Deterministic Factors Influencing Learners' Learning Behaviors and
Outcomes by Applying Information Technology-assisted Music
Curriculum
Jing Li
1,2
, Fang-Jie Shiu
2
and Hsiu-Chin Huang
2,3,*
1
Music and Film College, Tainjin Normal University, Binshui West Rd., Tianjin, China
2
Ph.D. Program in Management, Da-Yeh University, University Rd., Changhua, Taiwan
3
Department of Beauty Science, Chienkuo Technology University, Chiehshou North Rd., Changhua, Taiwan
Keywords: Applying Information Technology for Music Teaching, Learning Behaviors, Learning Outcomes, Fuzzy
Delphi Method, ISM with Fuzzy MICMAC.
Abstract: Due to the tendency of complex relationship between learners' learning behaviors and learning outcomes, this
research applies Fuzzy Delphi method and ISM with Fuzzy MICMAC to further understand deterministic
factors influencing learners' learning behaviors and outcomes by applying information technology-assisted
music curriculum. The results show that applying information technology-assisted music curriculum will
affect learners' learning behaviors, such as online learning attitude, music learning motivation and learning
engagement, as well as learning outcome factors such as learning satisfaction and learning effectiveness. In
addition, self-directed learning is the crucial factor of learning behaviors and learning outcomes, and learning
behaviors will affect learning outcome factors, such as learning effectiveness and learning satisfaction.
1 INTRODUCTION
The university music curriculum is a combination of
music theory, music art and performance skills
(learning by doing). It is difficult to use one single
type of information technology to integrate all music
teaching content and methods into the digital learning
system, but to develop music teaching system with
different information technology according to the
needs of different teaching content and methods.
Therefore, most of the research literature on applying
information technology-assisted teaching systems
studies applying information technology for music
teaching based on case studies or a single teaching
method. In the past, the literature has been less
explored from a systematic integration perspective of
music teaching (Tseng, 2009) and implemented
music knowledge content into online teaching
courses, such as music appreciation, music analysis,
and music history (Ho, 2007). Therefore, this
research takes the integrated music teaching system
as the research target of applying information
technology for music teaching.
*
Corresponding author. Tel.: [phone]
Learner’s learning behavior is a complex,
multidimensional and uncertain concept, and many
researchers have developed different measuring
methods to evaluate learners' learning behavior.
However, there is no consistent conclusion, because
learners' learning behavior has many indicators, it is
difficult to evaluate from a single point of view. So
measuring learners' learning behavior needs to
consider a variety of quantitative and qualitative
criteria. In addition, learning outcomes are measured
by subjective learning gains and objective learning
outcomes (Tu, Huang, & Chang, 2010). In order to
understand the relationship between learners'
complex learning behaviors and learning outcomes in
information technology-assisted teaching, this
research takes the integration of information
technology into music curriculum in universities in
Tianjin and Beijing in China, and in universities in
Taichung and Changhua in Taiwan, as the subject of
research. By Fuzzy Delphi method, scholars and
experts on learning behaviors and outcomes are
surveyed to obtain the indicator for applying
information technology for music teaching, learning
Li, J., Shiu, F. and Huang, H.
Deterministic Factors Influencing Learners’ Learning Behaviors and Outcomes by Applying Information Technology-assisted Music Curriculum.
DOI: 10.5220/0009413503270335
In Proceedings of the 12th International Conference on Computer Supported Education (CSEDU 2020) - Volume 2, pages 327-335
ISBN: 978-989-758-417-6
Copyright
c
 2020 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
327

behaviors and learning outcomes, and to establish the
structure of the influence of applying information
technology for music teaching on learners' learning
behaviors and learning outcomes. By using ISM
(Interpretive Structural Modelling) with Fuzzy
MICMAC (Matriced' Impacts Croisés Multiplication
Appliquée á un Classement), this research analyzes
the relationship between dimensions/criteria
influencing learners' learning behaviors and learning
outcomes, and identifies deterministic factors to
provide teaching strategies and curriculum design and
planning references for educational institutions and
schools to apply information technology for music
teaching or to make improvement. At the same time,
this research constructs a causality model between
learners' learning behaviors and learning outcomes,
which can be further explored in academic circles the
complex relationship between learning behaviors and
learning outcomes of online learners.
