changing mono lifestyle (L
6
) , skill
engagement (E
1
), motion engagement (E
2
),
performance engagement (E
3
), attitude engagement
(E
4
), interaction engagement (E
5
).
3.4 Operation Steps of ISM with Fuzzy
MICMAC
3.4.1 ISM Analysis
Interpretive Structural Modelling (ISM) is an
interactive learning process in which a set of varied,
but directly related, elements is structured into a
comprehensive, systemic model (Sage, 1977;
Warfield, 1974). ISM is a well-established
methodology for identifying relationships between
specific items that define a problem or an issue (Jia
et al., 2015).
The objective of this methodology is to construct
a structural model to capture the user’s best
perceptions of the situation” (Bouzon et al., 2015).
ISM has been applied by a number of researchers in
various fields to develop a better understanding of
complex systems, such as analysing vendor selection
criteria (Deshmukh and Mandal, 1994), exploring
the factors affecting flexibility in a flexible
manufacturing system (Raj, 2012), determining the
mutual relationships between the enablers of tourism
value (Lin and Yeh, 2013), analysing key reverse
logistics variables to improve computer hardware
supply chains (Ravi et al., 2005), and identifying the
barriers to the implementation of total productive
maintenance (Attri et al., 2013). Using ISM, this
study investigates the relationships between
dimensions/criteria in music teaching applying
information technology. (Farris and Sage, 1975):
Step 1: Defining a set of variables affecting the
system; Step 2: Developing Self-Structural
Interaction Matrix and establishing a contextual
relationship between these variables; Step 3:
Developing a Reachability Matrix, and checking the
matrix for transitivity; Step 4: Partitioning the
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Reachability Matrix into different levels; Step 5:
Forming a canonical form of matrix; Step 6:
Drawing a directed graph and removing the
transitive links; Step 7: Converting the resultant
digraph into an ISM by replacing variable nodes
with statements.
3.4.2 Fuzzy MICMAC Analysis
MICMAC is a systematic analysis tool for
categorizing variables based on hidden and indirect
relationships, as well as for assessing the extent to
which they influence each other (Hu et al., 2009;
Kanungo et al., 1999). Deshmukh and Mandal
(1994) claim that the primary goal of MICMAC
analysis is to analyse the driving power and
dependence of each variable. “Driving power” refers
to the degree of influence and “dependence” is
defined the extent one variable is influenced by
others (Arcade et al., 1999). Based on driving power
and dependence, a 2D driver-dependence diagram
can be created, (Lee et al., 2010). By using
MICMAC, the factors/criteria can be classified into
the following clusters. Autonomous factors/criteria:
with weak driving power and weak dependence.
These factors are relatively disconnected from the
system, with which they have only a few links.
Dependent factors/criteria: with weak driving power
but strong dependence. Linkage factors/criteria: with
strong driving power and strong dependence.
Independent factors/criteria: with strong driving
power but weak dependence. Factors with relatively
strong driving power are “key” variables clustered
into the category of independent or linkage factors.
The establishment of an Fuzzy MICMAC
involves a number of steps, which are well
documented in the literature (Katiyar et al., 2017):
Step 1: Developing fuzzy direct relationship matrix;
Step 2: Fuzzy indirect relationship analysis; Step 3:
Stabilizing fuzzy matrix; Step 4: Drawing driving-
dependence power graph.
4 ISM WITH FUZZY MICMAC
ANALYSIS
4.1 Analysis of Survey Questionnaire
The research units including universities at Tianjin
and Beijing areas in China and Taichung Chunghua
areas in Taiwan and aiming to the research scholars
and university teachers on music teaching
incorporating information technology, to them 40
questionnaire respondents were validly completed in
one month.
Using ISM with Fuzzy MICMAC, this research
analyses the 5 dimension and 30 criteria including
interacting relationships and strength between them.
4.2 Dimension Analysis
4.2.1 Structural Self-Interaction Matrix
(SSIM)
With the knowledge and experience, the
questionnaire respondents evaluate the relationships
between dimensions and criteria and fill the SSIM,
which are categorized into types of affecting
variables, variables affected, variables affecting each
other, and independent variables.
Through the development of the initial and final
reachability matrix and the partition procedure, the
conical matrix is shown as table 1.
Table 1: Conical Matrix.
