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American Journal of  Physical Education 
and Health Science (AJPEHS)

A Parsimonious Model of  Nursing Students’ Clinical Learning Environment and 
Self-Directed Learning: Basis for a Self-Directed Learning Development Program

Maria Majorie M. Castillo1*

Volume 3 Issue 1, Year 2025
ISSN: 2992-9679 (Online) 

DOI: https://doi.org/10.54536/ajpehs.v3i1.4807
https://journals.e-palli.com/home/index.php/ajpehs

Article Information ABSTRACT

Received: March 20, 2025

Accepted: April 22, 2025

Published: June 13, 2025

The importance of  self-directed learning (SDL) in nursing education has grown as 
students are ready for independent clinical practice and ongoing professional growth. 
Understanding the variables that impact SDL preparedness is essential for formulating 
efficient educational approaches. The present research used a descriptive-correlational 
methodology to investigate the association between the clinical education environment and 
nursing students’ preparedness for self-directed learning. The Dundee Ready Education 
Environment Measure (DREEM) and the Self-Directed Learning Readiness Scale (SDLRS) 
were used to examine data obtained from 300 nursing students. Stratified random selection 
guaranteed the inclusion of  second to fourth-year students. The data was studied using 
descriptive statistics, correlation analysis, and structural equation modeling (SEM). The 
findings indicated that most participants were female (82.70%), and their perception of  the 
clinical education setting was generally good, with notably high ratings regarding learning 
and teaching effectiveness. The students exhibited exceptional preparedness for self-directed 
learning, displaying outstanding self-management, a strong desire for learning, and self-
control. The results of  the correlation study indicated strong negative associations between 
the year level and the clinical education environment and self-directed learning preparation. 
Conversely, academic performance (GWA) exhibited favorable connections with both 
variables. SEM analysis revealed that the clinical education setting substantially impacted the 
preparedness for SDL. Enhanced fit indices of  the modified model indicate that a desirable 
clinical education setting benefits SDL preparedness. 

Keywords

Clinical Education Environment, 
Educational Interventions, Nursing 
Students, Program Development, 
Self-Directed Learning Readiness, 
Structural Equation Modeling

1 St. Paul University Manila, Philippines
* Corresponding author’s e-mail: mariamajoriec@gmail.com

INTRODUCTION 
In a clinical learning environment, nursing students’ 
educational experiences go beyond traditional classroom 
settings, encompassing hospitals, community health 
centers, and other real-world healthcare settings. These 
environments play a crucial role in shaping students’ 
competencies and professional readiness. Vasli (2023) 
Self-directed learning had a significant positive effect on 
clinical competence As Bates (2015) pointed out, a safe 
and vibrant learning environment is essential for students 
to develop competence, motivation, and encouragement, 
all of  which are influenced by interactions with peers and 
teachers. In clinical education, the environment is further 
characterized by hands-on learning, exposure to diverse 
patient care situations, and a need for critical thinking and 
adaptability (Jamshidi et al., 2016). This distinctive setting 
requires nursing students to be independent, innovative, 
and responsive, traits that align with the concept of  self-
directed learning.
Research has extensively examined the relationship 
between the clinical educational environment and nursing 
students’ competence, revealing a statistically significant 
and positive correlation between students’ perceptions 
of  their clinical environment and their competence 
(Visiers‐Jiménez et al., 2021). These findings highlight 
the importance of  optimizing clinical settings to support 
SDL, as students who perceive their environment 
positively tend to demonstrate greater competency in 

their clinical practice. Additionally, nursing students’ 
satisfaction with the clinical learning environment has 
been identified as a key factor contributing to their 
overall learning experience. Papastavrou et al. (2016) 
emphasized that satisfaction within clinical settings can 
lead to potential reforms aimed at improving learning 
activities and achievements. Moreover, the challenges 
faced by nursing students, such as the need for adequate 
self-confidence in caregiving, significantly impact their 
learning (Jamshidi et al., 2016). 
The impact of  SDL on problem-solving abilities has also 
been studied, revealing a complex relationship between 
self-directed learning readiness and problem-solving 
skills among nursing students (Luo et al., 2019). Although 
the correlation between these variables is low, the study 
underscores the necessity of  SDL in fostering critical 
thinking and problem-solving capabilities. Furthermore, 
Mirzawati et al. (2020) observed a positive and significant 
relationship between self-efficacy, the learning 
environment, and SDL, emphasizing the interconnected 
nature of  these variables. The learning environment 
plays a vital role in shaping students’ SDL strategies, as 
demonstrated by Schweder and Raufelder (2021), who 
found differences in how students engage in self-directed 
versus teacher-directed learning environments.
The necessity for SDL programs has been recognized 
in various educational fields, including pharmacy (Unni 
et al., 2019), pre-medical education (Kim et al., 2022), 



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and nursing education (Noh & Kim, 2019). In nursing, 
the effectiveness of  SDL programs utilizing blended 
coaching has been explored, demonstrating the need for 
further research on associated variables and long-term 
effects (Noh & Kim, 2019). Additionally, Vasli (2023) and 
Asadiparvar-Masouleh (2022) examined the mediating 
role of  the clinical learning environment in the effects of  
SDL on clinical competence among internship nursing 
students, reinforcing the significance of  clinical settings 
in supporting self-directed learning approaches.
Another aspect of  SDL is its relationship with nursing 
students’ satisfaction and competency self-efficacy. 
Ibrahim et al. (2019) established that students’ satisfaction 
with the clinical learning environment significantly 
impacts their confidence in their competencies. This 
finding illustrates the importance of  fostering a supportive 
and engaging clinical education environment where SDL 
can thrive. Moreover, clinical nursing education aims to 
develop students’ professional skills, critical thinking 
abilities, independence, and lifelong learning habits 
(Sebaee et al., 2017). Given these objectives, incorporating 
SDL into nursing education is imperative for producing 
competent and self-reliant nursing professionals.
However, despite the significance of  the clinical learning 
environment, a gap has been observed between the skills 
and traits nursing students need to thrive in these settings 
and their actual readiness for self-directed learning. Prior 
research, such as the studies by Ojekou and Okanlawon 
(2019) and Grande et al. (2022), has shown moderate 
levels of  self-directed learning readiness among Nigerian 
and Southeast Asian nursing students. These findings 
suggest that, while students may be motivated, there is 
often a disconnect between the learning environment 
and their ability to fully engage in self-directed learning. 
This gap becomes particularly evident in clinical settings, 
where students must navigate complex and unpredictable 
situations independently, yet may lack the necessary 
readiness to do so effectively.
To address this issue, the present study aimed to 
investigate the relationship between the clinical learning 
environment and self-directed learning among Filipino 
nursing students. The objective is to develop a structural 
equation model that illustrates the interrelationships 
between these two elements, filling a significant gap in the 
literature. By understanding these dynamics, this research 
seeks to contribute valuable insights to educational 
practices, informing the development of  tailored 
interventions that enhance self-directed learning among 
nursing students in the Filipino context.

