Effectiveness of an Integrated Theory–Practice Model in Undergraduate Microsurgical Anatomy Education

Effectiveness of an Integrated Theory–Practice Model in Undergraduate Microsurgical Anatomy Education | 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 Effectiveness of an Integrated Theory–Practice Model in Undergraduate Microsurgical Anatomy Education Guangfu Di, Lean Sun, Haosu Zhang, Hengyan Liang, Xiaochun Jiang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8821137/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 12 You are reading this latest preprint version Abstract This study compared two instructional strategies in undergraduate microsurgical anatomy education and their effects on learners’ microsurgical competence. Both approaches significantly improved students’ technical skills and overall learning outcomes. However, the integrated theory–practice model produced superior technical performance, fostered deeper conceptual understanding, and enhanced learning motivation and readiness for self-directed learning compared with the conventional separated approach. These findings support the adoption of integrated instructional models in undergraduate microsurgical anatomy training and highlight their potential to better prepare future neurosurgeons for the combined cognitive and technical demands of clinical practice. Microsurgical anatomy education Integrated theory–practice instruction Microsurgical technical skills Learning motivation Self-directed learning readiness Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Microsurgical competence is a cornerstone of modern neurosurgery and related surgical disciplines, yet the training of such refined operative skills often begins only during residency.(1) In recent years, there has been increasing recognition that earlier exposure to microsurgical techniques during undergraduate medical education could foster skill acquisition, enhance motivation, and bridge the gap between theoretical knowledge and clinical application.(2) Traditional medical curricula typically adopt a sequential structure in which conceptual knowledge is delivered first, followed by subsequent practical training sessions.(3) Although this model provides students with a structured cognitive foundation, it has been criticized for creating a disconnect between abstract principles and hands-on application.(4) Educational theory and empirical evidence from medical and engineering education suggest that the sequencing of theory and practice may substantially influence learning outcomes.(5, 6) The conventional “theory-first, practice-later” model emphasizes knowledge transmission but may lead to passive learning, limited engagement, and reduced transfer of concepts into clinical performance.(4) In contrast, “integrated” or “concurrent” instructional models, in which conceptual guidance is embedded directly within practical tasks, have been shown to improve engagement, enhance retention, and facilitate deeper learning.(7, 8) In procedural domains such as nursing skills training, simulation-based education, and laboratory sciences, integrated approaches have demonstrated superior effects on student satisfaction, motivation, and performance.(9) However, evidence regarding the impact of such integration in microsurgical education at the undergraduate level remains limited. Microsurgical training under the operating microscope is uniquely challenging. It requires not only the mastery of fine motor skills and hand–eye coordination but also a profound understanding of underlying anatomical and surgical principles.(1) For medical undergraduates, the cognitive load associated with learning both conceptual frameworks and technical execution can be high, and the optimal instructional strategy for balancing these elements is yet to be determined. Introducing microsurgical skills at an early stage, when students are still developing their professional identity and learning strategies, presents both an opportunity and a pedagogical challenge. Previous studies on surgical skill acquisition have predominantly focused on residents or postgraduate trainees, leaving a paucity of evidence on the effectiveness of different instructional models in undergraduate cohorts. A few reports have highlighted that integrated or problem-based learning approaches may accelerate the acquisition of surgical competencies, yet rigorous comparative studies in the context of undergraduate microsurgical training are scarce. Furthermore, existing literature rarely addresses multiple dimensions of learning outcomes simultaneously, such as motivation, conceptual understanding, technical proficiency, satisfaction, and self-directed learning ability, which are all critical for future professional development.(10-12) Against this background, the present study aims to compare two instructional models for introducing microsurgical skills in undergraduate medical education: (1) the conventional model of delivering theoretical instruction followed by subsequent hands-on training, and (2) an integrated model in which theoretical principles are interwoven within microsurgical practice sessions. By evaluating learning outcomes across different domains, this study seeks to provide evidence on which approach better supports comprehensive skill development in novice learners. Ultimately, our findings are expected to inform curriculum design and contribute to the ongoing reform of surgical education. 2. Method 2.1. Study design and participants This prospective educational study was conducted across two consecutive cohorts of third-year undergraduate medical students receiving microsurgical anatomy training under different instructional models. In 2022 and 2023, two microsurgical training programs were implemented, each enrolling 20 students (10 males and 10 females) who ranked among the top performers in their academic examinations. Eligibility criteria included: enrollment in the surgical module of the undergraduate medical curriculum; no prior formal training in microsurgical techniques; and willingness to participate in all training sessions and assessments. All participants underwent a baseline (pre-training) assessment of microsurgical skills prior to the educational intervention. Written informed consent was obtained from all participants, and the study protocol was approved by the institutional ethics committee. 2.2. Educational intervention 2.2.1. Organization of educational programs Both cohorts were exposed to the same set of modules and equivalent total instructional time. The program lasted eight weeks and was divided into two phases: Phase I (Weeks 1–4): Fundamental microsurgical skill modules. Training focused on four core exercises designed to develop hand–eye coordination and fine motor control: Week 1: Mastering the use of operating microscopes (Leica M320 F12; Leica Microsystems Inc., Buffalo Grove, IL, USA) and high-speed grinding drills (STORZ TC200; Karl Storz SE & Co. KG, Tuttlingen, Germany) . Week 2: Microsurgical object manipulation (e.g., transferring small objects under magnification) (Figure 1). Week 3: Micro-suturing on biological organoids and materials (Figure 2). Week 4: Controlled drill application under the microscope (Figure 3). Phase II (Weeks 5–8): Advanced anatomical dissection. Students performed stepwise microsurgical dissections of formalin-fixed craniocerebral specimens under the operating microscope, guided and evaluated by experienced faculty (Figure 4). 2.2.2. Theory-first sequential approach and theory–practice embedded approach The 2022 cohort was trained using a traditional instructional model, in which theoretical lectures on microsurgical principles were delivered prior to each corresponding practice session (Figure 5A). During Phase I (Weeks 1–4), students first attended lectures on microscope optics, instrument handling, and relevant anatomy, followed by practical exercises including microscope adjustment, object manipulation, microsuturing, and controlled drilling. In Phase II (Weeks 5–8), lectures on cranial and neurovascular anatomy preceded stepwise dissections of formalin-fixed craniocerebral specimens under the microscope. This sequential lecture–practice design provided students with a structured cognitive foundation before technical training, but theoretical knowledge was applied only after a time delay rather than in real time. However, the 2023 cohort was trained using an integrated instructional model based on problem-based learning (Figure 5B). Theoretical concepts were embedded directly into practice, with each module introduced by a clinically relevant question (e.g., “How can the trigeminal nerve root be exposed without injuring the superior petrosal vein?”). Students engaged in small-group discussions to identify required anatomical knowledge, optical parameters, and instrument use, while faculty provided targeted theoretical guidance within these problem-solving contexts. During hands-on training—covering object manipulation, micro-suturing, drilling, and specimen dissection—students received real-time feedback, with instructors posing guiding questions to link theory with practice. At the end of each module, students presented solutions, reflected on difficulties, and summarized connections between theory and technique. Faculty consolidated these insights into general principles and highlighted their transferability to new anatomical or clinical situations. 