2 LITERATURE REVIEW
2.1 Applying Information Technology
for Music Teaching
The connotation of IT integration into teaching is the
innovation of educational idea and teaching method.
Teachers use information technology to develop
multidimensional and creative teaching activities to
promote learners' active learning and cultivate the
knowledge of learners' information technology
application (Wang, 2010), to motivate learning
behaviors such as learning attitudes (Wang & Liao,
2008), learning motivation (Ho, 2007) and learning
engagement (Riordain, Johnston, & Walshe, 2016),
and improve learning outcomes (Chen & Tsai, 2009).
Applying information technology for music
teaching in this research means that teachers use
information technology tools to assist the teaching of
relevant music curriculum. Music curriculum can be
divided into two categories: music knowledge, such
as music theory, music appreciation, music research,
and music skills, such as music composition
recording, music performance, musical instrument
teaching. Applying information technology for music
teaching can be used in music theory, music creation,
music composition recording, music performance,
musical instrument teaching, music appreciation, and
music research (Tseng, 2009), and the music teaching
system and music curriculum used by the research
objects in this research include the above seven online
courses.
2.2 Learning Behaviors
In recent years, more and more attention has been
paid to self-directed learning in the higher education
environment (Shen, Chen, & Hu, 2014). The
characteristic of self-directed learners is active
learning, love of learning, fearless of difficulties, and
using resources to achieve learning goals
(Guglielmino, 1977). In addition, Knowles (1975)
believes that self-directed learning is the process by
which individuals actively diagnose learning needs
without or with the help of others, to plan learning
goals, to seek the human or material resources they
need, and to implement appropriate learning
strategies to evaluate learning outcomes. Therefore,
in this research, self-directed learning is a kind of
ability, which means the learner can trigger learning
on his own, and can carry out independently and
continuously. He has the ability of self-training, a
strong desire and confidence to learn, can use basic
learning skills, arrange appropriate learning steps,
develop learning plans, and use time to carry out. And
learners' self-directed learning affects their learning
motivation (Saranraj & Shahila, 2016), learning
attitudes (Zhang, Zeng, Chen, & Li, 2012), and
learning effectiveness (Tucker, 2018).
Learning attitudes are gradually formed in the
learner's learning activity process through interaction
with the surrounding environment, so the factors
affecting the learner's learning attitude are quite
complex (Huang, 2003). Learning attitudes refer to
the attitude of learners towards the interaction of their
learning environment, and, depending on their ability
and experience, learners’ persistent positive or
negative behavioral tendency or inner state for
learning various matters (Liu, Ting, & Cheng, 2010).
Online education is an important delivery method of
teaching in a variety of educational settings (Ku &
Lohr, 2003). Computers and the Internet designed for
education have fundamentally changed the university
education (Liaw & Huang, 2011). Learners' learning
attitudes affect not only online teaching (Chang,
2000), but also learners' learning attitudes, learning
satisfaction (Wang and Liao, 2008) and learning
effectiveness (Masgoret & Gardner, 2003). Some
other scholars believe that learning attitudes can
affect learning motivation in learning behaviors
(Maclntyre, Potter, & Burns, 2012) or self-directed
learning (Khodabandehlou, Jahandar, Seyedi, &
Abadi, 2012).
Learning motivation is the fundamental driving
force of learning behavior, which enables learners to
gain motive power to learn (Wu, 2016). Learning
motivation refers to the internal psychological
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process that causes students to learn, maintain
learning activities, and guide them toward the goals
set by teachers (Chen, 2007). And learners' learning
motivation not only affects learning behavior of
learning engagement (Wei & Huang, 2001), but also
affects their learning outcomes including learning
effectiveness (Takaloo & Ahmadj, 2017) and learning
satisfaction (Wang & Liao, 2008). Other scholars
believe that in learning behavior, learning motivation
can affect learning attitudes (Liu, 2009) or self-
directed learning (Hung, Chen, & Tsai, 2016).