Self-Directed Learning (SDL): History and Concepts
The practice of  self-directed learning can be traced back 
to classical antiquity with the self-study of  philosophers 
such as Socrates, Plato, and Aristotle. In Colonial 
America, individuals pursued learning independently 
due to the limited number of  formal schools (Hiemstra, 
1994). Formal academic interest in SDL emerged in the 
19th century, with contributions from Craik (1840) and 
Smiles (1859), but it was in the 1960s when Houle (1961) 

and his student Tough (1979) laid the modern foundation 
of  SDL as a field of  research. Tough’s work examined 
how adults learn independently, which influenced many 
studies worldwide. Knowles (1975) introduced the 
concept of  andragogy and emphasized adult learning 
principles, stating that learners are motivated by internal 
factors such as self-esteem and curiosity. Guglielmino 
(1977) developed the Self-Directed Learning Readiness 
Scale (SDLRS), a widely used tool in SDL research. 
Further research by Spear and Mocker (1984) and Long 
& Agyekum (1983) highlighted the role of  the learner’s 
environment and led to the establishment of  the 
International Symposium on Self-Directed Learning.

Concepts and Definitions of  SDL
SDL is a complex concept defined through multiple 
lenses. Kerka (1999) described it as a process in which 
autonomous individuals pursue learning for personal 
growth and as a social phenomenon influenced by context. 
Rowland and Volet (1996) also emphasized the cultural 
influences on SDL, stating that social and cultural contexts 
shape how learners engage in independent learning. Van 
der Walt (2012) proposed two approaches: one based 
on individual understanding and another that redefines 
SDL beyond Knowles’ original framework. Merriam 
et al. (2007) identified three core purposes of  SDL: 
enhancing self-determination, promoting deep learning, 
and fostering social change. Braman (1998) linked SDL to 
individualistic values and argued that both personal goals 
and cultural expectations shape it. Brookfield (2009) 
added that SDL involves not just actions like goal-setting 
and strategy use, but also internal changes in how learners 
perceive knowledge. These insights show that SDL is 
both a personal and social process requiring autonomy, 
critical thinking, and adaptability (O’Donnell, 2005).

LITERATURE REVIEW
The Process of  Self-Directed Learning
The University of  Waterloo outlines SDL as a four-
step process: (1) readiness to learn, (2) setting goals, 
(3) engaging in the process, and (4) evaluating learning 
(Centre for Teaching Excellence, University of  Waterloo, 
2023). Readiness involves assessing one’s environment 
and past experiences. Goal-setting requires coordination 
between student and mentor using tools like learning 
contracts. Engagement means students must be aware 
of  their learning styles and take responsibility for 
progress. Evaluation involves self-reflection, seeking 
feedback, and making necessary adjustments. Robinson 
and Persky (2020) emphasized that while SDL promotes 
independence, both students and faculty face challenges 
in its implementation. Faculty must shift from instructors 
to facilitators and support students through this 
transition. Sumuer (2018) noted that students must move 
away from rote or strategic learning and instead adopt 
deep, reflective approaches for effective SDL.

Self-Directed Learning in Nursing Education
In nursing, SDL is closely linked to clinical competence. 



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Studies show that a positive perception of  the clinical 
environment leads to higher competence among nursing 
students (Visiers-Jiménez et al., 2021). Student satisfaction 
with the clinical learning setting enhances learning 
outcomes (Papastavrou et al., 2016), while challenges like 
lack of  confidence can hinder progress (Jamshidi et al., 
2016). SDL readiness correlates with problem-solving 
ability, although the relationship is complex (Luo et al., 
2019). Other studies highlight the role of  self-efficacy 
and satisfaction in enhancing SDL (Mirzawati et al., 
2020; Schweder & Raufelder, 2021). The need for SDL 
programs is evident across fields such as pharmacy (Unni 
et al., 2019), pre-medical (Kim et al., 2022), and nursing 
education (Noh & Kim, 2019). Vasli & Asadiparvar-
Masouleh (2022, 2023) also demonstrated the mediating 
role of  the clinical learning environment in influencing 
clinical competence through SDL.

Learning Environment of  Nursing Students
Bates (2015) defined the learning environment as 
a combination of  physical, cultural, and contextual 
settings where learning takes place. These include 
student characteristics, teaching goals, learning activities, 
evaluation strategies, and institutional culture. For nursing 
students, physical environments range from traditional 
classrooms to home setups for online learning, especially 
during COVID-19. The psychological environment 
involves motivation, trust, and student-teacher interaction, 
while emotional environments focus on safety, self-
expression, and emotional support. Clinical Education 
Environments (CEEs) include real-life hospital settings 
and virtual platforms. These settings allow students to 
develop practical skills and are influenced heavily by 
the guidance of  clinical instructors (Zhang et al., 2022). 
The role of  CEEs is essential in preparing students for 
professional nursing roles and reinforces the need for 
supportive and structured learning environments that 
foster SDL.

