Exploring self-directed learning readiness and related factors: the role of time management skills in nursing students

Abstract

Background

Previous studies have reported varying levels of self-directed learning readiness and time management skills among nursing students, and the relationship between these two skills has not been extensively explored. Consequently, this study aimed to assess self-directed learning readiness and its influencing factors, with a specific emphasis on the role of time management skills among nursing students.

Methods

This cross-sectional study involved the participation of 110 undergraduate nursing students who were conveniently recruited for the research. The data collection tools included a demographic information form, Fisher’s Self-Directed Learning Readiness questionnaire, and the Time Management Inventory developed by Britton and Tesser. The collected data were analyzed using STATA-14 statistical software.

Results

The findings revealed that 96.4% of the nursing students demonstrated readiness for self-directed learning. The mean overall score for self-directed learning readiness was 162.3 ± 6.1 out of 200, indicating a relatively high level of readiness. The mean score for time management skills was 98.1 ± 5.0 out of 135, suggesting satisfactory proficiency in this area. In terms of the relationship between self-directed learning readiness and its associated factors, time management skills, academic probation history, grade point average, place of residence, and level of interest in the field of study collectively accounted for 9.2% of the variance in self-directed learning readiness. Among these factors, academic probation history, grade point average, and place of residence emerged as statistically significant predictors (P < 0.05).

Conclusions

The study findings indicate that the level of self-directed learning readiness and time management skills among the nursing students were considered acceptable. Academic probation history, grade point average, and place of residence emerged as significant predictors of self-directed learning readiness. These results highlight the importance of considering students’ academic background and living conditions to effectively enhance their level of self-directed learning readiness. Further research is recommended to explore additional factors that may influence self-directed learning readiness among nursing students.

Background

Self-Directed Learning (SDL) is an instructional approach where learners take responsibility for their learning process [1]. Within this method, learners identify their learning needs, set goals, locate relevant resources, utilize appropriate learning strategies, and evaluate their learning outcomes [2]. The effectiveness of SDL relies on learners possessing certain characteristics such as curiosity, critical thinking abilities, decision-making skills, and self-confidence, which collectively contribute to their Self-Directed Learning Readiness (SDLR). The term " SDLR " refers to an individual’s ability, motivation, willingness, and capacity to engage in self-directed learning activities without direct supervision or guidance from instructors [3]. It is crucial to be aware of nursing students’ SDLR levels for effective educational planning [4]. The existing literature demonstrates varying levels of SDL abilities and readiness among nursing students in different countries. Studies conducted in Saudi Arabia, the Philippines, and Thailand indicate that a majority of nursing students are ready for SDL [5]. In Saudi Arabia, nursing students were found to be above average in terms of SDLR, while in China, most students lacked the necessary readiness for SDL [3, 6]. Reports on SDL ability among South Korean and Chinese students indicate a moderate level [7, 8], whereas nursing students from Spain, Portugal, Italy, Finland, Slovakia, and the Czech Republic displayed higher levels of SDL ability [9]. In Iran, studies examining the readiness of nursing students for SDL have reported varied results, ranging from low to high levels [10–12]. Time management skills, including planning, prioritization, organization, goal setting, monitoring, and procrastination avoidance, are significant factors in enhancing the quality of learning [13], empowering individuals to effectively manage their time to achieve their goals [14, 15]. Effective time management is essential for nursing students, enabling them to succeed academically and thrive in their future careers [16, 17]. Studies on nursing students’ time management skills show varied findings, with reported levels ranging from average to high [16, 18, 19]. Furthermore, possessing strong time management skills can boost students’ self-efficacy for learning [20]. As a key component of self-management, time management is fundamental to SDLR [5, 21] and has been identified as a facilitator of SDL [21]. Studies from Turkey (2018) and India (2019) demonstrate a significant correlation between time management skills and SDLR [22, 23]. Similarly, research from South Korea (2015) suggests that students’ time management behaviors can predict SDL [24].

Given the importance of SDL and time management skills for academic success and clinical competence in nursing students, investigating their relationship can offer valuable insights into student performance and progress. While limited research has explored this association, existing findings suggest a positive relationship between SDLR and time management skills [22, 23]. Further research is needed to explore this relationship more comprehensively. Therefore, this study aimed to assess SDLR and its associated factors, focusing specifically on the role of time management skills in nursing students.

