Department of Education, University of Jaffna, Sri Lanka<\/em><\/p>\n <\/p>\n Abstract<\/strong><\/p>\n Despite institutional efforts, integrating self-directed learning within the Sri Lankan higher education system encounters challenges from entrenched conventional teaching practices. In response to these challenges, this correlational study adopts a quantitative approach to investigate the relationship between self-directed learning readiness (SDLR) and academic attainment (AA) among student-teachers. The study’s objectives include assessing levels of SDLR, examining demographic differences in SDLR, investigating the relationship between SDLR and AA, and exploring specific dimensions of SDLR associated with AA among student-teachers. The sample consisted of 138 participants selected through simple random sampling from a population of 426 Master of Education and Post Graduate Diploma in Education students at the University of Jaffna. Utilizing a self-generated questionnaire comprising 30 items and employing a 5-point Likert scale, SDLR was assessed across six dimensions: Self-Motivation (SM), Goal-Orientation (GO), Time Management (TM), Information Seeking (IS), Self-Regulation (SR), and Collaboration and Communication (CC). Descriptive statistical techniques, including mean and standard deviation, and inferential statistical techniques, such as independent sample t-test, one-way ANOVA test, and Pearson’s correlation test, were employed to analyze the collected data. Findings revealed a high level of SDLR among student-teachers (M <\/em>= 101.83, SD <\/em>= 10.574). Significant differences were found based on the course pursued (F <\/em>(2,137) = 8.037, p <\/em>= .001) and participants’ age (F <\/em>(4,133) =2.614, p <\/em>= .038). Age had significant relationships with SM (p <\/em>= .035), SR (p <\/em>= .019), and CC (p <\/em>= .048). However, no significant differences were found in gender, marital status, or the stream followed at the Advanced level, except for a significant difference in SR based on gender (p <\/em>= .022). Regarding the relationship between SDLR and AA, a statistically significant but weak correlation was found (r<\/em>= .201, n <\/em>= 138, p <\/em>= .021). SM (r <\/em>= .178, p <\/em>= .037), IS (r <\/em>= .220, p <\/em>= .009), and CC (r <\/em>= .229, p <\/em>= .018) showed significant relationships with AA, while the remaining dimensions did not exhibit significant associations. This study enriches understanding of the SDLR-AA relationship, emphasizing the need to cultivate SDL skills, particularly in SM, IS, and CC. Its insights inform institutions and policymakers, highlighting demographic influences and guiding educators in fostering SDLR among student-teachers.<\/p>\n Keywords:<\/em> \u00a0\u00a0 Self-directed Learning Readiness, Academic attainment, Academic Achievement, Self-Regulation, Self-Motivation<\/p>\n <\/p>\n Background of the Study<\/strong><\/p>\n Self-directed learning is characterized by students taking control of their learning process through planning, implementation, monitoring, and evaluation. Students’ readiness for self-directed learning (SDLR) is crucial in successfully implementing self-directed learning within the educational system.<\/p>\n The concept of self-directed learning (SDL) is deeply rooted in history, with its origins tracing back to the ancient Greek philosophers Socrates, Plato, and Aristotle, who epitomized the pursuit of knowledge through individual inquiry and reflection (Brockett & Hiemstra, 1991). Self-directed learning (SDL) has been an enduring concept in education, dating back to the inception of the theory of ‘Andragogy’ proposed by Malcolm Knowles in 1968. Bosch (2017) defines SDL as an educational approach wherein learners assume responsibility for their learning process. Boyer et al. (2014) elaborate further, delineating self-directed students as those who not only set their own learning goals but also select learning resources, employ preferred learning strategies, and critically reflect upon the outcomes of their learning endeavors.<\/p>\n Over time, SDL has garnered increasing attention, particularly in online learning environments. In the contemporary era marked by evolving societal changes and technological advancements, the availability of diverse knowledge sources worldwide has disrupted traditional learning approaches, leading to a growing emphasis on learner-centered paradigms. Self-Directed Learning (SDL) has emerged as a pivotal concept, mainly influenced by the advent of blended learning and the profound impact of the COVID-19 pandemic in Sri Lanka. To foster SDL, implementing a credit system and adopting an outcome-based education module have been introduced within the Sri Lanka Qualifications Framework (SLQF). However, despite these initiatives, higher education institutions in Sri Lanka predominantly adhere to conventional educational practices rooted in behavioral design principles (Bandara, 2022). This discrepancy signifies a significant barrier to establishing SDL within the university system, necessitating concerted efforts to bridge the awareness gap between students and teachers and facilitate the integration of SDL practices (Bandara, 2017).