Determinants of Learners' Self-Directed Learning and on-Line

Learning Attitudes in on-Line Learning

Jing Li

, Chi-Jen Chuang

and Chi-Hui Wu

Music and Film College, Tianjin Normal University, Tianjin, China

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

Department of Management and Information, National Open University, ZhongZheng Rd., New Taipei, Taiwan

Keywords: Self-Directed Learning, on-Line Learning Attitudes, Fuzzy Delphi Method, Fuzzy DEMATEL.

Abstract: Self-directed learning and online learning attitudes are important learning behavioral factors for learners in

online learning; meanwhile, they affect learning outcomes too. In addition, the criteria of self-directed

learning and online learning attitudes have complex, tangled, interconnected relationships. Therefore, this

study applied the Fuzzy Delphi method and Fuzzy DEMATEL method to clarify the complex relationship

between self-directed learning and online learning attitudes and to provide schools with teaching strategies

and curriculum design to motivate learners' learning behavior. The study revealed that, in terms of the

dimensions, "self-directed learning" is the defining dimension of learners' learning behavior, which

influences the "online learning attitudes" dimension. Furthermore, in the criteria section, "self-learning",

"computer and network confidence", "online learning", "computer and smartphone use" and "computer and

smartphone preferences" are the decisive criteria that influence the other seven criteria. Among them, online

learning, computer and smartphone use, and computer and smartphone preferences are the three key criteria

for learning, and therefore, enhancing learners' attitudes towards online learning is an important task.

1 INTRODUCTION

The Internet is a common learning platform for

learners and teachers to interact, communicate, and

collaborate in a specific way (Baran et al., 2011),

and the use of information technology (IT) in

teaching has been implemented worldwide for

decades. The purpose of developing online learning

is to use IT to enhance the quality of teaching and

learning, create a high-quality learning environment,

eliminate time and space constraints on learning,

improve the management of teaching resources, and

establish the integration of IT into teaching and

learning in various fields (Wu et al., 2019).

Although there is a large literature on the

association of learning behavior (Laer & Elen, 2019),

learner's learning behavior is still a complex

behavioral pattern and a complicated, multifaceted

and uncertain concept (Wu et al., 2019), learners' on-

line learning behaviors include self-directed learning

(Oddi, 1986; Chen & Liang, 2009), learning

motivation (Boshier, 1971; Chen & Lin, 2018),

learning attitudes (Loyd & Gressard, 1984;

Okwumabua et al., 2010), learning engagement

(Handelsman et al., 2005; Tsai, 2016), and self-

directed learning, on-line learning attitudes are

important dimensions of learning behavior (Wu et

al., 2019).

Since learners' self-directed learning affects their

motivation (Song & Hill, 2007; Saranraj & Shahila,

2016), learning attitudes (Zhang et al.,2012),

learning effectiveness (Khodabandehlou et al., 2012;

Chen et al., 2022), and that learning attitudes affect

motivation (Maclntyre et al., 2012), self-directed

learning (Khodabandehlou et al., 2012), learning

engagement (Lauren, 2017; Josephine et al., 2018),

learning satisfaction (Wang & Liao, 2008) and

learning effectiveness (Masgoret & Gardner, 2003;

Wang & Liao, 2008). Therefore, there is no

consistent conclusion on the relationship between

self-directed learning and online learning attitudes,

and there are many different indicators of self-

directed learning and online learning attitudes, and

measuring self-directed learning and online learning

attitudes requires consideration of multiple

quantitative and qualitative criteria (Wu et al., 2019).

In order to understand the complex relationships

and determinants between learners' self-directed

550

Li, J., Chuang, C. and Wu, C.

Determinants of Learners’ Self-Directed Learning and on-Line Learning Attitudes in on-Line Learning.

DOI: 10.5220/0011988900003470

In Proceedings of the 15th International Conference on Computer Supported Education (CSEDU 2023) - Volume 2, pages 550-557

ISBN: 978-989-758-641-5; ISSN: 2184-5026

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

learning and online learning attitudes in online

learning activities, this study used the Fuzzy Delphi

method to survey scholars and experts on learning

behavior to obtain indicators of self-directed

learning and online learning attitudes and to

establish a framework of self-directed learning and

online learning attitudes. The study also analyzed

the causal relationships between the dimensions and

criteria of self-directed learning and online learning

attitudes through the Fuzzy DEMATEL method and

identified the determinants in order to provide

educational institutions and schools with reference

for teaching strategies and curriculum design for the

integration of IT into online teaching. The study also

constructs a causal model of self-directed learning

and online learning attitudes for academics to further

explore the complex interrelationships between key

learning behaviors of learners who learn online.