This study is grounded in Self-Regulated Learning theory, which encompasses three key processes: metacognition, motivation, and strategic action. Metacognitive learners possess a strong understanding of their strengths, weaknesses, and learning strategies. Motivated learners exhibit diligence, perseverance, and confidence in their learning abilities. Strategic learners employ a variety of learning strategies tailored to their specific needs [25]. Time management is a crucial aspect of self-regulated learning, enabling learners to allocate their time effectively and achieve their learning goals [20].

This study aimed to address the following research questions:

1. What is the level of SDLR among nursing students?

2. What is the level of time management skills among nursing students?

3. What factors are associated with the level of SDLR among nursing students?

Materials and methods

Study design

This study utilized a cross-sectional descriptive-analytical research design, conducted between March 6, 2023, and April 22, 2023. The study adhered to the guidelines outlined in the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) [26] to ensure transparent and comprehensive reporting of the research methodology and results.

Sample and sampling method

The study population consisted of undergraduate nursing students enrolled in the School of Nursing and Midwifery at Kermanshah University of Medical Sciences during the second semester of 2023. Located in western Iran, this school has been providing nursing education since 1965 and offers undergraduate, master’s, and doctoral programs. The sample size for this study was determined based on the findings of Ertuğ and Faydali [22], considering a 95% confidence level, 90% test power, and a correlation coefficient of 0.5 between SDLR and time management. Initially, a sample size of 38 participants was calculated. However, taking into account the inclusion of five independent variables in the regression model and a potential dropout rate of 20%, the sample size was increased to 110 participants. The inclusion criteria for the study were being an undergraduate nursing student enrolled in the second semester or above and expressing willingness to participate. Participants who met these criteria were included in the study using convenience sampling, which ensured a practical and efficient recruitment process.

Instruments

The data collection instruments used in this study included a demographic information form, the Time Management Inventory, and the SDLR scale. The demographic information form gathered relevant details such as age, gender, marital status, school grade, academic probation history, Grade Point Average (GPA), place of residence, level of interest in the field of study, and willingness to pursue higher education. These demographic variables were considered essential in examining their potential influence on SDLR and time management skills.

To assess time management skills, the Time Management Inventory (TMI) developed by Britton and Tesser (1991) [27] was employed. This questionnaire has been previously validated, and its overall Cronbach’s alpha coefficient was reported as 0.87. The subscales of the TMI, including time planning, attitudes toward time, and time-wasting, demonstrated Cronbach’s alpha coefficients of 0.88, 0.66, and 0.47, respectively [28]. In the present study, the reliability of the TMI was assessed using Cronbach’s alpha, resulting in an overall alpha coefficient of 0.89. The calculated alpha coefficients for the subscales of time planning, attitudes toward time, and time-wasting were 0.90, 0.78, and 0.72, respectively, indicating good reliability of the instrument. The TMI comprises 27 items divided into three subscales: Time Planning (16 items), Time Attitudes (7 items), and Time Wasting (4 items). Participants rated each item on a five-point Likert scale ranging from 1 (never) to 5 (always). The total score on the TMI ranges from 27 to 135, with higher scores indicating better time management skills [16].

The SDLR questionnaire utilized in this study was developed by Fisher et al. (2001) [29]. This questionnaire consists of 40 items divided into three subscales: self-management, desire for learning, and self-control, comprising 13, 12, and 15 questions, respectively. Previous studies have validated the questionnaire’s reliability and validity [30, 31]. In a study conducted by Ballad et al. (2022), the internal consistency of the entire tool was reported with a Cronbach’s alpha coefficient of 0.87 [1]. The Persian version of the SDLR questionnaire has been validated in Iran, where Cronbach’s alpha coefficients for the overall tool and its subscales, including self-management, desire for learning, and self-control, were reported as 0.94, 0.88, 0.87, and 0.88, respectively [32]. The SDLR questionnaire employs a five-point Likert scale ranging from 1 (completely disagree) to 5 (completely agree) [1]. The total score on the scale ranges from 40 to 200, with scores above 150 indicating readiness for SDL [32]. Additionally, in the current study, the Grade Point Average (GPA) was reported based on the Iranian educational system, which utilizes a scale ranging from zero to 20.