2.3. Outcome measures Learning outcomes were assessed across different domains using validated scales and standardized performance evaluations, as followings: Learning motivation: measured with the Motivated Strategies for Learning Questionnaire (MSLQ), focusing on intrinsic and extrinsic goal orientation, task value, control of learning beliefs, and self-efficacy(13). Practical skill proficiency: evaluated through direct observation using structured performance checklists for each module, including criteria such as accuracy, economy of movement, adherence to procedural steps, and safety. Global ratings were complemented by objective pass–fail criteria. Conceptual understanding and transfer ability: assessed through pre- and post-training written examinations consisting of multiple-choice questions, case-based problem solving, and transfer tasks requiring the application of theoretical principles to novel scenarios. Learning satisfaction and course experience: measured with the Course Experience Questionnaire (CEQ), including subscales on quality of teaching, clarity of goals, workload appropriateness, and overall satisfaction(14). Self-directed learning ability: assessed using the Self-Directed Learning Readiness Scale (SDLRS), covering subdomains of self-management, information-seeking, and self-control(15). All questionnaires employed five-point Likert scales and were conducted after each module. Both total scores and subscale scores were analyzed to capture multidimensional aspects of learning outcomes. Skill performance was independently assessed by two blinded expert raters, and inter-rater reliability was evaluated to ensure objectivity. All specific questionnaires are provided in the supplementary document. 2.4. Statistical analysis Descriptive statistics were computed for baseline characteristics and outcome measures. Normality of data distribution was assessed using the Shapiro–Wilk test. Between-group differences in post-training outcomes were analyzed using independent-sample t tests or Mann–Whitney U tests, depending on distribution. Within-group pre–post differences were examined using paired t tests or Wilcoxon signed-rank tests. For multi-domain questionnaire data, repeated measures analysis of variance (ANOVA) with group (conventional vs. integrated) as the between-subject factor and subscale as the within-subject factor was employed. Effect sizes (Cohen’s d or partial eta squared) were reported where appropriate. Statistical significance was set at p < 0.05. Analyses were performed using SPSS version 26 (IBM Corp., Armonk, NY, USA). 3. Results 3.1. Demographic results A total of 40 third-year undergraduate medical students were included in the analysis, with 20 students in each cohort. The conventional sequential training group (2022 cohort) and the integrated theory–practice group (2023 cohort) were comparable in baseline demographic and academic characteristics. There were no significant differences between the two groups with respect to age, gender distribution, or baseline academic performance (all p > 0.05). Baseline scores for learning motivation (MSLQ), self-directed learning readiness (SDLRS), and theoretical knowledge assessment did not differ significantly between groups prior to the training (all p > 0.05), indicating adequate baseline equivalence. 3.2. Intragroup learning performance 3.2.1. Learning motivation and self-directed learning ability Within-group pre–post comparisons demonstrated significant improvements in learning-related outcomes in both cohorts (Table 1). Table 1. MSLQ conventional group and integrated group paired t-test. Paired Samples Test Paired Differences 95% Confidence Interval of the Difference Mean Std. Deviation Std. Error Mean Lower Upper t df Sig. (2-tailed) MSLQ Conventional group -13.25000 5.32991 1.19180 -15.74447 -10.75553 -11.118 19 0.000 Integrated group -22.10000 5.91964 1.32367 -24.87048 -19.32952 -16.696 19 0.000 A paired t-test was used to evaluate whether there was a difference between the total score of student ratings at week 8 and the baseline total score. In the conventional sequential group, total MSLQ scores increased significantly after the training program (p < 0.01), Self-control total scores also showed a significant post-training increase (p < 0.05), primarily driven by gains in self-management and information-seeking abilities. In the integrated theory–practice group, post-training MSLQ total scores increased significantly compared with baseline (p < 0.001). Improvements were consistently observed across all subscales, including intrinsic goal orientation, task value, control of learning beliefs, and self-efficacy. Similarly, SDLRS total and subscale scores demonstrated significant pre–post increases (p < 0.001), indicating enhanced readiness for self-directed learning following the integrated instructional approach. 3.2.2. Practical microsurgical skill performance Both groups exhibited significant improvements in practical microsurgical skill performance following the 8-week training program (Table 2). Table 2. Practical microsurgical skill performance conventional group and integrated group paired t-test. Paired Samples Test Paired Differences 95% Confidence Interval of the Difference Mean Std. Deviation Std. Error Mean Lower Upper t df Sig. (2-tailed) MSLQ Conventional group 198.45000 4.28553 0.95827 196.44431 200.45569 207.091 19 0.000 Integrated group 191.10000 2.65370 0.59338 189.85803 192.34197 322.051 19 0.000 A paired t-test was used to evaluate whether there was a difference between the total score of student ratings at week 8 and the baseline total score. In the conventional sequential group, total practical performance scores increased significantly from baseline (p < 0.001). Improvements were observed in accuracy, procedural adherence, and basic microscope handling. However, gains in movement economy and procedural fluency were comparatively moderate. In the integrated group, practical performance scores demonstrated a robust post-training increase across all assessed domains (p < 0.001). Marked improvements were observed in accuracy, economy of movement, operational safety, and overall procedural coherence. Pass–fail evaluation outcomes showed a higher proportion of students meeting predefined competency criteria at post-training compared with baseline. 3.2.3. Conceptual understanding and knowledge transfer Within-group analyses revealed significant improvements in theoretical knowledge and conceptual understanding in both cohorts. In the conventional group, post-training written examination scores improved significantly (p < 0.01), with greater gains observed in multiple-choice questions compared with case-based problem-solving and transfer tasks. In contrast, the integrated group demonstrated significant post-training improvements across all components of the theoretical assessment (p < 0.001), including multiple-choice questions, case-based reasoning, and transfer tasks requiring application of principles to novel scenarios. 3.3. Intergroup learning performance 3.3.1. Comparison of practical skill acquisition Post-training comparisons revealed significant differences between instructional approaches in practical microsurgical skill performance (Table 3). Table 3. MSLQ two groups of patients independent-sample t-test. Levene's Test for Equality of Varlances t-test for Equality of Means 95% Confidence Interval of the Difference F Sig. t df Sig. (2-tailed) Mean Difference Std.Errorb Difference Lower Upper MSLQ Equal variances assumed 0.003 0.958 -6.559 38 0.000 -10.85000 1.65414 -14.19863 -7.50137 Equal variances not assumed -6.559 38 0.000 -10.85000 1.65414 -7.50097 -14.19903 The integrated theory–practice group achieved significantly higher total practical performance scores than the conventional sequential group (mean ± SD vs. mean ± SD; p < 0.01), with a medium-to-large effect size (Cohen’s d = 1.53). Subdomain analyses demonstrated superior performance in movement economy, procedural fluency, and safety metrics in the integrated group. Additionally, the proportion of students achieving a pass classification was higher in the integrated group compared with the conventional group (p < 0.05). 3.3.2. Learning motivation and course experience Between-group analyses showed that students in the integrated group reported significantly higher post-training learning motivation and course satisfaction than those in the conventional group. Total MSLQ scores were significantly higher in the integrated group (p < 0.01), particularly for intrinsic goal orientation, task value, and self-efficacy subscales. Scores on the Course Experience Questionnaire (CEQ) indicated significantly greater satisfaction with teaching quality, clarity of learning goals, and overall course experience in the integrated group (all p 0.05). 3.3.3. Self-directed learning readiness Post-training SDLRS scores were significantly higher in the integrated group compared with the conventional group. Students exposed to the integrated instructional model demonstrated superior self-management, information-seeking, and self-control abilities, resulting in a higher total SDLRS score (p < 0.01). Effect size estimates indicated a moderate advantage of the integrated approach in fostering self-directed learning readiness. 3.3.4. Conceptual understanding and transfer performance Post-training theoretical examination results favored the integrated group. While no significant between-group difference was observed for multiple-choice question scores (p > 0.05), the integrated group achieved significantly higher scores on case-based problem-solving and transfer tasks compared with the conventional group (p < 0.01), reflecting enhanced ability to apply theoretical principles to unfamiliar anatomical and clinical contexts. 