MATERIALS AND METHODS
Research Design
This study used a correlational research design to explore 
the relationship between clinical learning environment 
and self-directed learning readiness among nursing 
students. Correlational designs examine associations but 
do not imply causation (Polit & Beck, 2017). Structural 
Equation Modeling (SEM) was used to analyze complex 
relationships between variables and assess model fit. SEM 
is commonly used in social sciences due to its ability to 
represent latent constructs and test hypotheses using 
empirical data (Beran & Violato, 2010; Hair et al., 2021).

Research Locale
The study was conducted at Luna Goco Colleges 
in Calapan City, a recognized institution in nursing 
education. The college offers access to various clinical 
settings, including hospitals and community centers. Its 
comprehensive clinical exposure made it a suitable site to 
study how learning environments influence students’ self-

directed learning. Findings are expected to benefit the 
institution and contribute to nursing education practices.

Research Instruments
Two validated tools were used: the Dundee Ready 
Education Environment Measure (DREEM) and the 
Self-Directed Learning Readiness Scale (SDLRS). 
The DREEM (Roff  et al., 1997) evaluates students’ 
perceptions of  their educational environment across five 
subscales, with high reliability (α = 0.91). SDLRS (Fisher, 
King, & Tague, 2001) assesses readiness for SDL through 
self-management, desire for learning, and self-control, 
with Cronbach’s alpha ranging from 0.83 to 0.924.

Population and Sampling
The study employed consecutive sampling, a non-
probability method suitable for ordered populations. 
Eligibility was based on students being enrolled in the 
nursing program during the data collection year (Bujang & 
Baharum, 2017). A total of  300 students were targeted to 
ensure statistical power. Informed consent was obtained, 
and ethical procedures were followed.

Ethical Considerations
The study complied with ethical guidelines from the 
university ethics committee. Participants remained 
anonymous and responses were kept confidential. 
Consent forms preceded the survey and detailed the 
study’s purpose, procedures, risks, and benefits. Ethical 
approval was obtained before data collection. No 
vulnerable groups were included (Gordon, 2020).

Data Collection Procedure
After ethical and administrative approval, questionnaires 
were distributed during students’ free time with teachers’ 
permission. Instructions were provided, and students 
were free to ask questions. Completed questionnaires 
were reviewed for completeness before analysis.

Data Analysis Procedure
Data analysis was conducted using IBM SPSS and 
AMOS version 20.0. A significance level of  0.05 was 
applied. Normality was assessed using Shapiro-Wilk 
and Doornik-Hansen tests. Pearson’s correlation was 
used to evaluate linear relationships. Structural Equation 
Modeling (SEM) was applied to assess model fit using 
CB-SEM with maximum likelihood estimation (Byrne, 
2010). Fit indices included χ2/df  ≤ 3.00, RMSEA ≤ 0.08, 
CFI and GFI ≥ 0.90, and PNFI (Huang et al., 2010). 
Path analysis determined direct, indirect, and total effects 
among variables.
 
RESULTS AND DISCUSSION
This chapter presented the discussion, analyses, and 
interpretation of  the data gathered mainly through 
survey questionnaires to determine the clinical learning 
environment readiness and self-directed learning of  the 
nursing students in Luna Goco Colleges. 



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Demographic Profile of  the Participants in Terms of
1. Gender
2. Year Level

3. General Weighted Average (GWA) in the previous 
semester

4. NCM Courses enrolled

Table 1: Demographic Profile of  Participants (N = 300)
Characteristics Frequency (Percentage) Mean (SD)
Sex (f, %)
Male 52 (17.30%)
Female 248 (82.70%)
Year Level (f, %)
Second Year 135 (45.00%)
Third Year 92 (30.70%)
Fourth Year 73 (24.30%)
General Weighted Average (x̅, SD) 83.77 (2.40)
Enrolled Courses (f, %)
NCM 109 135 (45.00%)
NCM 110 135 (45.00%)
NCM 115 92 (30.70%)
NCM 116 92 (30.70%)
NCM 117 92 (30.70%)
NCM 121 73 (24.30%)
NCM 122 73 (24.30%)

Abbreviations: x̅ = Mean, SD = Standard Deviation

The demographic profile shows that most nursing 
students are female (82.70%), consistent with global 
trends in nursing education (Smith & Leggat, 2007). This 
gender imbalance is important, as female students often 
encounter different stressors and learning experiences 
(Albaqawi et al., 2022). Addressing these differences 
through gender-sensitive strategies like mentorship 
can improve learning and well-being (Englund, 2023). 
Moreover, promoting gender diversity benefits student 
learning, patient care, and teamwork in clinical settings 
(Ramjan, 2023; Tekbaş & Pola, 2020). Inclusive practices 
can enrich the educational environment and prepare 
students for diverse healthcare teams (Dubs, 2023).
The year level distribution, second year (45%), third year 
(30.70%), and fourth year (24.30%)—reflects the typical 
progression in nursing education. Each level represents 
a unique phase: foundational learning in the second year, 
specialized training in the third, and final preparation for 
licensure and practice in the fourth (Smith & Leggat, 2007; 
Ramjan, 2023). Recognizing these stages helps educators 

design targeted instruction that supports students’ 
developmental and academic needs (Albaqawi et al., 2022).
The pattern of  course enrollment aligns with these 
academic stages. High enrollment in NCM 109 and 110 
(45%) emphasizes the focus on foundational nursing care, 
while NCM 115, 116, and 117 (30.70%) reflect advanced 
clinical topics (Lee & Thompson, 2019). NCM 121 and 
122 (24.30%) likely cover specialized or elective topics 
(Brown & Miller, 2020). Understanding this structure aids 
in aligning nursing curricula with healthcare needs and 
educational standards, ultimately producing competent, 
practice-ready graduates.