Learning engagement is the behavioral, cognitive
and emotional engagement components which
learners reflect in the learning process because of
experience and feeling (Fredricks, Blumenfeld, &
Paris, 2004). Learning engagement is an important
indicator of the learning situation of college students,
and many studies agree that the degree of learning
engagement affects their ability to acquire knowledge
and the result of cognitive development (Pascarella &
Terenzini, 2005). Glanville and Wildhagen (2007)
believe that learning engagement refers to the
behavior and psychological involvement of learners
in the school curriculum, and that the breadth of the
engagement can make it a powerful concept for
understanding the educational outcomes of students.
2.3 Learning Outcomes
Since learning outcomes are not limited to academic
achievement in learning effectiveness (Tu, Huang, &
Chang, 2010), but also include skills (Hsieh, Lee, &
Sung, 2017), application and creation (Chen & Liu,
2015), and learning satisfaction (Tu, Huang, & Chang,
2010). Therefore, learning outcomes are measured by
subjective learning gains (e.g. learning satisfaction)
and objective learning outcomes (e.g. learning
effectiveness) (Tu, Huang, & Chang, 2010).
Learning satisfaction is a feeling or attitude
towards learning activities. A feeling of being happy
or having a positive attitude is satisfaction, a feeling
of being not happy, or having a negative attitude is
dissatisfaction (Long, 1985). This feeling or attitude
is formed because learners have a fondness for
learning activities and a desire to learn, or, in the
learning process, achieve their desires, needs, or
learning goals, and generate a positive attitude or a
sense of satisfaction (Chi, Lee, Liu, & Hsu, 2007).
And learners' learning satisfaction affects their
learning effectiveness (Lee & Huang, 2007).
Therefore, learning satisfaction in this research refers
to the learning behavior generated by the learner's
learning desire and needs, and the subjective feeling
of whether the learner is happy with the learning
activity, and whether the learning effectiveness can
make the learner feel that his needs are being met.
Learning Effectiveness is a student's achievement
in knowledge or skills after learning (Hsieh, Lee, &
Sung, 2017); Tu, Huang and Chang (2010) believe
that learning effectiveness refers to the level to which
a learner acquires certain knowledge, skills, or
affection through learning or training in a particular
field at a specific time. This research is about the
effectiveness of music learning. Effectiveness of
music learning refers to the level of knowledge, skills
and affection reached. Hsieh, Lee, and Sung (2017)
point out in the research that learning effectiveness
includes musical skills and cultivation of affection.
Chen and Liu (2015) indicate in the research that
learning effectiveness includes memory and
comprehension ability, application and analysis
ability, evaluation ability and creativity.
3 METHODOLOGY AND MODEL
DEVELOPMENT
3.1 Research Framework
The design and framework of this research will be
divided into two stages for data collection and
analysis, in order to construct the evaluation
dimensions and criteria. First of all, the first stage is
based on the review of the relevant literature, we
compile the views of scholars, summarize the
influence of applying information technology for
music teaching on the learning behavior of learners, a
total of 7 dimensions and 41 criteria. Then we use
Fuzzy Delphi Method to make group decisions on
scholars and experts on applying information
technology for music teaching, to reach a consensus
of the vagueness of the influence of applying
information technology for music teaching on
learners' learning behavior. Therefore, taking
advantage of the knowledge and experience of
experts and scholars through their feedback, the
dimensions and criteria converge into a consistent and
reliable structure to screen out the relatively
important criteria and prepare a questionnaire. The
second stage is the questionnaire survey, which is
developed using ISM. Main respondents of the survey
are experts and teachers of online music curriculum
in universities in Tianjin and Beijing in China, and in
universities in Taichung and Changhua in Taiwan.
We construct the reachability matrix between
dimensions and criteria using ISM with MICMAC,
create the cause and effect diagram and draw the
Deterministic Factors Influencing Learners’ Learning Behaviors and Outcomes by Applying Information Technology-assisted Music
Curriculum
329

driving power and dependence graph, then find out
the deterministic factors influencing learners'
learning behaviors by applying information
technology for music teaching, and construct the
causal relationship between the deterministic factors
of technological innovation ability, as the research
structure of this research as a whole.