<\/p>\n Despite the global recognition of self-directed learning (SDL) as a transformative educational approach, its effective integration within the Sri Lankan higher education system still needs to be improved by various challenges. While initiatives such as the Sri Lanka Qualifications Framework (SLQF) and outcome-based education modules have been introduced to promote SDL, conventional teaching practices persist, distancing institutional directives and pedagogical realities. This incongruity underscores a pressing need to investigate the nexus between self- directed learning readiness (SDLR) and academic attainment among student teachers in Sri Lanka. Understanding the extent of SDLR among student teachers and its impact on academic achievement is essential for identifying barriers to SDL implementation and formulating targeted interventions to foster a conducive environment for self-directed learning within the Sri Lankan educational landscape.<\/p>\n <\/p>\n Review of Related Literature<\/strong><\/p>\n Theoretical Bases<\/strong><\/p>\n Prior to the establishment of formal educational institutions in the 1800s, self- directed learning (SDL) prevailed as individuals relied on personal initiative and resourcefulness to acquire knowledge (Candy, 2009). Throughout the 20th century, scholars such as Malcolm Knowles and Allen Tough contributed significantly to advancing the understanding and practice of SDL. Notably, scholars like Spear and Mocker (1984) emphasized the role of environmental factors in promoting SDL, highlighting its complex interplay with individual disposition and external circumstances (Bouchard, 1994).<\/p>\n Subsequent progress in SDL research has led to the development of various models, including Long’s Self-Directed Learning Instructional Model, Candy’s Self- Directed Learning Model, Brockett and Hiemstra’s Personal Responsibility Orientation Model, and Garrison’s Model. Long (1991) proposed an instructional design framework emphasizing the balance between pedagogical and psychological control, fostering an environment conducive to SDL.<\/p>\n Candy (1991) introduced a conceptualization of SDL comprising student control and autodidact, emphasizing personal autonomy, self-management, student control, and autodidact within his model. Brockett and Hiemstra (1991) emphasized personal responsibility as a critical determinant of SDL, distinguishing between personal responsibility in the teaching-learning process and personal responsibility in one’s thoughts and actions. Garrison (1997) delineated three core dimensions of SDL: self-management, self-monitoring, and motivation, underscoring the importance of reflective practices and critical reflection in facilitating SDL.<\/p>\n Self-motivation, goal orientation, and self-regulation were selected for this study, building on the multifaceted nature of SDL. These dimensions collectively contribute to learners’ autonomy, agency, and engagement in educational pursuits. Integrating these dimensions into the study’s theoretical framework provides a comprehensive understanding of SDLR among student-teachers.<\/p>\n In summary, SDL models highlight integral dimensions such as pedagogical and psychological control, personal autonomy, self-management, student control, autodidact, personal responsibility, self-monitoring, and motivation. These dimensions collectively shape learners’ engagement in educational pursuits, underscoring the multifaceted nature of SDL. The selection of self-motivation, goal orientation, and self-regulation as dimensions for this study aligns with the theoretical foundations of SDL. It enhances our understanding of SDLR among student-teachers.<\/p>\n <\/p>\n Empirical Investigation<\/strong><\/p>\n Research on self-directed learning (SDL) and self-directed learning readiness (SDLR) has identified numerous factors influencing students’ preparedness for engaging in self-directed learning. Nymbae et al. (2016) highlighted internal and external factors affecting SDL readiness, such as physical health, leisure availability, and familial support. Akaranithi (2007) emphasized the role of educational institutions, learning environments, and resource access in shaping self-directed learners. Similarly, Nurrokhmanti et al. (2016) identified familial and peer support, faculty resources, and environmental influences as critical determinants of SDL readiness.