The effect of Self-Directed Learning Readiness on Thai medical students’ self-reported National Licensing Examination Step 1 score

The effect of Self-Directed Learning Readiness on Thai medical students’ self-reported National Licensing Examination Step 1 score | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The effect of Self-Directed Learning Readiness on Thai medical students’ self-reported National Licensing Examination Step 1 score Khemmawit Siriwong, Thitipat Pattanaprateeb, Ramon Sawetratanasatien, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5369545/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Self-directed learning (SDL) is essential for medical students to adapt to continuous learning demands in clinical practice. Problem-based learning (PBL) has been widely used in Thailand’s medical education to foster SDL skills. Yet, limited research exists on how specific SDL components relate to academic success, particularly performance on the National Licensing Examination Step 1 (NLE1). This study examines associations between SDL readiness and self-reported NLE1 scores among Thai medical students. Methods In this cross-sectional study, 108 fourth- and fifth-year medical students at the Faculty of Medicine Siriraj Hospital, Mahidol University, completed the Self-Directed Learning Readiness Scale (SDLRS), assessing components such as self-management, willingness to learn, and self-control. Additionally, students completed the Time Management Questionnaire (TMQ). Self-reported NLE1 scores were collected as a measure of academic performance. Multiple linear regression was conducted to explore associations between SDL components and NLE1 scores, and Cohen’s Kappa was used to assess alignment between perceived and actual SDL readiness. Results Among the SDL components, only self-management skills were significantly associated with higher self-reported NLE1 scores (p = 0.005). Moderate agreement (Cohen’s Kappa = 0.41) was found between students’ perceived SDL readiness and actual SDL readiness scores, indicating that those perceiving themselves as SDL-ready tended to perform better on the NLE1. Although time management was common among SDL-ready students, it did not directly correlate with NLE1 scores. Discussion Our findings highlight self-management as the SDL component most closely associated with academic performance in medical students. This suggests that fostering self-management skills, including decision-making, resource utilization, and action planning, could enhance students’ academic competencies. The moderate agreement between perceived and actual SDL readiness also suggests that self-awareness plays a role in SDL effectiveness. Encouraging self-reflective practices and providing feedback on students' self-assessments could help bridge the gap between perception and reality. Future research might examine these relationships longitudinally or across diverse educational settings to clarify SDL’s broader impacts on academic outcomes. Self-directed learning SDL readiness self-management academic performance Figures Figure 1 Background Knowledge in the medical field evolves and expands rapidly, making it difficult for the conventional medical curriculum, limited by time and resources, to catch up. Thus, the medical curriculum needs to be revolutionized. Instead of providing a pre-digested body of knowledge, educators are encouraged to provide scaffolding—structured support for the learning process ( 1 ), transitioning from providing one-sided lectures to engaging in problem-based learning (PBL), which is expected to foster essential self-directed learning (SDL) skills, the critical tools for the future medical profession ( 2 ), and further promotes lifelong learning, an ultimate goal of medical education ( 3 ). In a local context, Thailand’s medical curriculum comprises six years of undergraduate programs, with three years of pre-clinical and another three years of clinical training. PBL has played a prominent role in Thailand’s medical education for nearly four decades, following global trends and transitioning the curriculum from traditional lectures to a more interactive approach ( 2 ). Despite the growing recognition of SDL's importance in medical education, research gaps remain in its evaluation. Previous studies have yet to identify reliable predictors of academic competency associated with SDL readiness. Conflicting evidence has been described due to the intricate interaction between components of the learning process. For example, academic performances are influenced by various factors such as self-determination or conscientiousness. ( 4 , 5 ) Some studies described a positive correlation attributing SDL-readiness to human characteristics, such as the lecturer's ability, teaching format, and desire for self-improvement among the students ( 6 – 9 ). In contrast, some reported no significant correlation due to undetermined components such as the depth of knowledge and learning methods, which are challenging to generalize ( 10 , 11 ). No clear relationships have been thoroughly explored among the complex components of SDL, including self-management skills, self-control, willingness to learn, time management skills, and conscientiousness. This study aims to examine the associations between SDL components and academic performance, as measured by self-reported National Licensing Examination step 1 (NLE1) scores, using multiple linear regression to understand better the individual contributions of each SDL component to academic competency. Although the NLE1 scores are self-reported, they provide valuable insights into students’ perceived academic abilities and allow exploring potential associations with SDL readiness. The findings from this study could inform curriculum development by identifying key SDL components associated with academic competency, thereby guiding targeted educational interventions to enhance lifelong learning ( 12 , 13 ). Methods Population structure and study design A total of 640 4th - and 5th -year medical students at the Faculty of Medicine, Siriraj Hospital, in the academic year 2023, were invited to participate in this study. As the NLE1 exam, which assesses knowledge gained during the preclinical years, was conducted at the end of the third year, Enrolling 4th - and 5th -year students ensured that all participants had completed the exam and received their scores. Additionally, selecting these students, rather than 6th -year students, helped minimize recall bias related to SDL readiness and NLE1 scores. All participants were contacted via the LINE application and completed an online survey on Google Forms. The survey included demographic data, the 40-item Fisher’s Self-Directed Learning Readiness Scale (SDLRS) ( 14 ), the 18-item Time Management Questionnaire (TMQ) ( 15 , 16 ), self-reported NLE1 score (out of 300), and self-perception of SDL readiness. Questionnaire tools SDLRS was chosen as the primary method for assessing self-directed learning readiness due to its validated reliability and comprehensive structure. Developed by Murray J. Fisher, the SDLRS consists of 40 items divided into three subscales: self-management, desire for learning, and self-control. Widely validated across diverse settings ( 17 , 18 ), the SDLRS provides an in-depth and reliable