D
5
D
3
D
4
D
1
D
2
Driving Power
D
5
1
0
0
0
0
1
D
3
1
1
1
0
0
3
D
4
1
1
1
0
0
3
D
1
1
1
1
1
0
4
D
2
1
1
1
0
1
4
Dependence Power
5
4
4
1
1
4.2.2 Development of Digraph and Building
the ISM-based Model
Based on the conical form of reachability matrix, an
initial digraph including transitivity links is
generated by nodes and lines of the edges. Suppose
there is a relationship between two dimensions, and
then it is shown by an arrow from one dimension to
another dimension.
Figure 1: ISM-based Model.
D
5
Learning
Engagement
D
3
Online Learning
Attitudes
D
4
Music Learning
Motivation
D
1
Applying Information
Technology for Music
Teaching
D
2
Self-Directed
Learning
Determinants Affecting Learner’s Behaviour in Music Education Applying Information Technology
427

The figure 1 shows that Applying Information
Technology for Music Instrument Teaching (D
1
) and
Self-Directed Learning (D
2
) are the most crucial
dimensions for online learning behaviour to learner as
it comes at the bottom of the ISM hierarchy. Learning
Engagement (D
5
) appeared at the top which indicate it
will influence the entire process of online learning
behaviour. The D
1
and D
2
lead to Online Learning
Attitudes (D
3
) and Music Learning Motivation (D
4
).
Similarly, D
3
and D
4
lead to D
5
.
4.2.3 Development of Fuzzy Direct
Relationship Matrix (FDRM)
The MICMAC considers only binary types of
relationships; therefore, at this stage, we have used the
fuzzy set theory to increase the earlier sensitivity.
With fuzzy MICMAC, an additional input of possible
interactions among the barriers is established. Similar
to Qureshi, Kumar and Kumar (2008), the possibility
of interaction is be defined by a qualitative
consideration on a 0 to 1 scale.
The possibility of the numerical value of
reachability is covered on the DRM to obtain a fuzzy
direct reachability matrix (FDRM). Further, the
binary direct reachability matrix (BDRM) is achieved
by examining the direct relationship between the
dimensions disregarding the transitivity and making
diagonal entries 0. FDRM is presented in Table 2.
Table 2: Fuzzy Direct Reachability Matrix.
D
1
D
2
D
3
D
4
D
5
D
1
0
0.7
0.5
0.7
0.5
D
2
0.1
0
0.7
0.7
0.7
D
3
0.3
0.1
0
0.7
0.3
D
4
0.3
0.3
0.7
0
0.9
D
5
0.5
0.5
0.5
0.1
0
4.2.4 Fuzzy Indirect Relationship Analysis
FDRM is used to find the fuzzy indirect relationship
between the dimensions. The matrix is multiplied
reproduced until the hierarchies of the driving and
dependence power are stabilized. According to the
fuzzy set theory, the product matrix will also be a
fuzzy matrix. Multiplication follows the given
following rule: the product of fuzzy set A and B is
fuzzy set C.
C=A*B= max k[min(a
ik
, b
kj
)], where A= (a
ik
)
and B=( b
kj
) are two fuzzy matrices.
4.2.5 Stabilization of Fuzzy Matrix
As discussed in the previous section, the FDRM
process and matrix multiplication are used to
stabilize the matrix as exhibited in Table 3. The ranks
of the driving power of the criterion decide the
hierarchy of criterion. The purpose of this
classification of the dimensions is to analyse the
driving and dependence power of the dimensions that
influence learners’ learning behaviour.
Table 3: Fuzzy MICMAC Stabilized Matrix.
D
1
D
2
D
3
D
4
D
5
Driving
Power
Rank
D
1
0.5
0.5
0.7
0.7
0.7
3.1
1
D
2
0.5
0.5
0.7
0.7
0.7
3.1
1
D
3
0.3
0.3
0.7
0.3
0.7
2.3
4
D
4
0.5
0.5
0.5
0.7
0.3
2.5
3
D
5
0.3
0.5
0.5
0.5
0.5
2.3
4
Dependence
Power
2.1
2.3
3.1
2.9
2.9
Rank
5
4
1
2
2
4.2.6 Key Indicators
Based on the information derived from the Fuzzy
MICMAC stabilized matrix, the indicators were
classified into four sectors in the Driving-Dependence
Graph (Figure 2). The indicators with the greatest
driving power in the stabilized matrix are the key
indicators, that nearest to the origin represents the
highest driving power. Identification and
classification of the key criterion is essential for
management to decide the course of action to be taken
for the system under studying. This method confirms
the importance of certain dimensions and also search
some hidden dimensions through direct classification,
which an important role on the system. Fuzzy
MICMAC examination of direct relationships also
reveals that criterion having strong impact can be
supressing hidden criterion.
