A multicohort study using an eLMS in the UAE The Impact of an eLearning platform on Medical Student Performance in Respiratory System Module: A Multi-Cohort Study in the United Arab Emirates

A multicohort study using an eLMS in the UAE The Impact of an eLearning platform on Medical Student Performance in Respiratory System Module: A Multi-Cohort Study in the United Arab Emirates | 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 A multicohort study using an eLMS in the UAE The Impact of an eLearning platform on Medical Student Performance in Respiratory System Module: A Multi-Cohort Study in the United Arab Emirates Mohammed Al-Houqani, Adonis Wazir, Susan Waller, S. Ayhan Çalişkan, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8121618/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract Background This study investigates how medical student engagement with an e-learning platform (Lecturio) correlates with academic performance in a respiratory system module at the College of Medicine and Health Sciences (CMHS), United Arab Emirates University (UAEU). Methods A retrospective cohort study analyzed six student cohorts (2018–2024) using engagement metrics from Lecturio and academic outcomes from internal assessments. Metrics included videos watched, logins, quiz and Question-bank (Q-bank) activity, and quiz accuracy. Results Platform engagement was highest in the first year and varied across subsequent cohorts. Students who actively used the platform achieved significantly higher final scores than those with minimal engagement (85.50 vs. 82.04, p < 0.001). Key engagement metrics—particularly quiz questions attempted and accuracy on first attempt—were significantly associated with academic performance. Conclusion Student interaction with e-learning tools is positively associated with academic success. This study highlights specific engagement patterns that correlate with improved outcomes, supporting the integration of digital platforms into blended medical curricula. Future research should explore long-term effects, qualitative student experiences, and the role of e-learning in professional skill development and lifelong learning. E-Learning Online Learning Engagement Medical Education Self-Directed Learning Figures Figure 1 Figure 2 Figure 3 Background The integration of e-learning platforms in medical education has significantly enhanced learning flexibility, accessibility, and interactivity. These platforms provide resources like video lectures, quizzes, and interactive case studies, accessible anytime and anywhere, as well as features that enable educators to optimize their teaching using assignments and learning analytics. The COVID-19 pandemic accelerated the adoption of e-learning in medical education, enabling continuity of studies despite disruptions ( 1 , 2 ). Studies have shown that students generally had positive perceptions of e-learning during the pandemic, appreciating its flexibility and accessibility despite the challenges of limited faculty interaction and clinical experiences ( 3 , 4 ). However, other studies reported on increased stress during this period ( 5 , 6 ) and medical students experiencing loneliness and social isolation ( 7 ). Beyond COVID-19, e-learning has been shown to be at least as effective as in person instructor-led methods in various medical education contexts, providing an appropriate alternative to in person methods and forming part of a blended learning strategy ( 1 , 2 ). The ability to repeatedly access learning materials and engage with interactive content allows for better retention and comprehension, as demonstrated by improved knowledge retention and student satisfaction in e-learning environments. ( 1 , 2 ). While research has demonstrated the effectiveness of e-learning in medical education, there has been growing recognition that comparisons between e-learning and in person learning should shift towards efforts to optimize its impact. Rather than focusing solely on whether e-learning works, there is increasing interest in understanding how to enhance its effectiveness through tailored content delivery, adaptive learning technologies, and improved student engagement. Studies suggest that the success of e-learning depends on factors such as course design, instructor-learner interaction ( 8 ), and institutional support ( 9 ). Additionally, research highlights the need for e-learning platforms to incorporate strategies that align with learner needs, ensure interactivity, and provide feedback mechanisms to enhance effectiveness ( 1 ). Our study addresses this gap by examining not just whether the e-learning platform was effective, but how students engaged with it and whether their usage patterns correlate with academic performance. By analyzing student interaction with the platform—such as time spent on modules, quiz attempts, and accuracy—we aimed to identify specific factors that contribute to improved learning outcomes. This approach shifts the focus from a binary comparison of e-learning versus in person learning to a more nuanced understanding of how digital learning tools can be optimized for maximum impact ( 10 ). By linking engagement data with student performance, our findings will contribute to the broader conversation on how e-learning platforms can be refined to support student success more effectively. The College of Medicine and Health Sciences (CMHS) of the United Arab Emirates University (UAEU) has been using Lecturio in the respiratory system module for its preclinical medical students since 2019. Lecturio is a medical education platform that provides digital resources and AI-augmented features for students and educators, including personalized learning paths, interactive quizzes, and progress tracking. This study aims to identify the key factors that contribute to effective learning outcomes and optimize the impact of digital education in medical training. It thus explores Lecturio usage trends by medical students over six cohorts from 2019 to 2024, focusing on their interaction patterns and usage metrics and the impact on academic performance in the respiratory system module. Objectives and specific aims of the study Primary objective Exploring the impact of Lecturio on student performance by: Comparing final grades between students active on the platform with those with minimal-to-no engagement. Comparing platform usage metrics between students who passed and those who did not achieve the passing score of 75 percent. Comparing platform usage metrics with student performance on the final course grade. Secondary objectives Examine trends of students’ performance on the Respiratory Medicine module over the years 2018–2024. Examine trends of students’ usage metrics on the Lecturio platform over the years 2019–2024. Methods This study is grounded in several key contructivist learning theories (CLTs), including Adaptive Learning Theory, Cognitive Load theory, and Self-Directed Learning (SDL)( 11 , 12 ). Additionally, it incorporates principles of technology-enhanced learning in medical education and is situated within the broader context of blended learning models. These frameworks emphasize the role of adaptive e-learning platforms, such as Lecturio, in enhancing medical students' academic performance and professional development. The theoretical framework guides the study by: ( 1 ) examining engagement metrics through the lens of self-directed and adapted learning principles, ( 2 ) evaluating how adaptive features cater to diverse learning needs and reduce cognitive load, and by ( 3 ) analyzing longitudinal trends to assess sustained improvements in academic outcomes. Design This retrospective observational cohort study was conducted at UAEU’s CMHS, involving third-year medical students completing the Respiratory System module (RSM) in third year of a 6 year prelicensure medical program from 2018 to 2024. Data was collected from Lecturio (platform usage metrics) and internal CMHS, UAEU sources (exam scores, passing rates, end of course marks). Setting UAEU is a public research-intensive university located in Al Ain, UAE made up of various colleges offering a wide variety of undergraduate, graduate programs and continuing education. The CMHS offers a six-year Doctor of Medicine program consisting of three, 2-year phases, premedical, preclinical and clinical. The five-weeks long RSM is part of the preclinical organ system integrated phase of the curriculum. It is structured around weekly case-based problems, incorporating problem-based learning (PBL) and team-based learning (TBL) approaches. Additional instructional methods include lectures, clinical skills sessions, and early clinical exposure to provide a comprehensive and applied learning experience. Assessment of the course include several instruments, weekly TBL/PBL assessment, mid-term and final multiple-choice questions (MCQs) assessments. At CMHS, UAEU, Lecturio, an AI-driven online learning platform, supports medical education through a subscription supported by the National Medical Library (NML). The platform offers video lectures, interactive study materials, quizzes, and flashcards covering diverse medical topics. Using adaptive learning algorithms powered by AI, Lecturio personalizes educational experiences by analyzing user interactions, performance, and preferences. It dynamically adjusts content, pace, and difficulty, continuously refining recommendations to address individual strengths and challenges. This approach enhances learning efficiency, engagement, and exam preparation for certifications such as the United States Medical Licensing Examination (USMLE). Educators can upload their own content and assign it in an interleaved fashion to their students, or assign videos for the students before the class, in a flipped classroom approach. CHMS has been using Lecturio during the RSM of the MD program for preclinical (Year 3) medical students from 2019 to the present. Participants Participants were third-year medical students, male and female with gender ratio of 70:30 (female/male) completing the RSM in the academic years from 2018 to 2024. Quantitative data was collected for all students, including platform usage data and grades. Data collection Data was collected from the following sources: Lecturio data: quantitative data on platform usage: videos watched (average videos watched per user, average % of video /course finished, time spent, recall questions answered, percentage correct, perceived confidence level, performance on the question-bank (Q-Bank) and frequency of activity. This was collected from students in the RSM for the following five cohorts, 2019–2024. Data was retrieved from uaeu.lecturio.com and filtered by dates, limited to specific respiratory system module content. Users retrieved that did not have recorded scores as a UAEU student were excluded (test users or faculty). Internal UAEU data: exam scores (Case based Tests, TBL, PBL, Midterm, Final), passing rates, and end of course marks for the same cohorts. Data analysis All data on student performance was collected in a Microsoft Excel spreadsheet. Lecturio usage data and student performance data were imported into SPSS 20 (IBM SPSS Statistics) for the purpose of descriptive analysis. Continuous data was recorded as means, standard deviations, levels of significance and categorical data as frequencies and percentages. Results Performance Trends The data analyzed covered 6 cohorts. There were 75 students in Cohort 1, 82 students in Cohort 2, 84 in Cohort 3, 76 in Cohort 4, 107 in Cohort 5, and 108 in Cohort 6, for a total of see. Average final performance on RSM ranged from 82.68% (2022–2023) to 88.13% (2018–2019). The structure of assessments evolved across cohorts. Earlier cohorts had separate tests for each case, while later cohorts had combined case based testing and incorporated teaching and learning activities such as Problem-Based Learning (PBL) and Team-Based Learning (TBL). Despite these changes in teaching and assessment structure, mean percentage scores across most assessments remained consistent, demonstrating similar learning outcomes. Figure 1 below illustrates the trends in PBL (Problem-Based Learning), Midterm exam, Final exam, and Final score percentages across the six cohorts (2018–2019 to 2023–2024) in the respiratory system module. Pass Rates and Grade Distribution Overall pass rates (A-C grades) were high across all cohorts, ranging from 88% (2022–2023) to 98% (2019–2020). The percentage of students receiving A grades generally decreased over the cohorts, from 43% in 2018–2019 to 14% in 2022–2023. There was a shift in grade distribution from earlier cohorts (more A’s and B’s) to later cohorts (more B’s and C’s). See Table 1 below. Table 1 Final score letter grades and pass rates for 6 student cohorts from 2018–2024 Cohort Letter grade – Number (%) Pass rate (%) A B+ B C+ C D+ D F Cohort 1 2018–2019 N = 75 32 (43) 27 (36) 9 ( 12 ) 5 ( 7 ) 1 ( 1 ) 1 ( 1 ) 0 0 97 Cohort 2 2019–2020 N = 82 20 ( 24 ) 23 (28) 22 (27) 15 ( 18 ) 2 ( 2 ) 0 0 0 98 Cohort 3 2020–2021 N = 84 27 (32) 23 (27) 17 ( 20 ) 8 ( 10 ) 7 ( 8 ) 0 0 2 ( 2 ) 89 Cohort 4 2021–2022 N = 76 29 (38) 17 ( 22 ) 20 ( 26 ) 5 ( 7 ) 2 ( 3 ) 1 ( 1 ) 0 2 ( 3 ) 93 Cohort 5 2022–2023 N = 107 15 ( 14 ) 26 ( 24 ) 30 (28) 23 ( 21 ) 12 ( 11 ) 1 ( 1 ) 0 0 88 Cohort 6 2023–2024 N = 108 19 ( 18 ) 39 (36) 31 (29) 14 ( 13 ) 4 ( 4 ) 0 0 1 ( 1 ) 95 Platform Usage Metrics and Trends The data analyzed covered cohorts 2–6 (Cohort 1 did not use Lecturio). There was a total of 457 students who used Lecturio over the study period. The first cohort had significantly higher usage as reflected in the number of logins (22.7), videos completed (31.0), quiz questions attempted (195.4). The number of Q-bank questions, however, was relatively low (2.2). Usage declined in subsequent cohorts but showed an increase in the most recent cohort, while Q-bank usage showed an increase over the years (see Fig. 2 below). It is worth noting that these are mean values and each cohort had a wide range of usage – while the minimum number of logins was 0 for all cohorts, the maximum ranged from 52 to 148 logins during the study period. Performance on quiz questions averaged 75% correctness across the cohorts, with a notable outlier of 80% during the academic year 2020–2021 (Cohort 2). This cohort also had the highest rate of correct quiz questions at the first attempt (75%), whereas the others ranged between 68% to 70%. The highest rate of mastered questions was also in the first cohort (10%). Q-bank performance followed a different trend. Data revealed the lowest performance in the first cohort (47% correct), which increased to 54% in Cohort 4 and then had a sharp increase to 73% in