<\/p>\n Ramli et al. (2018) proposed an interaction between internal and external factors, suggesting that supportive environments indirectly foster SDL readiness by positively impacting internal motivational factors. Leatemia et al. (2016) identified various factors affecting SDL readiness among Asian students, including problem- based learning processes and assessments.<\/p>\n Munasinghe et al. (2020) identified factors determining SDL among university management undergraduates, including language proficiency, resource accessibility, teaching methodologies, and familial support. Kim and Park (2011) highlighted the impact of self-esteem and belongingness on SDL among advanced practice nurse students.<\/p>\n Several scales have been developed to assess SDL readiness, such as Guglielmino’s (1977) initial scale, Fisher et al.’s (2001) scale for nurse learners, and Hoban et al.’s (2005) scale focusing on lifelong learning and self-confidence. Hendry and Ginns (2009) introduced a scale for medical students, which Fisher and King (2010) later refined for nursing education. Khiat (2015) developed a scale for adult learners in Singapore, while Torabi et al. (2013) and Lim et al. (2018) validated scales for measuring SDL readiness among teachers and language learners, respectively.<\/p>\n Drawing upon this theoretical background, the study explores six essential SDLR dimensions among student teachers: self-motivation, goal orientation, Time Management, Information Seeking, Self-Regulation, and Collaboration and Communication. By examining the relationship between SDLR and academic attainment, the study seeks to address gaps in the literature regarding the influence of demographic variables on SDLR among student teachers.<\/p>\n Several studies have investigated the relationship between SDLR and academic attainment. Khalid et al. (2020) compared SDLR and academic achievement among university students in online distance learning and traditional settings. Jaleel and<\/p>\n O.M. (2017) explored the relationship between SDLR and academic achievement in Information Technology among secondary-level students, while Grengia et al. (2022) and Hussain et al. (2019) examined SDLR among teacher education students. However, these studies often need to look into the influence of demographic variables on SDLR.<\/p>\n Koirala et al. (2021) examined factors affecting SDLR among undergraduate nursing students, incorporating various dimensions of SDLR and demographic variables. Nevertheless, the study did not specifically target student-teachers, highlighting a research gap in understanding SDLR within the context of teacher education.<\/p>\n Therefore, this research aims to bridge these gaps by investigating the relationship between SDLR and academic attainment among student teachers in Sri Lanka. By examining a comprehensive set of demographic variables and dimensions of SDLR, the study seeks to provide a nuanced understanding of how various factors influence SDLR among student teachers. This endeavor will contribute to the existing body of knowledge by shedding light on the unique dynamics of SDLR in the context of teacher education, offering valuable insights for educational policymakers, practitioners, and researchers.<\/p>\n Previous studies have explored SDL across various educational domains in the Sri Lankan context. Bandara (2022) examined challenges associated with implementing SDL policies in higher education institutions, while Bandara (2017) investigated academics’ perceptions regarding SDL practices. Munasinghe et al. (2019) and Dharmasena et al. (2022) explored factors influencing SDL readiness among undergraduate students, and Samarasooriya et al. (2019) investigated SDL among nurse learners. However, none of these studies specifically targeted student-teachers, highlighting a research gap in understanding SDLR within the teacher education context.<\/p>\n Recognizing the pivotal role of teachers in shaping student learning experiences, it becomes imperative to focus on enhancing SDL readiness among student teachers. By equipping student teachers with robust SDL skills, they can effectively model and promote SDL practices within classroom settings, thereby fostering a culture of lifelong learning among future generations of students.