The impact of learner autonomy on the performance in voluntary online cardiac auscultation courses

The impact of learner autonomy on the performance in voluntary online cardiac auscultation courses | 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 impact of learner autonomy on the performance in voluntary online cardiac auscultation courses Yudong Fang, Ligang Fang, Wenling Zhu, Xue Lin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4758934/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 This study explores the impact of learner autonomy on academic performance in a free, non-mandatory online heart sound auscultation course, emphasizing the enhancement of online learning outcomes through learner autonomy. Medical students and doctors were recruited via WeChat groups and participated in four 2-hour live sessions over four weeks, delivered through Plaso teaching software. Participants engaged with real heart sounds using in-ear headphones and were evaluated through random questions during lectures and a comparison of scores on ten heart sound auscultation questions before and after training. Results from 122 doctors and 77 medical students showed that 146 (73%) attended and 46 (23%) completed all sessions, with heart auscultation scores improving significantly from 40 to 70 (p = 0.000). Full participation and active engagement were key predictors of successful exam performance, while intrinsic motivation correlated with complete course attendance (P = 0.045). Moreover, ROC curve analysis revealed that outstanding learners spent more time reviewing post-class materials. The study concludes that while learner autonomy is crucial for success in voluntary online courses, sole reliance on autonomy may not suffice. Effective learning requires identifying intrinsic needs, full participation, active interaction, and additional review. Course designers are advised to recruit learners precisely, incorporate interactive elements, and promote post-class review to enhance learner autonomy. Learner autonomy Online learning Heart sound auscultation intrinsic motivation active engagement Academic performance Figures Figure 1 Figure 2 Background In an era marked by rapid advancements in medical technology and information, online education has emerged as the primary mode of continuing education for medical practitioners, especially following the COVID-19 outbreak(Goldberg and Crocombe 2017 ; O’Doherty et al. 2018 ; Zhu et al. 2023 ). With the rapid growth of online learning, an increasing number of theories and studies are exploring the key factors that influence online learning outcomes, with learner autonomy receiving significant attention(Pei and Wu 2019 ). In 1997, Moore's Theory of Transactional Distance highlighted that dialogue, structure, and learner autonomy constitute the fundamental elements of online learning(Moore n.d.). By 2023, the AMEE guide emphasized the importance of autonomous learning in asynchronous learning(MacNeill et al. 2024 ). Learner autonomy refers to the ability of learners to actively manage their own learning processes(“Learner autonomy” 2023). Most studies suggest that learner autonomy, through setting personal learning goals, controlling the learning process, and reflecting on their learning, can help learners achieve their learning objectives(“Learner Autonomy - an overview | ScienceDirect Topics” n.d.). However, there is currently limited research verifying the extent to which learner autonomy influences learning outcomes in online learning environments. Understanding the quantitative role of autonomy in learning outcomes can significantly inspire learners to study more effectively and assist teachers in designing content and methods for online courses. For medical practitioners, online education primarily takes two forms. One involves formal course training conducted by medical schools using online platforms with strict assessments. The other involves learners autonomously selecting courses that meet their needs, which can be either paid or free, without mandatory requirements on their results. Most busy clinical doctors tend to choose the latter option, which requires a higher degree of learner autonomy to complete the learning process. We believe that in non-mandatory, free, and on-demand online courses, recording learners' learning processes and exploring their impact on learning outcomes can help us understand the influence of learner autonomy on learning effectiveness. Cardiac auscultation is a crucial diagnostic tool in clinical practice, yet mastering it remains a significant challenge in medical education due to several reasons (de Giovanni et al. 2009 ; G et al. 2012; Vukanovic-Criley et al. 2006 ). Learners often lack access to authentic heart sounds, as reproductions via audio software frequently fail to accurately mimic the original sounds, complicating their understanding in a clinical context. Explanations focused solely on the acoustic properties of heart sounds do not adequately convey the underlying pathophysiological processes, often leaving learners confused and disinterested. Additionally, cardiac murmurs are relatively rare in clinical settings, providing limited practice opportunities and making the skill more difficult to master. These challenges in teaching heart auscultation in traditional settings highlight its potential for online instruction. To address these issues, we developed a course based on real heart sounds, integrating various imaging techniques to clearly explain the pathophysiological basis of heart sounds. The course includes a sufficient number of practice heart sounds and is offered free of charge to online learners. During the learning process, learners' full participation, classroom engagement, and frequency of post-course review are considered to be related to learner autonomy. Method Learner Recruitment This prospective, self-controlled, single-center study recruited doctors and medical students interested in free heart auscultation training through WeChat. The study was approved by Peking Union Medical College Hospital with clinical trial number I-23PJ1679, and each participant signed an informed consent form. Participants were informed that their learning process would be recorded during the classes, but specific personal information would not be disclosed. Teaching Process Teaching Contents The heart sounds used were actual recordings from patients, covering the majority of key heart sounds in internal medicine and diagnostics. These sounds were accessible through in-ear headphones, identified using sound recognition software, and were verified by two experienced cardiology professors to ensure they were not distorted. The heart sound tutorial employed various imaging methods, including animations and echocardiographic images, combined with case studies, to clearly explain the mechanisms and clinical significance of each heart sound. This comprehensive tutorial included 80 heart sounds, over 30 cases, 5 animations, and 100 echocardiographic images. Teaching Setting All teaching sessions were conducted using an online teaching software (Plaso, PLASO Network Technologies Co., LTD, Nanjing, China). The sessions were held once a week, each lasting two hours, for a total of four sessions. The teacher delivered the lectures via live streaming, and the online classroom facilitated interaction between teachers and students. At the beginning and end of the training, the teacher administered 10 heart sound tests online to assess participants. Each correct answer was scored as 10 points, with each test having a maximum score of 100 points. The heart sounds in the two tests were different but collectively covered all key heart sounds. During the lectures, the teacher sporadically asked questions to all students using online tools, and students responded via the answer panel on their interface. After class, lecture videos and review materials were