Evaluation of  the Nursing Students on the Education 
Environment in Terms of:

1. Learning
2. teacher 
3. academic
4. atmosphere 
5. social 

Table 2: Descriptive Statistics of  Clinical Education Environment among the Participants (N = 300)
Variables Mean (SD) Score Range Interpretationa

Overall Clinical Education Environment Score 159.28 (6.15) 0 to 200 Excellent
Student’s Perception of  Learning 37.73 (1.92) 0 to 48 Highly Positive Teaching
Student’s Perception of  Teachers 35.74 (2.23) 0 to 44 Model Teachers
Student’s Academic Self-Perceptions 26.01 (1.99) 0 to 32 Confident
Student’s Perception of  Atmosphere 39.06 (2.28) 0 to 48 Good Feeling Overall
Student’s Social Self-Perceptions 20.74 (1.41) 0 to 28 Not Too Bad



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The evaluation of  the clinical education environment 
among nursing students revealed key insights across five 
dimensions: learning, teachers, academic self-perception, 
atmosphere, and social self-perception are shown in Table 
2. The overall clinical education environment received an 
excellent rating, with a mean score of  159.28 (SD=6.15), 
indicating a highly supportive and effective educational 
setting (Vaughan et al., 2014).
In terms of  learning, students expressed a highly positive 
perception, with a mean score of  37.73 (SD=1.92), 
reflecting their satisfaction with the quality of  education. 
This indicated that students find the learning environment 
conducive to acquiring both theoretical and practical 
knowledge, crucial for academic engagement and success. 
Positive perceptions of  learning environments are known 
to enhance student motivation and overall academic 
performance (Cohen, 2010).
The perception of  teachers also received high praise, 
with a mean score of  35.74 (SD=2.23), categorizing 
the instructors as “model teachers.” This suggests that 
students view their teachers as effective mentors who 
provided essential support for professional development. 
The presence of  highly regarded teachers has been shown 
to positively influence students’ clinical confidence and 
preparedness (Kuh & Hu, 2001). Effective teaching in 
clinical education is vital, as it shapes students’ readiness 
to apply theoretical knowledge in real-world settings 
(Smith & Leggat, 2007).
Students’ academic self-perception scored 26.01 
(SD=1.99), reflecting a strong sense of  confidence in 
their academic abilities, which is crucial for fostering 
resilience and self-directed learning. Confidence in 
academic performance has been linked to better student 
outcomes, including higher persistence and success rates 
in demanding programs such as nursing (Pascarella & 
Terenzini, 2005). This level of  academic self-efficacy 
helped students navigate the challenges of  their studies, 
improving both their academic and clinical competencies 
(Albaqawi et al., 2022).

The atmosphere within the clinical education environment 
received the highest rating, with a mean score of  39.06 
(SD=2.28), signifying a “good feeling overall” (Vaughan 
et al., 2014). This suggests that students perceived their 
academic atmosphere as positive and conducive to learning. 
A positive learning atmosphere has been consistently 
associated with improved student satisfaction, academic 
achievement, and retention (Pascarella & Terenzini, 
2005). It provides a space where students feel supported, 
engaged, and motivated to succeed, which enhances their 
overall educational experience.
However, social self-perception was rated lower than the 
other dimensions, with a mean score of  20.74 (SD=1.41), 
indicating a “not too bad” experience. This suggests that 
while students do not feel overly negative about their social 
integration, there is room for improvement. Literature 
suggests that improving social self-perceptions, through 
initiatives such as peer mentoring and group learning 
activities, can foster a stronger sense of  belonging and 
support (Strange & Banning, 2001). A supportive social 
environment is key to mitigating feelings of  isolation 
and promoting students’ overall well-being, which can 
ultimately enhance their academic success and retention.
Lastly, the clinical education environment is viewed 
positively, particularly in terms of  the learning experience 
and academic atmosphere. While the social aspect required 
attention, the strong ratings in other areas indicated that 
students are generally well-supported in their academic 
journey. Addressing social integration through strategic 
interventions can further enhance the comprehensive 
educational experience, ensuring that nursing students 
are fully prepared for professional practice (Cleary, 2011; 
Macdonald & Callender, 2010).

SDL Readiness of  the Nursing Students in Terms of
3.1. self- management 
3.2. desire for learning
3.3. self-control 

The self-directed learning (SDL) readiness of  nursing 

Table 3: Descriptive Statistics of  Self-Directed Learning Readiness among the Participants (N = 300)
Variables Mean (SD) Score Range Interpretationa

Overall Self-Directed Learning Readiness Score 175.35 (6.42) 40 to 200 Ready for Self-Directed Learning
Self-Management 49.70 (3.20) 12 to 60 Excellent Self-Management
Desire for Learning 59.26 (2.45) 13 to 65 Excellent Desire for Learning
Self-Control 66.39 (2.74) 15 to 75 Excellent Self-Control

students was evaluated across three dimensions: self-
management, desire for learning, and self-control. Table 
3 presents the descriptive statistics for SDL readiness 
among the participants. The overall SDL readiness score 
had a mean of  175.35 (SD=6.42), which falls within the 
“Ready for Self-Directed Learning” category, as defined 
by Fisher et al. (2001). This indicated that the nursing 
students are well-prepared for SDL, an essential skill 
in healthcare education where continuous learning and 
adaptability are critical.
In terms of  self-management, the mean score was 49.70 

(SD=3.20), interpreted as “Excellent Self-Management.” 
This suggested that students are adept at organizing, 
planning, and regulating their learning activities. Effective 
self-management is crucial in SDL as it enables learners to 
take responsibility for their educational progress, making 
informed decisions about what and how to learn (Abd-El-
Fattah, (2010). With this high level of  self-management, 
students demonstrated the ability to manage their study 
schedules, set learning goals, and actively engage in 
learning without needing constant external direction.
For desire for learning, the mean score was 59.26 