the final cohort (see Fig. 3 below). Comparative Analyses Students active on the platform (269) achieved significantly higher final scores (mean 85.50) compared to those with minimal-to-no activity (188) (mean 82.04, p < 0.001). Students who achieved a passing score of 75% in the course participated in significantly more platform activities, including more hours per week (p < 0.001), logins (p < 0.001), videos completed (p = 0.003), quiz questions (p = 0.003), and Q-bank questions attempted (p = 0.037). Compared means are in Table 2 (see below). The binary logistic regression model of these factors was not statistically significant, however. Table 2 Comparison of usage metrics between students who passed (>75 score) and those that did not. Variable Pass (>75) Mean, SD Fail (<75) Mean, SD p-value Activity Hours per week Number of logins Logins per week 0.17, 0.32 11.13, 19.51 0.87, 1.52 0.06, 0.15 5.68, 14.10 0.44, 1.10 <0.001 <0.001 <0.001 Videos Videos started Videos completed Watched minutes 12.71, 24.10 9.59, 20.78 58.36, 127.81 4.59, 11.69 3.47, 9.48 18.91, 51.65 0.052 a 0.003 0.001 Quizzes Total questions answered Total unique questions % Correct answers % Correct at 1 st attempt % Questions mastered Average repetitions to mastery Answered with high confidence 62.55, 131.54 42.45, 83.24 76.03, 13.05 69.90, 16.66 4.10, 11.01 1.23, 0.30 100.29, 138.34 16.00, 37.60 12.29, 29.57 71.86, 8.09 64.00, 8.47 1.94, 9.51 1.3, 0.44 55.71, 52.63 0.003 0.003 0.402 a <0.353 a 0.026 0.855 0.571 Q-bank Total questions answered % Correct answers % Correct at 1 st attempt 13.25, 40.94 59.85, 23.45 64.78, 26.09 5.65, 21.08 52.50, 17.75 55.00, 23.37 0.037 0.536 a <0.247 Study Planner Items created % Completed on time 7.91, 9.59 14.47, 25.18 3.65, 7.80 2.08, 3.26 0.006 0.095 a p-values determined by Student’s t-test, rest by Mann-Whitney U test Correlation analysis revealed statistically significant, albeit mild, correlations between the activity metrics above and final course grades, including rate of correct quiz questions in addition to the above. The highest statistically significant correlation coefficient was noted for total Q-bank questions (0.185). Linear regression analysis showed that total quiz questions answered, and percentage correct at first attempt significantly predicted final scores (R-square = 0.114, p < 0.001). Discussion The integration of the Lecturio e-learning platform into the module curriculum has maintained consistent student performance across six cohorts, despite variations in teaching methodologies and pandemic-related challenges. The findings of this study highlight key trends in student engagement with the e-learning platform and their relationship with academic performance. A notable observation was the high usage of the platform in the first year, likely influenced by the shift to online learning due to the COVID-19 pandemic. Similar trends have been documented in the literature, where the transition to remote learning increased student reliance on digital tools ( 13 , 14 ) ). The peak increase in the most recent cohort, particularly in watched minutes, may suggest growing familiarity with the platform, an increase in assignment-driven engagement, or evolving instructional strategies by faculty. Prior research indicates that faculty involvement plays a crucial role in e-learning adoption and effectiveness ( 15 ), warranting further examination of how instructor practices influence student engagement. The variability in engagement levels could be influenced by other factors such as SDL capability, student motivation, prior academic performance, and integration of the platform within the broader curriculum ( 12 ). The lower pass rate in 2022–2023 (88%) may be linked to challenges posed by fully remote preclinical education during the pandemic, with limited access to laboratories and foundational in-person teaching impacting preparation for the respiratory module. The maintenance of learning outcomes despite pandemic disruptions suggests that the e-learning platform may have played a role in mitigating the impact on student performance. These findings highlight the potential of digital learning tools to support medical education, particularly in times of crisis ( 16 , 17 , 18 , 19 , 20 ), while underscoring the need for a balanced approach that integrates both online and face and in person teaching methods. Student performance on formative assessments has remained relatively consistent. This aligns with studies indicating that self-assessment tools such as quizzes contribute to learning retention but do not necessarily lead to substantial performance fluctuations unless coupled with targeted pedagogical interventions ( 21 ). The study demonstrates the potential of e-learning platforms to maintain academic performance in medical education, even during periods of significant disruption ( 16 , 17 , 18 , 19 , 20 ). The most significant finding relates to the positive association between e-learning platform activity and final course performance. Engagement with key learning activities—such as logins, video completion, and quiz participation—was significantly higher among students who achieved a passing score of 75% or more. These findings align with research demonstrating that frequent interaction with online learning resources enhances academic outcomes ( 22 , 23 ). However, the lack of statistical significance in the binary logistic regression model suggests that while engagement contributes to better performance, other confounding factors may influence success. Correlation analysis revealed statistically significant, albeit mild, relationships between activity metrics and final grades. The strongest correlation was observed for total Q-bank questions attempted (0.185), reinforcing the idea that active retrieval practice through quizzes benefits knowledge retention ( 24 ). Linear regression analysis further indicated that the number of quiz questions answered and the percentage correct at the first attempt were significant predictors of final scores (R² = 0.114, p < 0.001). This finding is consistent with prior studies showing that accuracy on initial attempts are strong indicators of academic performance ( 25 ). Limitations While this study provides valuable insights into the relationship between e-learning engagement and academic performance, several limitations must be acknowledged. First, as a retrospective observational cohort study, causality cannot be established; the associations observed may be influenced by unmeasured confounders such as prior academic performance, self-regulated learning ability, or intrinsic motivation. Second, although the study spans six cohorts, variability in teaching methods, assessment formats, and external factors like the COVID-19 pandemic could have impacted both platform usage and student performance, complicating direct comparisons across years. Third, engagement was measured through platform metrics like logins, video completions, and quiz attempts, which may not fully capture the depth or quality of learning. Additionally, qualitative data on student perceptions or motivations for platform use were not collected, limiting interpretation of the engagement patterns observed. The regression model's limited predictive value (R² = 0.114) also suggests that other important contributors to academic success were