Data collection

Potential participants were invited to participate in the study through a dedicated Telegram group for nursing students. The invitation included a link to an online questionnaire hosted on “http://rabit.kums.ac.ir/s/c9nVyc-VkoW.html” and a detailed information sheet outlining the study’s objectives, procedures, risks and benefits, data confidentiality measures, and the voluntary nature of participation. To ensure that students were fully informed, the information sheet emphasized the importance of confidentiality and explained how their data would be handled. Students were informed that completing and submitting the online questionnaire would serve as their informed consent. The questionnaire link was deactivated once the desired sample size was reached. The survey was accessible to students online, allowing them to participate from their preferred location.

Data analysis

In this study, we employed both descriptive and inferential statistics to analyze the data. Descriptive statistics such as frequencies, percentages, means, and standard deviations were used to describe participant characteristics. For inferential statistics, we used independent t-tests and one-way analysis of variance (ANOVA) to compare mean SDLR scores based on different demographic variables. To examine the relationship between SDLR and time management, we utilized the Pearson correlation coefficient. To assess the predictive role of demographic variables and time management on the mean SDLR score, we employed a linear regression model. Initially, we conducted univariate regression models and included only the variables with a p-value below 0.2 as potential predictors. Subsequently, these selected variables were included in the multiple linear regression model to evaluate their combined predictive influence on SDLR. The data analysis was performed using STATA-14 software, a widely used statistical software package known for its capabilities in data analysis and management. We adopted a significance level of less than 0.05 for all statistical tests.

Ethical considerations

The study received approval from the Ethics Committee of Kermanshah University of Medical Sciences (code: IR.KUMS.REC.1401.368). All participants were provided with a clear explanation of the study objectives, and the importance of maintaining the confidentiality of their personal information and responses was emphasized. This explanation included explicit mention of the researchers’ need to access student GPAs for the study. Participants were informed that their voluntary completion and submission of the questionnaires would serve as their written consent to participate in the study, including consent to access their GPAs. To ensure anonymity, only the primary researcher (first author) had access to identifiable data during the process of linking GPAs to questionnaire responses. Once the data were linked, student identifiers were removed, and all analyses were conducted using de-identified datasets. To avoid any potential for coercion and ensure voluntary participation, no academics involved in teaching or assessing the students were involved in the recruitment process.

Results

The sample had a mean age of 22.7 ± 3.0 years. The majority of participants were female (n = 64, 58.2%) and single (n = 99, 90%). Around 26.4% (n = 29) of students were in their first year of study. The majority of participants had no history of academic probation (n = 103, 93.6%), and 88.2% (n = 97) of the sample had a GPA above 15 (out of 20). Approximately 70% (n = 77) of the students resided in dormitories. In terms of interest in their field of study, 48.2% (n = 53) of the participants reported moderate interest. Moreover, 76.4% (n = 84) expressed a willingness to pursue higher education (Table 1).

Table 1.

Demographic characteristics of the nursing students (N = 110)

Variables		n (%)/ Mean ± SD
Age, years		22.7 ± 3.0
Sex	Female	64 (58.2)
	Male	46 (41.8)
Marital status	Single	99 (90.0)
	Married	11 (10.0)
School grade (year)	1st	29 (26.4)
	2nd	26 (23.6)
	3rd	28 (25.5)
	4th	27 (24.5)
Academic probation history	Yes	7 (6.4)
	No	103 (93.6)
Grade point average	≤ 15	13 (11.8)
	> 15	97 (88.2)
Residence	Dormitory	77 (70.0)
	With family	33 (30.0)
Level of interest in the field of study	Low	13 (11.8)
	Moderate	53 (48.2)
	High	44 (40.0)
The desire for further education at higher levels	Yes	84 (76.4)
	No	26 (23.6)

The mean score for the total SDLR was 162.3 ± 6.1 (out of 200), with 96.4% of students (n = 106) achieving scores above 150. The mean and standard deviation of the subscale scores for self-management, desire for learning, and self-control were reported as 50.5 ± 4.5, 48.3 ± 2.1, and 63.4 ± 2.0, respectively.

The findings revealed that the mean score for the total TMI was 98.1 ± 5.0 (out of 135). Additionally, the mean scores for the subscales of time planning, time attitudes, and time-wasting were reported as 57.7 ± 4.3, 22.9 ± 2.2, and 17.5 ± 1.3, respectively.

A significant difference was observed in the mean SDLR score between non-dormitory students and dormitory residents (p = 0.021). However, no significant correlations were found between the mean SDLR score and various demographic variables, such as age, gender, marital status, school grade, history of academic probation, GPA, level of interest in the field of study, and willingness to pursue further education (Table 2).

