Determinants Affecting Online Learning Behaviour and Learning

Effectiveness

Chi-Hui Wu

, Jing Li

, Reed-Joe Chang

and Tung-Jung Lin

Ph.D. Program in Management, Da-Yeh University, University Rd., Chunghua, Taiwan

Music and Film College, Tainjin Normal University, Binshui West Rd., Tianjin, China

Keywords: Online Teaching and Learning, Self-Directed Learning, Music Learning Motivation, Online Learning

Attitudes, Learning Engagement, Learning Satisfaction, Learning Effectiveness, Fuzzy Delphi Method,

Fuzzy DEMATEL.

Abstract: Using Fuzzy DEMATEL, this article investigates the learner’s behaviour of online learning that features

multiple characteristics which are complicated and interacting with each other, and between them clears the

relationships to provide or benefit schools with teaching strategies, courses design and planning activating

learners’ learning behaviour and achieving learning effectiveness. With respect to the dimensions, music

learning motivation, self-directed learning are the determinant dimensions of learners’ behaviour and

learning effectiveness that affecting other four dimensions; and to the criteria, they are preference and use

on computers and smart phones, online learning affecting other 31 factors

1 INTRODUCTION

The music teaching in university, integrating music

theory, music art, and playing kills, is a subject of

learning-by-doing. Thus, it is not sufficient to

introduce a single information technology for all

music teaching, but needs a multiple system with

different teaching contents and methods. Most of

literatures of IT-aided teaching system are case

studies. As such, this study is to investigate and

analyse an integrated music teaching system

incorporating information technology.

A learner’s behaviour is a concept of multiple

dimensions for many researchers who have

developed with a couple of measures to evaluate,

however, haven’t come to a conclusion. Thus, it is

suggetsted to measure learner’s behaviour with

multiple quantitative and qualitative criteria.

Additionally, learning effectiveness includes two

measurig methods: subjective learning achievments,

e.g. learning satisfaction, and objective learning

effectiveness (Tu et al., 2010). To understand the

relationships beteween complicated behaviours on

the music teaching applying information technology,

the universities located at China Tianjin and Beijing

areas, and Taichung & Chunghua areas in Taiwan

were selected to acquire the index and construct a

framework for the learner’s behaviour through

questionaire of Fuzzy Delphi method for the

students taking music courses, furthermore, to

analyze the causal effects between the dimensions

and criteria of learner’s behavior and learning

effectiveness by Fuzzy DEMATEL.

2 LITERATURE REVIEW

2.1 Online Teaching and Learning

IT is an innovative concept and method integrating

IT into teaching and education. Teachers are

applying IT to developing innovative teaching

activities, ability of IT application, and improving

learning effectiveness (Chang and Wang, 2008;

Wang, 2010).

Over the past decades, online courses have been

increasingly growing. With increasing demand for

online learning as well as more institutions of higher

learning, which continues to grow as a viable means

of providing increased access to a greater number of

students. Online learning is used to refer to web-

based training, e-learning, distributed learning,

internet-based learning, web-based instruction, or

net-based learning (Keengwe and Kidd, 2010).

Wu, C., Li, J., Chang, R. and Lin, T.

Determinants Affecting Online Learning Behaviour and Learning Effectiveness.

DOI: 10.5220/0007734703750382

In Proceedings of the 11th International Conference on Computer Supported Education (CSEDU 2019), pages 375-382

ISBN: 978-989-758-367-4

375

This article is to describe the integration of IT

into music teaching in terms of IT-aided tools, that

can be applied to 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

system and courses.

2.2 Self-Directed Learning

Self-directed learning (SDL) has been identified as

an approach to learning that received increasing

attention in recent years, particularly in the context

of higher education (Shen et al., 2014). Guglielmino

(1977) and Driscoll (1994) proposed that self-

directed learning implies an independent and

continual behaviour and characteristics affecting the

learning motivation, efforts and perseverance

(Mount et al., 2005; Gendron, 2006; Chen and Liang,

2009).

Knowles (1975) proposed that a self-directed

learning is a process that learners can actively

recognize learning requirements, plan learning goals,

seek for manpower and materials needed, and apply

proper strategies to evaluating learning results. And,

the learners’ self-directed learning will affect their

learning motivation (Liang, 2008; Mount et al., 2005;

Liang, 2008), learning effectiveness (Xu and Ren,

2005; Shen et al., 2014).

Based on self-directed learning scale,

Guglielmino (1977), and related research on self-

directed learning (Oddi, 1986; Liang, 2008; Chen

and Liang, 2009).