assessment, making it well-suited for evaluating SDL readiness in this study. In addition to SDL readiness, time management has been shown to influence academic performance significantly. While Fisher’s SDLRS partially addresses time management, its scope is limited. To address this gap, we incorporated two additional measures: the TMQ, a validated tool providing a more detailed assessment of time management skills ( 19 ), and a self-perception scale of SDL readiness, which offers participants’ subjective perspectives on their readiness ( 11 ). These instruments complement the objective evaluation provided by Fisher’s SDLRS, offering a more comprehensive analysis of the factors that may affect academic performance. Statistical analysis Data analyses were performed on SPSS Statistics 18.0 software and MS Excel. The Anderson-Darling test indicated that the NLE1 data were not normally distributed (A² = 4.37, which exceeds the critical value of 0.752). As a result, the Mann-Whitney U test was employed for group comparisons, while the two-tailed t-test was retained for comparison purposes. The internal validity of Fisher’s SDLRS and TMQ was assessed using Cronbach’s alpha (0.962 and 0.793, respectively). NLE1 scores between groups were compared using the two-tailed t-test, and hierarchical multiple regression analysis was performed to examine the correlation between various factors (school year, age, gender, SDLRS, TMQ scores) and NLE1 scores. Cohen’s kappa was used to evaluate the relationship between subjects' self-perception of SDL effectiveness and their actual SDLRS scores. Ethical Considerations This study was approved by the Siriraj Institutional Review Board [Protocol number 555/2566(Exempt)]. Participation was voluntary, with students informed that their responses would have no impact on their academic standing or personal evaluation, ensuring unbiased participation. No incentives were offered for survey completion. Result Of the 640 4th - and 5th -year medical students invited to complete the survey, 108 responded (response rate 16.9%). Of these, 60 (55.6%; 95% CI 45.7%-65.1%) scored at least 150 on Fisher’s SDLRS and were classified as SDL-ready. The population demographics are shown in Table 1 . Besides Fisher’s SDLRS, students were also asked to assess their perception of SDL readiness. The Cohen’s Kappa analysis showed moderate agreement (71.3%, Cohen’s Kappa = 0.41) between students’ self-reported SDL readiness and their actual Fisher’s SDLRS-based classification. Table 1 – Demographic data Parameter SDL- Ready (n = 60) Non SDL- Ready (n = 48) p-value Age (Year) 21.87 22.04 0.221 College Year (% 4th year) 30.00 22.92 0.409 Gender (% Male) 63.33 64.58 0.893 Self-Perception (% as SDL Ready) 76.67 35.42 - Based on Fisher’s SDLRS score, SDL-ready students scored significantly higher on the self-reported NLE1 than SDL-non-ready students (2-Sample unpaired t-test: mean 220.1 vs. 203.5; p = 0.005, Mann-Whitney U test: median 220.5 vs. 204; p = 0.004), adjusted for all baseline parameters. To further investigate the relationship between SDL components and academic performance, a sub-analysis of the SDLRS components was conducted: self-management skills, willingness to learn, and self-control. Multiple regression analysis revealed that only self-management skills were significantly associated with higher self-reported NLE1 scores (Fig. 1 ). Regarding time management, 41.7% of the study population had moderate time management skills, and 53.7% had high time management skills, based on the TMQ ( 15 , 16 ). However, no significant difference in self-reported NLE1 scores was observed between these groups, nor was there any significant association between self-reported NLE1 scores and individual time management components. Discussion Our study shows a significant correlation between SDL readiness and self-reported NLE1 scores. While this correlation does not imply causation, the findings suggest that enhancing SDL readiness in medical students may be associated with higher academic competency. Thus, improving SDL readiness fosters lifelong learning skills, such as metacognition, adaptability, and critical thinking, which are essential for continuous professional development ( 20 ). However, it is possible that factors such as prior academic achievement, intrinsic motivation, or other personality traits like conscientiousness could independently influence SDL readiness and NLE1 performance. Longitudinal studies or interventions are needed better to establish causal links between SDL readiness and academic competence. Among the various SDL components, only self-management skills showed a significant association with higher self-reported NLE1 scores. This finding suggests that self-management may be critical to medical students' academic success. Self-management in the medical context includes problem-solving, decision-making, resource utilization, forming a patient-provider partnership, action planning, and self-tailoring. For example, in clinical settings, problem-solving may involve identifying the best approach to patient care, while decision-making may require balancing treatment options based on patient preferences and medical evidence. Therefore, targeted interventions to strengthen these components could be particularly beneficial ( 21 ). There is growing evidence that self-efficacy, defined as ‘the core belief that one has the power to effect changes by one’s actions,’ strongly affects self-management skills ( 22 ). In medical education, enhancing self-efficacy can empower students to take more proactive steps in their learning, such as seeking out resources, setting realistic goals, and taking ownership of their educational journey. Self-efficacy plays a crucial role in SDL by influencing students' motivation and persistence in overcoming learning challenges. Programs should, therefore, focus not only on developing the skill sets mentioned earlier but also on fostering self-efficacy through specific strategies. For instance, providing real-time feedback during clinical simulations can help students recognize the immediate impact of their decisions, reinforcing the belief that their actions matter. Additionally, setting achievable learning goals and offering guidance on how to overcome setbacks can further strengthen self-efficacy ( 23 ). In the evaluation aspect, with targeted interventions aimed at enhancing self-management and self-efficacy, methods for the evaluation of SDL readiness must also evolve. A more concise version of the SDLRS focusing primarily on self-management skills could be considered to facilitate more accessible and more efficient assessments rather than administering the entire 40-item questionnaire. Focusing on self-management skills in the evaluation could provide more specific insights into students' abilities to navigate complex clinical scenarios and manage their learning effectively, which are critical aspects of SDL readiness in medical training. However, it should be noted that SDL components are not mutually exclusive, and future studies should further explore how self-management skills correlate with other SDL aspects ( 24 ). There was a moderate agreement between students' self-perception of SDL readiness and their actual SDL readiness scores, with students who perceived themselves as