4. Discussion The present prospective educational study compared a traditional theory-first sequential instructional model with an integrated theory–practice approach for undergraduate microsurgical anatomy training. While both instructional strategies effectively improved basic microsurgical competence, the integrated model demonstrated clear advantages in practical skill acquisition, conceptual understanding, learning motivation, and self-directed learning readiness. 4.1. Summary of principal findings Our results indicate that students trained using the integrated theory–practice model achieved significantly higher post-training performance in practical microsurgical skills than those trained using the conventional sequential approach. Notably, the integrated group demonstrated superior procedural fluency, economy of movement, and operational safety, suggesting that embedding theoretical reasoning directly within hands-on practice may facilitate more coherent and efficient skill execution. In addition to technical proficiency, the integrated instructional model was associated with enhanced conceptual understanding and transfer ability. While both groups showed comparable gains in factual knowledge, students exposed to the integrated approach performed significantly better on case-based problem-solving and transfer tasks, reflecting a deeper understanding of underlying principles and greater adaptability to novel anatomical and clinical scenarios. Furthermore, the integrated model led to higher intrinsic learning motivation, greater perceived task value, and improved self-directed learning readiness. These findings highlight the broader educational impact of instructional design beyond immediate skill acquisition. 4.2. Educational interpretation: integrating theory and practice From an educational perspective, the observed benefits of the integrated model are consistent with constructivist learning theory and problem-based learning frameworks. Embedding theoretical content within authentic microsurgical tasks encourages active knowledge construction, continuous hypothesis testing, and immediate feedback, thereby strengthening the linkage between declarative knowledge and procedural execution. In contrast, the theory-first sequential approach, although cognitively structured, separates knowledge acquisition from its practical application. This temporal separation may limit opportunities for real-time integration, potentially explaining the comparatively weaker performance in skill fluency and transfer tasks observed in the conventional group. Importantly, the integrated approach did not increase perceived workload or negatively affect course experience, suggesting that cognitive integration can be achieved without imposing additional learning burden when instructional design is carefully structured. 4.3. Implications for microsurgical and neurosurgical training Microsurgical competence in neurosurgery requires not only manual dexterity but also continuous decision-making informed by anatomical knowledge, optical parameters, and risk assessment. The superior performance of the integrated group in both technical execution and conceptual transfer suggests that early exposure to integrated learning paradigms may better reflect the cognitive demands of real-world neurosurgical practice. Moreover, the observed improvements in self-directed learning readiness are particularly relevant in the context of neurosurgical education, where continuous skill refinement and lifelong learning are essential. By fostering self-management and reflective learning habits at the undergraduate level, integrated instructional models may contribute to more sustainable professional development trajectories.(16) 4.4. Educational value and scalability The present study demonstrates that an integrated theory–practice model can be implemented within a structured undergraduate curriculum without requiring additional instructional time or resources. This scalability makes the approach particularly attractive for anatomy-based microsurgical training programs, where faculty time and laboratory resources are often limited. Furthermore, the use of standardized assessment tools and blinded performance evaluation strengthens the generalizability of our findings and supports the adoption of similar instructional designs in other surgical and procedural disciplines.(10) 4.5. Limitations and future directions Several limitations should be acknowledged. First, the study involved a relatively small sample size from a single institution, which may limit external generalizability. Second, long-term retention of skills and transfer to clinical performance were not assessed. Future studies should incorporate longitudinal follow-up and objective clinical performance metrics to further evaluate the durability and translational impact of integrated instructional models. Additionally, qualitative data exploring students’ learning experiences in greater depth may provide further insight into the mechanisms underlying the observed advantages. 4.6. Conclusions In conclusion, while both instructional strategies effectively improved microsurgical competence, the integrated theory–practice approach resulted in superior technical performance, deeper conceptual understanding, enhanced learning motivation, and stronger self-directed learning readiness. These findings support the adoption of integrated instructional models in undergraduate microsurgical anatomy education and underscore their potential value for preparing future neurosurgeons for the cognitive and technical demands of clinical practice. Abbreviations MSLQ Motivated Strategies for Learning Questionnaire CEQ Course Experience Questionnaire SDLRS Self-Directed Learning Readiness Scale ANOVA analysis of variance Declarations 5.1. Ethical review and consent to participate This study was approved by the Scientific Research and New Technology of Wannan Medical College Yijishan Hospital IRB (Ethical Approval No. LLSC-2022-236). The study was conducted in accordance with the ethical principles of the Declaration of Helsinki (as revised in 2013) and relevant institutional regulations. All participants provided written informed consent prior to enrollment. Participation was voluntary, and all data were collected and analyzed anonymously. 5.2. Consent for publication Not applicable. No identifiable individual data (including images or videos) are included in this manuscript. 5.3. Availability of data and materials The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request. 5.4. Competing interests The authors declare that they have no competing interests. 5.5. Authors’ contributions Xiaochun Jiang: Conceptualization, study design, methodology, investigation, data curation, writing—original draft. Guangfu Di: Instructional implementation, investigation, writing—review and editing. Lean Sun: Formal analysis, visualization, writing—review editing. Haosu Zhang: Supervision, project administration, resources, writing—review and editing. Hengya Liang: Recruitment and enrollment of students, resources All authors read and approved the final manuscript. Acknowledgements 5.6. Funding This study was supported by the Anhui Provincial University Research Program (2024AH040236); and Anhui Provincial Clinical Medical Research Transformation Special Project (202204295107020010). 5.7. Acknowledgements The authors thank Wannan medical collage for providing facilities and technical support, and we thank all students who participated in this study. References Ritschl LM, Grabenhorst A, Wolff C, Pippich K, Dick D, Berberat PO, et al. Influence and Longevity of a Microsurgery Course for Medical Students on Their Future Careers: A Retrospective Report of Up to 10 Years. J Reconstr Microsurg. 2026;42(1):24–9. Fulton M, Donnelly DT, Nkana ZH, Jung S, Zeng W, Dingle AM. The Impact of Early Exposure to Microsurgery Training on Undergraduates: A Pilot Course. WMJ. 2024;123(5):368–73. Atkins JL, Kalu PU, Lannon DA, Green CJ, Butler PE. Training in microsurgical skills: Does course-based learning deliver? Microsurgery. 2005;25(6):481–5. Stienen MN, Freyschlag CF, Schaller K, Meling T. Procedures performed during neurosurgery residency in Europe. Acta Neurochir (Wien). 2020;162(10):2303–11. Sakamoto Y, Okamoto S, Shimizu K, Araki Y, Hirakawa A, Wakabayashi T. Hands-on Simulation versus Traditional Video-learning in Teaching Microsurgery Technique. Neurol Med Chir (Tokyo). 2017;57(5):238–45. Stengel FC, Gandia-Gonzalez ML, Aldea CC, Bartek J Jr., Belo D, Ben-Shalom N, et al. Transformation of neurosurgical training from see one, do one, teach one to AR/VR & simulation - A survey by the EANS Young Neurosurgeons. Brain Spine. 2022;2:100929. Neyazi B, Amini A, Swiatek VM, Stein KP, Rashidi A, Sandalcioglu IE. Reshaping neurosurgical training: a novel simulation-based concept for structured skill acquisition and curriculum integration. Neurosurg Rev. 2025;48(1):517. Carciumaru TZ, Eşanu V, Tang C, Dindelegan GC, Velinov N, Dirven C, et al. Effectiveness of a 4-day intensive course for neurosurgeons in error reduction in microvascular anastomoses. Brain Spine. 2025;5:105623. Hirche C, Megerle K, Heitmann C, Rois J, Russe F, Froschauer SM, et al. 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The Development, Validation and Application of the Course Experience Questionnaire. Studies in Higher Education - STUD HIGH EDUC. 1997;22:33–53. Guglielmino LM. Development of the Self-Directed Learning Readiness Scale. US: ProQuest Information & Learning; 1978. Tyler BM, Liu A, Sankey EW, Mangraviti A, Barone MA, Brem H. The Johns Hopkins Hunterian Laboratory Philosophy: Mentoring Students in a Scientific Neurosurgical Research Laboratory. Acad Med. 2016;91(6):778–84. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 15 May, 2026 Reviews received at journal 10 May, 2026 Reviews received at journal 29 Apr, 2026 Reviewers agreed at journal 25 Apr, 2026 Reviews received at journal 23 Apr, 2026 Reviewers agreed at journal 17 Apr, 2026 Reviewers agreed at journal 16 Apr, 2026 Reviewers invited by journal 15 Apr, 2026 Editor invited by journal 20 Mar, 2026 Editor assigned by journal 12 Feb, 2026 Submission checks completed at journal 12 Feb, 2026 First submitted to journal 12 Feb, 2026 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. 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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-8821137","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":627914729,"identity":"592f2cce-f7c0-4a01-a7a4-45738b589603","order_by":0,"name":"Guangfu Di","email":"","orcid":"","institution":"First Affiliated Hospital of Wannan Medical College","correspondingAuthor":false,"prefix":"","firstName":"Guangfu","middleName":"","lastName":"Di","suffix":""},{"id":627914730,"identity":"17716a20-4875-4394-bdfa-9a6b545ef28c","order_by":1,"name":"Lean Sun","email":"","orcid":"","institution":"The First Affiliated Hospital of Wannan Medical Collage","correspondingAuthor":false,"prefix":"","firstName":"Lean","middleName":"","lastName":"Sun","suffix":""},{"id":627914731,"identity":"8e0ea132-00e5-4dbe-88f9-9c69e7120100","order_by":2,"name":"Haosu Zhang","email":"","orcid":"","institution":"The First Affiliated Hospital of Wannan Medical Collage","correspondingAuthor":false,"prefix":"","firstName":"Haosu","middleName":"","lastName":"Zhang","suffix":""},{"id":627914732,"identity":"77b588f1-7d48-4efd-a0dd-cde2d323f8da","order_by":3,"name":"Hengyan Liang","email":"","orcid":"","institution":"The First Affiliated Hospital of Wannan Medical Collage","correspondingAuthor":false,"prefix":"","firstName":"Hengyan","middleName":"","lastName":"Liang","suffix":""},{"id":627914733,"identity":"5a478042-0260-48ab-aaa5-b2d145dbf5bb","order_by":4,"name":"Xiaochun Jiang","email":"data:image/png;base64,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","orcid":"","institution":"First Affiliated Hospital of Wannan Medical College","correspondingAuthor":true,"prefix":"","firstName":"Xiaochun","middleName":"","lastName":"Jiang","suffix":""}],"badges":[],"createdAt":"2026-02-08 11:23:40","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8821137/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8821137/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107648782,"identity":"44b82c7a-e3be-42de-815b-072465f374fa","added_by":"auto","created_at":"2026-04-23 14:41:28","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":141669,"visible":true,"origin":"","legend":"\u003cp\u003eWeek 2: Microsurgical object manipulation. Clipping mung beans in a small flask by gun-type tweezers under the microscope.\u003c/p\u003e","description":"","filename":"image1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8821137/v1/ce0f07069b20f1dd7add976c.jpeg"},{"id":107648764,"identity":"6886360f-0371-4651-8a59-bacdbdffe631","added_by":"auto","created_at":"2026-04-23 14:41:16","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":151298,"visible":true,"origin":"","legend":"\u003cp\u003eWeek 3: Micro-suturing on biological organoids and materials. A. Suturing gauze and knotting sutures under the microscope. B. Suture latex gloves under the microscope.,add water to the sutured gloves to check the completion degree of the suture.\u003c/p\u003e","description":"","filename":"image2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8821137/v1/26cad3435cc7b6e81b97698a.jpeg"},{"id":107648823,"identity":"e3e293b6-9d9d-4bcf-ae91-ef2e285563a7","added_by":"auto","created_at":"2026-04-23 14:41:31","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":140178,"visible":true,"origin":"","legend":"\u003cp\u003eWeek 4: Controlled drill application under the microscope. Drilling eggshells under the microscope.\u003c/p\u003e","description":"","filename":"image3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8821137/v1/f1bfb5330f7d81c95db4fd3a.jpeg"},{"id":107648789,"identity":"5dbacb1f-fa80-45b3-b58b-39eea5c1b9c3","added_by":"auto","created_at":"2026-04-23 14:41:29","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":150542,"visible":true,"origin":"","legend":"\u003cp\u003e(Weeks 5–8): Advanced anatomical dissection. A. Dissecting cranial muscles under the microscope; B. Dissecting the skull under the microscope.\u003c/p\u003e","description":"","filename":"image4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8821137/v1/6935971a21ed88b97c6aa11c.jpeg"},{"id":107648771,"identity":"cbca8d56-7a81-4fb5-a094-c43f0ca105bd","added_by":"auto","created_at":"2026-04-23 14:41:21","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":169250,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTheory-first sequential approach and theory–practice embedded approach.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA. \u003c/strong\u003eThe 2022 cohort was trained using a traditional instructional model, in which theoretical lectures on microsurgical principles were delivered prior to each corresponding practice session. \u003cstrong\u003eB. \u003c/strong\u003eThe 2023 cohort was trained using an integrated instructional model based on problem-based learning.\u003c/p\u003e","description":"","filename":"image5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8821137/v1/a50e0a801eefa6f3f21019fb.jpeg"},{"id":107707894,"identity":"5ecd34c7-1fa4-479e-a6af-ed76942c28e7","added_by":"auto","created_at":"2026-04-24 09:21:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1016449,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8821137/v1/308bb2a0-bfdb-4a83-a21f-1c428bb20bb9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Effectiveness of an Integrated Theory–Practice Model in Undergraduate Microsurgical Anatomy Education","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMicrosurgical competence is a cornerstone of modern neurosurgery and related surgical disciplines, yet the training of such refined operative skills often begins only during residency.(1) In recent years, there has been increasing recognition that earlier exposure to microsurgical techniques during undergraduate medical education could foster skill acquisition, enhance motivation, and bridge the gap between theoretical knowledge and clinical application.(2) Traditional medical curricula typically adopt a sequential structure in which conceptual knowledge is delivered first, followed by subsequent practical training sessions.(3) Although this model provides students with a structured cognitive foundation, it has been criticized for creating a disconnect between abstract principles and hands-on application.(4)\u003c/p\u003e\n\u003cp\u003eEducational theory and empirical evidence from medical and engineering education suggest that the sequencing of theory and practice may substantially influence learning outcomes.(5, 6) The conventional “theory-first, practice-later” model emphasizes knowledge transmission but may lead to passive learning, limited engagement, and reduced transfer of concepts into clinical performance.(4) In contrast, “integrated” or “concurrent” instructional models, in which conceptual guidance is embedded directly within practical tasks, have been shown to improve engagement, enhance retention, and facilitate deeper learning.(7, 8) In procedural domains such as nursing skills training, simulation-based education, and laboratory sciences, integrated approaches have demonstrated superior effects on student satisfaction, motivation, and performance.(9) However, evidence regarding the impact of such integration in microsurgical education at the undergraduate level remains limited.\u003c/p\u003e\n\u003cp\u003eMicrosurgical training under the operating microscope is uniquely challenging. It requires not only the mastery of fine motor skills and hand–eye coordination but also a profound understanding of underlying anatomical and surgical principles.(1) For medical undergraduates, the cognitive load associated with learning both conceptual frameworks and technical execution can be high, and the optimal instructional strategy for balancing these elements is yet to be determined. Introducing microsurgical skills at an early stage, when students are still developing their professional identity and learning strategies, presents both an opportunity and a pedagogical challenge.\u003c/p\u003e\n\u003cp\u003ePrevious studies on surgical skill acquisition have predominantly focused on residents or postgraduate trainees, leaving a paucity of evidence on the effectiveness of different instructional models in undergraduate cohorts. A few reports have highlighted that integrated or problem-based learning approaches may accelerate the acquisition of surgical competencies, yet rigorous comparative studies in the context of undergraduate microsurgical training are scarce. Furthermore, existing literature rarely addresses multiple dimensions of learning outcomes simultaneously, such as motivation, conceptual understanding, technical proficiency, satisfaction, and self-directed learning ability, which are all critical for future professional development.(10-12)\u003c/p\u003e\n\u003cp\u003eAgainst this background, the present study aims to compare two instructional models for introducing microsurgical skills in undergraduate medical education: (1) the conventional model of delivering theoretical instruction followed by subsequent hands-on training, and (2) an integrated model in which theoretical principles are interwoven within microsurgical practice sessions. By evaluating learning outcomes across different domains, this study seeks to provide evidence on which approach better supports comprehensive skill development in novice learners. Ultimately, our findings are expected to inform curriculum design and contribute to the ongoing reform of surgical education.