not accounted for. Future studies would benefit from a mixed-methods approach and longer-term follow-up to assess impacts on clinical competence and professional development. Conclusion As digital education continues to evolve, understanding how e-learning platforms integrate into broader educational models is crucial, particularly with the growing role of artificial intelligence (AI). AI-driven education tools are increasingly transforming how students interact with content, making learning experiences more adaptive and personalized ( 26 ). To maximize the benefits of these tools, it is essential to deeply understand how students engage with them and how they can be optimized for learning outcomes. This study provides key insights into the specific ways students interact with e-learning platforms, revealing patterns of engagement that are linked to academic success. By analyzing platform activity metrics, we gain a clearer understanding of how digital tools can be leveraged to enhance learning and support student achievement. Further research is needed to explore the long-term impact of e-learning platform engagement on academic performance and skill retention, particularly in relation to more emphasis on technology assisted CLTinspired teaching and learning underpinning contemporary eand future education on the health professions. Additionally, investigating the role of self-regulated learning strategies facilitated by these platforms could provide valuable insights into student adaptability and preparedness for clinical practice. Qualitative assessments of student experiences, motivation, and perceived effectiveness of e-learning tools would refine digital education strategies and optimize their integration into blended learning environments in medical education. Abbreviations CMHS-College of Medicine and Health Sciences UAEU- United Arab Emirates University Question Bank-Q-bank Coronavirus disease-COVID-19 Self Directed Learning-SDL Respiratory System module-RSM Problem based Learning-PBL Team based learning-TBL Multiple-choice questions-MCQs United States Medical Licensing Examination-USMLE Nationa Medical Library-NML Artificial Intelligence-AI Constructivist Learning Theory (CLT) Declarations Ethics approval and consent to participate UAEU Social Science ethics committee reviewed, approved and assigned reference number ERSC_2022_787. The study was conducted in adherence to the principles of the Helsinki Declaration. Individual student consent was waived as the collection of data, LMS activity and assessment outcomes, is part of quality improvement and course review. On enrolment students consent to participate in quality improvement through program review. All data collected is collated before dissemination and no data is individually identifiable and hence the Social Sciences Committee of the UAEU waives further student consent for such low risk and quality improvement focused studies. Consent for publication Not applicable Availability of data and materials For further data, please contact the first author Dr Mohammed Al-Houqani on [email protected] Competing interests Adonis Wazir receives a stipend from Lecturio for research and scholarship. The remaining authors declare that they have no conflict of interest to disclose. Clinical trial number Not applicable because this was a qualitative study. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Authors and Affiliations Mohammed Al-Houqani 1* , Adonis Wazir, 2 Susan Waller 3, 4* , S.Ayhan Çalışkan 3, Mohi Eldin Magzoub 3 1. National Institute for Health Specialities, United Arab Emirates University, Al Ain, United Arab Emirates. 2. Surrey and Sussex Healthcare NHS Trust, Surrey, United Kingdom 3. Department of Medical Education, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates. 4. Monash Rural Health, Monash University, Warragul, Australia. * Corresponding authors: Susan Waller https://orcid.org/0000-0002-6309-0360 Mohammed Al-Houqani https://orcid.org/0000-0003-4481-3911 Authors' contributions MH: Conceptualization, Methodology, Data curation, Formal analysis, Project administration, Writing – original draft, Writing – review & editing. AW: Data curation, analysis, writing – original draft, Writing – review & editing. SW: Analysis, Writing – original draft, Writing – review & editing. SC: Analysis, Writing – original draft, Writing – review & editing. MM Analysis, Writing – original draft, Writing – review & editing. 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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-8121618","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":588333569,"identity":"2fbde443-9051-4b6c-a32e-c05b98d93049","order_by":0,"name":"Mohammed Al-Houqani","email":"","orcid":"","institution":"United Arab Emirates University","correspondingAuthor":false,"prefix":"","firstName":"Mohammed","middleName":"","lastName":"Al-Houqani","suffix":""},{"id":588333570,"identity":"b49f9308-fb53-45dd-98af-b8810ff38295","order_by":1,"name":"Adonis Wazir","email":"","orcid":"","institution":"Surrey and Sussex Healthcare NHS Trust","correspondingAuthor":false,"prefix":"","firstName":"Adonis","middleName":"","lastName":"Wazir","suffix":""},{"id":588333571,"identity":"52b59593-5c08-412b-b03b-f9adb19a5c48","order_by":2,"name":"Susan Waller","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIiWNgGAWjYDADPnYg8QHM5AFiNiK0sDEzMDDOIFkLMw8xWuRnpD/8XMBwWJ6NmfnYZ9scmzz+/rMHGD6UHcapxeBGjrH0DIbDhm3MbMmzc7elFUvcyEtgnHEOjxaJHAZpHobbjG3MPMbMudsOJzbc4DFg5m3DrQXosMe/gVrs25j5PzNbArXMP3/GgPkvHi0MNxLMQLYkAm1hZmYEatlwIMeAmRGPFoMzb8yseQz+JwP9YszYuy0tceONHIODPefScTusPf3xbZ6KNNt+9ubHDD+32STOO3/G8MGPMmvcDoPYhcY/QED9KBgFo2AUjAICAACIO065JbMzaAAAAABJRU5ErkJggg==","orcid":"","institution":"United Arab Emirates University","correspondingAuthor":true,"prefix":"","firstName":"Susan","middleName":"","lastName":"Waller","suffix":""},{"id":588333572,"identity":"2cca93a2-e56f-4adf-9573-a88bc57f37fc","order_by":3,"name":"S. Ayhan Çalişkan","email":"","orcid":"","institution":"United Arab Emirates University","correspondingAuthor":false,"prefix":"","firstName":"S.","middleName":"Ayhan","lastName":"Çalişkan","suffix":""},{"id":588333575,"identity":"70dd69b9-f700-432f-81d4-69b1279a0745","order_by":4,"name":"Mohi Eldin Magzoub","email":"","orcid":"","institution":"United Arab Emirates University","correspondingAuthor":false,"prefix":"","firstName":"Mohi","middleName":"Eldin","lastName":"Magzoub","suffix":""}],"badges":[],"createdAt":"2025-11-15 11:23:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8121618/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8121618/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102330719,"identity":"8e203b15-11e8-48a8-a035-cd8656f4e22b","added_by":"auto","created_at":"2026-02-10 15:12:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":48816,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTrends across cohorts for PBL, Midterm exams, Final exams, and Final scores\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8121618/v1/61436796bbe87cea85758e1d.png"},{"id":102330671,"identity":"b242becc-478f-4a6a-9ede-56e2542dd571","added_by":"auto","created_at":"2026-02-10 15:12:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":46600,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLogins, videos completed, quiz questions attempted, and Q-bank questions attempted, compared across 5 cohorts.