SDL-ready reporting significantly higher NLE1 scores. This suggests that self-awareness is associated with better academic performance. However, while there was moderate agreement, self-assessments’ accuracy can be influenced by factors such as cultural influences and individual differences. One study describes that students who possess self-awareness have a positive change in attitude toward SDL, yet it does not describe whether it is associated with improvement in academic outcomes ( 25 ). Still, there is the implication that a positive attitude toward learning will eventually lead to higher academic competency. Further research is needed to understand how these factors impact self-assessment accuracy ( 26 , 27 ). Moreover, cognitive biases, such as the Dunning-Kruger effect, may influence moderate agreement, where individuals with lower skills overestimate their abilities. Understanding these biases can help educators design interventions to improve students' self-assessment accuracy. In addition, self-awareness is influenced by various factors. One of these traits is conscientiousness, which is crucial to developing medical professionalism. Conscientiousness can affect self-awareness through the mechanisms associated with dispositional self-consciousness (DSC) and situational self-awareness (SSA). Conscientiousness is associated with a heightened tendency for self-reflection and a stable self-concept, contributing to self-awareness as a reflective process. This trait supports SDL behaviors by fostering systematic approaches to planning, time management, and perseverance, which are essential in medical education. Thus, it may play a role in bridging the gap between perceived and actual SDL readiness and warrants further investigation on the issue ( 28 ). Enhancing self-reflective skills could improve students' ability to assess their SDL readiness accurately. Lastly, time management is crucial for effective SDL, as it helps students allocate time efficiently, set priorities, and balance tasks. While our study found no direct correlation between time management skills and academic competency, these skills may indirectly influence academic success by enhancing SDL components like self-management and goal setting. As such, it remains imperative for educators to educate their students on the importance of these skills ( 29 ). There are a few limitations to this study. First, the participants were 4th - and 5th -year medical students who had taken the NLE1 examination 1 and 2 years prior, respectively, resulting in a potential recall bias. Second, due to confidentiality constraints, we were unable to obtain official NLE1 scores and had to rely on self-reported data, which may have affected the accuracy of the results. Third, the NLE1 is a multiple-choice examination that mainly assesses medical knowledge and may not fully capture all aspects of academic competency. Finally, the study was conducted at a single institution. Given the variability in medical curricula across different institutions in Thailand and globally, the findings may not be generalizable to all settings. Conclusion Our study demonstrates a significant correlation between SDL readiness, particularly in self-management skills, and self-reported NLE1 scores, suggesting that enhancing SDL readiness may result in higher academic competency. Additionally, an interesting association exists between self-perception of SDL readiness and better academic performance. This highlights the potential importance of targeted interventions focusing on self-management skills. By focusing on self-management, self-efficacy, and self-awareness, educational programs can better equip students for the demands of modern medical practice, fostering skills that will benefit them throughout their careers. Future studies are still needed to assess the other aspects of medical competencies, such as clinical skills, as well as to explore the robustness of different thresholds and examine other factors that may influence the relationship between SDL readiness and academic outcomes. Abbreviations NLE1 National Licensing Examination step 1 PBL problem-based learning SDL self-directed learning SDLRS Self-Directed Learning Readiness Scale TMQ Time Management Questionnaire Declarations Ethics approval and consent to participate This study was approved by the Siriraj Institutional Review Board [Protocol number 555/2566(Exempt)]. Participation was voluntary, with students informed that their responses would have no impact on their academic standing or personal evaluation, ensuring unbiased participation. No incentives were offered for survey completion. Consent for publication Not applicable Availability of data and materials The data used in this study are available from the corresponding author upon request. Competing interests The authors declare that they have no competing interests. Funding The authors did not receive any funding for this study. Authors' contributions KS conceptualized the study. KS TP RS collected, analyzed, and interpreted the data, and were the major contributors in writing the manuscript. KI assisted with ethics approval, supervised the project, and revised the manuscript. All authors read and approved the final manuscript. Acknowledgments The authors acknowledge the use of ChatGPT, an AI language model developed by OpenAI, for language editing assistance. The authors also acknowledge Dr. Yodying Dangprapai and Assoc. Prof. Cherdsak Iramaneerat (Siriraj Health Science Education Excellence Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Thailand) for their valuable consultation. References Hmelo-Silver C, Eberbach C. Learning Theories and Problem-Based Learning. 2012. pp. 3–17. Tawanwongsri W. Challenges in Problem-Based Learning and Suggested Solutions at the School of Medicine, Walailak University: A Mixed-Methods Study. Ramathibodi Med J. 2018(41):001–8. Kim Y. Application of Social Constructivism in Medical Education. Korean Med Educ Rev. 2024;26(Suppl1):S31–9. AlRadini F, Ahmad N, Ejaz Kahloon L, Javaid A, Al Zamil N. Measuring Readiness for Self-Directed Learning in Medical Undergraduates. Adv Med Educ Pract. 2022;13:449–55. Schrempft S, Piumatti G, Gerbase MW, Baroffio A. Pathways to performance in undergraduate medical students: role of conscientiousness and the perceived educational environment. Adv Health Sci Educ Theory Pract. 2021;26(5):1537–54. John V, Michael J. J. Self Directed Learning Practices and Academic Performance among Undergraduate Nursing Students. Int J Res Appl Sci Eng Technol. 2018;6. Broadbent J, Poon WL. Self-regulated learning strategies & academic achievement in online higher education learning environments: A systematic review. Internet High Educ. 2015;27:1–13. Kidane HH, Roebertsen H, van der Vleuten CPM. Students' perceptions towards self-directed learning in Ethiopian medical schools with new innovative curriculum: a mixed-method study. BMC Med Educ. 2020;20(1):7. Tekkol İA, Demirel M. An Investigation of Self-Directed Learning Skills of Undergraduate Students. Front Psychol. 2018;9:2324. Premkumar K, Pahwa P, Banerjee A, Baptiste K, Bhatt H, Lim HJ. Does medical training promote or deter self-directed learning? A longitudinal mixed-methods study. Acad Med. 2013;88(11):1754–64. Taylor TAH, Kemp K, Mi M, Lerchenfeldt S. Self-directed learning assessment practices in undergraduate health professions education: a systematic review. Med Educ Online. 