Table 2.

Demographic characteristics of students based on the mean of self-directed learning readiness (n = 110)

Variables		N (%)	Self-directed Learning Readiness (mean ± SD)	Statistical test	P-value
Age, years	17–22	62 (56.4)	162.7 ± 6.2	t = 0.82	0.412
	23–38	48 (43.6)	161.7 ± 6.0
Sex	Female	64 (58.2)	162.3 ± 6.7	t = 0.05	0.961
	Male	46 (41.8)	162.2 ± 5.2
Marital status	Single	99 (90)	162.2 ± 5.9	t = -0.36	0.716
	Married	11 (10)	162.9 ± 7.7
School grade (year)	1st	29 (26.4)	162.7 ± 5.9	F = 0.10	0.960
	2nd	26 (23.6)	161.9 ± 6.2
	3rd	28 (25.5)	162.0 ± 7.0
	4th	27 (24.5)	162.4 ± 5.5
Academic probation history	Yes	7 (6.4)	166.4 ± 4.6	t = -1.89	0.062
	No	103 (93.6)	162.0 ± 6.1
Grade point average	≤ 15	13 (11.8)	160.0 ± 7.5	t = -1.44	0.152
	> 15	97 (88.2)	162.6 ± 5.8
Residence	Dormitory	77 (70.0)	161.4 ± 5.9	t = -2.34	0.021
	With family	33 (30.0)	164.3 ± 6.1
Level of interest in the field of study	Low	13 (11.8)	162.9 ± 5.8	F = 1.84	0.160
	Moderate	53 (48.2)	161.1 ± 5.4
	High	44 (40.0)	163.5 ± 6.7
The desire for further education at higher levels	Yes	84 (76.4)	162.4 ± 6.2	t = -0.37	0.712
	No	26 (23.6)	161.9 ± 5.8

Furthermore, in the current study, no significant correlations were identified between the total score and subscales of SDLR and the total score and subscales of the TMI (Table 3).

Table 3.

Relationships between Self-directed learning readiness (SDLR) and Time management (TM) and its subscales

SDLR and subscales		TM and subscales
		Time planning	Time attitudes	Time-wasting	TMI total
Self-management	r	0.078	-0.037	0.076	0.072
	p	0.417	0.703	0.432	0.458
Desire for learning	r	0.061	0.028	-0.135	0.028
	p	0.530	0.772	0.158	0.771
Self-control	r	0.131	-0.049	0.069	0.110
	p	0.173	0.611	0.476	0.254
SDLR total	r	0.123	-0.034	0.033	0.100
	p	0.199	0.723	0.736	0.299

To examine the relationship between time management skills, demographic variables, and SDLR, a linear regression analysis was conducted. Initially, based on the p-values obtained from the univariate linear regression, the variables of academic probation history, GPA, place of residence, level of interest in the field of study, and time management skills were selected for inclusion in the multiple regression analysis.

The results of the multiple regression analysis revealed that individuals with a history of academic probation had a mean SDLR score 5.33 times higher than those without a history of academic probation, assuming the other variables remain constant (p = 0.034). Among students with a GPA above 15, the mean SDLR score was 3.95 times higher compared to students with a GPA of 15 or below (p = 0.029). Additionally, the mean SDLR score in students who did not reside in dormitories was 2.46 times higher than those residing in dormitories (p = 0.047). However, no significant statistical relationship was found between the level of interest in the field of study and time management skills with SDLR (p = 0.362 for low-interest level, p = 0.098 for high-interest level, and p = 0.151 for time management skills).

The variables of academic probation history, GPA, place of residence, level of interest in the field of study, and time management skills collectively explained 9.2% of the variance in the SDLR score (Table 4).

Table 4.

Determining predictive factors for self-directed learning readiness based on linear regression