2 THEORETICAL

BACKGROUND

Self-directed learning is an effective learning

method, it is flexible and not limited by time and

space, and learners can continuously enrich their

professional knowledge, diagnose their learning

needs, find learning resources through their learning

goals, and implement appropriate learning strategies

to achieve learning outcomes (Chen et al., 2021).

Enhancing learners' self-directed learning can

motivate learners to learn (Chen et al., 2021), and

the higher the propensity for self-directed learning,

the higher the satisfaction level of learners (Chen et

al., 2021); in addition, the higher the self-directed

learning, the better the learning outcomes (Chen et

al., 2021; Chen et al., 2022).

Researchers who study self-directed learning

have different perspectives, for instance, some

scholars have adopted the readiness argument

(Fisher et al., 2001; Chen, 2021), and self-directed

learning readiness refers to the attitudes, abilities,

and attributes that one possesses when engaging in

self-directed learning (Chen, 2021). Scholars who

have studied self-directed learning readiness have

different views on its criteria. Fisher et al. (2001),

Chen (2021) suggested that the criteria of self-

directed learning readiness include self-management,

desire for learning, and self-control. However, Chen,

(2021) considered that the criteria for self-directed

learning readiness include hope for the future,

understanding of the self, active learning, self-

confidence in learning things, and self-management.

Chang & Chang (2010) and Liang & Lai (2007),

suggested that the criteria for self-directed learning

readiness are effective learning, enjoyment of

learning, motivation of learning, active learning,

independent learning, and creative learning.

Other scholars (Tough, 1979; Tang et al., 2022)

have applied the ability argument that self-directed

learning ability affects online learning performances

(Chou, 2012), and self-directed learning ability is

often seen as valuable skill in school settings (Rees

& Bary, 2006; Chou, 2012). Tang et al. (2022)

suggest that the criteria for self-directed learning

ability include self-management ability, information

ability, and cooperative learning ability.

Some scholars (Knowles, 1975) have suggested

the learning contract theory, which is considered to

be the process by which learners, with or without the

assistance of others, can diagnose their own learning

needs, set learning goals, identify learning resources,

select and implement appropriate learning strategies,

and evaluate learning outcomes (Knowles, 1975).

Self-directed learning from the learning contract

theory can be applied to the effective planning of

teaching and learning (Siriwongs, 2015), and

emphasizes learner autonomy, two-way interaction

between teacher and learner, and learner-centered

teaching to develop learners' independent and

autonomous learning skills (Chen et al., 2022).

This study integrates different perspectives on

self-directed learning and classifies it into seven

criteria: self-learning, continuous learning,

efficiency learning, independent learning, self-

understanding, planning learning, and favorite

learning.

Learning attitudes are determined by the

interaction between learners and their surroundings

during the learning process; therefore, the factors

that influence learners' attitudes are complex (Huang,

2003). Learning attitudes refer to learners' attitudes

toward their interactions with the learning

environment and, depending on their abilities and

experiences, their more persistent affirmative or

negative behavioral tendencies or internal states

toward learning things (Liu et al., 2010).

Online education is an important delivery

method in various educational settings (Ku & Lohr,

2003), and computers and the Internet designed for

education have fundamentally changed university

education (Liaw & Huang, 2011), with learner

attitudes affecting not only online teaching (Chang,

2000) but also learning satisfaction (Wang & Liao,

2008) and learning outcomes (Masgoret & Gardner,

2003), while some other scholars argue that learning

attitudes affect motivation in learning behavior

Determinants of Learners’ Self-Directed Learning and on-Line Learning Attitudes in on-Line Learning

551

(Maclntyre et al., 2012; Çevik & Bakioğlu, 2022) or

self-directed learning (Khodabandehlou et al., 2012).

Online learning is the use of computers and

smartphones as a medium of transmission, providing

a diverse teaching environment where different

learners have different problems and attitudes when

using computers for learning. Rainer and Miller

(1996) suggested that one of the most important

factors in computer use is the learner's attitude

towards the computer, so building positive learning

attitudes and computer skills can have a positive

effect on the learner's learning outcomes. Hignite

(1990) argued that computer attitudes refer to

learners' general perceptions of personal and social

use of computers. This study was conducted on

learners who were taking online music lessons, so

online learning attitudes are defined as learners'

willingness, interest, and emotional response to

learning and interacting with computers and the

Internet, as well as their ability to use computers and

the Internet to equip and operate computers at speed.