Figure 2: Driving-Dependence Power Graph for Factors.
6
5.5
5
4.5
4
3.5
3
2.5
2
1.5
1
0.5
0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6
2.66
2.66
D
1
D
2
D
4
D
3
Driving
Power
Dependence
Power
D
5
Driver
Factors
Linkage
Factors
Autonomous
Factors
Dependent
Factors
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4.3 Criteria Analysis
By conducting analysis of 30 criteria and complying
step 1 to step 9 corresponding those in dimension
analysis, the calculation results of Driving-
Dependence Graph is as Figure 3 shown.
Figure 3: Driving-Dependence Power Graph for Criteria.
5 RESULTS AND DISCUSSION
This research investigates determinants and criteria
of IT-aided teaching and provides teaching course
designers of education institutions with a
complicated relation model of IT-aided teaching to
understand the model and the key factors during
online learning. First, based on the literature reviews
and interviewing scholars and experts in online
learning by fuzzy Delphi method on the 5 affecting
factors and 30 criteria, and then, interview 20
experts to construct ISM model. and interviewing
scholars and experts to construct ISM model.
As for dimensions, the ISM Model is shown
Figure 1. Music teaching applying information
technology (D
1
), and Self-directed learning (D
2
) will
directly affecting Online learning attitudes (D
3
) and
Music learning motivation (D
4
), in addition, Online
learning attitudes (D
3
) and Music learning
motivation (D
4
) affects learning engagement (D
5
)，
and Online learning attitudes (D
3
) and Music
learning motivation (D
4
) will affect each other, thus,
Music teaching applying information technology
(D
1
), and Self-directed learning (D
2
) will indirectly
affect learning engagement (D
5
).
Fuzzy MICMAC method uses Driving power
and Dependence power to divide dimension values
in Fuzzy MICMAC Stabilized matrix (as Table 3)
into four clusters (as Fig. 2). Figure 2 shows Music
teaching applying information technology (D
1
), and
Self-directed learning (D
2
) are critical dimensions
affecting learners’ learning behaviour. And, Music
learning motivation (D
4
), learning engagement (D
5
)，
and Online learning attitudes (D
3
) are affected by
other dimensions of learning behaviour.
To further understand the complicated
relationships of learners’ learning behaviour, criteria
analysis results were shown as Figure 3, where
music theory (M
1
), music composition creation (M
2
).
music composition recording (M
3
), music
performance (M
4
), music appreciation (M
6
), music
research (M
7
), self-learning) (S
1
), efficiency learning
(S
3
), transforming monotonous life (L
6
), attitudes
engagement (E
4
) are 10 higher driving power criteria,
which are the key criteria affecting learners’ learning
behaviour. And planning learning (S
6
), loving
learning (S
7
), 、network use (O
2
), self-growth (L
2
),
professional advancement (L
4
), social conformity
(L
5
), performance engagement (E
3
), interaction
engagement (E
5
) 10 criteria are affected by the
above 10 criteria of music theory and so forth.
6 CONCLUSIONS
Music teaching applying information technology
provides learners individual learning opportunities to
broaden more extensive experience of music
learning. Therefore, it is suggested that take account
the learners’ learning background, experience, and
ability combining the features of network learning
environments before designing the music courses
and materials applying information technology.
Since the learners’ learning behaviour features
complicated relationships. Apart from the IT-aided
music teaching, self-directed learning is also an
important factor affecting learners’ learning
behaviour, thus, strengthening learners’ self-directed
learning, independent and consistent learning and
successfully the learning attitudes, music learning
motivation and learning engagement will be the
critical missions for the high education involving the
online teaching course.
30
29
28
27
26
25
24
23
22
21
20
19
18
17
16
15
14
20.5 21.0 21.5 22.0 22.5 23.0 23.5 24.0 24.5 25.0 25.5 26.0 26.5 27.0 27.5
25.8
25.8
M
1
M
2
M
6
M
5
Dependence Power
S
1
M
3
M
4
M
7
S
2
S
3
S
5
S
6
S
4
S
7
O
1
O
2
O
3
O
4
O
5
L
1
L
2
L
3
L
4
L
5
L
6
E
1
E
2
E
3
E
5
E
4
Driving
Power
Driver
Criteria
Autonomous
Criteria
Dependent
Criteria
Linkage
Criteria
Determinants Affecting Learner’s Behaviour in Music Education Applying Information Technology
429

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