<\/p>\n Hence, this study seeks to address this research gap by examining the SDL readiness of student-teachers in Sri Lanka. By elucidating the unique challenges, opportunities, and factors influencing SDLR within the teacher education context, this research endeavors to contribute valuable insights that can inform educational policies, practices, and interventions aimed at enhancing SDL among student- teachers and, by extension, fostering SDL practices among school students in Sri Lanka.<\/p>\n <\/p>\n Aim of the study<\/strong><\/p>\n This study aims to investigate the relationship between SDLR and academic attainment (AA) among student teachers in Sri Lanka.<\/p>\n <\/p>\n Objectives of the study<\/strong><\/p>\n <\/p>\n Materials and methods<\/strong><\/p>\n Research Design<\/strong><\/p>\n Aligned with the stated objectives, the present investigation employed a quantitative research methodology and a correlational research design. This approach facilitated the systematic data collection through structured surveys administered to student-teachers in Sri Lanka. Through this investigation, the research sought to uncover specific dimensions of SDLR that may be associated with AA, providing insights into the factors influencing academic performance in this population. By employing a survey research design, the study enabled a comprehensive examination of the multifaceted relationships between SDLR, demographic factors, and academic achievement among student-teachers in Sri Lanka.<\/p>\n <\/p>\n Population and Sample<\/strong><\/p>\n The population consisted of 426 students enrolled in the Master of Education (MEd) and Postgraduate Diploma in Education (PGDE) Programmes at the University of Jaffna in 2021 and 2022. A random sample of 138 participants, 51 males and 87 females, was selected (Table 1).[\/vc_column_text][vc_column_text]Table 1<\/strong><\/p>\n Distribution of Sample<\/em><\/p>\n Data Collection<\/strong><\/p>\n A self-generated questionnaire consisting of 30 items was developed to assess SDLR. The questionnaire demonstrated high internal consistency with a Cronbach’s alpha coefficient 0.922. The questionnaire’s validity was examined using the item- total correlation method and with a significance level of \u03b1 = 0.05, df=41, and N=43 which constitutes 10% of the population (N=426), and a critical Pearson Correlation value of approximately 0.3008, all 30 items displayed statistically significant positive correlations with the overall score (p < 0.05), affirming their connection to the underlying construct. Participants responded to the questionnaire using a 5-point Likert scale. The SDLR construct was measured across six SDLR dimensions: Self-Motivation, goal orientation, Time Management, Information Seeking, Self-Regulation, and Collaboration and Communication.<\/p>\n <\/p>\n Analysis of Data and Interpretation of Results<\/strong><\/p>\n Mean scores were calculated for SDLR to determine the level of SDLR among student-teachers. Independent sample t-tests were conducted to investigate potential differences in SDLR based on gender and marital status. Additionally, one-way ANOVA tests were used to examine variations in SDLR among student-teachers based on age category, course pursued, and stream followed at the advanced level. Furthermore, bivariate correlation coefficients were computed to explore the relationships between SDLR and academic attainment and identify the specific SDLR dimensions significantly associated with AA among student-teachers. Statistical Package for Social Sciences (SPSS) version 25 was used to analyze the data.<\/p>\n <\/p>\n The Level of SDLR among Student-Teachers<\/strong><\/p>\n Statistical measures, including the mean and standard deviation, were computed to evaluate the extent of self-directed learning readiness (SDLR) among student teachers. The overall mean score of student-teacher SDLR is 101.83, and the standard deviation is 10.574. The highest level of SDLR is 125.00, and the lowest level is 61.00 (Table 2). It was concluded that student-teachers displayed a high readiness for self-directed learning. The mean values of all the selected SDLR dimensions are high. Furthermore, the mean values of all selected SDLR dimensions indicate high readiness levels among participants.