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8121618/v1/5155e2b61376b56a99895003.png"},{"id":102330664,"identity":"a8a632c9-0ba7-4794-b600-fc76fb6d212a","added_by":"auto","created_at":"2026-02-10 15:12:43","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":56942,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePerformance metrics on Quiz and Q-bank questions.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8121618/v1/81292e7832ef375249b9be45.png"},{"id":102397705,"identity":"d857862e-f8dd-46be-a68d-c589db6176d8","added_by":"auto","created_at":"2026-02-11 10:19:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1024703,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8121618/v1/b69a26a3-cba1-43b9-b3f7-913840a00b63.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eA multicohort study using an eLMS in the UAE The Impact of an eLearning platform on Medical Student Performance in Respiratory System Module: A Multi-Cohort Study in the United Arab Emirates\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eThe integration of e-learning platforms in medical education has significantly enhanced learning flexibility, accessibility, and interactivity. These platforms provide resources like video lectures, quizzes, and interactive case studies, accessible anytime and anywhere, as well as features that enable educators to optimize their teaching using assignments and learning analytics. The COVID-19 pandemic accelerated the adoption of e-learning in medical education, enabling continuity of studies despite disruptions (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Studies have shown that students generally had positive perceptions of e-learning during the pandemic, appreciating its flexibility and accessibility despite the challenges of limited faculty interaction and clinical experiences (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). However, other studies reported on increased stress during this period (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) and medical students experiencing loneliness and social isolation (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Beyond COVID-19, e-learning has been shown to be at least as effective as in person instructor-led methods in various medical education contexts, providing an appropriate alternative to in person methods and forming part of a blended learning strategy (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). The ability to repeatedly access learning materials and engage with interactive content allows for better retention and comprehension, as demonstrated by improved knowledge retention and student satisfaction in e-learning environments. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhile research has demonstrated the effectiveness of e-learning in medical education, there has been growing recognition that comparisons between e-learning and in person learning should shift towards efforts to optimize its impact. Rather than focusing solely on whether e-learning works, there is increasing interest in understanding how to enhance its effectiveness through tailored content delivery, adaptive learning technologies, and improved student engagement. Studies suggest that the success of e-learning depends on factors such as course design, instructor-learner interaction (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e), and institutional support (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Additionally, research highlights the need for e-learning platforms to incorporate strategies that align with learner needs, ensure interactivity, and provide feedback mechanisms to enhance effectiveness (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur study addresses this gap by examining not just whether the e-learning platform was effective, but how students engaged with it and whether their usage patterns correlate with academic performance. By analyzing student interaction with the platform—such as time spent on modules, quiz attempts, and accuracy—we aimed to identify specific factors that contribute to improved learning outcomes. This approach shifts the focus from a binary comparison of e-learning versus in person learning to a more nuanced understanding of how digital learning tools can be optimized for maximum impact (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). By linking engagement data with student performance, our findings will contribute to the broader conversation on how e-learning platforms can be refined to support student success more effectively.\u003c/p\u003e \u003cp\u003eThe College of Medicine and Health Sciences (CMHS) of the United Arab Emirates University (UAEU) has been using Lecturio in the respiratory system module for its preclinical medical students since 2019. Lecturio is a medical education platform that provides digital resources and AI-augmented features for students and educators, including personalized learning paths, interactive quizzes, and progress tracking.\u003c/p\u003e \u003cp\u003eThis study aims to identify the key factors that contribute to effective learning outcomes and optimize the impact of digital education in medical training. It thus explores Lecturio usage trends by medical students over six cohorts from 2019 to 2024, focusing on their interaction patterns and usage metrics and the impact on academic performance in the respiratory system module.\u003c/p\u003e\n\u003ch3\u003eObjectives and specific aims of the study\u003c/h3\u003e\n\u003cp\u003ePrimary objective\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eExploring the impact of Lecturio on student performance by:\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eComparing final grades between students active on the platform with those with minimal-to-no engagement.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eComparing platform usage metrics between students who passed and those who did not achieve the passing score of 75 percent.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eComparing platform usage metrics with student performance on the final course grade.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003cp\u003e\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eSecondary objectives\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eExamine trends of students’ performance on the Respiratory Medicine module over the years 2018–2024.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eExamine trends of students’ usage metrics on the Lecturio platform over the years 2019–2024.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e "},{"header":"Methods","content":"\u003cp\u003eThis study is grounded in several key contructivist learning theories (CLTs), including Adaptive Learning Theory, Cognitive Load theory, and Self-Directed Learning (SDL)(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Additionally, it incorporates principles of technology-enhanced learning in medical education and is situated within the broader context of blended learning models. These frameworks emphasize the role of adaptive e-learning platforms, such as Lecturio, in enhancing medical students' academic performance and professional development. The theoretical framework guides the study by:\u003c/p\u003e\u003cp\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) examining engagement metrics through the lens of self-directed and adapted learning principles,\u003c/p\u003e\u003cp\u003e(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) evaluating how adaptive features cater to diverse learning needs and reduce cognitive load, and by\u003c/p\u003e\u003cp\u003e(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) analyzing longitudinal trends to assess sustained improvements in academic outcomes.