distributed to each student through the system, and all materials were available for review in the system for one month after the course ended. Definition of Key Variables in the Learning Process Learning motivation was categorized into intrinsic and extrinsic. On the WeChat recruitment page, doctors chose between: "Are you participating in this training because you have never mastered it before, always regretted it, and therefore want to understand it thoroughly?" Students chose between: "interested in learning about heart sounds (intrinsic motivation)" or "because you need it for work or exams (extrinsic motivation)?" Participation in the training was defined as attending at least one lecture or reviewing the post-class material at least once. Full participation was defined as attending all four lectures. The main assessment during the lecture process was the duration of time participants spent attending, which was recorded by the system. Participants' classroom engagement was determined by the number of times they answered random questions posed by the teacher, as tracked by the system. Post-class review indicators were assessed by the number of times and duration participants watched the lecture videos and the number of sets of materials reviewed. Learning time was calculated by summing all the time participants spent in the online classroom, including attending lectures and reviewing. Training effectiveness was evaluated by the change in participants' scores before and after the training. Learning outcomes were determined by the final quiz scores. Excellent learners were defined as those whose score improvement was higher than that of 90% of other participants. Statistical Analysis The Shapiro-Wilk test was used to evaluate the normality of the distribution of continuous variables. Data adhering to a normal distribution were reported as mean ± standard deviation, while non-normally distributed data were expressed as median (interquartile range, IQR) or median (minimum, maximum). Categorical variables were presented as frequencies and percentages. The Wilcoxon signed-rank test, a nonparametric method, was used for non-normally distributed values. Differences in categorical variables were assessed using either the chi-square test or Fisher’s exact test, as appropriate. Spearman’s correlation coefficient and multivariate linear regression were applied to determine factors affecting final scores. Variables included in the multivariate regression were chosen based on Spearman’s correlation coefficients with P < 0.05. Collinearity testing was performed in the multivariate regression analysis, and variables inducing collinearity were excluded. Factors influencing excellent participants were expressed using ROC curves. All statistical analyses were conducted using IBM SPSS Statistics for Windows, Version 23 (IBM Corp., Armonk, NY, USA) or GraphPad Prism, Version 10.1.2 (GraphPad Software, San Diego, CA, USA). Statistical tests were two-sided, with significance set at P < 0.05. Results A total of 199 individuals voluntarily participated in the training, including 122 doctors and 77 medical students. Recruiting doctors took a total of 2 days, while recruiting students took a month. All registrants expressed that they had not mastered the skill of heart sound auscultation. The doctors were significantly older than the medical students and had considerably more years of experience studying heart sound auscultation. From initial registration to participation in training, a higher proportion of doctors participated compared to medical students (79% vs. 65%, P = 0.025). Overall, 73% of those registered participated in the training, but only 23% of the registrants attended all sessions (Fig. 1 ), with no significant difference in full participation between doctors and medical students. Regarding motivation for participation, doctors were more driven by intrinsic motivation to attend the training than medical students (79% vs. 19%, P = 0.000). Intrinsic motivation was significantly related to age (r = 0.394, P = 0.004) and total study time (r = 0.145, P = 0.041), but not to the number of random questions answered in classes (r = 0.153, P = 0.111). Chi-square test results showed that intrinsic motivation was associated with full participation (χ2 = 4.025, P = 0.045). A significant increase in scores from before to after the training suggests the effectiveness of the training (Table 1 ). Table 1 Characteristics of Participants in Online Heart Auscultation Training. Total Doctor Medical students P^ Enrolled Participant 199 122 77 0.00 Age(years old) 26(23,31) 29(26,35) 22(20,25) 0.000 Gender(Female, %) 136(68%) 91(75%) 49(63%) 0.258 Years of Learning Heart Sound Auscultation(Y) 5(3,8) 7(5,11) 2(1, 4) 0.000 Training Participants(N,%)* 146(73%) 96(79%) 50(65%) 0.025 Full participant** 46(23%) 30(25%) 16(21%) 0.606 Participation Motivation Intrinsic Motivation Extrinsic Motivation 104(52%) 95(48%) 89(73%) 33(27%) 15(19%) 62(81%) 0.000 Pre-training Score 40(20,50) 40(20,50) 30(20,50) 0.468 Post-training Score 70(50,83) $ 70(50,90) $ 50(50,75) $ 0.287 Individual Score Change 30(10,45) 35(10,50) 30(0,40) 0.662 *Defined as the number of people who attended at least one session of class or reviewed the material after class at least once; **Defined as participation in all four training sessions; ^Comparison between doctors and medical students groups, P < 0.05 indicates a significant difference;$: Comparison of scores before and after training shows a significant difference, P = 0.000 During the four classes, a total of 16 random questions were asked, and 11 sets of review materials were distributed online after each class. The system automatically recorded the number of times participants answered questions during class and accessed review materials afterward. A total of 49 participants (33%) watched the lecture videos after class; however, only 10 participants (7%) watched all four full sessions. One hundred eleven participants (76%) reviewed the lecture materials after class, but only 20 participants (14%) reviewed all the lecture materials. The duration of viewing the lecture videos after class varied greatly among participants. Age was significantly positively correlated with total study time (r = 0.366, P = 0.000), time spent reviewing recorded videos (r = 0.330, P = 0.000), and the number of review materials accessed (r = 0.355, P = 0.000) (Table 2 ). Table 2 Assessment Results for Classroom Attendance and Post-Class Review Total Doctor Medical students P^ Number of Live Class Participation 2(1,4) 2(1,4) 2.5(1,4) 0.408 Total Duration of Live Class Participation (minutes) 191(84, 384) 183(79, 375) 210(84, 404) 0.771 Number of Times Answering Random Questions in Class* 5(1,16) 5(1,14) 5(3,16) 0.234 Number of Course Materials reviewed After Class* 1(0,3) 1(0, 5) 0(0,1) 0.01 Duration of Watching Lecture Videos(minutes) 0(0,578) 0(0,578) 0(0,169) 0.01 Total study time(minutes) 202(86, 422) 192(86,424) 210(88,416) 0.888 Data is limited to training attendees only *Represents the expression method for maximum value, minimum value, and median. ^Comparison between doctors and medical students groups, P < 0.05 indicates a significant difference Analysis of Factors Affecting Training Scores We conducted a univariate correlation analysis to assess the relationship between various factors—such as learning motivation, overall participation, number of class attendances, duration of class participation, frequency of answering random questions in classes, number of post-class material reviews, number of times watching lecture videos, and total study time—and the final scores. The results showed significant correlations between the final scores and full participation (r = 0.351, P = 0.023), frequency of answering random questions in classes (r = 0.431, P = 0.004), and number of post-class material reviews (r = 0.345, P = 0.025). Factors such as