Variables	Variable levels	Univariate					Multivariate
		B	SE	t	95% CIa	P-value	B	SE	t	95% CI	P-value
Age, (Year)	17–22	Ref.	-	-	-	-	-	-	-	-	-
	23–38	-0.96	1.17	-0.82	-3.29, 1.36	0.412	-	-	-	-	-
Sex	Female	Ref.	-	-	-	-	-	-	-	-	-
	Male	-0.06	1.18	-0.05	-2.40, 2.28	0.961	-	-	-	-	-
Marital status	Single	Ref.	-	-	-	-	-	-	-	-	-
	Married	0.71	1.94	0.36	-3.14, 4.55	0.716	-	-	-	-	-
School grade	1st	Ref.	-	-	-	-	-	-	-	-	-
	2nd	-0.81	1.66	-0.48	-4.10, 2.49	0.629	-	-	-	-	-
	3rd	-0.65	1.63	-0.40	-3.89, 2.58	0.689	-	-	-	-	-
	4th	-0.25	1.65	-0.15	-3.51, 3.02	0.882	-	-	-	-	-
Academic probation history	No	Ref.	-	-	-	-	Ref.	-	-	-	-
	Yes	4.43	2.34	1.89	-0.22, 9.10	0.061	5.33	2.48	2.15	0.41, 10.25	0.034
Grade point average	≤ 15	Ref.	-	-	-	-	Ref.	-	-	-	-
	> 15	2.58	1.79	1.44	-0.97, 6.12	0.152	3.95	1.79	2.21	0.41, 7.50	0.029
Residence	Dormitory	Ref.	-	-	-	-	Ref.	-	-	-	-
	With family	2.90	1.24	2.34	0.44, 5.36	0.021	2.46	1.22	2.01	0.04, 4.88	0.047
Level of interest in the field of study	Moderate	Ref.	-	-	-	-	Ref.	-	-	-	-
	Low	1.79	1.87	0.96	-1.91, 5.49	0.340	1.80	1.97	0.92	-2.10, 5.71	0.362
	High	2.32	1.23	1.89	-0.12, 4.76	0.062	1.99	1.19	1.67	-0.37, 4.35	0.098
The desire for further education at higher levels	No	Ref.	-	-	-	-	-	-	-	-	-
	Yes	0.51	1.37	0.37	-2.21, 3.23	0.712	-	-	-	-	-
Time management-Total	-	0.12	0.12	1.04	-0.11, 0.35	0.299	0.16	0.11	1.45	-0.06, 0.39	0.151

Note R² = 0.134, Adjusted R² = 0.092; ^aCI Confidence Interval

Discussion

This cross-sectional study aimed to examine the level of SDLR and its associated factors, with a specific focus on the role of time management skills among nursing students.

This cross-sectional study aimed to examine the level of SDLR and its associated factors, with a specific focus on the role of time management skills among nursing students. While previous research has indicated varied levels of SDLR among nursing students globally [1, 6, 12, 33], there remains limited understanding of the specific factors contributing to this readiness, particularly regarding time management skills. This study addresses this gap by investigating the relationship between time management skills and SDLR in a sample of Iranian nursing students. Our findings revealed that most nursing students in our study demonstrated a high level of readiness for SDL, consistent with several previous studies conducted in Iran and Oman [1, 12]. This finding underscores the potential for fostering SDL in this student population. However, it is essential to acknowledge that other studies, such as the one conducted in China [6], have reported significantly lower levels of SDLR. This discrepancy highlights the potential influence of contextual factors, such as cultural learning styles and educational systems, on SDL development.

This study found that nursing students demonstrated acceptable and desirable levels of time management skills. This finding aligns with research from various countries, including Turkey [16, 22, 34], Indonesia [35], and Iran [18], which consistently report moderate to high levels of time management skills among nursing students. This proficiency may stem from the demanding nature of the nursing profession, which necessitates effective management of multiple tasks and emergencies [17, 18]. However, despite these adequate time management skills, our study revealed no significant relationship between time management and SDLR among nursing students. This finding contradicts previous studies that have established a positive correlation between time management skills and both SDLR and SDL [22–24]. For example, Ertuğ and Faydali (2018) found a moderate positive correlation between SDLR and time management values [22]. This discrepancy could be attributed to several factors. First, our study focused specifically on nursing students, while previous research may have included students from various disciplines. Nursing education’s demanding and structured nature might necessitate a certain baseline level of time management skills for all students, potentially masking any additional impact of time management on SDLR within this specific population. Second, the lack of a significant relationship might indicate the importance of other factors in influencing SDLR, particularly within the context of nursing education. As Grandinetti (2015) suggests, factors such as motivation, learner independence, and intellectual curiosity are predictive of SDLR in nursing students [36]. Future research should explore these factors in more depth to gain a more nuanced understanding of the complex interplay between time management and other predictors of SDLR. Despite the non-significant relationship observed in this study, integrating practical and theoretical time management training into nursing curricula remains crucial. Given the positive impact of time management skills on academic achievements, learning motivation, anxiety reduction, and patient care [16, 17], fostering these skills can benefit students in various ways, even if their direct impact on SDLR requires further investigation. Incorporating active and interactive teaching methods can further enhance these skills, ultimately better preparing students for the multifaceted demands of their future careers.