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(SD=2.45), categorized as “Excellent Desire for 
Learning.” This dimension reflects the students’ strong 
motivation and enthusiasm to acquire new knowledge. 
Desire for learning is an intrinsic drive that pushes 
students to seek out educational opportunities and 
deepen their understanding of  clinical practices (Deci & 
Ryan, 2000). The high score in this area indicated that the 
participants possess a robust internal motivation, which is 
fundamental for SDL since motivated learners are more 
likely to engage in self-directed educational activities and 
sustain learning efforts over time.
The mean score for self-control was 66.39 (SD=2.74), 
also interpreted as “Excellent Self-Control.” This shows 
that students have a high capacity to regulate their 
behaviors, emotions, and learning processes. Self-control 
plays a critical role in SDL readiness by allowing students 
to stay focused on their goals, resist distractions, and 
persevere through challenges (Duckworth et al., 2007). 
The participants’ strong self-control suggests that they 
are capable of  maintaining discipline in their studies, 
which is essential for success in the often rigorous and 
demanding clinical education environment.
Collectively, these high mean scores across the three 
dimensions reflected the students’ readiness for SDL. 
With a total SDL readiness score of  175.35, the nursing 
students demonstrate that they are equipped with the 
necessary skills to take charge of  their learning. These 
findings aligned with Fisher et al.’s (2001) interpretation 
that individuals with scores above 150 are considered 
ready for SDL. The readiness for SDL is particularly 
important in clinical education, where nursing students 
must continuously update their skills and knowledge to 

stay current with healthcare advancements.
The high levels of  self-management, desire for learning, 
and self-control indicate that the students have developed 
the competencies required for SDL. Educators can build 
upon this readiness by implementing strategies that 
further promote SDL, such as problem-based learning 
(PBL) and case-based learning (CBL), which encouraged 
students to take initiative in solving real-world clinical 
problems. Moreover, given the strong desire for learning 
among students, fostering an environment that supports 
lifelong learning through continuous feedback and 
opportunities for self-reflection will reinforce these 
positive SDL behaviors.
Lastly, nursing students in this study exhibit a high level 
of  SDL readiness, with strengths in self-management, 
desire for learning, and self-control. These findings 
suggested that students are well-prepared to manage 
their educational journey independently, and further 
educational interventions can help sustain and enhance 
these capabilities. Creating supportive learning 
environments that encourage self-regulation and intrinsic 
motivation will be key in fostering continued student 
success in SDL (Zimmerman, 2002)

Significant Relationship between the Participants’ 
SD Profile and Their Self-Directed Learning
Table 4 presents the correlation coefficients between 
participants’ demographic characteristics, the dimensions 
of  the clinical education environment, and the 
dimensions of  self-directed learning readiness (SDLR). 
The dimensions of  SDLR examined included self-
management, desire for learning, and self-control. The 

Table 4: Correlation Coefficients of  the Correlations between the Demographic Characteristics, the Dimensions 
of  Clinical Education Environment, and the Dimensions of  Self-Directed Learning Readiness (SDLR) among the 
Participants (N = 300)

Va
ria

bl
es

1 2 3 4 5 6 7 8 9 10 11 12 13

1. Sex (Female)

– – – – – – – – – – – –

2. Year Levels

0.
07

(0
.2

38
)

– – – – – – – – – – – –

3. General 
Weighted Average

–0
.0

1
(0

.8
90

)

–0
.3

5*
(0

.0
01

)

– – – – – – – – – – –

4.Course (NCM 
109 and 110)

–0
.1

4*
(0

.0
20

)

–0
.1

4*
(0

.0
20

)

0.
32

*
(0

.0
01

)

– – – – – – – – – –

5. Course (NCM 
115, 116, and 117)

0.
17

*
(0

.0
03

)

0.
83

*
(0

.0
01

–0
.0

8
(0

.1
60

–0
.6

1*
(0

.0
01

)

– – – – – – – – –

6. Course (NCM 
121 and 122)

–0
.0

3
(0

.6
34

)

0.
85

*
(0

.0
01

)

–0
.2

8*
(0

.0
01

)

–0
.5

1*
(0

.0
01

)

–0
.3

8*
(0

.0
01

)
– – – – – – – –



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7. Student’s 
Perception of  
Learning 
(Clinical Education 
Environment) 0.

12
*

(0
.0

41
)

–0
.3

7*
(0

.0
01

)

0.
13

*
(0

.0
33

)

0.
21

*
(0

.0
01

)

0.
20

*
(0

.0
01

)
–0

.4
6*

(0
.0

01

– – – – – – –

8. Student’s 
Perception of  
Teachers
(Clinical Education 
Environment) –0

.0
1

(0
.9

27
)

–0
.1

3*
(0

.0
21

)

0.
07

(0
.2

55
)

0.
05

(0
.3

68
)

0.
12

*
(0

.0
01

)
–0

.1
9*

(0
.0

01
)

0.
53

*
(0

.0
01

)

– – – – – –

9. Student’s 
Academic Self-
Perceptions
(Clinical Education 
Environment) –0

.0
8

(0
.1

53
)

–0
.3

4*
(0

.0
01

)

0.
13

*
(0

.0
29

)

0.
36

*
(0

.0
01

)

–0
.1

8*
(0

.0
02

)
–0

.2
3*

(0
.0

01
)

0.
21

*
(0

.0
01

)

0.
16

 *
(0

.0
05

)

– – – – –

10. Student’s 
Perception of  
Atmosphere
(Clinical Education 
Environment) 0.

00
(0

.9
97

)

–0
.2

9*
 (0

.0
01

)

0.
21

*
(0

.0
14

)

0.
19

*
(0

.0
01

)

0.
09

(0
.1

03
)

–0
.3

2*
 (0

.0
01

)

0.
21

*
(0

.0
01

)

0.
27

*
(0

.0
02

)

0.
15

*
(0

.0
09

)

– – – –

11. Student’s Social 
Perceptions
(Clinical Education 
Environment) 0.

05
(0

.4
07

)

–0
.3

0*
(0

.0
01

)

0.
10

(0
.0

90
)

0.
14

*
(0

.0
13

)

0.
22

*
(0

.0
01

)
–0

.4
0*

(0
.0

01
)

0.
24

*
(0

.0
01

)

0.
21

*
(0

.0
01

)

0.
18

*
(0

.0
02

)

0.
42

*
(0

.0
01

)
– – –

12. Self-
Management (Self-
Directed Learning 
Readiness) 0.

03
(0

.5
55

)

–0
.2

8*
(0

.0
01

)

0.
13

*
(0

.0
29

)

0.
12

*
(0

.0
36

)

0.
21

*
(0

.0
01

)
–0

.3
7*

(0
.0

01
)

0.
25

*
(0

.0
01

)

0.
19

*
(0

.0
01

)

0.
26

*
(0

.0
01

)

0.
30

*
(0

.0
01

)
0.