age, whether the participants were doctors or students, years of studying heart auscultation, motivation for participating in the training, and time spent attending live classes did not significantly correlate with the final scores. Further multivariate linear regression analysis indicated that only full participation and frequency of answering random questions in classes were significantly associated with the final scores, while the number of post-class material reviews did not significantly affect the final grades. Table 3 Multivariate linear regression results for factors affecting the final scores in heart auscultation. Regression Variables B β 95%(CI) P value Constant Full participation Times of answering random questions in classes -21.789 41.547 4.794 0.602 0.695 -50.855-7.277 24.426–58.667 3.054–6.445 0·131 0·000 0·000 *Adjusted R 2 = 0.483 Factors Influencing Becoming an Excellent Learner Six participants increased their scores by more than 60 points after the training, placing them in the top 10% of all participants and defining them as excellent learners. ROC curve analysis suggests that total study time, actively answering questions in class, full participation, and the extent of post-class review are all significantly related to achieving excellent learner status (Fig. 2 ). Discussion This single-center, prospective, self-controlled study suggests that learner autonomy, such as full participation and active in-class interaction, is closely related to achieving good results. However, learners who exhibit such high levels of autonomy are rare; the study found that only 23% of participants completed the entire training, which was associated with the presence of intrinsic motivation. This implies that online course design should strategically enhance learner autonomy, and learners should also focus on developing their autonomy. The overall participation rate in voluntary, self-directed online courses is very low, suggesting that it is difficult for most people to complete these courses solely relying on learner autonomy. Specifically, the low completion rate may be related to the following factors. Before the training began, 53 participants (27%) who registered did not attend the training. This suggests that they may not have thoroughly assessed their needs, schedules, or interests, leading to their final decision not to participate. During the training, many learners found the course content challenging. The heart sound course involves the pathophysiology of the cardiovascular system and includes echocardiography images, which require a certain foundation in internal medicine, posing a difficulty for some learners. Some learners provided feedback indicating they found the material "a bit difficult," which contributed to their inability to complete the course. This indicates that defining the learners' knowledge background more clearly when preparing the course could help avoid high dropout rates. Maintaining course engagement is crucial for ensuring full participation, which will be discussed in the next chapter. Focusing on lectures during the course is a key factor in achieving good results in online learning. We used random questioning to verify that attendees were still listening, not to check if they could correctly answer the questions. In fact, among all participants, only one person answered all the teacher's questions. Despite this, statistics still confirmed that those who paid more attention during lectures were more likely to achieve better results. Maintaining classroom engagement requires the combined efforts of both teachers and students. Experiencing flow during learning is a hallmark of effective learning(Csikszentmihalyi 1990 ). However, computer-mediated communication exhaustion significantly hinders learners' concentration(de Oliveira Kubrusly Sobral et al. 2022 ). In this heart sound training course, although the instructor designed some interactive moments, the course was still primarily teacher-controlled with few opportunities for student discussions and questions. This is a key difference between face-to-face and online teaching: the lack of a shared physical space makes it challenging for instructors to motivate students. Therefore, it is recommended that instructors strategically engage learners, using nonverbal behaviors to enhance communication efficiency(Mottet 2000 ). Establishing opportunities to foster a sense of community in distance learning allows students to feel connected to their instructors, classmates, and the content itself(“Importance of Developing Community in Distance Education Courses | TechTrends” n.d.). As Knowles mentioned, most learners acquire knowledge within a structured school system, and only a small number of adults become fully autonomous learners(“a_The_ Modern_Practice_of_Adult_Education.pdf” n.d.). This means that teachers still need to support the learners' autonomous learning process. For learners, a positive attitude towards training can help prevent fatigue during the learning process, allowing them to engage more deeply(Oducado et al. 2022 ). Even though intrinsic motivation is not directly related to final grades, it plays a very positive role in self-directed learning behavior. In this study, intrinsic motivation was associated with quicker enrollment in training, full participation, and longer study times. Interestingly, however, we did not find a correlation between intrinsic motivation and learners actively answering teachers' questions in class. Most of those driven by intrinsic motivation to participate in training were doctors, who were older than medical students. We also found that age was associated with longer study times and active review. These results effectively characterize some traits of highly autonomous individuals participating in online learning: they are relatively older, tend to be silent in class, but spend more time on self-directed learning outside of class. This aligns with previous research findings, such as intrinsically motivated individuals tending to be lifelong learners(“Motivation and Lifelong Learning: Educational Psychologist: Vol 26, No 2” n.d.), students in the school system being trained to be dependent learners who need to undergo a process of reorientation to adult learning(“a_The_ Modern_Practice_of_Adult_Education.pdf” n.d.) and stable personality traits favor good results in online learning(Oducado et al. 2022 ). These results explain why doctors are more actively participating in online training. This implies that when designing online training, different content should be tailored to different motivational mechanisms. For example, courses for students lacking intrinsic motivation should focus on developing their self-directed learning abilities, while those for individuals with strong self-directed learning skills should include modules that help them delve deeper into the subject matter. Limitations of the study: The outcomes of education have a delayed effect, and deliberate practice can enhance learning outcomes(Morris 2020 ). However, our study could not assess the impact of learners' deliberate practice in their actual work on learning outcomes, which requires long-term tracking. Due to the very low number of students who engaged in post-class review, we only used the ROC curve to verify the role of post-class review among top-performing learners. This highlights an objective fact about this online training: few participants actively review after class. Consequently, our study cannot definitively determine the impact of post-class review on learning outcomes if a larger number of participants were to engage in it. For voluntary online courses, learner autonomy plays a crucial role in learning outcomes. However, relying solely on learner autonomy is unlikely to benefit the majority of learners. For learners, identifying their intrinsic needs, fully participating in the course, and actively interacting