\u003c/p\u003e\u003ch3\u003eDesign\u003c/h3\u003e\u003cp\u003eThis retrospective observational cohort study was conducted at UAEU’s CMHS, involving third-year medical students completing the Respiratory System module (RSM) in third year of a 6 year prelicensure medical program from 2018 to 2024. Data was collected from Lecturio (platform usage metrics) and internal CMHS, UAEU sources (exam scores, passing rates, end of course marks).\u003c/p\u003e\u003ch3\u003eSetting\u003c/h3\u003e\u003cp\u003eUAEU is a public research-intensive university located in Al Ain, UAE made up of various colleges offering a wide variety of undergraduate, graduate programs and continuing education. The CMHS offers a six-year Doctor of Medicine program consisting of three, 2-year phases, premedical, preclinical and clinical. The five-weeks long RSM is part of the preclinical organ system integrated phase of the curriculum. It is structured around weekly case-based problems, incorporating problem-based learning (PBL) and team-based learning (TBL) approaches. Additional instructional methods include lectures, clinical skills sessions, and early clinical exposure to provide a comprehensive and applied learning experience. Assessment of the course include several instruments, weekly TBL/PBL assessment, mid-term and final multiple-choice questions (MCQs) assessments.\u003c/p\u003e\u003cp\u003eAt CMHS, UAEU, Lecturio, an AI-driven online learning platform, supports medical education through a subscription supported by the National Medical Library (NML). The platform offers video lectures, interactive study materials, quizzes, and flashcards covering diverse medical topics. Using adaptive learning algorithms powered by AI, Lecturio personalizes educational experiences by analyzing user interactions, performance, and preferences. It dynamically adjusts content, pace, and difficulty, continuously refining recommendations to address individual strengths and challenges. This approach enhances learning efficiency, engagement, and exam preparation for certifications such as the United States Medical Licensing Examination (USMLE). Educators can upload their own content and assign it in an interleaved fashion to their students, or assign videos for the students before the class, in a flipped classroom approach. CHMS has been using Lecturio during the RSM of the MD program for preclinical (Year 3) medical students from 2019 to the present.\u003c/p\u003e\u003ch3\u003eParticipants\u003c/h3\u003e\u003cp\u003eParticipants were third-year medical students, male and female with gender ratio of 70:30 (female/male) completing the RSM in the academic years from 2018 to 2024. Quantitative data was collected for all students, including platform usage data and grades.\u003c/p\u003e\u003ch3\u003eData collection\u003c/h3\u003e\u003cp\u003eData was collected from the following sources:\u003c/p\u003e\u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eLecturio data: quantitative data on platform usage: videos watched (average videos watched per user, average % of video /course finished, time spent, recall questions answered, percentage correct, perceived confidence level, performance on the question-bank (Q-Bank) and frequency of activity. This was collected from students in the RSM for the following five cohorts, 2019–2024.\u003c/p\u003e\u003cp\u003eData was retrieved from uaeu.lecturio.com and filtered by dates, limited to specific respiratory system module content. Users retrieved that did not have recorded scores as a UAEU student were excluded (test users or faculty).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cli\u003e \u003cp\u003eInternal UAEU data: exam scores (Case based Tests, TBL, PBL, Midterm, Final), passing rates, and end of course marks for the same cohorts.\u003c/p\u003e \u003c/li\u003e \u003c/ol\u003e\u003ch2\u003eData analysis\u003c/h2\u003e\u003cp\u003eAll data on student performance was collected in a Microsoft Excel spreadsheet. Lecturio usage data and student performance data were imported into SPSS 20 (IBM SPSS Statistics) for the purpose of descriptive analysis. Continuous data was recorded as means, standard deviations, levels of significance and categorical data as frequencies and percentages.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003ePerformance Trends\u003c/h2\u003e \u003cp\u003eThe data analyzed covered 6 cohorts. There were 75 students in Cohort 1, 82 students in Cohort 2, 84 in Cohort 3, 76 in Cohort 4, 107 in Cohort 5, and 108 in Cohort 6, for a total of see.\u003c/p\u003e \u003cp\u003eAverage final performance on RSM ranged from 82.68% (2022\u0026ndash;2023) to 88.13% (2018\u0026ndash;2019).\u003c/p\u003e \u003cp\u003eThe structure of assessments evolved across cohorts. Earlier cohorts had separate tests for each case, while later cohorts had combined case based testing and incorporated teaching and learning activities such as Problem-Based Learning (PBL) and Team-Based Learning (TBL). Despite these changes in teaching and assessment structure, mean percentage scores across most assessments remained consistent, demonstrating similar learning outcomes.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e below illustrates the trends in PBL (Problem-Based Learning), Midterm exam, Final exam, and Final score percentages across the six cohorts (2018\u0026ndash;2019 to 2023\u0026ndash;2024) in the respiratory system module.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePass Rates and Grade Distribution\u003c/h2\u003e \u003cp\u003eOverall pass rates (A-C grades) were high across all cohorts, ranging from 88% (2022\u0026ndash;2023) to 98% (2019\u0026ndash;2020). The percentage of students receiving A grades generally decreased over the cohorts, from 43% in 2018\u0026ndash;2019 to 14% in 2022\u0026ndash;2023. There was a shift in grade distribution from earlier cohorts (more A\u0026rsquo;s and B\u0026rsquo;s) to later cohorts (more B\u0026rsquo;s and C\u0026rsquo;s). See Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e below.