34
*

(0
.0

01
)

– –

13. Desire for 
Learning (Self-
Directed Learning 
Readiness) –0

.0
6

(0
.2

78
)

–0
.8

0*
(0

.0
01

)

0.
31

*
(0

.0
01

)

0.
77

*
(0

.0
01

)

–0
.2

6*
(0

.0
01

)
–0

.6
1*

(0
.0

01
)

0.
31

*
(0

.0
01

)

0.
19

*
(0

.0
01

)

0.
36

*
(0

.0
01

)

0.
27

*
(0

.0
01

)
0.

27
*

(0
.0

01
)

0.
29

*
(0

.0
01

)

–

14. Self-Control 
(Self-Directed 
Learning 
Readiness) –0

.0
9

(0
.1

25
)

–0
.6

8*
(0

.0
01

)

0.
23

*
(0

.0
01

)

0.
74

*
(0

.0
01

)

–0
.4

3*
(0

.0
01

)
–0

.4
1*

(0
.0

01
)

0.
20

*
(0

.0
01

)

0.
11

*
(0

.0
01

)

0.
27

*
(0

.0
01

)

0.
23

*
(0

.0
01

)
0.

23
*

(0
.0

01
)

0.
16

*
(0

.0
07

)

0.
63

*
(0

.0
01

)

Note: Values are represented as r-value (p-value). *Significant at 0.05

correlation analysis revealed several key findings regarding 
these relationships.
Sex demonstrated minimal correlations with both the 
dimensions of  the clinical education environment and 
SDLR dimensions, with r-values ranging from –0.01 
to 0.12. This suggested that sex does not significantly 
influence the participants’ perceptions of  their clinical 
education environment or their readiness for self-directed 
learning, aligning with previous studies indicating no 
substantial gender differences in SDL capabilities (Murad 
et al., 2010).
In contrast, year level exhibited significant negative 
correlations with various aspects of  the clinical education 
environment and the SDLR dimensions. Specifically, 
year level had correlations ranging from –0.13 to –0.37 
with the clinical education environment and from –0.28 
to –0.80 with the SDLR dimensions. This negative 
association implies that as students’ progress through 

their academic program, their perception of  the clinical 
environment and their readiness for SDL tend to decrease. 
This could be attributed with the increase academic and 
clinical pressures faced by upper-year students, leading to 
higher stress levels and less favorable perceptions of  their 
learning environment. (Fung et al., 2014).
The general weighted average (GWA) showed positive 
correlations with the dimensions of  the clinical education 
environment, ranging from 0.07 to 0.21, and with the 
SDLR dimensions, ranging from 0.13 to 0.31. This 
indicates that students with higher academic achievement 
tend to have a more positive view of  their clinical learning 
environment and exhibit greater readiness for SDL. This 
supports the notion that academic success is linked with 
favorable learning experiences and proactive learning 
behaviors (Stewart, 2017).
The courses enrolled, specifically NCM 109/110, NCM 
115/116/117, and NCM 121/122, showed perfect 



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correlations among themselves and significant associations 
with year level, with correlations ranging from –0.89 
to 0.85. This multicollinearity issue necessitated their 
exclusion from the structural equation modeling (SEM) 
analysis to maintain model validity.
The correlations between the dimensions of  the clinical 
education environment and SDLR were consistently 
positive and statistically significant, ranging from 0.11 
to 0.36. This suggested that a more positive perception 
of  the clinical education environment is associated with 
higher self-management, greater desire for learning, and 
improved self-control among the students.
These findings have several implications for educational 
practice. The negative correlation between year level 
and both the clinical education environment and SDLR 
suggested that targeted interventions may be necessary 
to support upper-year students. Institutions might 
consider implementing stress management programs, 
offering additional resources, and creating a supportive 
learning environment to help mitigate the pressures faced 
by senior students (Vidal et al., 2020). Additionally, the 
positive correlation between GWA and SDL dimensions 
underscores the importance of  academic support services. 
Providing tutoring, mentorship, and other academic 
support can enhance students’ academic performance, 
which in turn could improve their perceptions of  the 
learning environment and their readiness for SDL.
Lastly, while sex does not significantly impact SDL 
readiness, year level and GWA do show notable 
relationships. The findings suggested that academic 
support and stress management strategies could be 
beneficial in enhancing SDL readiness, particularly for 
upper-year students facing increased academic pressures.

Significant Relationship between the Participants’ 
Evaluation of  the Clinical Education Environment 
and Self-Directed Learning
The hypothesized model of  the associations of  
the demographic characteristics, clinical education 
environment, and self-directed learning readiness are 
presented in Figure 1. As previously mentioned, only 
sex and year level were the demographic characteristics 
included in the modeling analyses due to issues on linearity 
and multicollinearity of  other demographic variables. 
Initial analysis of  the hypothesized model indicated very 
poor model fit indices (Table 5). Results also showed 
that the association of  general weighted average to 
clinical education environment (β=0.02, p=0.606) and 
self-directed learning readiness (β=0.02, p=0.717) were 
not statistically significant. Modification indices also 
recommended a covariance term between the error 
terms of  the dimensions of  perceptions of  learning and 
teachers (MI=30.85, Par. Change=2.66); perceptions of  
atmosphere and social self-perceptions (MI=19.03, Par. 
Change=1.14); and, self-management and self-control 
(MI=4.06 Par. Change=–0.83). These initial results were 
used to trim and respecify the hypothesized model. 
The initial analysis of  the hypothesized model revealed 
significant limitations, as indicated by the poor model 