<\/p>\n <\/p>\n Table 2<\/strong><\/p>\n SDLR Level of Student-Teachers<\/em><\/p>\n Influence of Demographic Factors on SDLR among Student-Teachers Influence of Gender on SDLR<\/em><\/strong><\/p>\n An independent sample t-test was conducted to investigate the potential impact of gender on self-directed learning readiness (SDLR). The findings of this analysis are presented in Table 3. According to the findings delineated in Table 3, the study asserts that no statistically significant discrepancy exists in self-directed learning readiness (SDLR) between male and female student-teachers (t<\/em>= 1.658, p <\/em>= .100). Among the dimensions of SDLR examined, only self-regulation exhibited a notable difference (t <\/em>= 2.314, p <\/em>= .022), with males displaying higher SDLR (M <\/em>= 20.49) compared to females (M <\/em>= 19.55). Conversely, the dimensions of SDLR, including Self-Motivation (t <\/em>= 2.314, p <\/em>= .022), Goal-Orientation (t <\/em>= 1.700, p <\/em>= .091), Time Management (t <\/em>= .661, p <\/em>= .510), Information Seeking (t<\/em>= 1.610, p <\/em>= .110), and Collaboration and Communication (t <\/em>= .655, p <\/em>= .514) did not reveal statistically significant distinctions.<\/p>\n <\/p>\n Table 3<\/strong><\/p>\n Difference on the Basis of Gender<\/em><\/p>\n Influence of Marital Status on SDLR<\/strong><\/p>\n The study utilized an independent sample t-test to explore the potential influence of marital status on self-directed learning readiness (SDLR). The outcomes of this statistical analysis are delineated in Table 4. Based on the data provided in Table 4, the study concludes that no significant disparity exists in self-directed learning readiness (SDLR) between male and female student-teachers (t <\/em>= .777, p <\/em>= .439). Furthermore, the analysis reveals no statistically significant variances in any of the selected SDLR dimensions between male and female student-teachers: Self- Motivation (t <\/em>= 1.671, p <\/em>= .097), Goal-Orientation (t<\/em>= .222, p <\/em>= .825), Time Management (t <\/em>= .943, p <\/em>= .347), Information Seeking (t<\/em>= .576, p <\/em>= .566), Self- regulation (t <\/em>= -0.014, p <\/em>= .989), and Collaboration and Communication (t <\/em>= .405, p <\/em>= .686).<\/p>\n <\/p>\n Table 4<\/strong><\/p>\n Difference on the Basis of Marital Status<\/em><\/p>\n Influence of Course Pursued on SDLR<\/strong><\/p>\n A one-way ANOVA test was employed to explore the potential influence of the course pursued on self-directed learning readiness (SDLR). The outcomes of this statistical analysis are delineated in Table 5. Based on the data presented in Table 5, the study reveals a statistically significant difference in SDLR among student- teachers based on the course persuade, including MEd, PGDE (Part-time), and PGDE (Full-time) (F <\/em>(2, 135) = 8.037, p <\/em>= .001). Moreover, the analysis indicates statistically significant differences in all of the selected SDLR dimensions across the courses mentioned above persuaded by student-teachers: Self-Motivation (F <\/em>= 5.278, p <\/em>= .006), Goal-Orientation (F <\/em>= 4.479, p <\/em>= .013), Time Management (F <\/em>= 5.054, p <\/em>= .008), Information Seeking (F<\/em>= 8.887, p <\/em>= .001), Self-regulation (F <\/em>= 4.707, p <\/em>= .011), and Collaboration and Communication (F <\/em>= 5.889, p <\/em>= .004).<\/p>\n <\/p>\n Table 5<\/strong><\/p>\n Difference on the Basis of Course pursued<\/em><\/p>\n A post hoc test using Tukey’s HSD was conducted to identify the specific courses wherein significant differences occurred. The results of this analysis are presented in Table 6.<\/p>\n <\/p>\n Table 6<\/strong><\/p>\n Tukey Test Results for Difference on the Basis of Course pursued<\/em><\/p>\n Based on the results presented in Table 6, significant differences were observed between the PGDE full-time (M <\/em>= 103.86, SD <\/em>= 11.041) and PGDE part-time (M <\/em>= 97.30, SD <\/em>= 11.152) courses (MD <\/em>= 6.561, p <\/em>= .009), as well as between the PGDE part-time and M.Ed. (M <\/em>= 104.79, SD <\/em>= 8.076) courses (MD <\/em>= -7.488, p <\/em>= .001). Conversely, no significant difference was found between the PGDE full-time and M.Ed. courses (MD <\/em>= -0.927, SD <\/em>= .905). Consequently, full-time student-teachers exhibit higher levels of self-directed learning readiness (SDLR) than their part-time counterparts.<\/p>\n <\/p>\n Influence of Age Category on SDLR<\/strong><\/p>\n The study utilized a one-way ANOVA test to investigate the potential impact of age category on self-directed learning readiness (SDLR). The results of this statistical analysis are presented in Table 7.<\/p>\n Based on the data provided in Table 7, the study identifies a statistically significant disparity in self-directed learning readiness (SDLR) among student-teachers based on the age category (F(4,133) = 2.614, p = .038). Additionally, the analysis reveals notable differences in the dimensions of Self-motivation (F (4,133) = 2.667, p = .035), Self-regulation (F (4,133) =3.043, p = .019), and Collaboration and Communication (F (4,133) = 2.460, p = .048). Conversely, other dimensions, including Goal orientation (F (4,133) = 1.625, p = .172), Time Management (F (4,133) = 1.709, p = .152), and Information-seeking (F (4,133) = 1.846, p = .124) do not exhibit significant differences.