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFinal score letter grades and pass rates for 6 student cohorts from 2018\u0026ndash;2024\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCohort\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"8\" nameend=\"c9\" namest=\"c2\"\u003e \u003cp\u003eLetter grade \u0026ndash; Number (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePass rate (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB+\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC+\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eD+\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCohort 1\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e2018\u0026ndash;2019\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eN\u0026thinsp;=\u0026thinsp;75\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32 (43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5 (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCohort 2\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e2019\u0026ndash;2020\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eN\u0026thinsp;=\u0026thinsp;82\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22 (27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15 (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2 (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCohort 3\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e2020\u0026ndash;2021\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eN\u0026thinsp;=\u0026thinsp;84\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 (32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17 (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8 (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7 (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2 (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCohort 4\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e2021\u0026ndash;2022\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eN\u0026thinsp;=\u0026thinsp;76\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5 (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2 (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2 (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCohort 5\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e2022\u0026ndash;2023\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eN\u0026thinsp;=\u0026thinsp;107\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30 (28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23 (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12 (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCohort 6\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e2023\u0026ndash;2024\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eN\u0026thinsp;=\u0026thinsp;108\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39 (36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31 (29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14 (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4 (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1 (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePlatform Usage Metrics and Trends\u003c/h2\u003e \u003cp\u003eThe data analyzed covered cohorts 2\u0026ndash;6 (Cohort 1 did not use Lecturio). There was a total of 457 students who used Lecturio over the study period.\u003c/p\u003e \u003cp\u003eThe first cohort had significantly higher usage as reflected in the number of logins (22.7), videos completed (31.0), quiz questions attempted (195.4). The number of Q-bank questions, however, was relatively low (2.2). Usage declined in subsequent cohorts but showed an increase in the most recent cohort, while Q-bank usage showed an increase over the years (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e below). It is worth noting that these are mean values and each cohort had a wide range of usage \u0026ndash; while the minimum number of logins was 0 for all cohorts, the maximum ranged from 52 to 148 logins during the study period.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePerformance on quiz questions averaged 75% correctness across the cohorts, with a notable outlier of 80% during the academic year 2020\u0026ndash;2021 (Cohort 2). This cohort also had the highest rate of correct quiz questions at the first attempt (75%), whereas the others ranged between 68% to 70%. The highest rate of mastered questions was also in the first cohort (10%). Q-bank performance followed a different trend. Data revealed the lowest performance in the first cohort (47% correct), which increased to 54% in Cohort 4 and then had a sharp increase to 73% in the final cohort (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e below).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eComparative Analyses\u003c/h2\u003e \u003cp\u003eStudents active on the platform (269) achieved significantly higher final scores (mean 85.50) compared to those with minimal-to-no activity (188) (mean 82.04, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Students who achieved a passing score of 75% in the course participated in significantly more platform activities, including more hours per week (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), logins (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), videos completed (p\u0026thinsp;=\u0026thinsp;0.003), quiz questions (p\u0026thinsp;=\u0026thinsp;0.003), and Q-bank questions attempted (p\u0026thinsp;=\u0026thinsp;0.037). Compared means are in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e (see below). The binary logistic regression model of these factors was not statistically significant, however.\u003c/p\u003e \u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Comparison of usage metrics between students who passed (\u0026gt;75 score) and those that did not.\u003c/strong\u003e\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePass (\u0026gt;75)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMean, SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFail (\u0026lt;75)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMean, SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eActivity\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eHours per week\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNumber of logins\u003c/p\u003e\n \u003cp\u003eLogins per week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.17, 0.32\u003c/p\u003e\n \u003cp\u003e11.13, 19.51\u003c/p\u003e\n \u003cp\u003e0.87, 1.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.06, 0.15\u003c/p\u003e\n \u003cp\u003e5.68, 14.10\u003c/p\u003e\n \u003cp\u003e0.44, 1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVideos\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eVideos started\u003c/p\u003e\n \u003cp\u003eVideos completed\u003c/p\u003e\n \u003cp\u003eWatched minutes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e12.71, 24.10\u003c/p\u003e\n \u003cp\u003e9.59, 20.78\u003c/p\u003e\n \u003cp\u003e58.36, 127.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e4.59, 11.69\u003c/p\u003e\n \u003cp\u003e3.47, 9.48\u003c/p\u003e\n \u003cp\u003e18.91, 51.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.052\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQuizzes\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eTotal questions answered\u003c/p\u003e\n \u003cp\u003eTotal unique questions\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e% Correct answers\u003c/p\u003e\n \u003cp\u003e% Correct at 1\u003csup\u003est\u003c/sup\u003e attempt\u003c/p\u003e\n \u003cp\u003e% Questions mastered\u003c/p\u003e\n \u003cp\u003eAverage repetitions to mastery\u003c/p\u003e\n \u003cp\u003eAnswered with high confidence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e62.55, 131.54\u003c/p\u003e\n \u003cp\u003e42.45, 83.24\u003c/p\u003e\n \u003cp\u003e76.03, 13.05\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e69.90, 16.66\u003c/p\u003e\n \u003cp\u003e4.10, 11.01\u003c/p\u003e\n \u003cp\u003e1.23, 0.30\u003c/p\u003e\n \u003cp\u003e100.29, 138.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e16.00, 37.60\u003c/p\u003e\n \u003cp\u003e12.29, 29.57\u003c/p\u003e\n \u003cp\u003e71.86, 8.09\u003c/p\u003e\n \u003cp\u003e64.00, 8.47\u003c/p\u003e\n \u003cp\u003e1.94, 9.51\u003c/p\u003e\n \u003cp\u003e1.3, 0.44\u003c/p\u003e\n \u003cp\u003e55.71, 52.