fit indices. The lack of  significant associations between 
GWA and both the clinical education environment and 
SDLR suggested that GWA may not be a critical factor 
in understanding these relationships. This finding is 
consistent with previous studies that have found mixed 
results regarding the impact of  academic performance 
on learning environment perceptions and self-directed 
learning readiness (Dunlap, 2005).
The suggested modifications, based on the modification 
indices, indicated potential improvements to the model 
through the inclusion of  covariance terms between 
certain error terms. Specifically, the high modification 
index (MI) and parameter change between the perceptions 
of  learning and perceptions of  teachers (MI=30.85, Par. 
Change=2.66) suggest a significant overlap between these 
dimensions. This overlap could be due to the integral 
role that teacher quality plays in shaping students’ overall 
learning experiences (Ginns et al., 2007).
Similarly, the covariance between perceptions of  
atmosphere and social self-perceptions (MI=19.03, Par. 
Change=1.14) implies that a positive learning atmosphere 
is closely linked to students’ social well-being and self-
perceptions. A supportive and inclusive atmosphere can 
enhance students’ social interactions and their confidence 
in self-directed learning.
The covariance between self-management and self-
control (MI=4.06, Par. Change=–0.83) underscores the 
interconnected nature of  these constructs. Both self-
management and self-control are crucial components 
of  self-directed learning, as they involve regulating one’s 
behavior and learning processes (Zimmerman, 2000). 
These findings align with the theoretical frameworks that 
emphasize the synergy between different self-regulation 
strategies (Pintrich, 2004).
The initial poor fit of  the hypothesized model and the 
non-significant associations involving GWA highlight the 
need to consider other variables that might better explain 
the relationships among demographic characteristics, the 
clinical education environment, and SDLR. Educational 
institutions should focus on enhancing the clinical 
education environment and supporting students’ self-
directed learning readiness through targeted interventions 
rather than relying solely on academic performance 
indicators.
The modifications suggested by the modification indices 
offer valuable insights for improving educational strategies. 
Emphasizing the quality of  teaching and creating a 
positive learning atmosphere can significantly impact 
students’ perceptions and their readiness for self-directed 
learning. Training programs for educators that focus on 
effective teaching strategies and creating inclusive learning 
environments can be beneficial (Hattie, 2009).
Furthermore, the close relationship between self-
management and self-control suggests that programs 
aimed at enhancing these skills should be integrated 
into the curriculum. Workshops and courses on time 
management, goal setting, and self-regulation can 
help students develop the competencies necessary for 
successful self-directed learning.



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Parsimonious Model Developed among the Profile, 
Clinical Education Environment and Self-Directed 
Learning
Figure 2 depicted the emerging model of  the associations 
of  demographic characteristics, clinical education 
environment, and self-directed learning readiness. After 
two iterations of  model re-specification and trimming, 
the emerging model showed acceptable model fit 
parameters (Table 6). It can be noted that year level had 
a strong, negative influence on both clinical education 
environment (β=–0.73, p=0.003) and self-directed 
learning readiness (β=–0.70, p=0.049). These results 
denoted that as year level increases by 1-unit (e.g., from 
second year to third year), clinical education environment 
decreases by 0.73-unit and self-directed learning readiness 
by 0.70-unit. Results also showed that year level had 

a negative, indirect effect on self-directed learning 
readiness (β=–0.22, p=0.036) through the mediation of  
clinical education environment, indicating an indirect 
decrease in self-directed learning readiness by 0.22-unit 
for every 1-unit increase in year level. It can also be 
noted from the emerging model that clinical education 
environment had a moderate and positive influence on 
self-directed learning readiness (β=0.30, p=0.044), which 
indicates that every 1-unit increase in clinical education 
environment leads to a 0.30-unit increase in self-directed 
learning readiness. Analyses also showed that year level 
alone measured 53.20% of  the variance of  clinical 
education environment, while both year level and clinical 
education environment measured 88.50% of  the R2-value 
or explained variance of  self-directed learning readiness. 

Table 5: Model Fit Parameters of  the Hypothesized and Emerging Models (N = 300)
Model CMIN RMSEA 90% CI CFI GFI PNFI

χ2 df χ2/df  
(p-value)

RMSEA
(p-value)

Lower 
Bound

Upper 
Bound

Acceptable 
Threshold

– – ≤3.00
(>0.05)

≤0.08
(>0.05)

– – ≥0.90 ≥0.90 EM>
HM

Hypothesized 
Model

144.25 31 4.65
(0.001)

0.111
(0.001)

0.093 0.129 0.873 0.916 0.583

Emerging Model 73.90 30 2.46
(0.001)

0.070
(0.051)

0.050 0.090 0.951 0.954 0.614

Abbreviations: χ2 = Chi-Squared Value; df  = Degrees of  Freedom; RMSEA = Root Mean Square Error of  Approximation; CFI 
= Comparative Fit Index; GFI = Goodness-of-Fit Index; PNFI = Parsimonious Normal Fit Index; EM = Emerging Model; HM 
= Hypothesized Model

Table 6: Path Analyses of  the Total, Direct, and Indirect Effects among the Study Variables (N = 300)
Outcomes Year Level Clinical Education Environment

Indirect 
Effect

Direct Effect Total Effect Indirect 
Effect

Direct 
Effect

Total 
Effect

Clinical Education 
Environment

– –0.73* (0.003) –0.73* (0.003) – – –

Self-Directed Learning 
Readiness

–0.22* (0.036) –0.70* (0.049) –0.92* (0.010) – 0.30* (0.044) 0.30* (0.044)

Note: Values are presented as standardized regression or beta coefficient (p-value). *Significant at 0.05 level

Figure 1: Hypothesized Model of  the Associations of  Demographic Characteristics, Clinical Education Environment, 
and Self-Directed Learning Readiness