2.3 Music Learning Motivation

Learning motivation is an elementary driving force

motivating a learner to learn (Wu, 2016). Learning

motivation is a mental experience to activate,

maintain learning activities, and direct them toward

the learning objective designated (Chen, 2007). And,

learners’ learning motivation will affect their

learning effectiveness including learning

effectiveness (Hsieh et al., 2017) and learning

satisfaction (Chen 2007; Lee and Huang, 2007).

Learners’ learning motivation not only affect the

behaviour of the learning engagement (Wei and

Huang, 2001), but the learning effectiveness (Hsieh

et al., 2017) and learning satisfaction (Chen 2007;

Lee and Huang, 2007).

The motivation of learning music this article

indicates is a process that learners actively learn

music activities, maintain, and promote those toward

a mental experience. 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).

2.4 Online Learning Attitudes

The attitudes of learners affect the learning

satisfaction (

Chi et al., 2007) and learning

effectiveness (Kuo and Lee, 2008). 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 altered 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.

This study is to aim 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

constructed five dimensions in online learning

attitudes: Computer and network confidence,

network use, online learning, computer/smart phone

use, computer/smart phone preferences.

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). Learning engagement is a critical index

reflecting the learning status of undergraduate

students, the degree of engagement of that will affect

knowledge acquisition and cognitive development

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(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. it benefits the instruction

and courses design to fully understand the degree

that students have engaged. And, learners’ learning

engagement will affect their learning effectiveness

(Tsai, 2016; Wonglorsaichon et al., 2014; Tsai,

2016).

Apart from emotion engagement, it needs to take

account the strategy, effectiveness 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,

effectiveness engagement, attitudes engagement,

interaction engagement (Handelsman et al., 2005;

Lin and Huang, 2012; Tsai, 2016).

2.6 Learning Satisfaction

Learning satisfaction is a sense or attitude to

learning activities; a pleasant sense or positive

attitude means satisfactory, an unpleasant sense or

negative sense means unsatisfactory (Tough, 1982;

Long, 1985), the production of attitude comes from

learning activities and consequently have the

positive attitude and sense of satisfaction (Chi et al.,

2007). And, learners’ learning satisfaction will affect

learning effectiveness (Lee and Huang, 2007). This

study aims to the learning satisfaction that learners’

learning behaviour is stimulated by the wishes and

needs, and finally see whether the learners will reach

a pleasant sense and subjectively feel satisfactory

with their learning effectiveness.

This study synthesizes some scholars’

viewpoints (Long, 1985; Chadwick and Jame 1987;

Chen, 2007; Wu, 2016) to come to five dimensions

for learning satisfactory: Instructor's teaching ability,

learning content and teaching materials,

interpersonal interaction, teaching website learning

environment, administrative services.

2.7 Learning Effectiveness

Learning effectiveness means achievements of

students obtain on knowledge or skills after learning

(Hsieh et al., 2017); Tu et al. (2010) supposes that

learning effectiveness means to what degree that

learners’ learned knowledge, skills or emotion in a

certain discipline during a period of time.

This study aims to the learning effectiveness

including the degree to that learners’ knowledge,

skills, and emotion can reach. Hsieh et al. (2017)

pointed out that learning effectiveness comprises

music skills, affection cultivating; Chen and Liu

(2015) proposed that learning effectiveness consists

of memory and comprehension ability, application

and analysis ability, evaluation and creative ability.

Thus, learning effectiveness categorizes to six

dimensions: learning effectiveness, music skill,

affection cultivating, memory and comprehension

ability, application and analysis ability, evaluation

and creative ability.

3 DEMATEL METHODOLOGY

AND MODEL DEVELOPMENT

3.1 Research Framework

The research designs a framework comprising

dimensions and criteria evaluation, data collection

and analysis. First, based on literature reviews,

previous viewpoints of scholars were synthesized for

the determinants affecting learning behaviour and

effectiveness, which were categorized into 6

dimensions and 34 criteria and, through Fuzzy

Delphi method, the group decision making by

experts and scholars who are good in online learning

behaviour and effectiveness were conducted to solve

the fuzzy problems affecting learners’ learning

behaviour and effectiveness. Second, referring to the

universities located at Mainland China Tainjin and

Beijing areas, and Taichung Chunghua areas in

Taiwan, the questionnaire by DEMANTEL method

follows, and aims to the learners in Tianjin

University Mainland China for constructing relation

matrix between dimensions and criteria, depicting

causal effects graph and performing causal effects

route analysis and further ascertaining determinants

affecting learners’ online learning behaviour and

effectiveness completing the research framework.