This study focuses on the integration of IT into

teaching and learning, where learners have to use

computer devices and the Internet to learn the

content of music lessons; therefore, it refers to the

computer attitude scale (Loyd & Gressard, 1984),

the online teaching attitude scale (Graff, 2003), and

related online learning attitude studies (Okwumabua

et al., 2010), the online learning attitudes were

categorized into five components: computer and

network confidence, network use, online learning,

computer and smartphone use, and computer and

smartphone preference.

3 RESEARCH METHODOLOGY

AND DESIGN

In this study, the main factors of learners' self-

directed learning and online learning attitudes were

summarized based on the literature review, with a

total of 12 criteria in two major dimensions. The

main targets of the study were scholars and experts

in western Taiwan and Beijing-Tianjin, China, who

studied online learning (IT-assisted music teaching).

The study was conducted by using the Fuzzy Delphi

method first to select the criteria with higher relative

importance and then the Fuzzy DEMATEL method

to explore the relationship among the dimensions

and the criteria, construct a matrix of the relationship

among the dimensions and the criteria, draw a cause-

effect relationship diagram, and analyze the path of

the cause-effect relationship. This study aims to

explore the determinants of learner self-directed

learning and the attitudes of learners in online

learning.

This study uses the fuzzy Delphi method to

screen out relatively important criteria of self-

directed learning and online leering attitudes. The

Fuzzy Delphi Method (Lin et al., 2020) is a four-step

process. Step 1: Gather the views of the decision-

making community; Step 2: Create a triangular

fuzzy number; Step 3: Defuzzification; and Step 4:

Selection of evaluation criteria. The retention

dimensions and criteria questionnaire were

distributed to 20 academics and practical experts

with more than ten years of experience in studying

online music learning programs at universities, who

used their knowledge and experience to determine

whether to retain the criteria. The threshold used in

this study is 70% (Wu et al., 2022), meaning that the

criterion will be kept if more than 70% of academics

and experts agree to keep it. The two dimensions

and 12 criteria identified in this study have all been

kept because more than 70% of experts and

academics agree to keep them, as shown in Table 1.

Table 1: Fuzzy Delphi Method Questionnaire Item

Statistics.

No.

Self-directed learning

(S)/ online learning

attitudes (O)

Thresh hold

(Fuzzy performance

values）

Retain /

Delete

1 Self-learning (S1) 0.870 Retain

Continuous learning

(S2)

0.889 Retain

Efficiency learning

(S3)

0.726 Retain

Independent learning

(S4)

0.744 Retain

Self-understanding

(S5)

0.844 Retain

6 Planning learning (S6) 0.898 Retain

7 Favorite learning (S7) 0.825 Retain

Computer and network

confidence (O1)

0.836 Retain

9 Network use (O2) 0.879 Retain

10 Online learning (O3) 0.870 Retain

Computer/smart phone

use (O4)

0.853 Retain

Computer/smart phone

preferences (O5)

0.799 Retain

The Fuzzy DEMATEL is a method that

combines fuzzy semantic variables and DEMATEL

method. The formula and calculation steps (Wu et

al., 2020) as followed have seven steps.

CSEDU 2023 - 15th International Conference on Computer Supported Education

552

Step 1: Define the evaluation criteria and design

a fuzzy semantic scale, and

Step 2: Create a direct association matrix.

Step 3: Build and analyze the structural model.

Step 4: Total association matrix

Step 5: Defuzzification.

Step 6: Centrality and Causality.

Step 7: Result Analysis.

4 ANALYSIS AND DISCUSSION

OF THE FINDINGS

In this phase, 20 scholars and practical experts with

more than ten years of experience studying online

music learning programs at universities were invited

to take the survey. The questionnaires were then

distributed on-spot to these researchers and

practitioners for completion. After three months of

the survey, there were 20 valid questionnaires,

including 10 for researchers and 10 for practitioners.

The results of the various dimensions and criteria

were then analysed.