2023;28(1):2189553. Rathmann K, Herke M, Bilz L, Rimpelä A, Hurrelmann K, Richter M. Class-Level School Performance and Life Satisfaction: Differential Sensitivity for Low- and High-Performing School-Aged Children. Int J Environ Res Public Health [Internet]. 2018; 15(12). Buppasiri P, Kuhiranratn P, Engchanil C, Kaewpila P, Sila-on S, Pratipanawatr T. Achievement in National Licensing Examination Steps I and II of Khon Kaen Medical Students from Various Programs. Srinagarind Med J. 2013;28(4):516–23. Fisher M, King J, Tague G. Development of a self-directed learning readiness scale for nursing education. Nurse Educ Today. 2001;21(7):516–25. Britton B, Tesser A. Effects of Time-Management Practices on College Grades. J Educ Psychol. 1991;83:405–10. Khatib A. Time Management and Its Relation To Students’ Stress, Gender and Academic Achievement Among Sample of Students at Al Ain University of Science and Technology. UAE Int J Bus social Res. 2014;4:47–58. Fisher M, King J. The Self-Directed Learning Readiness Scale for nursing education revisited: a confirmatory factor analysis. Nurse Educ Today. 2009;30:44–8. Kumar AP, Omprakash A, Mani PKC, Swaminathan N, Maheshkumar K, Maruthy KN, et al. Validation of Internal structure of Self-Directed Learning Readiness Scale among Indian Medical Students using factor analysis and the Structural equation Modelling Approach. BMC Med Educ. 2021;21(1):614. Claessens BJC, van Eerde W, Rutte CG, Roe RA. A review of the time management literature. Personnel Rev. 2007;36(2):255–76. Charokar K, Dulloo P. Self-directed Learning Theory to Practice: A Footstep towards the Path of being a Life-long Learne. J Adv Med Educ Prof. 2022;10(3):135–44. Lorig KR, Holman HR. Self-management education: History, definition, outcomes, and mechanisms. Ann Behav Med. 2003;26(1):1–7. Klassen RM, Klassen JRL. Self-efficacy beliefs of medical students: a critical review. Perspect Med Educ. 2018;7(2):76–82. Aoki S, Shikama Y, Yasui K, Moroi Y, Sakamoto N, Suenaga H, et al. Optimizing simulated interviews and feedback to maximize medical students' self-efficacy in real time. BMC Med Educ. 2022;22(1):438. Zhu M, Doo MY. The relationship among motivation, self-monitoring, self-management, and learning strategies of MOOC learners. J Comput High Educ. 2022;34(2):321–42. Roberts M, Darden A, Wiskur B, Hill M. A Longitudinal Assessment of Self-directed Learning Readiness and Development in Medical Students. J Med Educ Curric Dev. 2024;11:23821205241242261. Gabbard T, Romanelli F. The Accuracy of Health Professions Students' Self-Assessments Compared to Objective Measures of Competence. Am J Pharm Educ. 2021;85(4):8405. Blanch-Hartigan D. Medical students’ self-assessment of performance: Results from three meta-analyses. Patient Educ Couns. 2011;84(1):3–9. Majolo M, Gomes WB, DeCastro TG. Self-Consciousness and Self-Awareness: Associations between Stable and Transitory Levels of Evidence. Behav Sci (Basel). 2023;13(2). Al-Otaibi M. Medical Student’s time management skills and strategies in Connecting to self-directed learning: A Qualitative Study People. Int J Social Sci. 2019;5(2):222–37. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5369545","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":373652084,"identity":"d544afcf-f536-4a34-ac3e-7eaaabc4d096","order_by":0,"name":"Khemmawit Siriwong","email":"","orcid":"","institution":"Siriraj Hospital","correspondingAuthor":false,"prefix":"","firstName":"Khemmawit","middleName":"","lastName":"Siriwong","suffix":""},{"id":373652085,"identity":"d8f4713c-0f60-43ef-a4c8-cbb425f73c86","order_by":1,"name":"Thitipat Pattanaprateeb","email":"","orcid":"","institution":"Siriraj Hospital","correspondingAuthor":false,"prefix":"","firstName":"Thitipat","middleName":"","lastName":"Pattanaprateeb","suffix":""},{"id":373652091,"identity":"2c8fbc4c-4c95-43bc-b1c3-0e68ebe5bce0","order_by":2,"name":"Ramon Sawetratanasatien","email":"","orcid":"","institution":"Siriraj Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ramon","middleName":"","lastName":"Sawetratanasatien","suffix":""},{"id":373652092,"identity":"0217285f-d67e-486a-bc89-e23d409507a7","order_by":3,"name":"Korakrit Imwattana","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCklEQVRIiWNgGAWjYBACCSBmbABRzEDWBwaGBDaGBMYDcDk8WiRAWhhnQLQwgLXw4NcCMZGZB6iFgZAWyfbehx9n7rGoY2DnTpO2zbHL42NPPnCAocaOwV66AasWaZ7jxpIbnoEcxrtNOndbcjEbz7OEAwzHkhl4ZA5g1SInkcYg+eAARMvt3G3MiW0SOQYHGNgOAB2WgEsL80+4Fstt9VAt/3BrkZZIY5PcANPCuO0wRAtjG24tkj3H2CxnHJCQbGPm3f6zd9vxxDaQXxL7knl4bmDXInG8jflmz4E6fn7+s5sNfm6rTpzfnnzwwYdvdnLsM7BrgQM2FB5QMQ9+9aNgFIyCUTAK8AEA4jhXRN5Vx/QAAAAASUVORK5CYII=","orcid":"","institution":"Siriraj Hospital","correspondingAuthor":true,"prefix":"","firstName":"Korakrit","middleName":"","lastName":"Imwattana","suffix":""}],"badges":[],"createdAt":"2024-11-01 00:08:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5369545/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5369545/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":68908471,"identity":"6e39dfce-26ab-46a5-896c-1cc219456481","added_by":"auto","created_at":"2024-11-13 11:10:39","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":17011,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation between SDL effectiveness and NLE1 score. \u003c/strong\u003eUnivariate linear regression analysis of factors associated with NLE1 among Thai medical students. A forest plot demonstrates the effect size of each factor with a 95% confidence interval.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5369545/v1/e078803fa18ba24ab8782650.png"},{"id":100966980,"identity":"3fd86d3d-e4e8-418c-a9ae-c20c5e2f79db","added_by":"auto","created_at":"2026-01-23 09:28:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":468983,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5369545/v1/9e569624-3bc7-453b-842c-7a9b88e5d08f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The effect of Self-Directed Learning Readiness on Thai medical students’ self-reported National Licensing Examination Step 1 score","fulltext":[{"header":"Background","content":"\u003cp\u003eKnowledge in the medical field evolves and expands rapidly, making it difficult for the conventional medical curriculum, limited by time and resources, to catch up. Thus, the medical curriculum needs to be revolutionized. Instead of providing a pre-digested body of knowledge, educators are encouraged to provide scaffolding\u0026mdash;structured support for the learning process (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e), transitioning from providing one-sided lectures to engaging in problem-based learning (PBL), which is expected to foster essential self-directed learning (SDL) skills, the critical tools for the future medical profession (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e), and further promotes lifelong learning, an ultimate goal of medical education (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn a local context, Thailand\u0026rsquo;s medical curriculum comprises six years of undergraduate programs, with three years of pre-clinical and another three years of clinical training. PBL has played a prominent role in Thailand\u0026rsquo;s medical education for nearly four decades, following global trends and transitioning the curriculum from traditional lectures to a more interactive approach (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite the growing recognition of SDL's importance in medical education, research gaps remain in its evaluation. Previous studies have yet to identify reliable predictors of academic competency associated with SDL readiness. Conflicting evidence has been described due to the intricate interaction between components of the learning process. For example, academic performances are influenced by various factors such as self-determination or conscientiousness. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) Some studies described a positive correlation attributing SDL-readiness to human characteristics, such as the lecturer's ability, teaching format, and desire for self-improvement among the students (\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). In contrast, some reported no significant correlation due to undetermined components such as the depth of knowledge and learning methods, which are challenging to generalize (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNo clear relationships have been thoroughly explored among the complex components of SDL, including self-management skills, self-control, willingness to learn, time management skills, and conscientiousness. This study aims to examine the associations between SDL components and academic performance, as measured by self-reported National Licensing Examination step 1 (NLE1) scores, using multiple linear regression to understand better the individual contributions of each SDL component to academic competency. Although the NLE1 scores are self-reported, they provide valuable insights into students\u0026rsquo; perceived academic abilities and allow exploring potential associations with SDL readiness. The findings from this study could inform curriculum development by identifying key SDL components associated with academic competency, thereby guiding targeted educational interventions to enhance lifelong learning (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePopulation structure and study design\u003c/h2\u003e \u003cp\u003e A total of 640 4th - and 5th -year medical students at the Faculty of Medicine, Siriraj Hospital, in the academic year 2023, were invited to participate in this study. As the NLE1 exam, which assesses knowledge gained during the preclinical years, was conducted at the end of the third year, Enrolling 4th - and 5th -year students ensured that all participants had completed the exam and received their scores. Additionally, selecting these students, rather than 6th -year students, helped minimize recall bias related to SDL readiness and NLE1 scores. All participants were contacted via the LINE application and completed an online survey on Google Forms. The survey included demographic data, the 40-item Fisher\u0026rsquo;s Self-Directed Learning Readiness Scale (SDLRS) (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e), the 18-item Time Management Questionnaire (TMQ) (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e), self-reported NLE1 score (out of 300), and self-perception of SDL readiness.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eQuestionnaire tools\u003c/h3\u003e\n\u003cp\u003eSDLRS was chosen as the primary method for assessing self-directed learning readiness due to its validated reliability and comprehensive structure. Developed by Murray J. Fisher, the SDLRS consists of 40 items divided into three subscales: self-management, desire for learning, and self-control. Widely validated across diverse settings (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), the SDLRS provides an in-depth and reliable assessment, making it well-suited for evaluating SDL readiness in this study.\u003c/p\u003e \u003cp\u003eIn addition to SDL readiness, time management has been shown to influence academic performance significantly. While Fisher\u0026rsquo;s SDLRS partially addresses time management, its scope is limited. To address this gap, we incorporated two additional measures: the TMQ, a validated tool providing a more detailed assessment of time management skills (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e), and a self-perception scale of SDL readiness, which offers participants\u0026rsquo; subjective perspectives on their readiness (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). These instruments complement the objective evaluation provided by Fisher\u0026rsquo;s SDLRS, offering a more comprehensive analysis of the factors that may affect academic performance.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eData analyses were performed on SPSS Statistics 18.0 software and MS Excel. The Anderson-Darling test indicated that the NLE1 data were not normally distributed (A\u0026sup2; = 4.37, which exceeds the critical value of 0.752). As a result, the Mann-Whitney U test was employed for group comparisons, while the two-tailed t-test was retained for comparison purposes. The internal validity of Fisher\u0026rsquo;s SDLRS and TMQ was assessed using Cronbach\u0026rsquo;s alpha (0.962 and 0.793, respectively). NLE1 scores between groups were compared using the two-tailed t-test, and hierarchical multiple regression analysis was performed to examine the correlation between various factors (school year, age, gender, SDLRS, TMQ scores) and NLE1 scores. Cohen\u0026rsquo;s kappa was used to evaluate the relationship between subjects' self-perception of SDL effectiveness and their actual SDLRS scores.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEthical Considerations\u003c/h3\u003e\n\u003cp\u003eThis study was approved by the Siriraj Institutional Review Board [Protocol number 555/2566(Exempt)]. Participation was voluntary, with students informed that their responses would have no impact on their academic standing or personal evaluation, ensuring unbiased participation. No incentives were offered for survey completion.\u003c/p\u003e"},{"header":"Result","content":"\u003cp\u003eOf the 640 4th - and 5th -year medical students invited to complete the survey, 108 responded (response rate 16.9%). Of these, 60 (55.6%; 95% CI 45.7%-65.1%) scored at least 150 on Fisher\u0026rsquo;s SDLRS and were classified as SDL-ready. The population demographics are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Besides Fisher\u0026rsquo;s SDLRS, students were also asked to assess their perception of SDL readiness. The Cohen\u0026rsquo;s Kappa analysis showed moderate agreement (71.3%, Cohen\u0026rsquo;s Kappa\u0026thinsp;=\u0026thinsp;0.41) between students\u0026rsquo; self-reported SDL readiness and their actual Fisher\u0026rsquo;s SDLRS-based classification.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u0026ndash; Demographic data\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSDL- Ready\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;60)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon SDL- Ready\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;48)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (Year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.221\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege Year (% 4th year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.409\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (% Male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e63.