The present study found no statistically significant relationship between gender, marital status, and SDLR scores. This finding aligns with several previous studies that reported no significant association between gender and SDLR [1, 6, 22, 33, 37], or between marital status and SDLR [1, 33, 38]. However, this finding should be interpreted cautiously, given the contradictory results in the existing literature. Several studies have reported a significant relationship between gender and SDLR, with female students exhibiting higher SDLR scores than male students [5, 12, 39]. This discrepancy in findings warrants further examination. One possible explanation could be the influence of sociocultural factors that shape learning behaviors and attitudes differently for men and women [40]. The inconsistent findings regarding the relationship between gender, marital status, and SDLR highlight the need for more nuanced and contextually grounded research in this area. Future studies should consider employing larger, more diverse samples and utilizing qualitative methods to explore the lived experiences and perspectives of students from different genders and marital backgrounds. Investigating the potential mediating roles of sociocultural factors, learning motivations, and institutional support systems could provide a more comprehensive understanding of how these demographic variables interact with SDLR. By acknowledging these complexities and engaging in more rigorous and contextually sensitive research, we can gain valuable insights for educational planners and instructors to develop inclusive and tailored approaches that effectively enhance SDLR among all students.

The present study found no statistically significant relationship between age, academic year, and SDLR. This finding aligns with several previous studies that reported no significant association between age and SDLR [1, 33, 37], or between academic year and SDLR [1, 5, 22, 33, 39]. However, this finding should be viewed in light of contrasting evidence from the existing literature. Notably, a 2010 Thai study found that senior nursing students exhibited significantly higher SDLR scores compared to their junior counterparts [41]. This discrepancy might be attributed to the progressive development of SDL skills throughout nursing education. As Alkorashy and Assi (2017) and Slater and Cusick (2017) argue, senior nursing students, particularly those with extensive clinical experience, tend to develop greater independence, decision-making abilities, and a deeper understanding of their learning needs [42, 43]. This progressive development of SDL skills may not be fully captured when comparing age or academic year in isolation. Therefore, while our study did not find a significant association between age, academic year, and SDLR, it is crucial to acknowledge the potential influence of cumulative learning experiences and clinical exposure on SDL development. This highlights the need for longitudinal studies that track students’ SDL development throughout their nursing education journey, considering the dynamic interplay between individual characteristics, educational experiences, and contextual factors. Despite the mixed findings, fostering SDL skills from the outset of nursing education remains paramount. As Boyer et al. (2014) emphasize, integrating SDL principles and practices into the curriculum, providing appropriate support and resources, and emphasizing the value of SDL can empower students to become active and engaged learners [44]. By cultivating SDL skills early on, students can effectively navigate their educational journey, develop essential competencies for lifelong learning, and thrive in their future nursing careers [45].

The present study found that students with a GPA higher than 15 demonstrated significantly higher levels of SDLR compared to those with a GPA below 15. This finding aligns with several studies that have established a positive correlation between academic achievement and SDLR. For instance, Ballad et al. (2022) reported a significant correlation between overall GPA and SDLR scores using bivariate regression analysis [1]. Similarly, Abdulghani et al. (2019) and Calucag et al. (2023) found a direct relationship between overall GPA and SDLR scores [46, 47]. However, this relationship between GPA and SDLR is not always consistent within the literature. Two Turkish studies conducted in 2018 and 2019 found no significant correlation between overall GPA and readiness for SDL [22, 48]. This discrepancy in findings warrants further investigation into the factors that might moderate the relationship between academic achievement and SDLR. One possible explanation for the positive association observed in our study and other supporting studies could be the influence of academic self-efficacy and motivation. Students with higher GPAs often possess a stronger belief in their learning abilities and are more likely to be intrinsically motivated to excel academically [49]. This heightened motivation can translate into a greater inclination towards self-directed learning, as they actively seek opportunities to expand their knowledge and skills. As Subaş et al. [50] argue, nursing students, in particular, exhibit high levels of academic motivation, driven by the desire to adapt to the evolving healthcare landscape [50]. This intrinsic drive for professional development could further contribute to the higher SDLR scores observed among high-achieving students. Therefore, while a higher GPA might not directly cause higher SDLR, it could indicate underlying motivational factors and learning behaviors that contribute to a greater readiness for SDL.