4.1 Results of the Analysis of Each

Dimensions

The evaluative dimensions are self-directed learning

(S) and online learning attitudes (O). First is

defining the evaluative dimensions, designing the

fuzzy semantic scales, establishing the direct

association matrix, building and analysing the

structural model, and creating the total association

matrix and defuzzification. The formulae and

calculations, and the defuzzification matrix for each

dimension, are shown in Table 2. The column and

row values of each dimension are shown in Table 3

after the calculation of the centrality and causality.

Then, after obtaining the values of d+r (centrality)

and d-r (causality). The value of d+r (centrality)

represents the strength of the influence between the

dimensions, the higher the value, the stronger the

influence. When the value of d-r is positive and if

the value is higher, it represents the "cause" of the

influence of other dimensions, and when d-r is

negative and if the value is lower, it stands for the

"effect" of the influence of other dimensions.

In the causality (d-r) section, according to the

value of d-r (causality), the dimensions of self-

directed learning and online learning attitudes are

classified into cause and effect clusters. Those

dimensions with positive d-r (causality) values are

classified as cause groups. The positive value of the

self-directed learning (S) dimension directly affects

another dimension. Therefore, schools, educational

institutions, and teachers should consider this

dimension as the main dimension in developing

learners' learning behaviors in online learning

programs.

Table 2: Matrix of Defuzzied Total Correlations of the

Dimensions.

Dimension

Self-directed

Learning (S)

Online Learning

Attitudes (O)

Self-directed

Learning (S)

6.941 7.558*

Online Learning

Attitudes (O)

7.094 6.941

Note: * Indicates above the threshold value of 7.133.

Table 3: Collation of Column and Row Values of

Dimension.

Dimension

(column

values )

(row

values)

d+r

(column

sums)

d-r

(column

difference)

Quadrant

Causal

relationship

S 14.499 14.035 28.534 0.464 2

Affects another

dimension

O 14.035 14.499 28.534 -0.464 3

Independence

dimension

Average 28.534 0

Note: Self-directed learning (S), Online learning attitudes (O).

The main purpose of learners' learning behaviors

is to enforce the dimension in cause groups, namely,

self-directed learning, so as to improve self-directed

learning. Hence, self-directed learning (S) is the

strongest affecting dimension and should be listed as

the main dimension which could strengthen a

learner’s learning behavior. While another

dimension with negative d-r (causality) values was

categorized as an effect cluster, namely, online

learning attitudes (O). This means that it was

affected by others, and the extent to which this

dimension was affected was greater than its own

influence, so schools, educational institutions, and

teachers can therefore consider the online learning

attitude dimension as a problem to be solved in the

long-term development of learners' learning

behaviors. The highest positive value of d-r is self-

directed learning (S), which represents the "cause"

of the most influence on the other dimensions, while

online learning attitudes (O) are the "effect" of the

most influence from the other dimensions. As such,

the higher the value of self-directed learning (S), the

stronger the online learning attitudes (O). Hence, the

self-directed learning dimension is the foundation of

the learner’s learning behavior. In terms of overall

consideration, if learners want to improve their

learning behavior in an online learning course, they

Determinants of Learners’ Self-Directed Learning and on-Line Learning Attitudes in on-Line Learning

553

should choose the most influential dimension,

namely, "self-directed learning (S)", which directly

affects the dimension "online learning attitudes" (O).

From Table 2, we can find that self-directed

learning (S) affects online learning attitudes (O), and

it is clear that the direction of the arrow of self-

directed learning (S) towards the online learning

attitudes (O) directly and strongly. Hence, learners

should cultivate their self-directed learning to

enforce their learning attitudes, in order to perfect

their online learning course behavior.

4.2 Results of the Analysis of the

Criteria

The assessment criteria are self-learning (S1),

continuous learning (S2), efficiency learning (S3),

independent learning (S4), self-understanding (S5),

planning learning (S6), favorite learning (S7),

computer and network confidence (O1), network use

(O2), online learning (O3), computer and

smartphone use (O4), and computer and smartphone

preferences (O5). There are a total of 12 criteria.

After defining the criteria and designing the fuzzy

semantic scale, establishing a direct association

matrix, building and analysing the structural model,

the total association matrix, and defuzzification, the

defuzzified total association matrix among the

criteria is shown in Table 4. Once d+r (centrality)

and d-r (causality) have been obtained, the cause-

effect relationship diagram.