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The hypothesized model, depicted in Figure 3, illustrated 
the relationships between self-directed learning 
readiness (SDLR), the clinical education environment, 
and demographic characteristics. The model suggested 
that the clinical education environment and SDLR are 
primarily influenced by demographic characteristics, 
such as age, gender, and prior educational background. 
The relationship between SDLR and demographic 
characteristics is hypothesized to be mediated by the 
clinical education environment, which includes factors 
such as mentorship quality, available resources, and 
institutional support. The model’s directional arrows 
suggested that SDLR is anticipated to be improved by a 
supportive clinical education environment and favorable 
demographic characteristics.
The hypothesized model posits that SDLR is substantially 
influenced by demographic characteristics. The notion 
that age and prior educational experiences influence 
learning styles and aptitude for self-directed learning 
is supported by recent research. As a result of  their 
extensive educational and life experiences, mature pupils 
frequently demonstrated higher SDLR (Guglielmino & 
Long, 2011). Furthermore, research has demonstrated 
that gender differences in learning preferences exist, with 
certain studies suggesting that female students may be 
more self-directed than their male counterparts (Murad 
et al., 2010). 
The clinical education environment is proposed to serve 
as a critical mediator between SDLR and demographic 
characteristics. Students’ motivation and conviction to 
participate in self-directed learning can be improved 
by a positive educational environment that includes 
constructive feedback, supportive mentorship, and 
adequate resources (Brydges et al., 2012). Williams and 
Beovich (2019) conducted a study that found that nursing 
students’ SDLR was substantially enhanced by supportive 
mentorship and access to learning resources. This 
underscores the significance of  a well-structured clinical 
education environment.
The significance of  demographic characteristics in the 
development of  educational strategies is emphasized by the 
results of  the hypothesized model. Mentorship programs 
that are customized to meet the unique requirements of  
various age groups and genders may prove to be more 
advantageous. Additionally, it is imperative to establish 
a clinical education environment that is supportive and 
provides sufficient resources and mentorship. To cultivate 
an environment that is conducive to learning, institutions 
should allocate resources to mentorship programs of  
high quality and ensure that there are adequate learning 
resources (Brydges et al., 2012). 
The model depicted in Figure 4 emerged through a 
series of  data-driven refinements and iterations aimed 
at explaining the relationships between demographic 
characteristics, the clinical education environment, and 
self-directed learning readiness (SDLR). Initially, the 
hypothesized model suggested that demographic factors, 
such as Year Level and General Weighted Average 

(GWA), would directly influence both the Clinical 
Education Environment and SDLR, with the Clinical 
Education Environment acting as a mediator between 
these variables.
Data were collected from 300 participants, capturing key 
variables such as perceptions of  the clinical education 
environment (including perceptions of  learning, teachers, 
academic self-perception, and atmosphere) and SDLR 
(including self-management, desire for learning, and self-
control). The first iteration of  path analysis tested these 
relationships, but non-significant paths were removed in 
subsequent rounds of  model specification.
Through model trimming, Year Level was found to have 
a significant, negative direct impact on both the Clinical 
Education Environment (β = –0.73, p = 0.003) and SDLR 
(β = –0.70, p = 0.049). This indicated that as students 
advanced in their year levels, both their perception of  the 
clinical education environment and their readiness for 
self-directed learning decreased. Importantly, the model 
also revealed an indirect effect of  Year Level on SDLR (β 
= –0.22, p = 0.036) mediated by the Clinical Education 
Environment, further suggesting that the declining quality 
of  the clinical environment as students progressed had an 
additional negative impact on SDLR.
The final model highlighted the positive influence of  
the Clinical Education Environment on SDLR (β = 
0.30, p = 0.044), showing that a supportive educational 
environment could enhance students’ readiness for self-
directed learning, despite the negative influence of  year 
progression. With these relationships established, the 
model demonstrated that Year Level accounted for 53.20% 
of  the variance in the Clinical Education Environment, 
while Year Level and the Clinical Education Environment 
together explained 88.50% of  the variance in SDLR.
This model emerged after refining the initial hypothesis, 
validating the significant pathways, and trimming non-
significant ones, ultimately providing a more focused 
understanding of  how year level and clinical education 
interact to influence self-directed learning readiness.
The initial hypotheses are expanded upon by the emerging 
model, which incorporates empirical data to disclose 
more intricate interactions between the variables. For 
example, the model implies that SDLR is more strongly 
associated with antecedent educational attainment than 
was previously believed. Stewart (2017) has recently 
discovered that learners with higher educational 
credentials are more proficient at self-directed learning as 
a result of  their developed critical thinking and problem-
solving skills.
The emerging model also identified the quality of  clinical 
placements and the availability of  simulation-based 
learning as critical factors that influence SDLR. SDLR 
is considerably improved by simulation-based education, 
which offers realistic and engaging learning experiences 
(Fey et al., 2014). 
The necessity for a more sophisticated strategy to 
improve SDLR is underscored by the emergent model. 
Educational interventions should be customized to 



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the unique demographic characteristics of  students, 
with a particular emphasis on their prior educational 
achievements. Furthermore, the integration of  simulation-
based learning and the provision of  high-quality clinical 
placements can substantially enhance SDLR. Simulation-
based learning should be incorporated into the curricula 
of  institutions, and clinical placements should provide 
supportive and enriching learning experiences (Fey et al., 
2014; Vidal et al., 2020). 

CONCLUSIONS
The demographic profile revealed that most nursing 
students were female and maintained consistent 
academic performance. This highlights the need for 
teaching strategies that consider gender diversity 
and support learning in clinical settings. The clinical 
education environment was generally rated positively, 
though improvements in student social interaction 
are recommended to enhance the overall learning 
experience. Nursing students showed a strong readiness 
for self-directed learning (SDL), indicating the value of  
incorporating SDL-based activities into the curriculum 
to develop their independence. The study also found 
that demographic factors and the clinical environment 
significantly influence SDL readiness. Therefore, 
continuous monitoring and customized interventions 
based on students’ year levels are essential. While the 
original hypothesized model did not fit well with the 
data, the revised model better reflected the relationship 
between the clinical environment and SDL readiness. 
These findings reinforce the importance of  creating 
supportive, student-centered environments and tailoring 
education strategies to foster self-directed learning among 
nursing students.

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