during class can lead to better results. Achieving excellence requires even more review. To encourage learner autonomy, course designers should more precisely recruit learners, incorporate more interactive elements in the class, and encourage learners to review after class. Declaration: We declare no competing interests. The study was approved by Peking Union Medical College Hospital with clinical trial number I-23PJ1679, and each participant signed an informed consent form. This project is supported by the Educational Reform Project of Peking Union Medical College, Chinese Academy of Medical Sciences, under the grant numbers 2023zlgl032 and 2021zlgc0110. Declarations We declare no competing interests. The study was approved by Peking Union Medical College Hospital with clinical trial number I-23PJ1679 , and each participant signed an informed consent form. Funding: This project is supported by the Educational Reform Project of Peking Union Medical College, Chinese Academy of Medical Sciences, under the grant numbers 2023zlgl032 and 2021zlgc0110. Authors contributions: Xue Lin and Yudong Fang conceived and designed the study. Ligang Fang and Wenling Zhu verified all the heart sounds and reviewed the lecture content. Yudong Fang recruited participants and conducted classroom data collection. Xue Lin analyzed all the data and drafted the manuscript. All authors had full access to all the data in the study. Xue Lin took responsibility for the integrity of the data and the accuracy of the data analysis. All authors critically revised the manuscript for important intellectual content and gave final approval for the version to be published. All authors agree to take responsibility for all aspects of the work, ensuring that any questions regarding the accuracy or integrity of any part of the work are appropriately investigated and resolved. 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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-4758934","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":334294692,"identity":"fbb544dc-b8d3-4201-aa14-cc2e24b61d1f","order_by":0,"name":"Yudong Fang","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yudong","middleName":"","lastName":"Fang","suffix":""},{"id":334294693,"identity":"87d2c66e-59bb-4cef-b42f-b4b22ea196de","order_by":1,"name":"Ligang Fang","email":"","orcid":"","institution":"Chinese Academy of Medical Science and Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Ligang","middleName":"","lastName":"Fang","suffix":""},{"id":334294694,"identity":"3a88a8c2-6058-4758-b776-abe89ffef1bb","order_by":2,"name":"Wenling Zhu","email":"","orcid":"","institution":"Chinese Academy of Medical Science and Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Wenling","middleName":"","lastName":"Zhu","suffix":""},{"id":334294695,"identity":"bdf75e59-2296-452c-8c1a-395ac75307a6","order_by":3,"name":"Xue Lin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuUlEQVRIiWNgGAWjYBACPmY4k4fxAVFa2JC0MBsQpwXB5GGTIE4LO+8xaZ6KO4lr288eq3hTxiDPL3aAkMP4ko15zjxL3HYmL+3mnHMMhjNnJxDSwmP4mLftcOK2Gzxmt3nbGBIMbhPWYnCY9x9ESzGxWoC2NEC0MBOrxdhwzrFnxtvO5BhLzjknQdgv/PxnzCTe1NyR3Xb8jOGHN2U28vzSBLRAwQEIRWzUoGghWscoGAWjYBSMIAAAg1w9SZJqd90AAAAASUVORK5CYII=","orcid":"","institution":"Chinese Academy of Medical Science and Peking Union Medical College","correspondingAuthor":true,"prefix":"","firstName":"Xue","middleName":"","lastName":"Lin","suffix":""}],"badges":[],"createdAt":"2024-07-17 22:59:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4758934/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4758934/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":63807887,"identity":"8ab98f7e-d468-448b-a66f-50307e36e0f2","added_by":"auto","created_at":"2024-09-02 13:45:33","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":72620,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram of the change in the number of doctors, medical students, and all participants from registration to full participation\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4758934/v1/05d020cb6f650479e96186a9.png"},{"id":63807888,"identity":"8db1750e-2471-4747-b5f7-18b83b4b215c","added_by":"auto","created_at":"2024-09-02 13:45:33","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":151225,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve analysis of factors related to excellent performance. All P \u0026lt; 0.05\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4758934/v1/556f62ba4ee43fc94846c457.png"},{"id":81426936,"identity":"63ff7ec5-7bb9-48fe-a5c9-fbe2ae7224dc","added_by":"auto","created_at":"2025-04-26 07:46:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":624145,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4758934/v1/b8219970-2078-4ccd-8283-ea4dd56b607d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The impact of learner autonomy on the performance in voluntary online cardiac auscultation courses","fulltext":[{"header":"Background","content":"\u003cp\u003eIn an era marked by rapid advancements in medical technology and information, online education has emerged as the primary mode of continuing education for medical practitioners, especially following the COVID-19 outbreak(Goldberg and Crocombe \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; O\u0026rsquo;Doherty et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Zhu et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). With the rapid growth of online learning, an increasing number of theories and studies are exploring the key factors that influence online learning outcomes, with learner autonomy receiving significant attention(Pei and Wu \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In 1997, Moore's Theory of Transactional Distance highlighted that dialogue, structure, and learner autonomy constitute the fundamental elements of online learning(Moore n.d.). By 2023, the AMEE guide emphasized the importance of autonomous learning in asynchronous learning(MacNeill et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Learner autonomy refers to the ability of learners to actively manage their own learning processes(\u0026ldquo;Learner autonomy\u0026rdquo; 2023). Most studies suggest that learner autonomy, through setting personal learning goals, controlling the learning process, and reflecting on their learning, can help learners achieve their learning objectives(\u0026ldquo;Learner Autonomy - an overview | ScienceDirect Topics\u0026rdquo; n.d.). However, there is currently limited research verifying the extent to which learner autonomy influences learning outcomes in online learning environments. Understanding the quantitative role of autonomy in learning outcomes can significantly inspire learners to study more effectively and assist teachers in designing content and methods for online courses.\u003c/p\u003e \u003cp\u003eFor medical practitioners, online education primarily takes two forms. One involves formal course training conducted by medical schools using online platforms with strict assessments. The other involves learners autonomously selecting courses that meet their needs, which can be either paid or free, without mandatory requirements on their results. Most busy clinical doctors tend to choose the latter option, which requires a higher degree of learner autonomy to complete the learning process. We believe that in non-mandatory, free, and on-demand online courses, recording learners' learning processes and exploring their impact on learning outcomes can help us understand the influence of learner autonomy on learning effectiveness.