3.2 The Operation and Steps of Fuzzy
Delphi Method
This research uses fuzzy Delphi method to screen out
relatively important items in the dimensions and
criteria of learners’ learning behaviors and outcomes.
The steps of fuzzy Delphi method (Liang, Lee, &
Huang, 2010) are as follows.
Step 1: Collecting the opinions of the decision-
making group; Step 2: Establishing triangular fuzzy
number; Step 3: Defuzzification; Step 4: Screening
evaluation criteria.
In the part of screening evaluation criteria, it is
necessary to establish the threshold value and
statistical judgment standard for the consensus of
expert opinions (Yeh, Pai, & Peng, 2017), and select
appropriate criteria from numerous criteria by
threshold value. Generally, the standard adopted is
60% to 80% of the maximum value (Liang, Lee, &
Huang, 2010). In this research we use 65% as the
threshold value.
3.3 Questionnaire Design and Object
After compiling the data collected through literature
review, 7 dimensions are sorted out: Applying
information technology in music teaching (D
1
), Self-
directed learning (D
2
), Online learning attitudes (D
3
),
Music learning motivation (D
4
), Learning
engagement (D
5
), Learning Satisfaction (D
6
) and
Learning Effectiveness (D
7
) then the 7 dimensions
and 41 criteria are screened by fuzzy Delphi method.
The questionnaire was distributed to 20 scholars and
experts in applying information technology in music
teaching, and they judged whether to retain the
dimension based on their knowledge and experience.
The threshold value used in this research is 65%,
which means that a dimension should be retained if
more than 65% (13 scholars) agreed to retain it. As
for the 7 dimensions and 41 criteria compiled in this
research, more than 65% of experts and scholars
agreed on each of them, so all dimensions were
retained. Finally, the 41 criteria are as follows: Music
theory (M
1
), Music composition creation (M
2
), Music
composition recording (M
3
), Music performance
(M
4
), Musical instrument teaching (M
5
), Music
appreciation (M
6
), Music research (M
7
), Self-learning
(S
1
), Continuous learning (S
2
), Efficiency learning
(S
3
), Independent learning (S
4
), Self-understanding
(S
5
), Planning learning)(S
6
), Favorite learning (S
7
),
Computer and network confidence (O
1
), Network use
(O
2
), Online learning (O
3
), Computer / smart phone
use (O
4
), Computer / smart phone preferences (O
5
),
Cognitive interest (L
1
), Self-growth (L
2
),
Interpersonal facilitation (L
3
), Professional
advancement) (L
4
), Social conformity (L
5
),
Transforming monotonous life (L
6
), Skills
engagement (E
1
), Emotional engagement (E
2
),
Performance engagement (E
3
), Attitudes engagement
(E
4
), Interaction engagement (E
5
), Instructor's
teaching ability) (A
1
), Learning content and teaching
materials (A
2
), Interpersonal interaction (A
3
),
Teaching website learning environment (A
4
),
Administrative services (A
5
), Academic achievement
(F
1
), Music skills (F
2
), Cultivation of affection (F
3
),
Ability to remember and to understand (F
4
),
Application and analysis ability (F
5
), Evaluation and
creativity (F
6
).
3.4 The Operation and Steps of ISM
with Fuzzy MICMAC
3.4.1 ISM Analysis
This research uses the interpretive structure model
(ISM) to explore the relationship between
dimensions/criteria of learners' learning behaviors
and learning outcomes influenced by information-
assisted music teaching. The establishment of an ISM
involves a number of steps, which are well
documented in the literature (Farris & 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 (obtained in the
content analysis); Step 3: Developing a Reachability
Matrix, and checking the matrix for transitivity; Step
4: Partitioning the 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.
The use of ISM helps to explore the relationship
between dimensions/criteria of learners' learning
behaviors and learning outcomes influenced by
information-assisted music teaching. The Details of
the development of the structural models for each of
the clusters are provided in the results section.