\u003c/p\u003e"},{"header":"2. Method","content":"\u003cp\u003e\u003cstrong\u003e2.1.\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Study design and participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis prospective educational study was conducted across two consecutive cohorts of third-year undergraduate medical students receiving microsurgical anatomy training under different instructional models. In 2022 and 2023, two microsurgical training programs were implemented, each enrolling 20 students (10 males and 10 females) who ranked among the top performers in their academic examinations.\u003c/p\u003e\n\u003cp\u003eEligibility criteria included:\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eenrollment in the surgical module of the undergraduate medical curriculum;\u003c/li\u003e\n \u003cli\u003eno prior formal training in microsurgical techniques; and\u003c/li\u003e\n \u003cli\u003ewillingness to participate in all training sessions and assessments.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eAll participants underwent a baseline (pre-training) assessment of microsurgical skills prior to the educational intervention. Written informed consent was obtained from all participants, and the study protocol was approved by the institutional ethics committee.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Educational intervention\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.1.\u0026nbsp; \u0026nbsp;Organization of educational programs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBoth cohorts were exposed to the same set of modules and equivalent total instructional time. The program lasted eight weeks and was divided into two phases:\u003c/p\u003e\n\u003cp\u003ePhase I (Weeks 1\u0026ndash;4): Fundamental microsurgical skill modules. Training focused on four core exercises designed to develop hand\u0026ndash;eye coordination and fine motor control:\u003c/p\u003e\n\u003cp\u003eWeek 1: Mastering the use of operating microscopes (Leica M320 F12; Leica Microsystems Inc., Buffalo Grove, IL, USA) and high-speed grinding drills \u0026nbsp;(STORZ TC200; Karl Storz SE \u0026amp; Co. KG, Tuttlingen, Germany) .\u003c/p\u003e\n\u003cp\u003eWeek 2: Microsurgical object manipulation (e.g., transferring small objects under magnification) (Figure 1).\u003c/p\u003e\n\u003cp\u003eWeek 3: Micro-suturing on biological organoids and materials (Figure 2).\u003c/p\u003e\n\u003cp\u003eWeek 4: Controlled drill application under the microscope (Figure 3).\u003c/p\u003e\n\u003cp\u003ePhase II (Weeks 5\u0026ndash;8): Advanced anatomical dissection. Students performed stepwise microsurgical dissections of formalin-fixed craniocerebral specimens under the operating microscope, guided and evaluated by experienced faculty (Figure 4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.2.\u0026nbsp; \u0026nbsp;Theory-first sequential approach and theory\u0026ndash;practice embedded approach\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 2022 cohort was trained using a traditional instructional model, in which theoretical lectures on microsurgical principles were delivered prior to each corresponding practice session (Figure 5A). During Phase I (Weeks 1\u0026ndash;4), students first attended lectures on microscope optics, instrument handling, and relevant anatomy, followed by practical exercises including microscope adjustment, object manipulation, microsuturing, and controlled drilling. In Phase II (Weeks 5\u0026ndash;8), lectures on cranial and neurovascular anatomy preceded stepwise dissections of formalin-fixed craniocerebral specimens under the microscope. This sequential lecture\u0026ndash;practice design provided students with a structured cognitive foundation before technical training, but theoretical knowledge was applied only after a time delay rather than in real time.\u003c/p\u003e\n\u003cp\u003eHowever, the 2023 cohort was trained using an integrated instructional model based on problem-based learning (Figure 5B). Theoretical concepts were embedded directly into practice, with each module introduced by a clinically relevant question (e.g., \u0026ldquo;How can the trigeminal nerve root be exposed without injuring the superior petrosal vein?\u0026rdquo;). Students engaged in small-group discussions to identify required anatomical knowledge, optical parameters, and instrument use, while faculty provided targeted theoretical guidance within these problem-solving contexts. During hands-on training\u0026mdash;covering object manipulation, micro-suturing, drilling, and specimen dissection\u0026mdash;students received real-time feedback, with instructors posing guiding questions to link theory with practice. At the end of each module, students presented solutions, reflected on difficulties, and summarized connections between theory and technique. Faculty consolidated these insights into general principles and highlighted their transferability to new anatomical or clinical situations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3.\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Outcome measures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLearning outcomes were assessed across different domains using validated scales and standardized performance evaluations, as followings:\u003c/p\u003e\n\u003cp\u003eLearning motivation: measured with the Motivated Strategies for Learning Questionnaire (MSLQ), focusing on intrinsic and extrinsic goal orientation, task value, control of learning beliefs, and self-efficacy(13).\u003c/p\u003e\n\u003cp\u003ePractical skill proficiency: evaluated through direct observation using structured performance checklists for each module, including criteria such as accuracy, economy of movement, adherence to procedural steps, and safety. Global ratings were complemented by objective pass\u0026ndash;fail criteria.\u003c/p\u003e\n\u003cp\u003eConceptual understanding and transfer ability: assessed through pre- and post-training written examinations consisting of multiple-choice questions, case-based problem solving, and transfer tasks requiring the application of theoretical principles to novel scenarios.\u003c/p\u003e\n\u003cp\u003eLearning satisfaction and course experience: measured with the Course Experience Questionnaire (CEQ), including subscales on quality of teaching, clarity of goals, workload appropriateness, and overall satisfaction(14).\u003c/p\u003e\n\u003cp\u003eSelf-directed learning ability: assessed using the Self-Directed Learning Readiness Scale (SDLRS), covering subdomains of self-management, information-seeking, and self-control(15).\u003c/p\u003e\n\u003cp\u003eAll questionnaires employed five-point Likert scales and were conducted after each module. Both total scores and subscale scores were analyzed to capture multidimensional aspects of learning outcomes. Skill performance was independently assessed by two blinded expert raters, and inter-rater reliability was evaluated to ensure objectivity. All specific questionnaires are provided in the supplementary document.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4.\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Statistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDescriptive statistics were computed for baseline characteristics and outcome measures. Normality of data distribution was assessed using the Shapiro\u0026ndash;Wilk test. Between-group differences in post-training outcomes were analyzed using independent-sample t tests or Mann\u0026ndash;Whitney U tests, depending on distribution. Within-group pre\u0026ndash;post differences were examined using paired t tests or Wilcoxon signed-rank tests. For multi-domain questionnaire data, repeated measures analysis of variance (ANOVA) with group (conventional vs. integrated) as the between-subject factor and subscale as the within-subject factor was employed. Effect sizes (Cohen\u0026rsquo;s d or partial eta squared) were reported where appropriate. Statistical significance was set at p \u0026lt; 0.05. Analyses were performed using SPSS version 26 (IBM Corp., Armonk, NY, USA).\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1. \u0026nbsp; \u0026nbsp; \u0026nbsp;Demographic results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 40 third-year undergraduate medical students were included in the analysis, with 20 students in each cohort. The conventional sequential training group (2022 cohort) and the integrated theory\u0026ndash;practice group (2023 cohort) were comparable in baseline demographic and academic characteristics.\u003c/p\u003e\n\u003cp\u003eThere were no significant differences between the two groups with respect to age, gender distribution, or baseline academic performance (all p \u0026gt; 0.05). Baseline scores for learning motivation (MSLQ), self-directed learning readiness (SDLRS), and theoretical knowledge assessment did not differ significantly between groups prior to the training (all p \u0026gt; 0.05), indicating adequate baseline equivalence.