3.2 Fuzzy Delphi Method

This study uses Fuzzy Delphi method to screen out

the relatively important items from the dimensions

and criteria of learners’ learning behaviour and

effectiveness. The steps of Fuzzy Delphi method are

as follows (Liang et al., 2010):

Determinants Affecting Online Learning Behaviour and Learning Effectiveness

377

Step1: Collecting group decisive opinions: Using

semantic variables in questionnaire, the measure

index for the importance of various criteria can be

obtained. For the measure criteria, this study uses

Likert’s 5 scale to evaluate the ability of

technological innovation and adopts the geometric

mean to integrate expert opinions.

Step2: Constructing fuzzy triangle: Calculating the

fuzzy triangles of the importance of various criteria,

Klir and Yuan (1995) proposed geometric mean

from general models’ arithmetic mean as Fuzzy

Delphi method for calculating group decisive

consensus.

Step3: Solving problems by defuzzification: A fuzzy

number is a quantity whose value is imprecise.

Therefore, we must perform defuzzification for

fuzzy numbers before operating on them. The

process of defuzzification is to find the best non-

fuzzy performance value, BNP.

Step4: Screening out the evaluation criteria: For

criteria evaluation, 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

This study figures out 6 dimensions: Self-directed

learning, online learning attitude, music learning

motivation, learning engagement, learning

satisfactory, and learning effectiveness, and the

corresponding 34 criteria, the Fuzzy Delphi method

was applied to do the screening. The questionnaires

were issued to scholars and experts specializing in

online learning, who make decisions for taking the

dimension or not by their knowledge and experience

with a threshold value of 70%, that means at least 14

scholars agree with the dimensions. This study

shows the six dimensions and 34 criteria, to which

over 70% experts or scholars are favourable, as a

result, all dimensions in this study are taken.

The operational definitions of 34 criteria follows:

Self-learning (S1), Persistent learning (S2),

Efficient learning (S3), Independent learning (S4),

Self-understanding (S5), Learning planning (S6),

Loving learning (S7), Learning Confidence on

computers/smart phones and networks (O1), Using

networks (O2), Loving to use computer/smart

phones (O5), Cognition of interests (L1), Self-

growth (L2), Social relationships (L3), Job progress

(L4), Expectations of others (L5), Changing routine

in lifestyle (L6), Skill engagement (E1), Emotion

engagement (E2), Performance engagement (E3),

Attitude engagement (E4), Interaction engagement

(E5), Teaching methods of teachers (A1), Learning

contents and materials (A2), Social interaction (A3),

Environments of teaching websites (A4), Public

services (A5), Academic achievements (F1), Music

skills (F2), Affection cultivating (F3), Memory and

comprehension ability (F4), Application and

analysis ability (F5), Evaluation and creative ability

(F6).

3.4 Operation Steps of Fuzzy

DEMATEL

As for Fuzzy DEMATEL, the linguistics scale and

triangular fuzzy numbers comply with the

categorization of Li (1999). The linguistics scale is

divided into 5 levels: Very high effect (VH), high

effect (H), low effect (L), very low effect (VL), no

effect (No). To facilitate respondents to answer

questionnaire, the values are among 0-4.

The operation steps of DEMATEL method are as

below:

Step 1: Defining the evaluation criteria and

designing the fuzzy linguistics scale.

Step 2: Establishing the direct-relation matrix and

obtaining the initial one after comparison by

respondents and experts.

(1)

where, is triangular fuzzy

numbers, those, , on left diagonal

are (0, 0, 0).

Step 3: Establishing analytic structural model and

converting linear scale to normal equation for

comparison.

and (2)

Get normalized fuzzy direct-relation matrix as:

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Step 4: By (3), the total relation matrix can be

obtained as following equations:

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CSEDU 2019 - 11th International Conference on Computer Supported Education

378

, (4)

Step 5: Solving problems by defuzzification and

obtaining total relation matrix by (5)

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Step 6: Obtaining column and row values by

defining d and r:

(6)

Step 7: Conducting result analysis.

The relation graph can be depicted after

calculating d+r and d-r. The value of d+r stands for

the effect strength between dimension and criteria

called centricity indicating relation strength of a

certain dimension related to others. The greater the

centricity, the stronger the relationships between

them. As for d-r, it stands for the interaction

relationships between dimensions and criteria called

causality indicating the strength difference between

dimensions/criteria affects others and those affected.

The high value of d-r represents that the dimension

is the cause affecting others; the low ones mean the

dimension is the effect of other dimensions.