In the current study, it was observed that students with a history of academic probation exhibited a significantly higher level of readiness for SDL compared to students without such a history. Academic probation refers to a situation in which a student has failed to achieve passing grades in a sufficient number of courses during an academic semester [51]. This status typically serves as a warning signal, indicating to the student the need to reassess their learning strategies and seek necessary support, such as guidance from academic advisors [52]. This finding contrasts with the results of a study conducted in Oman in 2022, which reported that students on academic probation had significantly lower average scores on the SDLR scale [1]. The unexpected increase in SDLR scores among students with academic probation in our study may be influenced by various factors. For instance, the experience of being placed on academic probation might prompt students to become more self-aware of their learning needs and seek greater autonomy in their studies. Additionally, factors such as perceived family support, financial challenges, academic motivation, and interest in courses could play a role [52, 53]. Students facing financial strain, for example, might need to take on additional responsibilities outside of school, potentially fostering a greater need for SDL. It is important to note that based on the findings of this study, it cannot be definitively concluded that students on academic probation cannot navigate and manage their learning. Nonetheless, it is essential to provide adequate support to these students to enhance their academic performance. Institutions can adopt strategies such as early intervention initiatives, academic counseling, peer mentoring, and workshops on time management to assist students encountering academic difficulties [54].

This study revealed a significant difference in SDLR scores based on living arrangements, with students living with their families demonstrating higher SDLR compared to those in dormitories. This finding aligns with a 2019 Indian study that also reported higher SDLR scores among students living at home [23]. However, it contradicts previous research from Iran (2017) and India (2014), which found no significant relationship between living arrangements and SDLR [11, 55]. This discrepancy warrants further investigation. One possible explanation for the higher SDLR observed among students living at home may be the role of parental support and guidance [56]. A 2023 Chinese study by Li et al. highlights this influence, suggesting that parental presence can provide a structured and supportive learning environment where students receive encouragement, assistance with academic tasks, and guidance in developing effective learning strategies [57]. It is crucial to acknowledge, however, that the impact of family support on SDLR likely varies depending on cultural contexts and individual family dynamics [56]. While our findings suggest a potential benefit of family support for SDLR, further research is needed to explore the nuanced interplay of family dynamics, cultural factors, and individual student characteristics in shaping SDLR.

The present study found that the majority of nursing students exhibited moderate to high levels of interest in their field and a desire to pursue higher education. Interestingly, this interest and aspiration did not correlate with their readiness for SDL, which was consistently high across the sample. This finding aligns with a 2020 Korean study that also reported no significant relationship between interest in nursing and SDL [8]. However, it contrasts with other studies that have established a positive correlation between interest in the field of study and SDLR. For example, a 2019 Saudi Arabian study found that students interested in their field demonstrated significantly higher SDLR [6]. Similarly, a 2021 Chinese study revealed that nursing students with a strong interest in the profession exhibited a greater capacity for SDL compared to their less interested counterparts [7]. Several factors might explain these conflicting findings. Firstly, it is crucial to consider the influence of external factors on students’ choice of study. Aschbacher et al. (2010) argue that factors such as financial necessity or familial pressure, rather than genuine interest, can often drive students toward particular fields [58]. This external pressure might motivate students to develop SDLR out of concern for academic and career success, regardless of their inherent interest in the field. This could explain the high SDLR observed in our study, despite the lack of correlation with interest levels. Secondly, the learning environment and teaching methodologies employed within different educational contexts can significantly influence students’ SDL development [40]. The high SDLR scores observed in our study could be attributed to a supportive learning environment that fosters SDL skills, as suggested by Rui et al. [59]. This highlights the need for further research to investigate the specific pedagogical approaches and institutional factors that contribute to higher SDLR, regardless of students’ initial interest levels.