\u003c/p\u003e \u003cp\u003eCardiac auscultation is a crucial diagnostic tool in clinical practice, yet mastering it remains a significant challenge in medical education due to several reasons (de Giovanni et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; G et al. 2012; Vukanovic-Criley et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Learners often lack access to authentic heart sounds, as reproductions via audio software frequently fail to accurately mimic the original sounds, complicating their understanding in a clinical context. Explanations focused solely on the acoustic properties of heart sounds do not adequately convey the underlying pathophysiological processes, often leaving learners confused and disinterested. Additionally, cardiac murmurs are relatively rare in clinical settings, providing limited practice opportunities and making the skill more difficult to master.\u003c/p\u003e \u003cp\u003eThese challenges in teaching heart auscultation in traditional settings highlight its potential for online instruction. To address these issues, we developed a course based on real heart sounds, integrating various imaging techniques to clearly explain the pathophysiological basis of heart sounds. The course includes a sufficient number of practice heart sounds and is offered free of charge to online learners. During the learning process, learners' full participation, classroom engagement, and frequency of post-course review are considered to be related to learner autonomy.\u003c/p\u003e "},{"header":"Method","content":"\u003cp\u003eLearner Recruitment\u003c/p\u003e \u003cp\u003eThis prospective, self-controlled, single-center study recruited doctors and medical students interested in free heart auscultation training through WeChat. The study was approved by Peking Union Medical College Hospital with clinical trial number I-23PJ1679, and each participant signed an informed consent form. Participants were informed that their learning process would be recorded during the classes, but specific personal information would not be disclosed.\u003c/p\u003e \u003cp\u003eTeaching Process\u003c/p\u003e \u003cp\u003eTeaching Contents\u003c/p\u003e \u003cp\u003eThe heart sounds used were actual recordings from patients, covering the majority of key heart sounds in internal medicine and diagnostics. These sounds were accessible through in-ear headphones, identified using sound recognition software, and were verified by two experienced cardiology professors to ensure they were not distorted. The heart sound tutorial employed various imaging methods, including animations and echocardiographic images, combined with case studies, to clearly explain the mechanisms and clinical significance of each heart sound. This comprehensive tutorial included 80 heart sounds, over 30 cases, 5 animations, and 100 echocardiographic images.\u003c/p\u003e \u003cp\u003eTeaching Setting\u003c/p\u003e \u003cp\u003eAll teaching sessions were conducted using an online teaching software (Plaso, PLASO Network Technologies Co., LTD, Nanjing, China). The sessions were held once a week, each lasting two hours, for a total of four sessions. The teacher delivered the lectures via live streaming, and the online classroom facilitated interaction between teachers and students. At the beginning and end of the training, the teacher administered 10 heart sound tests online to assess participants. Each correct answer was scored as 10 points, with each test having a maximum score of 100 points. The heart sounds in the two tests were different but collectively covered all key heart sounds.\u003c/p\u003e \u003cp\u003eDuring the lectures, the teacher sporadically asked questions to all students using online tools, and students responded via the answer panel on their interface. After class, lecture videos and review materials were distributed to each student through the system, and all materials were available for review in the system for one month after the course ended.\u003c/p\u003e \u003cp\u003eDefinition of Key Variables in the Learning Process\u003c/p\u003e \u003cp\u003eLearning motivation was categorized into intrinsic and extrinsic. On the WeChat recruitment page, doctors chose between: \"Are you participating in this training because you have never mastered it before, always regretted it, and therefore want to understand it thoroughly?\" Students chose between: \"interested in learning about heart sounds (intrinsic motivation)\" or \"because you need it for work or exams (extrinsic motivation)?\"\u003c/p\u003e \u003cp\u003eParticipation in the training was defined as attending at least one lecture or reviewing the post-class material at least once. Full participation was defined as attending all four lectures.\u003c/p\u003e \u003cp\u003eThe main assessment during the lecture process was the duration of time participants spent attending, which was recorded by the system. Participants' classroom engagement was determined by the number of times they answered random questions posed by the teacher, as tracked by the system.\u003c/p\u003e \u003cp\u003ePost-class review indicators were assessed by the number of times and duration participants watched the lecture videos and the number of sets of materials reviewed.\u003c/p\u003e \u003cp\u003eLearning time was calculated by summing all the time participants spent in the online classroom, including attending lectures and reviewing.\u003c/p\u003e \u003cp\u003eTraining effectiveness was evaluated by the change in participants' scores before and after the training. Learning outcomes were determined by the final quiz scores. Excellent learners were defined as those whose score improvement was higher than that of 90% of other participants.\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eThe Shapiro-Wilk test was used to evaluate the normality of the distribution of continuous variables. Data adhering to a normal distribution were reported as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, while non-normally distributed data were expressed as median (interquartile range, IQR) or median (minimum, maximum). Categorical variables were presented as frequencies and percentages. The Wilcoxon signed-rank test, a nonparametric method, was used for non-normally distributed values. Differences in categorical variables were assessed using either the chi-square test or Fisher\u0026rsquo;s exact test, as appropriate.\u003c/p\u003e \u003cp\u003eSpearman\u0026rsquo;s correlation coefficient and multivariate linear regression were applied to determine factors affecting final scores. Variables included in the multivariate regression were chosen based on Spearman\u0026rsquo;s correlation coefficients with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Collinearity testing was performed in the multivariate regression analysis, and variables inducing collinearity were excluded. Factors influencing excellent participants were expressed using ROC curves.\u003c/p\u003e \u003cp\u003eAll statistical analyses were conducted using IBM SPSS Statistics for Windows, Version 23 (IBM Corp., Armonk, NY, USA) or GraphPad Prism, Version 10.1.2 (GraphPad Software, San Diego, CA, USA). Statistical tests were two-sided, with significance set at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 199 individuals voluntarily participated in the training, including 122 doctors and 77 medical students. Recruiting doctors took a total of 2 days, while recruiting students took a month. All registrants expressed that they had not mastered the skill of heart sound auscultation. The doctors were significantly older than the medical students and had considerably more years of experience studying heart sound auscultation. From initial registration to participation in training, a higher proportion of doctors participated compared to medical students (79% vs. 65%, P\u0026thinsp;=\u0026thinsp;0.025). Overall, 73% of those registered participated in the training, but only 23% of the registrants attended all sessions (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), with no significant difference in full participation between doctors and medical students.