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2. \u0026nbsp; \u0026nbsp; \u0026nbsp;Intragroup learning performance \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2.1. \u0026nbsp; Learning motivation and self-directed learning ability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWithin-group pre\u0026ndash;post comparisons demonstrated significant improvements in learning-related outcomes in both cohorts (Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eMSLQ\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003econventional\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003egroup and\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eintegrated\u003c/strong\u003e \u003cstrong\u003egroup paired t-test.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"140%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"12\" valign=\"top\"\u003e\n \u003cp\u003ePaired Samples Test\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"12\" valign=\"top\"\u003e\n \u003cp\u003ePaired Differences\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e95% Confidence Interval of the Difference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStd. Deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStd. Error\u003c/p\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLower\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUpper\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSig. (2-tailed)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eMSLQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eConventional group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e-13.25000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.32991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.19180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-15.74447\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-10.75553\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-11.118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eIntegrated group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-22.10000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.91964\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.32367\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-24.87048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-19.32952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-16.696\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA paired t-test was used to evaluate whether there was a difference between the total score of student ratings at week 8 and the baseline total score.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the conventional sequential group, total MSLQ scores increased significantly after the training program (p \u0026lt; 0.01), Self-control total scores also showed a significant post-training increase (p \u0026lt; 0.05), primarily driven by gains in self-management and information-seeking abilities.\u003c/p\u003e\n\u003cp\u003eIn the integrated theory\u0026ndash;practice group, post-training MSLQ total scores increased significantly compared with baseline (p \u0026lt; 0.001). Improvements were consistently observed across all subscales, including intrinsic goal orientation, task value, control of learning beliefs, and self-efficacy. Similarly, SDLRS total and subscale scores demonstrated significant pre\u0026ndash;post increases (p \u0026lt; 0.001), indicating enhanced readiness for self-directed learning following the integrated instructional approach.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2.2. \u0026nbsp; Practical microsurgical skill performance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBoth groups exhibited significant improvements in practical microsurgical skill performance following the 8-week training program (Table 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Practical microsurgical skill performance\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003econventional\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003egroup and\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eintegrated\u003c/strong\u003e \u003cstrong\u003egroup paired t-test.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"12\" valign=\"top\"\u003e\n \u003cp\u003ePaired Samples Test\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"12\" valign=\"top\"\u003e\n \u003cp\u003ePaired Differences\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e95% Confidence Interval of the Difference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStd. Deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStd. Error\u003c/p\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLower\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUpper\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSig. (2-tailed)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eMSLQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eConventional group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e198.45000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.28553\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.95827\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e196.44431\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e200.45569\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e207.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eIntegrated group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e191.10000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.65370\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.59338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e189.85803\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e192.34197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e322.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA paired t-test was used to evaluate whether there was a difference between the total score of student ratings at week 8 and the baseline total score.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the conventional sequential group, total practical performance scores increased significantly from baseline (p \u0026lt; 0.001). Improvements were observed in accuracy, procedural adherence, and basic microscope handling. However, gains in movement economy and procedural fluency were comparatively moderate.\u003c/p\u003e\n\u003cp\u003eIn the integrated group, practical performance scores demonstrated a robust post-training increase across all assessed domains (p \u0026lt; 0.001). Marked improvements were observed in accuracy, economy of movement, operational safety, and overall procedural coherence. Pass\u0026ndash;fail evaluation outcomes showed a higher proportion of students meeting predefined competency criteria at post-training compared with baseline.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2.3. \u0026nbsp; Conceptual understanding and knowledge transfer\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWithin-group analyses revealed significant improvements in theoretical knowledge and conceptual understanding in both cohorts.\u003c/p\u003e\n\u003cp\u003eIn the conventional group, post-training written examination scores improved significantly (p \u0026lt; 0.01), with greater gains observed in multiple-choice questions compared with case-based problem-solving and transfer tasks.\u003c/p\u003e\n\u003cp\u003eIn contrast, the integrated group demonstrated significant post-training improvements across all components of the theoretical assessment (p \u0026lt; 0.001), including multiple-choice questions, case-based reasoning, and transfer tasks requiring application of principles to novel scenarios.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3. \u0026nbsp; \u0026nbsp; \u0026nbsp;Intergroup learning performance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3.1. \u0026nbsp; Comparison of practical skill acquisition\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePost-training comparisons revealed significant differences between instructional approaches in practical microsurgical skill performance (Table 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eMSLQ two groups of patients independent-sample t-test.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"772\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eLevene\u0026apos;s Test for Equality of Varlances\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"7\" valign=\"top\"\u003e\n \u003cp\u003et-test for \u0026nbsp;Equality of Means\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e95% Confidence Interval of the Difference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSig.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSig. (2-tailed)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMean Difference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStd.Errorb Difference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLower\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUpper\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eMSLQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEqual variances assumed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.958\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-6.559\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-10.85000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.65414\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-14.19863\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-7.50137\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEqual variances not assumed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-6.559\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-10.85000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.65414\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-7.50097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-14.19903\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe integrated theory\u0026ndash;practice group achieved significantly higher total practical performance scores than the conventional sequential group (mean \u0026plusmn; SD vs. mean \u0026plusmn; SD; p \u0026lt; 0.01), with a medium-to-large effect size (Cohen\u0026rsquo;s d = 1.53). Subdomain analyses demonstrated superior performance in movement economy, procedural fluency, and safety metrics in the integrated group.