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e64.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.893\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-Perception (% as SDL Ready)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e76.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBased on Fisher\u0026rsquo;s SDLRS score, SDL-ready students scored significantly higher on the self-reported NLE1 than SDL-non-ready students (2-Sample unpaired t-test: mean 220.1 vs. 203.5; p\u0026thinsp;=\u0026thinsp;0.005, Mann-Whitney U test: median 220.5 vs. 204; p\u0026thinsp;=\u0026thinsp;0.004), adjusted for all baseline parameters. To further investigate the relationship between SDL components and academic performance, a sub-analysis of the SDLRS components was conducted: self-management skills, willingness to learn, and self-control. Multiple regression analysis revealed that only self-management skills were significantly associated with higher self-reported NLE1 scores (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRegarding time management, 41.7% of the study population had moderate time management skills, and 53.7% had high time management skills, based on the TMQ (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). However, no significant difference in self-reported NLE1 scores was observed between these groups, nor was there any significant association between self-reported NLE1 scores and individual time management components.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study shows a significant correlation between SDL readiness and self-reported NLE1 scores. While this correlation does not imply causation, the findings suggest that enhancing SDL readiness in medical students may be associated with higher academic competency. Thus, improving SDL readiness fosters lifelong learning skills, such as metacognition, adaptability, and critical thinking, which are essential for continuous professional development (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). However, it is possible that factors such as prior academic achievement, intrinsic motivation, or other personality traits like conscientiousness could independently influence SDL readiness and NLE1 performance. Longitudinal studies or interventions are needed better to establish causal links between SDL readiness and academic competence.\u003c/p\u003e \u003cp\u003eAmong the various SDL components, only self-management skills showed a significant association with higher self-reported NLE1 scores. This finding suggests that self-management may be critical to medical students' academic success. Self-management in the medical context includes problem-solving, decision-making, resource utilization, forming a patient-provider partnership, action planning, and self-tailoring. For example, in clinical settings, problem-solving may involve identifying the best approach to patient care, while decision-making may require balancing treatment options based on patient preferences and medical evidence. Therefore, targeted interventions to strengthen these components could be particularly beneficial (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThere is growing evidence that self-efficacy, defined as \u0026lsquo;the core belief that one has the power to effect changes by one\u0026rsquo;s actions,\u0026rsquo; strongly affects self-management skills (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). In medical education, enhancing self-efficacy can empower students to take more proactive steps in their learning, such as seeking out resources, setting realistic goals, and taking ownership of their educational journey. Self-efficacy plays a crucial role in SDL by influencing students' motivation and persistence in overcoming learning challenges. Programs should, therefore, focus not only on developing the skill sets mentioned earlier but also on fostering self-efficacy through specific strategies. For instance, providing real-time feedback during clinical simulations can help students recognize the immediate impact of their decisions, reinforcing the belief that their actions matter. Additionally, setting achievable learning goals and offering guidance on how to overcome setbacks can further strengthen self-efficacy (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the evaluation aspect, with targeted interventions aimed at enhancing self-management and self-efficacy, methods for the evaluation of SDL readiness must also evolve. A more concise version of the SDLRS focusing primarily on self-management skills could be considered to facilitate more accessible and more efficient assessments rather than administering the entire 40-item questionnaire. Focusing on self-management skills in the evaluation could provide more specific insights into students' abilities to navigate complex clinical scenarios and manage their learning effectively, which are critical aspects of SDL readiness in medical training. However, it should be noted that SDL components are not mutually exclusive, and future studies should further explore how self-management skills correlate with other SDL aspects (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThere was a moderate agreement between students' self-perception of SDL readiness and their actual SDL readiness scores, with students who perceived themselves as SDL-ready reporting significantly higher NLE1 scores. This suggests that self-awareness is associated with better academic performance. However, while there was moderate agreement, self-assessments\u0026rsquo; accuracy can be influenced by factors such as cultural influences and individual differences. One study describes that students who possess self-awareness have a positive change in attitude toward SDL, yet it does not describe whether it is associated with improvement in academic outcomes (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Still, there is the implication that a positive attitude toward learning will eventually lead to higher academic competency. Further research is needed to understand how these factors impact self-assessment accuracy (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Moreover, cognitive biases, such as the Dunning-Kruger effect, may influence moderate agreement, where individuals with lower skills overestimate their abilities. Understanding these biases can help educators design interventions to improve students' self-assessment accuracy.\u003c/p\u003e \u003cp\u003eIn addition, self-awareness is influenced by various factors. One of these traits is conscientiousness, which is crucial to developing medical professionalism. Conscientiousness can affect self-awareness through the mechanisms associated with dispositional self-consciousness (DSC) and situational self-awareness (SSA). Conscientiousness is associated with a heightened tendency for self-reflection and a stable self-concept, contributing to self-awareness as a reflective process. This trait supports SDL behaviors by fostering systematic approaches to planning, time management, and perseverance, which are essential in medical education. Thus, it may play a role in bridging the gap between perceived and actual SDL readiness and warrants further investigation on the issue (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Enhancing self-reflective skills could improve students' ability to assess their SDL readiness accurately.