<\/p>\n <\/p>\n Table 7<\/strong><\/p>\n Difference based on age category<\/p>\n To ascertain the particular age categories wherein significant differences were observed, a post hoc test utilizing Tukey’s HSD was administered. The results of this analysis are detailed in Table 8.<\/p>\n <\/p>\n Table 8<\/strong><\/p>\n Tukey Test Results for Difference on the Basis of Age Category<\/em><\/p>\n According to the findings outlined in Table 8, notable discrepancies were solely identified between the age category of less than 31(M <\/em>= 93.11, SD <\/em>= 13.157) and 36 to 40 (M <\/em>= 105.18, SD <\/em>= 12.717, MD <\/em>= -12.071, p <\/em>= .019). Conversely, no significant differences were observed among the remaining age categories.<\/p>\n <\/p>\n Influence of stream followed in Advanced-level on SDLR<\/strong><\/p>\n The study employed a one-way ANOVA test to examine the potential influence of the stream pursued during Advanced-level education on self-directed learning readiness (SDLR). The outcomes of this statistical analysis are delineated in Table 9.<\/p>\n <\/p>\n Table 9<\/strong><\/p>\n Differences on the Basis of stream followed in Advanced-level<\/em><\/p>\n Based on the data provided in Table 9, the study concludes that there is no significant difference in self-directed learning readiness (SDLR) among student- teachers based on the stream followed during Advanced-level education (F <\/em>(3,137) = 2.053, p <\/em>= .109). Furthermore, the analysis reveals no statistically significant differences in any of the selected SDLR dimensions, including Self-motivation (F <\/em>(3,137) = 1.655, p <\/em>= .180), Goal orientation (F <\/em>(3,137) = 2.451, p <\/em>= .066), Time management (F<\/em>(3,137) = 2.224, p <\/em>= .088), Information seeking (F <\/em>(3,137) = 1.358,p <\/em>= .258), Self-regulation (F <\/em>(3,137) = 1.533, p <\/em>= .209), and Collaboration and Communication (F <\/em>(3,137) = .537, p <\/em>= .657), among the different streams followed during Advanced-level education.<\/p>\n <\/p>\n Relationship between SDLR and AA<\/strong><\/p>\n Pearson’s correlation test was utilized to investigate the relationship between self- directed learning readiness and academic attainment among student-teachers. The outcomes of this statistical analysis are presented in Table 10.<\/p>\n <\/p>\n Table 10<\/strong><\/p>\n Relationship between SDLR and AA<\/em><\/p>\n Based on the data provided in Table 10, the study reveals a statistically significant albeit weak correlation between self-directed learning readiness and academic attainment among student-teachers (r<\/em>= .201, N <\/em>= 138, p <\/em>= .018). Certain dimensions of self-directed learning readiness exhibit significant relationships with academic attainment. Specifically, Self-motivation (r <\/em>= .178,N <\/em>= 138, p <\/em>= .037), Information seeking (r<\/em>= 0.220, N <\/em>= 138, p <\/em>= .009), and Collaboration and Communication (r <\/em>= 0.229, N <\/em>= 138, p <\/em>= .007) demonstrate noteworthy associations with academic attainment. Conversely, other dimensions including Goal orientation (r <\/em>= .128, N <\/em>= 138, p <\/em>= .134), Time management (r <\/em>= .166, N <\/em>= 138, p <\/em>= .052), and Self-regulation (r<\/em>= .154, N <\/em>= 138, p <\/em>= .072) do not display significant associations with academic attainment.<\/p>\n <\/p>\n Findings of the Study<\/strong><\/p>\n The research revealed that student-teachers in Sri Lanka demonstrated a notably high level of self-directed learning readiness, with a mean score of 101.83 and a standard deviation of 10.574. This suggests that, on average, the participants exhibited a substantial preparedness for engaging in self-directed learning practices. Such findings underscore a positive attribute within teacher education, indicating a commendable level of readiness among student-teachers for autonomous learning pursuits.<\/p>\n The study investigated the influence of demographic factors on self-directed learning readiness (SDLR) among student teachers. Notably, significant differences in SDLR were observed based on the course pursued (F <\/em>(2, 135) = 8.037, p <\/em>= .001) and the age category (F <\/em>(4,133) = 2.614, p <\/em>= .038). These findings highlight the impact of educational program choice and age on SDLR among student teachers.