In terms of centrality (d+r), these three criteria,

computer and network confidence (O1), online

learning (O3), and computer and smartphone

preferences (O5), are the most important. In terms of

the causality (d-r), the value of these criteria self-

learning (S1), computer and network confidence

(O1), online learning (O3), computer and

smartphone use (O4), and computer and smartphone

preferences (O5) are positive values, which mean

that these are the cause criteria. Among them, the

strongest are online learning (O3), computer and

smartphone use (O4), and computer and smartphone

preferences (O5). Conversely, the values of these

seven criteria - continuous learning (S2), efficiency

learning (S3), independent learning (S4), self-

understanding (S5), planning learning (S6), favorite

learning (S7), and network use (O2), are negative,

which means these criteria are effect criteria. Among

these criteria, independent learning (S4), planning

learning (S6), and network use (O2) have the highest

negative values.

Table 4: Collation of Column and Row Values of Criteria.

Criteria d r d+r d-r Quadrant

Causal

relationship

S1 4.876 4.418 9.293 0.458 1

Core criteria

S2 4.403 4.428 8.831 -0.024 3

Independence

criteria

S3 4.329 4.491 8.820 -0.162 3

Independence

criteria

S4 3.757 4.462 8.219 -0.705 3

Independence

criteria

S5 4.033 4.464 8.497 -0.430 3

Independence

criteria

S6 4.042 4.623 8.665 -0.581 3

Independence

criteria

S7 4.252 4.653 8.905 -0.401 3

Independence

criteria

O1 4.961 4.497 9.458 0.464 1

Core criteria

O2 4.075 4.530 8.605 -0.456 3

Independence

criteria

O3 4.966 4.395 9.361 0.570 1

Core criteria

O4 4.916 4.403 9.319 0.512 1

Core criteria

O5 5.140 4.385 9.525 0.754 1

Core criteria

Avera

e 8.958 0

Note: Self-learning (S1), Continuous learning (S2), Efficiency learning

(S3), Independent learning (S4), Self-understanding (S5), Planning learning

(S6), Favorite learning (S7), Computer and network confidence (O1),

Network use (O2), Online learning (O3), Computer/smart phone use (O4),

Computer/smart phone preferences (O5)

According to the causal relationships obtained

from the combined centrality and causality analyses,

computer and smartphone preferences (O5) have the

strongest influence, while the most influential

criterion is independent learning (S4). Among the

criteria of self-directed learning and online learning

attitudes, Online learning (O3), Computer and

smartphone use (O4), and Computer and smartphone

preferences (O5) are the most influential criteria and

are the main criteria for improving learner’s self-

directed learning, online learning attitudes.

In the causality (d-r) section, the 12 criteria of

self-directed learning and online learning attitudes

can be grouped into cause-effect clusters based on

the d-r (causality) values. Criteria with positive d-r

(causality) values are categorized as cause clusters,

with a total of five criteria categorized. Positive

criteria have a direct impact on the other criteria.

Therefore, scholars should consider these criteria as

important targets for enhancing self-directed

learning and online learning attitudes and strengthen

the criteria ability of the cause group to enhance the

other criteria of self-directed learning and online

learning attitudes. The most influential criteria are

"online learning (O3), computer and smartphone use

(O4), and computer and smartphone preferences

(O5)." These three criteria are the most influential

criteria and should be treated as the most important

criteria for self-directed learning and online learning

attitudes and the most influential "cause" of the other

criteria. The higher the proportion of online learning,

computer and smartphone use, and computer and

CSEDU 2023 - 15th International Conference on Computer Supported Education

554

smartphone preferences, the stronger the influence

of other criteria on self-directed learning and online

learning attitudes. Therefore, the learners' online

learning, computer and smartphone use, and

computer and smartphone preferences are the basis

for self-directed learning and online learning

attitudes. The negative value of d-r (causality) is

classified as the effect cluster. A total of seven

criteria were categorized as "effect clusters,"

representing the extent to which they are influenced

by other criteria. The extent of being affected by

these seven criteria is greater than their own

influence; therefore, schools, educational institutions,

and teachers can consider these seven criteria as the

long-term development of learners' self-directed

learning and online learning attitudes to be

addressed in online learning programs.

5 CONCLUSIONS

Learners' self-directed learning and online learning

attitudes are complex, multi-criteria indicators of

competence that cannot be precisely defined and

measured, and there are a complex and entangled

relationships among criteria. The results of this

study show that the dimension of self-directed

learning influences the dimension of online learning

attitude, that the criteria for self-directed learning

and online learning attitude are correlated with each

other, and that the degree of influence on online

learning attitude varies between criteria.