\u003c/p\u003e \u003cp\u003eLastly, time management is crucial for effective SDL, as it helps students allocate time efficiently, set priorities, and balance tasks. While our study found no direct correlation between time management skills and academic competency, these skills may indirectly influence academic success by enhancing SDL components like self-management and goal setting. As such, it remains imperative for educators to educate their students on the importance of these skills (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThere are a few limitations to this study. First, the participants were 4th - and 5th -year medical students who had taken the NLE1 examination 1 and 2 years prior, respectively, resulting in a potential recall bias. Second, due to confidentiality constraints, we were unable to obtain official NLE1 scores and had to rely on self-reported data, which may have affected the accuracy of the results. Third, the NLE1 is a multiple-choice examination that mainly assesses medical knowledge and may not fully capture all aspects of academic competency. Finally, the study was conducted at a single institution. Given the variability in medical curricula across different institutions in Thailand and globally, the findings may not be generalizable to all settings.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study demonstrates a significant correlation between SDL readiness, particularly in self-management skills, and self-reported NLE1 scores, suggesting that enhancing SDL readiness may result in higher academic competency. Additionally, an interesting association exists between self-perception of SDL readiness and better academic performance. This highlights the potential importance of targeted interventions focusing on self-management skills. By focusing on self-management, self-efficacy, and self-awareness, educational programs can better equip students for the demands of modern medical practice, fostering skills that will benefit them throughout their careers. Future studies are still needed to assess the other aspects of medical competencies, such as clinical skills, as well as to explore the robustness of different thresholds and examine other factors that may influence the relationship between SDL readiness and academic outcomes.\u003c/p\u003e "},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNLE1\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Licensing Examination step 1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePBL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eproblem-based learning\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSDL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eself-directed learning\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSDLRS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSelf-Directed Learning Readiness Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTMQ\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTime Management Questionnaire\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Siriraj Institutional Review Board [Protocol number 555/2566(Exempt)]. Participation was voluntary, with students informed that their responses would have no impact on their academic standing or personal evaluation, ensuring unbiased participation. No incentives were offered for survey completion.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eThe data used in this study are available from the corresponding author upon request.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThe authors did not receive any funding for this study.\u003c/p\u003e\n\u003cp\u003eAuthors\u0026apos; contributions\u003c/p\u003e\n\u003cp\u003eKS conceptualized the study. KS TP RS collected, analyzed, and interpreted the data, and were the major contributors in writing the manuscript. KI assisted with ethics approval, supervised the project, and revised the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eThe authors acknowledge the use of ChatGPT, an AI language model developed by OpenAI, for language editing assistance. The authors also acknowledge Dr. Yodying Dangprapai and Assoc. Prof. Cherdsak Iramaneerat (Siriraj Health Science Education Excellence Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Thailand) for their valuable consultation.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHmelo-Silver C, Eberbach C. Learning Theories and Problem-Based Learning. 2012. pp. 3\u0026ndash;17.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTawanwongsri W. Challenges in Problem-Based Learning and Suggested Solutions at the School of Medicine, Walailak University: A Mixed-Methods Study. Ramathibodi Med J. 2018(41):001\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim Y. Application of Social Constructivism in Medical Education. Korean Med Educ Rev. 2024;26(Suppl1):S31\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlRadini F, Ahmad N, Ejaz Kahloon L, Javaid A, Al Zamil N. Measuring Readiness for Self-Directed Learning in Medical Undergraduates. Adv Med Educ Pract. 2022;13:449\u0026ndash;55.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchrempft S, Piumatti G, Gerbase MW, Baroffio A. Pathways to performance in undergraduate medical students: role of conscientiousness and the perceived educational environment. Adv Health Sci Educ Theory Pract. 2021;26(5):1537\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJohn V, Michael J. J. Self Directed Learning Practices and Academic Performance among Undergraduate Nursing Students. Int J Res Appl Sci Eng Technol. 2018;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBroadbent J, Poon WL. Self-regulated learning strategies \u0026amp; academic achievement in online higher education learning environments: A systematic review. Internet High Educ. 2015;27:1\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKidane HH, Roebertsen H, van der Vleuten CPM. Students' perceptions towards self-directed learning in Ethiopian medical schools with new innovative curriculum: a mixed-method study. BMC Med Educ. 2020;20(1):7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTekkol İA, Demirel M. An Investigation of Self-Directed Learning Skills of Undergraduate Students. Front Psychol. 2018;9:2324.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePremkumar K, Pahwa P, Banerjee A, Baptiste K, Bhatt H, Lim HJ. Does medical training promote or deter self-directed learning? A longitudinal mixed-methods study. Acad Med. 2013;88(11):1754\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTaylor TAH, Kemp K, Mi M, Lerchenfeldt S. Self-directed learning assessment practices in undergraduate health professions education: a systematic review. Med Educ Online. 2023;28(1):2189553.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRathmann K, Herke M, Bilz L, Rimpel\u0026auml; A, Hurrelmann K, Richter M. Class-Level School Performance and Life Satisfaction: Differential Sensitivity for Low- and High-Performing School-Aged Children. Int J Environ Res Public Health [Internet]. 2018; 15(12).