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3.4.2 MICMAC Analysis
Developed by Duperrin and Godet (1973), 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, Shao, Chiu, & Yen, 2009). Mandal and
Deshmukh (1994) claim that the primary goal of
MICMAC analysis is to analyze the driving power
and dependence of each variable. “Driving power”
refers to the degree of influence that one variable has
exceeded another, and “dependence” is defined as the
extent to which one variable is influenced by others
(Arcade, Godet, Meunier, & Roubelat, 1999). Based
on driving power and dependence, a 2D driver-
dependence diagram can be created, with the
horizontal axis representing the extent of dependence
and the vertical axis representing the extent of the
driver (Lee, Chao, & Lin, 2010).
The establishment of a Fuzzy MICMAC involves
a number of steps, which are well documented in the
literature (Katiyar, Barua, & Meena, 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 Dimensions
4.1.1 Developing the Conical Matrix
After the first to fourth steps of the ISM, perform the
conical matrix step. The conical matrix is computed
by clubbing all dimensions based on their levels
across the columns and rows of the final reachability
matrix. Later, the conical matrix is used to develop
the final digraph and structural model. For example,
the dimension D
7
is found at the level 1, D
3
and D
6
at
the level 2, D
5
at the level 3, D
4
at the level 4 and D
1
and D
2
at the level 5.
Further, the dependence power and driving power
of a dimension are determined as explained in the
previous section. Next, ranks are calculated by giving
the highest rank to the dimensions that have the
maximum number of 1s in the rows and columns
indicating the driving and dependence power. After
rearrangements, the conical matrix is obtained.
4.1.2 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.
This figure shows that D
1
and D
2
are the most
crucial dimensions for online learning behavior and
outcomes to learner as it comes at the bottom of the
ISM hierarchy. D
6
and D
7
appeared at the top which
indicate it will influence the entire process of online
learning behavior and outcomes. The D
1
and D
2
lead
to D
4
. Similarly, D
4
lead to D
5
and D
3
.
Figure 1: ISM-based Model.
4.1.3 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 up 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 in the digraph, disregarding the
transitivity and making diagonal entries 0.
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
D
6
Learning
Satisfaction
D
7
Learning
Effectiveness
Deterministic Factors Influencing Learners’ Learning Behaviors and Outcomes by Applying Information Technology-assisted Music
Curriculum
331

4.1.4 Fuzzy Indirect Relationship Analysis
FDRM is used to start the procedure of finding the
fuzzy indirect relationship between the dimensions.
The matrix is multiplied or repeatedly reproduced up
to a power until the hierarchies of the driving and
dependence power are stabilized. According to the
fuzzy set theory, when two fuzzy matrices are
multiplied, 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.
4.1.5 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 1. The dependence
power, driving power and ranks are determined as
discussed in the earlier section. The ranks of the
driving power of the criterion decide the hierarchy of
criterion in the system. The purpose of this
classification of the dimensions is to analyze the
driving and dependence power of the dimensions that
influence learning behavior and outcomes to learner.
Table 1: Fuzzy MICMAC Stabilized Matrix.
Dimensions D
1
D
2
D
3
D
4
D
5
D
6
D
7
Driving
Power
Rank
D
1
0.5 0.5 0.7 0.7 0.9 0.7 0.9 4.9 1
D
2
0.5 0.5 0.7 0.5 0.7 0.7 0.7 4.3 2
D
3
0.3 0.3 0.5 0.3 0.5 0.5 0.5 2.9 6
D
4
0.5 0.5 0.5 0.5 0.3 0.9 0.9 4.1 3
D
5
0.3 0.5 0.5 0.5 0.5 0.5 0.9 3.7 4
D
6
0.3 0.3 0.5 0.5 0.5 0.5 0.5 3.1 5
D
7
0.1 0.5 0.5 0.3 0.1 0.1 0.5 2.1 7
Dependence
Power
2.5 3.1 3.9 3.3 3.5 3.9 4.9
Rank 7 6 2 5 4 2 1
4.1.6 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. The key indicator that is nearest to
the origin in the graph 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
study. Comparing the hierarchy of dimensions in the
various classifications like direct, indirect and
potential, one can decide how much importance
should be given to each criterion for influencing
learning behavior and outcomes to learner. This
method confirms the importance of certain
dimensions and also searches some hidden
dimensions those cannot be identified through direct
classification. These hidden dimensions also play an
important role because of their indirect actions on the
system under consideration. These Fuzzy MICMAC
examination of direct relationships also reveals that
criterion having strong impact can be suppressing
hidden criterion.