\u003c/p\u003e \u003cp\u003eRegarding motivation for participation, doctors were more driven by intrinsic motivation to attend the training than medical students (79% vs. 19%, P\u0026thinsp;=\u0026thinsp;0.000). Intrinsic motivation was significantly related to age (r\u0026thinsp;=\u0026thinsp;0.394, P\u0026thinsp;=\u0026thinsp;0.004) and total study time (r\u0026thinsp;=\u0026thinsp;0.145, P\u0026thinsp;=\u0026thinsp;0.041), but not to the number of random questions answered in classes (r\u0026thinsp;=\u0026thinsp;0.153, P\u0026thinsp;=\u0026thinsp;0.111). Chi-square test results showed that intrinsic motivation was associated with full participation (χ2\u0026thinsp;=\u0026thinsp;4.025, P\u0026thinsp;=\u0026thinsp;0.045). A significant increase in scores from before to after the training suggests the effectiveness of the training (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \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\u003eCharacteristics of Participants in Online Heart Auscultation Training.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDoctor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMedical students\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP^\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnrolled Participant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(years old)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26(23,31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29(26,35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22(20,25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender(Female, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e136(68%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91(75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49(63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.258\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYears of Learning Heart Sound Auscultation(Y)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5(3,8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(5,11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2(1, 4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTraining Participants(N,%)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e146(73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96(79%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50(65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFull participant**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46(23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30(25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16(21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.606\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParticipation Motivation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntrinsic Motivation\u003c/p\u003e \u003cp\u003eExtrinsic Motivation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104(52%)\u003c/p\u003e \u003cp\u003e95(48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89(73%)\u003c/p\u003e \u003cp\u003e33(27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15(19%)\u003c/p\u003e \u003cp\u003e62(81%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-training Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40(20,50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40(20,50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30(20,50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.468\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-training Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70(50,83)\u003csup\u003e$\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70(50,90)\u003csup\u003e$\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50(50,75)\u003csup\u003e$\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.287\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndividual Score Change\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30(10,45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35(10,50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30(0,40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.662\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e*Defined as the number of people who attended at least one session of class or reviewed the material after class at least once; **Defined as participation in all four training sessions; ^Comparison between doctors and medical students groups, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicates a significant difference;$: Comparison of scores before and after training shows a significant difference, P\u0026thinsp;=\u0026thinsp;0.000\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eDuring the four classes, a total of 16 random questions were asked, and 11 sets of review materials were distributed online after each class. The system automatically recorded the number of times participants answered questions during class and accessed review materials afterward. A total of 49 participants (33%) watched the lecture videos after class; however, only 10 participants (7%) watched all four full sessions. One hundred eleven participants (76%) reviewed the lecture materials after class, but only 20 participants (14%) reviewed all the lecture materials.\u003c/p\u003e \u003cp\u003eThe duration of viewing the lecture videos after class varied greatly among participants. Age was significantly positively correlated with total study time (r\u0026thinsp;=\u0026thinsp;0.366, P\u0026thinsp;=\u0026thinsp;0.000), time spent reviewing recorded videos (r\u0026thinsp;=\u0026thinsp;0.330, P\u0026thinsp;=\u0026thinsp;0.000), and the number of review materials accessed (r\u0026thinsp;=\u0026thinsp;0.355, P\u0026thinsp;=\u0026thinsp;0.000) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssessment Results for Classroom Attendance and Post-Class Review\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDoctor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMedical students\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP^\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of Live Class Participation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2(1,4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(1,4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.5(1,4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.408\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Duration of Live Class Participation (minutes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e191(84, 384)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e183(79, 375)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e210(84, 404)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.771\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of Times Answering Random Questions in Class*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5(1,16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(1,14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5(3,16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.234\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of Course Materials reviewed After Class*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(0,3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(0, 5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0(0,1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration of Watching Lecture Videos(minutes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0(0,578)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(0,578)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0(0,169)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal study time(minutes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e202(86, 422)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e192(86,424)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e210(88,416)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.888\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eData is limited to training attendees only\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*Represents the expression method for maximum value, minimum value, and median.