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBuppasiri P, Kuhiranratn P, Engchanil C, Kaewpila P, Sila-on S, Pratipanawatr T. Achievement in National Licensing Examination Steps I and II of Khon Kaen Medical Students from Various Programs. Srinagarind Med J. 2013;28(4):516\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFisher M, King J, Tague G. Development of a self-directed learning readiness scale for nursing education. Nurse Educ Today. 2001;21(7):516\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBritton B, Tesser A. Effects of Time-Management Practices on College Grades. J Educ Psychol. 1991;83:405\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhatib A. Time Management and Its Relation To Students\u0026rsquo; Stress, Gender and Academic Achievement Among Sample of Students at Al Ain University of Science and Technology. UAE Int J Bus social Res. 2014;4:47\u0026ndash;58.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFisher M, King J. The Self-Directed Learning Readiness Scale for nursing education revisited: a confirmatory factor analysis. Nurse Educ Today. 2009;30:44\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKumar AP, Omprakash A, Mani PKC, Swaminathan N, Maheshkumar K, Maruthy KN, et al. Validation of Internal structure of Self-Directed Learning Readiness Scale among Indian Medical Students using factor analysis and the Structural equation Modelling Approach. BMC Med Educ. 2021;21(1):614.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eClaessens BJC, van Eerde W, Rutte CG, Roe RA. A review of the time management literature. Personnel Rev. 2007;36(2):255\u0026ndash;76.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCharokar K, Dulloo P. Self-directed Learning Theory to Practice: A Footstep towards the Path of being a Life-long Learne. J Adv Med Educ Prof. 2022;10(3):135\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLorig KR, Holman HR. Self-management education: History, definition, outcomes, and mechanisms. Ann Behav Med. 2003;26(1):1\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKlassen RM, Klassen JRL. Self-efficacy beliefs of medical students: a critical review. Perspect Med Educ. 2018;7(2):76\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAoki S, Shikama Y, Yasui K, Moroi Y, Sakamoto N, Suenaga H, et al. Optimizing simulated interviews and feedback to maximize medical students' self-efficacy in real time. BMC Med Educ. 2022;22(1):438.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu M, Doo MY. The relationship among motivation, self-monitoring, self-management, and learning strategies of MOOC learners. J Comput High Educ. 2022;34(2):321\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoberts M, Darden A, Wiskur B, Hill M. A Longitudinal Assessment of Self-directed Learning Readiness and Development in Medical Students. J Med Educ Curric Dev. 2024;11:23821205241242261.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGabbard T, Romanelli F. The Accuracy of Health Professions Students' Self-Assessments Compared to Objective Measures of Competence. Am J Pharm Educ. 2021;85(4):8405.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlanch-Hartigan D. Medical students\u0026rsquo; self-assessment of performance: Results from three meta-analyses. Patient Educ Couns. 2011;84(1):3\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMajolo M, Gomes WB, DeCastro TG. Self-Consciousness and Self-Awareness: Associations between Stable and Transitory Levels of Evidence. Behav Sci (Basel). 2023;13(2).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAl-Otaibi M. Medical Student\u0026rsquo;s time management skills and strategies in Connecting to self-directed learning: A Qualitative Study People. Int J Social Sci. 2019;5(2):222\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Self-directed learning, SDL readiness, self-management, academic performance","lastPublishedDoi":"10.21203/rs.3.rs-5369545/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5369545/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eSelf-directed learning (SDL) is essential for medical students to adapt to continuous learning demands in clinical practice. Problem-based learning (PBL) has been widely used in Thailand\u0026rsquo;s medical education to foster SDL skills. Yet, limited research exists on how specific SDL components relate to academic success, particularly performance on the National Licensing Examination Step 1 (NLE1). This study examines associations between SDL readiness and self-reported NLE1 scores among Thai medical students.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn this cross-sectional study, 108 fourth- and fifth-year medical students at the Faculty of Medicine Siriraj Hospital, Mahidol University, completed the Self-Directed Learning Readiness Scale (SDLRS), assessing components such as self-management, willingness to learn, and self-control. Additionally, students completed the Time Management Questionnaire (TMQ). Self-reported NLE1 scores were collected as a measure of academic performance. Multiple linear regression was conducted to explore associations between SDL components and NLE1 scores, and Cohen\u0026rsquo;s Kappa was used to assess alignment between perceived and actual SDL readiness.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong the SDL components, only self-management skills were significantly associated with higher self-reported NLE1 scores (p\u0026thinsp;=\u0026thinsp;0.005). Moderate agreement (Cohen\u0026rsquo;s Kappa\u0026thinsp;=\u0026thinsp;0.41) was found between students\u0026rsquo; perceived SDL readiness and actual SDL readiness scores, indicating that those perceiving themselves as SDL-ready tended to perform better on the NLE1. Although time management was common among SDL-ready students, it did not directly correlate with NLE1 scores.\u003c/p\u003e\u003ch2\u003eDiscussion\u003c/h2\u003e \u003cp\u003eOur findings highlight self-management as the SDL component most closely associated with academic performance in medical students. This suggests that fostering self-management skills, including decision-making, resource utilization, and action planning, could enhance students\u0026rsquo; academic competencies. The moderate agreement between perceived and actual SDL readiness also suggests that self-awareness plays a role in SDL effectiveness. Encouraging self-reflective practices and providing feedback on students' self-assessments could help bridge the gap between perception and reality. Future research might examine these relationships longitudinally or across diverse educational settings to clarify SDL\u0026rsquo;s broader impacts on academic outcomes.\u003c/p\u003e","manuscriptTitle":"The effect of Self-Directed Learning Readiness on Thai medical students’ self-reported National Licensing Examination Step 1 score","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-13 11:02:34","doi":"10.21203/rs.3.rs-5369545/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5153306f-cfd7-4d6c-8299-bd1d2d5334e0","owner":[],"postedDate":"November 13th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-23T09:27:51+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-13 11:02:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5369545","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5369545","identity":"rs-5369545","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}