4 FUZZY DEMATEL ANALYSIS

4.1 Analysis of Survey Questionnaire

The research units include universities at Tianjin and

Beijing areas in Mainland China and Taichung

Chunghua areas in Taiwan and aim to the research

scholars and university teachers on music teaching

incorporating information technology, to them

questionnaires were explained and then issued, in

which 40 were validly completed in one month.

Using Fuzzy DEMATEL, this research analyses

the 6 dimension and 34 criteria for the online

learning behaviour and learning effectiveness

including interacting relationships and strength

between dimensions and criteria.

4.2 Dimension Analysis

Step 1: Defining dimensions for evaluation and

design fuzzy linguistics scale: Dimensions for

evaluation include: Self-directed learning (D1),

online learning attitudes (D2), motivation of music

learning (D3), learning engagement (D4), learning

satisfaction (D5), and learning effectiveness (D6).

The linguistics scale and the corresponding fuzzy

numbers, attribute functions referring to the

classification by Li (1999).

Step 2: Constructing direct-relation matrix.

Step 3: Constructing structural models for analysis.

Step 4: Constructing total fuzzy relation matrix.

Step 5: Solving problems by defuzzification.

Step 6: Summing values of columns and rows: The

values for row (d), columns (r), sum of columns and

rows (d+r), difference of columns and rows (d-r) are

summarized as table 1.

Table 1: Column and row values of dimensions.

(row

values)

(column

values)

d+r

(centricity

)

d-r

(causality

)

quadrant

Causal

relation

D1 18.482 17.879 36.361 0.603

quad

Affecting criteria

D2 17.809 18.282 36.091 -0.473

quad

Independent

D3 18.845 18.184 37.030 0.661

1st quad

Core criteria

D4 18.767 18.638 37.405 0.128

1st quad

Core criteria

D5 18.233 18.679 36.912 -0.445

quad

Independent

D6 18.865 19.339 38.203 -0.474

4th quad

Criteria

affected

Avera

37.001 0.000

Step 7: Results analysis: After obtaining d+r

(centricity) and d-r (causality), causal figures are

drawn as figure 1.

Figure 1: Causal relationships between dimensions.

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35.5 36.0 36.5

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-1.0

Determinants Affecting Online Learning Behaviour and Learning Effectiveness

379

4.3 Criteria Analysis

To further understand the complicated relationships

between learners’ learning behaviour and learning

effectiveness, this research conducts the criteria

analysis for each dimension.

Step 1: Defining evaluation criteria and design fuzzy

linguistics scale, and evaluating 34 criteria from

self-learning (S1) to Evaluation and creative ability

(F6).

Step 2 to Step 7: Complying with the step 2 to step 7

as those used for the dimension analysis, the criteria

analysis is conducted and the values of row values

(d), column values (r), sum of column and row (d+r),

difference of column and row (d-r) are synthesized

as table 2 shown.

Table 2: Column and row values.

d(row)

r(colum

d+r(centricit

r(causality

)