\u003c/p\u003e\n\u003cp\u003eAdditionally, the proportion of students achieving a pass classification was higher in the integrated group compared with the conventional group (p \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3.2. \u0026nbsp; Learning motivation and course experience\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBetween-group analyses showed that students in the integrated group reported significantly higher post-training learning motivation and course satisfaction than those in the conventional group.\u003c/p\u003e\n\u003cp\u003eTotal MSLQ scores were significantly higher in the integrated group (p \u0026lt; 0.01), particularly for intrinsic goal orientation, task value, and self-efficacy subscales. Scores on the Course Experience Questionnaire (CEQ) indicated significantly greater satisfaction with teaching quality, clarity of learning goals, and overall course experience in the integrated group (all p \u0026lt; 0.05). Perceived workload did not differ significantly between groups (p \u0026gt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3.3. \u0026nbsp; Self-directed learning readiness\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePost-training SDLRS scores were significantly higher in the integrated group compared with the conventional group.\u003c/p\u003e\n\u003cp\u003eStudents exposed to the integrated instructional model demonstrated superior self-management, information-seeking, and self-control abilities, resulting in a higher total SDLRS score (p \u0026lt; 0.01). Effect size estimates indicated a moderate advantage of the integrated approach in fostering self-directed learning readiness.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3.4. \u0026nbsp; Conceptual understanding and transfer performance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePost-training theoretical examination results favored the integrated group.\u003c/p\u003e\n\u003cp\u003eWhile no significant between-group difference was observed for multiple-choice question scores (p \u0026gt; 0.05), the integrated group achieved significantly higher scores on case-based problem-solving and transfer tasks compared with the conventional group (p \u0026lt; 0.01), reflecting enhanced ability to apply theoretical principles to unfamiliar anatomical and clinical contexts.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe present prospective educational study compared a traditional theory-first sequential instructional model with an integrated theory\u0026ndash;practice approach for undergraduate microsurgical anatomy training. While both instructional strategies effectively improved basic microsurgical competence, the integrated model demonstrated clear advantages in practical skill acquisition, conceptual understanding, learning motivation, and self-directed learning readiness.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.1. Summary of principal findings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur results indicate that students trained using the integrated theory\u0026ndash;practice model achieved significantly higher post-training performance in practical microsurgical skills than those trained using the conventional sequential approach. Notably, the integrated group demonstrated superior procedural fluency, economy of movement, and operational safety, suggesting that embedding theoretical reasoning directly within hands-on practice may facilitate more coherent and efficient skill execution.\u003c/p\u003e\n\u003cp\u003eIn addition to technical proficiency, the integrated instructional model was associated with enhanced conceptual understanding and transfer ability. While both groups showed comparable gains in factual knowledge, students exposed to the integrated approach performed significantly better on case-based problem-solving and transfer tasks, reflecting a deeper understanding of underlying principles and greater adaptability to novel anatomical and clinical scenarios.\u003c/p\u003e\n\u003cp\u003eFurthermore, the integrated model led to higher intrinsic learning motivation, greater perceived task value, and improved self-directed learning readiness. These findings highlight the broader educational impact of instructional design beyond immediate skill acquisition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2. Educational interpretation: integrating theory and practice\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrom an educational perspective, the observed benefits of the integrated model are consistent with constructivist learning theory and problem-based learning frameworks. Embedding theoretical content within authentic microsurgical tasks encourages active knowledge construction, continuous hypothesis testing, and immediate feedback, thereby strengthening the linkage between declarative knowledge and procedural execution.\u003c/p\u003e\n\u003cp\u003eIn contrast, the theory-first sequential approach, although cognitively structured, separates knowledge acquisition from its practical application. This temporal separation may limit opportunities for real-time integration, potentially explaining the comparatively weaker performance in skill fluency and transfer tasks observed in the conventional group.\u003c/p\u003e\n\u003cp\u003eImportantly, the integrated approach did not increase perceived workload or negatively affect course experience, suggesting that cognitive integration can be achieved without imposing additional learning burden when instructional design is carefully structured.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.3. Implications for microsurgical and neurosurgical training\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMicrosurgical competence in neurosurgery requires not only manual dexterity but also continuous decision-making informed by anatomical knowledge, optical parameters, and risk assessment. The superior performance of the integrated group in both technical execution and conceptual transfer suggests that early exposure to integrated learning paradigms may better reflect the cognitive demands of real-world neurosurgical practice.\u003c/p\u003e\n\u003cp\u003eMoreover, the observed improvements in self-directed learning readiness are particularly relevant in the context of neurosurgical education, where continuous skill refinement and lifelong learning are essential. By fostering self-management and reflective learning habits at the undergraduate level, integrated instructional models may contribute to more sustainable professional development trajectories.(16)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.4. Educational value and scalability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present study demonstrates that an integrated theory\u0026ndash;practice model can be implemented within a structured undergraduate curriculum without requiring additional instructional time or resources. This scalability makes the approach particularly attractive for anatomy-based microsurgical training programs, where faculty time and laboratory resources are often limited.\u003c/p\u003e\n\u003cp\u003eFurthermore, the use of standardized assessment tools and blinded performance evaluation strengthens the generalizability of our findings and supports the adoption of similar instructional designs in other surgical and procedural disciplines.(10)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.5. Limitations and future directions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSeveral limitations should be acknowledged. First, the study involved a relatively small sample size from a single institution, which may limit external generalizability. Second, long-term retention of skills and transfer to clinical performance were not assessed. Future studies should incorporate longitudinal follow-up and objective clinical performance metrics to further evaluate the durability and translational impact of integrated instructional models.\u003c/p\u003e\n\u003cp\u003eAdditionally, qualitative data exploring students\u0026rsquo; learning experiences in greater depth may provide further insight into the mechanisms underlying the observed advantages.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.6. Conclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn conclusion, while both instructional strategies effectively improved microsurgical competence, the integrated theory\u0026ndash;practice approach resulted in superior technical performance, deeper conceptual understanding, enhanced learning motivation, and stronger self-directed learning readiness. These findings support the adoption of integrated instructional models in undergraduate microsurgical anatomy education and underscore their potential value for preparing future neurosurgeons for the cognitive and technical demands of clinical practice.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMSLQ\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMotivated Strategies for Learning Questionnaire\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCEQ\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCourse Experience Questionnaire\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\"\u003eANOVA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eanalysis of variance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e5.1.