The present study’s findings revealed no statistically significant relationship between overall scores and the subscales of time management and SDLR. This result, further corroborated by multiple regression analysis, contradicts previous research that has established a significant positive correlation between time management skills and both SDLR and SDL [22–24]. This discrepancy warrants further examination. One possible explanation lies in the multifaceted nature of self-management, of which time management is only one component. While effective time management is crucial for SDLR [21], other self-management skills, such as goal setting, resource management, and learning strategy selection, may have played a more dominant role in our study. This aligns with Wong et al. [40], who found that other self-management skills, including problem-solving ability, self-efficacy, goal setting, and learning strategy selection, are important for SDLR [40]. Future research could further disentangle the relative contributions of these individual self-management components to SDLR. Furthermore, contextual factors may have influenced the observed findings, often overlooked in previous studies. Teaching methodologies, learning environments, available resources, and support systems can significantly impact students’ readiness for SDL [1, 5, 21, 60]. For instance, Kek and Huijser [61] demonstrated that personal, family, learning environment, and teacher factors are influential on SDLR [61]. These factors might have acted as moderators or mediators in our study, potentially obscuring the direct relationship between time management and SDLR. Future studies should consider controlling for these contextual factors or exploring their specific influence on the time management-SDLR relationship. Finally, the high scores observed in the self-control subscale within our study suggest that our sample may possess a higher baseline of self-regulation compared to the samples used in previous studies. This pre-existing self-control might have reduced the relative importance of time management skills in predicting SDLR within our specific context. This finding aligns with Maryam et al. (2022), who argued that self-control, desire to learn, and self-management have a positive correlation with SDLR [62]. Future research could investigate whether the relationship between time management and SDLR differs across varying levels of self-control, providing a more nuanced understanding of this complex interplay.

Limitations

When interpreting the results of this study, it is crucial to acknowledge several limitations. Firstly, the sample was selected using convenience sampling, which may limit the generalizability of the findings to a broader population. Secondly, the data collection relied on self-report measures, which can introduce response bias. However, steps were taken to minimize this bias by ensuring the anonymity and confidentiality of the questionnaires. Thirdly, the online data collection method may have introduced self-selection bias, as students with higher levels of self-directed learning or better time management skills might have been more likely to participate. To mitigate this potential bias, strategies such as demographic data collection to assess the diversity of the sample and identify any potential patterns related to self-selection were employed. Fourthly, the study’s cross-sectional design does not allow for establishing causal relationships between the variables. Fifthly, the lower reliability scores for some subscales of the TMI may have limited the accuracy and precision of the measurements, potentially affecting the strength of the relationships between these variables and SDLR. Additionally, the validity of the instruments used in the study may be a concern, as mixed reliability scores were reported. Future research could address these limitations by using alternative instruments with stronger evidence of validity and reliability. Lastly, it is important to acknowledge that SDL and SDLR are influenced by multiple factors, such as motivation, learning environment, and individual characteristics. This study focused on a limited number of factors and may not capture the full complexity of SDL and SDLR.

Conclusions

Our findings revealed a satisfactory level of self-directed learning readiness (SDLR) and time management skills among our participants. However, despite adequate time management skills, these did not significantly predict overall SDL readiness. This suggests that while time management is important, other factors may play a more influential role in shaping students’ SDL preparedness. This study contributes to the international literature by identifying the complex interplay of factors influencing SDL and suggesting that future research should explore a broader range of potential predictors. However, it is crucial to acknowledge the inherent limitations of our cross-sectional research design, which may affect the generalizability of our findings. Future research endeavors should prioritize addressing these limitations by employing more robust sampling techniques and utilizing longitudinal designs to elucidate causal relationships. By doing so, future studies can contribute to a more nuanced understanding of SDL and inform the development of effective interventions to foster this essential skillset in nursing students.

Author contributions

NS, MJ, SR, MR, and AK contributed to designing the study. MR and MJ collected the data, and the data was analyzed by SR. The final report and manuscript were written by NS, MJ, SR, MR, and AK. All the authors read and approved the version for submission.

Funding

The research was financially supported by Kermanshah University of Medical Sciences (WWW.KUMS.AC.IR) under Grant No. 50001718, with Alireza Khatony as the grant recipient.

Data availability

The identified datasets analyzed during the current study are available from the corresponding author upon reasonable request.

Declarations

Ethics approval and consent to participate

The study was approved by the Ethics Committee of Kermanshah University of Medical Sciences, with the code IR.KUMS.REC.1401.368. Participants provided written informed consent by voluntarily completing and submitting the questionnaires. All methods were conducted in accordance with the applicable guidelines and regulations.

Consent for publication

Not Applicable.

Declaration of generative AI and AI-assisted technologies in the writing process

During the preparation of this work, the authors used Gemini-1.5-Pro to improve language and readability. After using this tool/service, the authors reviewed and edited the content as needed and took full responsibility for the content of the publication.

Competing interests

The authors declare no competing interests.