\u003c/p\u003e \u003cp\u003e^Comparison between doctors and medical students groups, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicates a significant difference\u003c/p\u003e \u003cp\u003eAnalysis of Factors Affecting Training Scores\u003c/p\u003e \u003cp\u003eWe conducted a univariate correlation analysis to assess the relationship between various factors\u0026mdash;such as learning motivation, overall participation, number of class attendances, duration of class participation, frequency of answering random questions in classes, number of post-class material reviews, number of times watching lecture videos, and total study time\u0026mdash;and the final scores. The results showed significant correlations between the final scores and full participation (r\u0026thinsp;=\u0026thinsp;0.351, P\u0026thinsp;=\u0026thinsp;0.023), frequency of answering random questions in classes (r\u0026thinsp;=\u0026thinsp;0.431, P\u0026thinsp;=\u0026thinsp;0.004), and number of post-class material reviews (r\u0026thinsp;=\u0026thinsp;0.345, P\u0026thinsp;=\u0026thinsp;0.025).\u003c/p\u003e \u003cp\u003eFactors such as age, whether the participants were doctors or students, years of studying heart auscultation, motivation for participating in the training, and time spent attending live classes did not significantly correlate with the final scores. Further multivariate linear regression analysis indicated that only full participation and frequency of answering random questions in classes were significantly associated with the final scores, while the number of post-class material reviews did not significantly affect the final grades.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate linear regression results for factors affecting the final scores in heart auscultation.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegression\u003c/p\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95%(CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\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\u003eConstant\u003c/p\u003e \u003cp\u003eFull participation\u003c/p\u003e \u003cp\u003eTimes of answering random questions in classes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-21.789\u003c/p\u003e \u003cp\u003e41.547\u003c/p\u003e \u003cp\u003e4.794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.602\u003c/p\u003e \u003cp\u003e0.695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-50.855-7.277\u003c/p\u003e \u003cp\u003e24.426\u0026ndash;58.667\u003c/p\u003e \u003cp\u003e3.054\u0026ndash;6.445\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u0026middot;131\u003c/p\u003e \u003cp\u003e0\u0026middot;000\u003c/p\u003e \u003cp\u003e0\u0026middot;000\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\u003e*Adjusted R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.483\u003c/p\u003e \u003cp\u003eFactors Influencing Becoming an Excellent Learner\u003c/p\u003e \u003cp\u003eSix participants increased their scores by more than 60 points after the training, placing them in the top 10% of all participants and defining them as excellent learners. ROC curve analysis suggests that total study time, actively answering questions in class, full participation, and the extent of post-class review are all significantly related to achieving excellent learner status (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis single-center, prospective, self-controlled study suggests that learner autonomy, such as full participation and active in-class interaction, is closely related to achieving good results. However, learners who exhibit such high levels of autonomy are rare; the study found that only 23% of participants completed the entire training, which was associated with the presence of intrinsic motivation. This implies that online course design should strategically enhance learner autonomy, and learners should also focus on developing their autonomy.\u003c/p\u003e \u003cp\u003eThe overall participation rate in voluntary, self-directed online courses is very low, suggesting that it is difficult for most people to complete these courses solely relying on learner autonomy. Specifically, the low completion rate may be related to the following factors.\u003c/p\u003e \u003cp\u003eBefore the training began, 53 participants (27%) who registered did not attend the training. This suggests that they may not have thoroughly assessed their needs, schedules, or interests, leading to their final decision not to participate. During the training, many learners found the course content challenging. The heart sound course involves the pathophysiology of the cardiovascular system and includes echocardiography images, which require a certain foundation in internal medicine, posing a difficulty for some learners. Some learners provided feedback indicating they found the material \"a bit difficult,\" which contributed to their inability to complete the course. This indicates that defining the learners' knowledge background more clearly when preparing the course could help avoid high dropout rates. Maintaining course engagement is crucial for ensuring full participation, which will be discussed in the next chapter.\u003c/p\u003e \u003cp\u003eFocusing on lectures during the course is a key factor in achieving good results in online learning. We used random questioning to verify that attendees were still listening, not to check if they could correctly answer the questions. In fact, among all participants, only one person answered all the teacher's questions. Despite this, statistics still confirmed that those who paid more attention during lectures were more likely to achieve better results. Maintaining classroom engagement requires the combined efforts of both teachers and students.\u003c/p\u003e \u003cp\u003eExperiencing flow during learning is a hallmark of effective learning(Csikszentmihalyi \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1990\u003c/span\u003e). However, computer-mediated communication exhaustion significantly hinders learners' concentration(de Oliveira Kubrusly Sobral et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In this heart sound training course, although the instructor designed some interactive moments, the course was still primarily teacher-controlled with few opportunities for student discussions and questions. This is a key difference between face-to-face and online teaching: the lack of a shared physical space makes it challenging for instructors to motivate students. Therefore, it is recommended that instructors strategically engage learners, using nonverbal behaviors to enhance communication efficiency(Mottet \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Establishing opportunities to foster a sense of community in distance learning allows students to feel connected to their instructors, classmates, and the content itself(\u0026ldquo;Importance of Developing Community in Distance Education Courses | TechTrends\u0026rdquo; n.d.).\u003c/p\u003e \u003cp\u003eAs Knowles mentioned, most learners acquire knowledge within a structured school system, and only a small number of adults become fully autonomous learners(\u0026ldquo;a_The_ Modern_Practice_of_Adult_Education.pdf\u0026rdquo; n.d.). This means that teachers still need to support the learners' autonomous learning process. For learners, a positive attitude towards training can help prevent fatigue during the learning process, allowing them to engage more deeply(Oducado et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEven though intrinsic motivation is not directly related to final grades, it plays a very positive role in self-directed learning behavior. In this study, intrinsic motivation was associated with quicker enrollment in training, full participation, and longer study times. Interestingly, however, we did not find a correlation between intrinsic motivation and learners actively answering teachers' questions in class. Most of those driven by intrinsic motivation to participate in training were doctors, who were older than medical students. We also found that age was associated with longer study times and active review.