Quadrant

Causal

relation

S1 5.308 4.901 10.209 0.406

1st quad

Core criteria

S2 5.178 4.868 10.046 0.310

quad

Affecting criteria

S3 5.121 4.896 10.017 0.225

quad

Affecting criteria

S4 4.834 4.920 9.754 -0.086

quad

Independent

S5 5.084 4.912 9.996 0.172

quad

Affecting criteria

S6 5.071 4.975 10.046 0.096

quad

Affecting criteria

S7 4.985 5.065 10.049 -0.080

quad

Independent

O1 5.380 4.928 10.308 0.453

1st quad

Core criteria

O2 4.942 4.943 9.885 -0.001

quad

Independent

O3 5.484 4.883 10.367 0.601

1st quad

Core criteria

O4 5.460 4.929 10.389 0.531

1st quad

Core criteria

O5 5.591 4.906 10.497 0.685

1st quad

Core criteria

L1 5.489 5.084 10.572 0.405

1st quad

Core criteria

L2 4.594 5.105 9.699 -0.510

quad

Independent

L3 4.400 4.975 9.375 -0.575

quad

Independent

L4 4.494 5.161 9.655 -0.667

quad

Independent

L5 5.076 5.135 10.211 -0.059

4th quad

Criteria affected

L6 4.794 5.143 9.936 -0.349

quad

Independent

E1 5.621 5.152 10.773 0.469

1st quad

Core criteria

E2 5.470 5.168 10.639 0.302

1st quad

Core criteria

E3 4.819 5.211 10.031 -0.392

quad

Independent

E4 5.292 5.233 10.525 0.060

1st quad

Core criteria

E5 4.678 5.213 9.891 -0.535

quad

Independent

A1 5.742 5.315 11.057 0.427

1st quad

Core criteria

A2 5.423 5.308 10.731 0.115

1st quad

Core criteria

A3 4.519 5.236 9.754 -0.717

quad

Independent

A4 5.277 5.134 10.411 0.143

1st quad

Core criteria

A5 4.864 4.972 9.836 -0.109

quad

Independent

F1 5.590 5.292 10.882 0.298

1st quad

Core criteria

F2 5.289 5.231 10.519 0.058

1st quad

Core criteria

F3 4.965 5.200 10.165 -0.236

4th quad

Criteria affected

F4 4.877 5.280 10.157 -0.403

4th quad

Criteria affected

F5 5.300 5.393 10.693 -0.093

4th quad

Criteria affected

F6 4.478 5.420 9.898 -0.942

quad

Independent

Averag

10.205 0.000

5 RESULTS AND DISCUSSION

5.1 Results of Dimension Analysis

With respect to d+r (centricity), Figure 1 show that

the value of Learning effectiveness (D6) is the

greatest compared to the other five dimensions that

means the strongest in all dimensions a learner must

learn music skills, application and creative ability to

enhance the learning effectiveness that is the

learner’s ultimate achievements. As for Online

learning attitudes (D2), its value 6.091 having the

least strength. The influence strength in the 6

dimensions is ranking from high to low as Learning

effectiveness (D6) to Online learning attitude (D2).

As for the causality (d-r), based on the values of

d-r (causality), the dimensions can be classified into

cause group and effect group; the dimensions with

positive d-r (causality) values called cause group

including Music learning motivation (D3), Self-

directed learning (D1), Learning engagement (D4),

directly affecting other dimensions which will be the

critical objectives. And, the dimensions with negative

values of d-r (causality) are categorized into effect

group including Learning effectiveness (D6), Online

learning attitudes (D2), Learning satisfaction (D5),

which are affected by other dimensions and are the

problems needed to be solved. Among the values of

d-r, ranking from high to low as Music learning

motivation (D3), Self-directed learning (D1), indica-

tes that they most affect other dimensions; Learning

effectiveness (D6), Online learning attitude, and

Learning satisfaction (D5) are affected most as effects

of other dimensions. The higher the Music learning

motivation, the stronger the Learning effectiveness

(D6), Online learning attitudes (D2), and Learning

satisfaction which is the basis of learners’ learning

behaviour and learning effectiveness.

Overall consideration, Music learning motivation

(D3) is the optimum option in all dimensions that

directly affects four dimensions: the learning engage-

ment and online learning attitudes. From Figure 1, it

is found that the six dimensions are interrelated and

clearly understood the Music learning motivation (D3)

dimension are strongly pointing to other dimensions

except Self-directed learning (D1), and only slightly

affected by other dimensions. Therefore, learners

should cultivate self-directed learning to activate

learning behaviour and learning effectiveness apart

from the motivations of cognition of interests and

self-growth that comprise Music learning motivation.

However, Learning effective (D6) is a dimension

affected that will be enhanced when the problems

with the other five dimensions are solved.

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5.2 Results of Criteria Analysis

Referring to centricity (d-r), the 34 criteria can be

classified into cause group and effect group; 18

criteria with positive d-r (causality) categorized into

cause group directly affecting other criteria which

needed to be enhanced to strengthen other criteria

like Online learning (O3), Using computers/smart

phones (O4), Loving to use computer/smart phones

(O5) which are most influential causes positively

affecting other criteria, and the bases of learners’

learning behaviour and learning effectiveness. And,

16 criteria with negative d-r (causality) categorized

into effect group which will be affected by other

criteria and are problems needed to be solved.

6 CONCLUSION

Learners’ behaviour features complicated, and

interrelated relationships between them that it’s not

easy to be precisely evaluated. This research shows

the dimensions and criteria for the learning

behaviour and learning effectiveness. In this

research, recommendations are proposed as follows.

1. It is necessary to understand learners’ learning

behaviour and provide information for the

implementation of online learning courses.

2. Cultivating learners’ self-directed online

learning ability, and learning independence

with strong learning desire and confidence.

3. Promoting online learning attitudes on

computer and network.

4. Properly planning music courses to enhance

learning motivation.

The model created in this research is subjected to

the environments of online music teaching, the

determinants of learners’ learning behaviour and

learning effectiveness.

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