Figure 2: Driving-Dependence Power Graph for Factors.
4.2 Analysis of Criteria
The analysis of the 41 criteria is calculated according
to steps 1-11 of the above dimension analysis. Fuzzy
MICMAC Stabilized matrix and Driving -
Dependence Graph are presented in Table 2 and
Figure 3.
Figure 3: Driving-Dependence Power Graph for Criteria.
1.0
0.5
0.5 1.0
3.59
3.59
D
1
D
2
Dependence Power
2.0
1.5
3.0
2.5
4.0
3.5
5.0
4.5
1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0
D
3
D
4
D
5
D
7
D
6
Driver
Factors
Linkage
Factors
Dependent
Factors
Autonomous
Factors
29.0
27.5
17.5 20.0
38.07
38.07
M
1
M
2
M
3
E
1
E
2
L
5
L
6
A
1
F
1
Dependence Power
32.0
30.5
35.0
33.5
38.0
36.5
41.0
39.5
22.5 25.0 27.5 30.0 32.5 35.0 37.5 40.0
Driver
Factors
Linkage
Factors
Dependent
Factors
Autonomous
Factors
42.5
S
1
E
4
O
3
S
3
S
4
E
5
S
5
S
6
S
7
O
1
O
2
F
6
F
5
F
4
F
3
A
5
S
2
F
2
O
4
M
4
M
5
M
6
M
7
O
5
A
3
A
4
E
3
A
2
L
1
L
2
L
3
L
4
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Table 2: Fuzzy MICMAC Stabilized Matrix.
Criteria
Drive
Power
Dependence
Power
Criteria
Drive
Power
Dependenc
e Power
M
1
40.3 39.3 L
3
36.9 37.8
M
2
40.3 39.8 L
4
28.7 39.3
M
3
40.0 39.7 L
5
39.8 39.1
M
4
40.3 39.9 L
6
40.5 39.0
M
5
40.2 39.9 E
1
41.0 38.7
M
6
40.2 39.9 E
2
40.6 39.0
M
7
40.3 39.9 E
3
31.0 39.0
S
1
40.3 36.1 E
4
40.2 37.2
S
2
39.3 39.6 E
5
37.5 39.1
S
3
38.8 37.6 A
1
40.3 39.1
S
4
37.2 38.1 A
2
39.1 37.7
S
5
37.5 37.1 A
3
35.2 39.2
S
6
39.2 36.6 A
4
40.3 16.2
S
7
35.9 35.7 A
5
40.3 34.6
O
1
38.1 38.6 F
1
40.3 39.1
O
2
37.1 40.0 F
2
39.9 38.1
O
3
40.2 38.0 F
3
28.1 39.1
O
4
39.7 38.2 F
4
34.5 40.1
O
5
40.2 38.8 F
5
38.8 40.4
L
1
41.0 38.0 F
6
26.7 40.1
L
2
34.9 38.0
5 RESULTS AND DISCUSSION
This research explores the determinants and criteria
that influence learners' learning behaviors and
learning outcomes in information technology-assisted
teaching, and provides a model of complex
relationship between information technology-assisted
teaching and learners' learning behaviors and learning
outcomes for teaching curriculum designers, teachers
and heads of educational institutions, to understand
the learning behavior and learning outcome patterns
that learners produce while learning online, and
which factors and criteria are crucial in influencing
learning behaviors and learning outcomes. First,
based on the literature review and interviews with
scholars and experts on online learning in Tianjin and
Beijing in China, and in Taichung and Changhua in
Taiwan, the Fuzzy Delphi method is used to select 7
influencing factors and 41 criteria. Secondly, an
expert questionnaire interview was conducted with 20
scholars and experts to establish the relationship
matrix and serve as a prerequisite for building the
ISM-based model.