In terms of the dimension, firstly, self-directed

learning influences the online learning attitude. In

terms of the dimension level, self-directed learning

is the cause that influences another dimension, and

online learning attitudes are the effect that is

influenced by it. Therefore, to strengthen learning

behavior in online learning, learners can start by

constructing a self-directed learning dimension.

Secondly, self-directed learning is the main

determinant dimension of learners' learning behavior,

and it directly influences the online learning attitude

and is a fundamental factor in enhancing learners'

learning behavior. Therefore, learners need to

develop self-directed learning to establish the

foundation of their learning behavior in online

learning.

In the criteria section, firstly, self-learning,

computer and network confidence, online learning,

computer and smartphone use, and computer and

smartphone preferences are the main influencing

criteria for the other criteria. In particular, computer

and smartphone preferences are the most influential

criteria, and among self-learning, computer and

network confidence, online learning, computer and

smartphone use, and computer and smartphone

preferences, these criteria affect each other and also

affect other criteria. In addition, the criterion of

computer and smartphone preferences is the

strongest influencing criterion for the other criteria.

Learners can start with the strongest and most

influential computer and smartphone preferences to

enhance their online learning attitudes by getting

learners to enjoy accessing and operating computers

and smartphones. Learners can also enhance self-

directed learning through self-learning to develop

skills for continuous learning, efficient learning, and

other skills.

Self-learning, computer and network confidence,

online learning, computer, and smartphone use, and

computer and smartphone preferences are the main

influences on the other criteria of online learning

attitudes and self-directed learning. Therefore,

learners should have the skills of computer and

network confidence, online learning, computer and

smartphone use, computer and smartphone

preferences, etc. Furthermore, learners need to

develop self-learning skills. Secondly, self-learning,

computer and network confidence, online learning,

computer and smartphone use, and computer and

smartphone preferences are key determinants of

online learning attitudes and self-directed learning.

Learners should be able to grasp learning

opportunities and overcome barriers to learning;

learners should be confident in their learning

abilities and performance on computers and

smartphones and the Internet; learners should enjoy

and look forward to learning online; and learners

should be able to use computers and smartphones in

their studies, life, and work and enjoy accessing and

operating them.

The determinants and interactions of online

learning attitudes and self-directed learning have

been less explored in previous studies, online

learning attitudes and self-directed learning are

important dimensions that influence learners'

learning behavior. In addition, scholars who study

online learning attitudes and self-directed learning

have different theoretical perspectives. To

understand the problems mentioned, this study

combines the Fuzzy Delphi method and the Fuzzy

DEMATEL method to propose a more

comprehensive and complete set of determinants of

self-directed learning and online learning attitudes.

There is no research paper on this subject, so this

study has academic value. In summary, the academic

value of the findings of this study includes 1. The

Determinants of Learners’ Self-Directed Learning and on-Line Learning Attitudes in on-Line Learning

555

study integrates theoretical perspectives on self-

directed learning and online learning attitudes and

uses a wide range of perspectives to collect and

analyze relevant literature to select indicators of self-

directed learning and online learning attitudes and to

identify the dimensions and criteria of self-directed

learning and online learning attitudes by integrating

the views of researchers and experts in online

learning. 2. Using the Fuzzy DEMATEL method to

evaluate the dimensions and criteria of self-directed

learning and online learning attitudes, the cause-

effect diagrams computed and analyzed provide a

clear and easy understanding of the complex cause-

effect structure between the dimensions and criteria

of self-directed learning and online learning attitudes

and the strength and extent of the influence of these

factors.

In terms of practical implications, the findings of

this study reveal a number of important implications

for the learning behavior of learners in online

learning. Schools, educational institutions, and

teachers can use the results of this study to identify

the structural interrelationships and causal

relationships among the indicators of learners' self-

directed learning and online learning attitudes and to

select the most important key indicators of self-

directed learning and online learning attitudes,

which will help schools, educational institutions, and

teachers to understand learners' learning behaviors in

online learning programs, target learners' self-

directed learning and online learning attitudes, and

improve online learning programs. This will help

schools, educational institutions, and teachers

understand learners' learning behaviors in online

learning programs, focus on the key criteria of

learners' self-directed learning and online learning

attitudes, improve online learning programs, and

cultivate the key criteria of learners' self-directed

learning and online learning attitudes, which can

effectively enhance learners' self-directed learning

and online learning attitudes, and potentially

improve learners' learning behaviors and learning

outcomes.

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