\u003c/p\u003e \u003cp\u003eThese results effectively characterize some traits of highly autonomous individuals participating in online learning: they are relatively older, tend to be silent in class, but spend more time on self-directed learning outside of class. This aligns with previous research findings, such as intrinsically motivated individuals tending to be lifelong learners(\u0026ldquo;Motivation and Lifelong Learning: Educational Psychologist: Vol 26, No 2\u0026rdquo; n.d.), students in the school system being trained to be dependent learners who need to undergo a process of reorientation to adult learning(\u0026ldquo;a_The_ Modern_Practice_of_Adult_Education.pdf\u0026rdquo; n.d.) and stable personality traits favor good results in online learning(Oducado et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These results explain why doctors are more actively participating in online training. This implies that when designing online training, different content should be tailored to different motivational mechanisms. For example, courses for students lacking intrinsic motivation should focus on developing their self-directed learning abilities, while those for individuals with strong self-directed learning skills should include modules that help them delve deeper into the subject matter.\u003c/p\u003e \u003cp\u003eLimitations of the study:\u003c/p\u003e \u003cp\u003eThe outcomes of education have a delayed effect, and deliberate practice can enhance learning outcomes(Morris \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, our study could not assess the impact of learners' deliberate practice in their actual work on learning outcomes, which requires long-term tracking. Due to the very low number of students who engaged in post-class review, we only used the ROC curve to verify the role of post-class review among top-performing learners. This highlights an objective fact about this online training: few participants actively review after class. Consequently, our study cannot definitively determine the impact of post-class review on learning outcomes if a larger number of participants were to engage in it.\u003c/p\u003e \u003cp\u003eFor voluntary online courses, learner autonomy plays a crucial role in learning outcomes. However, relying solely on learner autonomy is unlikely to benefit the majority of learners. For learners, identifying their intrinsic needs, fully participating in the course, and actively interacting during class can lead to better results. Achieving excellence requires even more review. To encourage learner autonomy, course designers should more precisely recruit learners, incorporate more interactive elements in the class, and encourage learners to review after class.\u003c/p\u003e \u003cp\u003eDeclaration:\u003c/p\u003e \u003cp\u003eWe declare no competing interests.\u003c/p\u003e \u003cp\u003eThe study was approved by Peking Union Medical College Hospital with clinical trial number I-23PJ1679, and each participant signed an informed consent form.\u003c/p\u003e \u003cp\u003eThis project is supported by the Educational Reform Project of Peking Union Medical College, Chinese Academy of Medical Sciences, under the grant numbers 2023zlgl032 and 2021zlgc0110.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eWe declare no competing interests.\u003c/p\u003e\n\u003cp\u003eThe study was approved by Peking Union Medical College Hospital with clinical trial number I-23PJ1679 , and each participant signed an informed consent form.\u003c/p\u003e\n\u003cp\u003eFunding:\u003c/p\u003e\n\u003cp\u003eThis project is supported by the Educational Reform Project of Peking Union Medical College, Chinese Academy of Medical Sciences, under the grant numbers 2023zlgl032 and 2021zlgc0110.\u003c/p\u003e\n\u003cp\u003eAuthors contributions: \u0026nbsp;Xue Lin and Yudong Fang conceived and designed the study. Ligang Fang and Wenling Zhu verified all the heart sounds and reviewed the lecture content. Yudong Fang recruited participants and conducted classroom data collection. Xue Lin analyzed all the data and drafted the manuscript. All authors had full access to all the data in the study. Xue Lin took responsibility for the integrity of the data and the accuracy of the data analysis. All authors critically revised the manuscript for important intellectual content and gave final approval for the version to be published. All authors agree to take responsibility for all aspects of the work, ensuring that any questions regarding the accuracy or integrity of any part of the work are appropriately investigated and resolved.\u003c/p\u003e\n\u003cp\u003eAcknowledgements：We appreciate all the participants in this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ea_The_ Modern_Practice_of_Adult_Education.pdf. 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The irreplaceable role of medical massive open online courses in China during the COVID-19 pandemic. BMC Med Educ. 2023;23(1):323. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12909-023-04315-z\u003c/span\u003e\u003cspan address=\"10.1186/s12909-023-04315-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\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":"Learner autonomy, Online learning, Heart sound auscultation intrinsic motivation, active engagement, Academic performance","lastPublishedDoi":"10.21203/rs.3.rs-4758934/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4758934/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study explores the impact of learner autonomy on academic performance in a free, non-mandatory online heart sound auscultation course, emphasizing the enhancement of online learning outcomes through learner autonomy. Medical students and doctors were recruited via WeChat groups and participated in four 2-hour live sessions over four weeks, delivered through Plaso teaching software. Participants engaged with real heart sounds using in-ear headphones and were evaluated through random questions during lectures and a comparison of scores on ten heart sound auscultation questions before and after training. Results from 122 doctors and 77 medical students showed that 146 (73%) attended and 46 (23%) completed all sessions, with heart auscultation scores improving significantly from 40 to 70 (p\u0026thinsp;=\u0026thinsp;0.000). Full participation and active engagement were key predictors of successful exam performance, while intrinsic motivation correlated with complete course attendance (P\u0026thinsp;=\u0026thinsp;0.045). Moreover, ROC curve analysis revealed that outstanding learners spent more time reviewing post-class materials. The study concludes that while learner autonomy is crucial for success in voluntary online courses, sole reliance on autonomy may not suffice. Effective learning requires identifying intrinsic needs, full participation, active interaction, and additional review. Course designers are advised to recruit learners precisely, incorporate interactive elements, and promote post-class review to enhance learner autonomy.\u003c/p\u003e","manuscriptTitle":"The impact of learner autonomy on the performance in voluntary online cardiac auscultation courses","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-02 13:45:27","doi":"10.21203/rs.3.rs-4758934/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":"57c0063d-7d1d-4719-aacf-d4372431e06c","owner":[],"postedDate":"September 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-04-26T07:38:36+00:00","versionOfRecord":[],"versionCreatedAt":"2024-09-02 13:45:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4758934","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4758934","identity":"rs-4758934","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}