On the part of dimension, its ISM-based Model is
shown in Figure 1. D
1
and D
2
will directly affect D4,
and D
4
will directly affect D
3
and D
5
. And D
5
directly
affects D
6
and D
7
, and finally D
3
directly affects D
7
,
so D
1
and D
2
indirectly affects D
5
, and D
3
, D
6
and D
7
.
The fuzzy MICMAC method uses driving power
and dependence power to classify the indicators of the
Fuzzy MICMAC Stabilized matrix (as in Table 1)
into four clusters (see Figure 2). Figure 2 shows that
D
1
, D
2
, D
4
, D
5
are driver factors with higher driving
power, so these four dimensions are the crucial
dimensions that influence learners' learning behaviors
and learning outcomes, while the three dimensions of
D
3
, D
6
, and D
7
are dependent factors with weak
drivers. The three dimensions are influenced by other
factors of learning behaviors and learning outcomes,
namely, D
1
, D
2
will influence D
3
, D
4
, D
5
, D
6
, D
7
these
predispositions of learning behaviors and learning
outcomes.
To further understand the complex relationship of
learners' learning behaviors, an analysis between the
criteria is carried out, and the results are shown in
Figure 3. S
1
, S
3
, S
6
, O
3
, L
1
, E
4
, A
2
, A
4
, and A
5
are
driver criteria with higher driving power. Therefore,
these 9 criteria are crucial criteria that influence
learners' learning behaviors and learning outcomes.
While S
4
, O
2
, L
4
, E
3
, E
5
, A
3
, F
3
, F
4
, and F
6
are
dependent criteria with weak drivers. These 9 criteria
are influenced by the above 9 learning behaviors and
learning outcomes criteria such as self-learning. The
results of this research point out that the music
teaching courses and teaching materials through the
application of information technology should take
into account the learning background, ability and
demand of learners' on music and computer, and
carefully choose which music courses are suitable for
applying information technology in teaching.
Learners' learning behaviors and learning outcomes
have a variety of characteristics and complex
relationships. Diversified learning activities can be
adopted to cultivate the crucial factors/criteria
influencing learners' learning behaviors and learning
outcomes, so as to improve learners' learning
behaviors and learning outcomes.
In addition, the model developed in this research
represents how applying information technology into
music teaching can influence learners' learning
behaviors and learning outcomes, which can be
applied not only to online music teaching courses, but
also to online teaching courses in other fields
6 CONCLUSIONS
Applying information technology for music teaching
provides learners with more personalized learning
opportunities and broaden their music learning
experience. Therefore, the learning background,
experience and abilities of learners, including music
and computer, should be taken into account before
designing music curriculum and teaching materials in
combination with information technology. And
teaching topics should be carefully planned to suit
Deterministic Factors Influencing Learners’ Learning Behaviors and Outcomes by Applying Information Technology-assisted Music
Curriculum
333

learners' interests and needs, in line with the
characteristics of the online learning environment.
Diversified learning activities should be adopted,
both independent learning and cooperative learning
should be taken into account, and attention should be
paid to increase the opportunities for learner to
interact and feedback.
Because the learners' learning behaviors and
learning outcomes have a variety of characteristics
and tendency of complex relationship, except
information technology-assisted music teaching, self-
directed learning is one of the important factors
influencing learners' learning behaviors and learning
outcomes. Therefore, learners' self-directed learning
should be enhanced. Self-directed learning is the
ability of learners to trigger learning on their own and
to be able to learn efficiently and systematically. And
then self-directed learning can influence learning
behaviors such as online learning attitudes, music
learning motivation and learning engagement, as well
as learning outcomes such as learning satisfaction and
learning effectiveness. So to cultivate learners' self-
directed learning is one of the important tasks of
institutions of higher education to focus on online
teaching courses.
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