<\/p>\n However, demographic factors such as gender (t <\/em>= 1.658, p <\/em>= .100), marital status (t<\/em>= .777, p <\/em>= .439), and the stream followed in Advanced level (F <\/em>(3,137) = 2.053, p <\/em>= .109) did not show significant differences in SDLR. Nonetheless, among the dimensions of SDLR examined, only self-regulation displayed a notable difference (t <\/em>= 2.314, p <\/em>= .022), with males exhibiting higher SDLR (M <\/em>= 20.49) compared to females (M<\/em>= 19.55). These findings underscore the nuanced relationship between demographic factors and SDLR among student-teachers, emphasizing the importance of considering various factors in understanding SDLR dynamics.<\/p>\n The study unveiled a statistically significant yet weak correlation between self- directed learning readiness (SDLR) and academic attainment, suggesting a discernible relationship between these variables (r <\/em>= 0.201, N <\/em>= 138, p <\/em>= 0.05). Notably, specific dimensions of SDLR exhibited significant associations with academic attainment. Self-motivation (r <\/em>= .178, N <\/em>= 138, p <\/em>= .037), Information seeking (r <\/em>= .220, N <\/em>= 138, p <\/em>= .009), and Collaboration and Communication (r <\/em>= .229, N <\/em>= 138, p <\/em>= .007) demonstrated notable correlations with academic achievement. In contrast, Goal orientation (r <\/em>= .128, N <\/em>= 138, p <\/em>= .134), Time management (r<\/em>= .166, N <\/em>= 138, p <\/em>= .052), and Self-regulation (r<\/em>= .154, N <\/em>= 138, p <\/em>= .072) did not display significant associations with academic attainment. These findings suggest that elevated levels of SDLR were moderately linked to enhanced academic performance among student-teachers.<\/p>\n Upon delving into the dimensions of self-directed learning readiness (SDLR), the investigation revealed significant associations between certain dimensions, namely Self-motivation (SM), Information Seeking (IS), and Collaboration and Communication (CC), with academic attainment (AA). These findings suggest that these specific dimensions exert an influence on the academic performance of student-teachers. Conversely, the remaining dimensions did not demonstrate significant associations with academic attainment, underscoring the intricate relationship between SDLR dimensions and academic success.<\/p>\n In conclusion, this study sheds light on the multifaceted dynamics of self-directed learning readiness (SDLR) among student-teachers in Sri Lanka. The findings underscore the significance of demographic factors such as the course pursued and age category in influencing SDLR. While certain dimensions of SDLR, such as self-motivation, information seeking, collaboration, and communication, were significantly associated with academic attainment, others displayed weaker or non- significant correlations. These insights contribute to a deeper understanding of the intricate interplay between SDLR dimensions and academic performance, emphasizing the importance of tailored interventions and support mechanisms to foster optimal SDLR development among student-teachers. Further research endeavors in this domain could offer additional insights into enhancing SDLR and improving educational outcomes within teacher education programs.<\/p>\n <\/p>\n Discussion and Conclusions<\/strong><\/p>\n The findings of this research underscore a notably high level of self-directed learning readiness (SDLR) among student-teachers in Sri Lanka, reflecting a mean score of 101.83 and a standard deviation of 10.574. Similarly, the study conducted by Hussain et al. (2019) in Pakistan reported a high SDLR level among participants, with a mean score of 100.79 and a standard deviation of 10.74. These consistent findings across different geographical contexts emphasize the prevalence of high SDLR among student-teachers, indicating a robust trend in readiness for self- directed learning within teacher education cohorts.<\/p>\n The alignment between this study and the research conducted by Hussain et al. (2019) highlights the resilience of SDLR among student-teachers, irrespective of regional variations. This consistency suggests a solid foundation for fostering self- directed learning abilities among student-teachers, potentially contributing to their effectiveness as future educators. Furthermore, these findings underscore the significance of recognizing and nurturing SDLR among student-teachers, given its pivotal role in their professional development and academic success.<\/p>\n However, the study also reveals a significant difference in SDLR among student- teachers based on age category (F <\/em>(4,133) = 2.614, p <\/em>= .038) and the course pursued (\n
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