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eEthical review and\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003econsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Scientific Research and New Technology of Wannan Medical College Yijishan Hospital IRB (Ethical Approval No. LLSC-2022-236). The study was conducted in accordance with the ethical principles of the Declaration of Helsinki (as revised in 2013) and relevant institutional regulations. All participants provided written informed consent prior to enrollment. Participation was voluntary, and all data were collected and analyzed anonymously.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.2.\u003c/strong\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. No identifiable individual data (including images or videos) are included in this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.3. Availability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.4. Competing interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.5. Authors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXiaochun Jiang: Conceptualization, study design, methodology, investigation, data curation, writing\u0026mdash;original draft.\u003c/p\u003e\n\u003cp\u003eGuangfu Di: Instructional implementation, investigation, writing\u0026mdash;review and editing.\u003c/p\u003e\n\u003cp\u003eLean Sun: Formal analysis, visualization, writing\u0026mdash;review editing.\u003c/p\u003e\n\u003cp\u003eHaosu Zhang: Supervision, project administration, resources, writing\u0026mdash;review and editing.\u003c/p\u003e\n\u003cp\u003eHengya Liang: Recruitment and enrollment of students, resources\u003c/p\u003e\n\u003cp\u003eAll authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.6. Funding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Anhui Provincial University Research Program (2024AH040236); and Anhui Provincial Clinical Medical Research Transformation Special Project (202204295107020010).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.7. \u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Acknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank Wannan medical collage for providing facilities and technical support, and we thank all students who participated in this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRitschl LM, Grabenhorst A, Wolff C, Pippich K, Dick D, Berberat PO, et al. Influence and Longevity of a Microsurgery Course for Medical Students on Their Future Careers: A Retrospective Report of Up to 10 Years. J Reconstr Microsurg. 2026;42(1):24\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFulton M, Donnelly DT, Nkana ZH, Jung S, Zeng W, Dingle AM. The Impact of Early Exposure to Microsurgery Training on Undergraduates: A Pilot Course. WMJ. 2024;123(5):368\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAtkins JL, Kalu PU, Lannon DA, Green CJ, Butler PE. Training in microsurgical skills: Does course-based learning deliver? Microsurgery. 2005;25(6):481\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStienen MN, Freyschlag CF, Schaller K, Meling T. Procedures performed during neurosurgery residency in Europe. Acta Neurochir (Wien). 2020;162(10):2303\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSakamoto Y, Okamoto S, Shimizu K, Araki Y, Hirakawa A, Wakabayashi T. Hands-on Simulation versus Traditional Video-learning in Teaching Microsurgery Technique. Neurol Med Chir (Tokyo). 2017;57(5):238\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStengel FC, Gandia-Gonzalez ML, Aldea CC, Bartek J Jr., Belo D, Ben-Shalom N, et al. Transformation of neurosurgical training from see one, do one, teach one to AR/VR \u0026amp; simulation - A survey by the EANS Young Neurosurgeons. Brain Spine. 2022;2:100929.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNeyazi B, Amini A, Swiatek VM, Stein KP, Rashidi A, Sandalcioglu IE. Reshaping neurosurgical training: a novel simulation-based concept for structured skill acquisition and curriculum integration. Neurosurg Rev. 2025;48(1):517.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarciumaru TZ, Eşanu V, Tang C, Dindelegan GC, Velinov N, Dirven C, et al. Effectiveness of a 4-day intensive course for neurosurgeons in error reduction in microvascular anastomoses. Brain Spine. 2025;5:105623.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHirche C, Megerle K, Heitmann C, Rois J, Russe F, Froschauer SM, et al. [Consensus of the German-Speaking Society for Microsurgery of Peripheral Nerves and Vessels (DAM) on minimum standards for microsurgical training courses and accreditation - Minimum Standards for Microsurgical Training Courses and Accreditation]. Handchir Mikrochir Plast Chir. 2020;52(2):135\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMattar T, Santos GBD, Telles JPM, Rezende MR, Wei TH. Mattar J\u0026uacute;nior R. Structured evaluation of a comprehensive microsurgical training program. Clin (Sao Paulo). 2021;76:e3194.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZyluk A, Szlosser Z, Puchalski P. Undergraduate microsurgical training: a preliminary experience. Handchir Mikrochir Plast Chir. 2019;51(6):477\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAkhigbe T, Zolnourian A, Bulters D. Mentoring models in neurosurgical training: Review of literature. J Clin Neurosci. 2017;45:40\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePintrich P, Smith D, Duncan T, McKeachie W. A Manual for the Use of the Motivated Strategies for Learning Questionnaire (MSLQ). Ann Arbor Mich. 1991;48109:1259.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilson K, Lizzio A, Ramsden P. The Development, Validation and Application of the Course Experience Questionnaire. Studies in Higher Education - STUD HIGH EDUC. 1997;22:33\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuglielmino LM. Development of the Self-Directed Learning Readiness Scale. US: ProQuest Information \u0026amp; Learning; 1978.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTyler BM, Liu A, Sankey EW, Mangraviti A, Barone MA, Brem H. The Johns Hopkins Hunterian Laboratory Philosophy: Mentoring Students in a Scientific Neurosurgical Research Laboratory. Acad Med. 2016;91(6):778\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-medical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meed","sideBox":"Learn more about [BMC Medical Education](http://bmcmededuc.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meed/default.aspx","title":"BMC Medical Education","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Microsurgical anatomy education, Integrated theory–practice instruction, Microsurgical technical skills, Learning motivation, Self-directed learning readiness","lastPublishedDoi":"10.21203/rs.3.rs-8821137/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8821137/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study compared two instructional strategies in undergraduate microsurgical anatomy education and their effects on learners\u0026rsquo; microsurgical competence. Both approaches significantly improved students\u0026rsquo; technical skills and overall learning outcomes. However, the integrated theory\u0026ndash;practice model produced superior technical performance, fostered deeper conceptual understanding, and enhanced learning motivation and readiness for self-directed learning compared with the conventional separated approach. These findings support the adoption of integrated instructional models in undergraduate microsurgical anatomy training and highlight their potential to better prepare future neurosurgeons for the combined cognitive and technical demands of clinical practice.\u003c/p\u003e","manuscriptTitle":"Effectiveness of an Integrated Theory–Practice Model in Undergraduate Microsurgical Anatomy Education","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-23 14:39:38","doi":"10.21203/rs.3.rs-8821137/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-15T05:58:53+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-10T14:43:04+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-29T11:53:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"82642014522681604502068765819429940614","date":"2026-04-25T14:48:10+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-23T06:19:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"251426370079413608847685021728225437435","date":"2026-04-17T11:18:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"45140876358737011311949077229173389499","date":"2026-04-16T06:29:05+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-15T15:04:53+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-20T09:50:44+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-12T06:55:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-12T06:47:54+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Education","date":"2026-02-12T06:44:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meed","sideBox":"Learn more about [BMC Medical Education](http://bmcmededuc.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meed/default.aspx","title":"BMC Medical Education","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cd1ac988-e6bc-4e4d-ac9b-06a94455cffe","owner":[],"postedDate":"April 23rd, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-15T05:58:53+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-10T14:43:04+00:00","index":77,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-29T11:53:12+00:00","index":75,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-15T06:09:48+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-23 14:39:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8821137","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8821137","identity":"rs-8821137","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}