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003cp\u003e0.402\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.353\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003cp\u003e0.855\u003c/p\u003e\n \u003cp\u003e0.571\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ-bank\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eTotal questions answered\u003c/p\u003e\n \u003cp\u003e% Correct answers\u003c/p\u003e\n \u003cp\u003e% Correct at 1\u003csup\u003est\u003c/sup\u003e attempt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e13.25, 40.94\u003c/p\u003e\n \u003cp\u003e59.85, 23.45\u003c/p\u003e\n \u003cp\u003e64.78, 26.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e5.65, 21.08\u003c/p\u003e\n \u003cp\u003e52.50, 17.75\u003c/p\u003e\n \u003cp\u003e55.00, 23.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003cp\u003e0.536\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.247\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStudy Planner\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eItems created\u003c/p\u003e\n \u003cp\u003e% Completed on time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e7.91, 9.59\u003c/p\u003e\n \u003cp\u003e14.47, 25.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3.65, 7.80\u003c/p\u003e\n \u003cp\u003e2.08, 3.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 520px;\"\u003e\n \u003cp\u003e\u003csup\u003ea\u003c/sup\u003ep-values determined by Student\u0026rsquo;s t-test, rest by Mann-Whitney U test\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e \u003cp\u003eCorrelation analysis revealed statistically significant, albeit mild, correlations between the activity metrics above and final course grades, including rate of correct quiz questions in addition to the above. The highest statistically significant correlation coefficient was noted for total Q-bank questions (0.185). Linear regression analysis showed that total quiz questions answered, and percentage correct at first attempt significantly predicted final scores (R-square\u0026thinsp;=\u0026thinsp;0.114, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe integration of the Lecturio e-learning platform into the module curriculum has maintained consistent student performance across six cohorts, despite variations in teaching methodologies and pandemic-related challenges.\u003c/p\u003e \u003cp\u003eThe findings of this study highlight key trends in student engagement with the e-learning platform and their relationship with academic performance. A notable observation was the high usage of the platform in the first year, likely influenced by the shift to online learning due to the COVID-19 pandemic. Similar trends have been documented in the literature, where the transition to remote learning increased student reliance on digital tools (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) ). The peak increase in the most recent cohort, particularly in watched minutes, may suggest growing familiarity with the platform, an increase in assignment-driven engagement, or evolving instructional strategies by faculty. Prior research indicates that faculty involvement plays a crucial role in e-learning adoption and effectiveness (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e), warranting further examination of how instructor practices influence student engagement.\u003c/p\u003e \u003cp\u003eThe variability in engagement levels could be influenced by other factors such as SDL capability, student motivation, prior academic performance, and integration of the platform within the broader curriculum (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). The lower pass rate in 2022\u0026ndash;2023 (88%) may be linked to challenges posed by fully remote preclinical education during the pandemic, with limited access to laboratories and foundational in-person teaching impacting preparation for the respiratory module. The maintenance of learning outcomes despite pandemic disruptions suggests that the e-learning platform may have played a role in mitigating the impact on student performance. These findings highlight the potential of digital learning tools to support medical education, particularly in times of crisis (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e), while underscoring the need for a balanced approach that integrates both online and face and in person teaching methods.\u003c/p\u003e \u003cp\u003eStudent performance on formative assessments has remained relatively consistent. This aligns with studies indicating that self-assessment tools such as quizzes contribute to learning retention but do not necessarily lead to substantial performance fluctuations unless coupled with targeted pedagogical interventions (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe study demonstrates the potential of e-learning platforms to maintain academic performance in medical education, even during periods of significant disruption (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). The most significant finding relates to the positive association between e-learning platform activity and final course performance. Engagement with key learning activities\u0026mdash;such as logins, video completion, and quiz participation\u0026mdash;was significantly higher among students who achieved a passing score of 75% or more. These findings align with research demonstrating that frequent interaction with online learning resources enhances academic outcomes (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). However, the lack of statistical significance in the binary logistic regression model suggests that while engagement contributes to better performance, other confounding factors may influence success.\u003c/p\u003e \u003cp\u003eCorrelation analysis revealed statistically significant, albeit mild, relationships between activity metrics and final grades. The strongest correlation was observed for total Q-bank questions attempted (0.185), reinforcing the idea that active retrieval practice through quizzes benefits knowledge retention (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Linear regression analysis further indicated that the number of quiz questions answered and the percentage correct at the first attempt were significant predictors of final scores (R\u0026sup2; = 0.114, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This finding is consistent with prior studies showing that accuracy on initial attempts are strong indicators of academic performance (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eWhile this study provides valuable insights into the relationship between e-learning engagement and academic performance, several limitations must be acknowledged. First, as a retrospective observational cohort study, causality cannot be established; the associations observed may be influenced by unmeasured confounders such as prior academic performance, self-regulated learning ability, or intrinsic motivation. Second, although the study spans six cohorts, variability in teaching methods, assessment formats, and external factors like the COVID-19 pandemic could have impacted both platform usage and student performance, complicating direct comparisons across years. Third, engagement was measured through platform metrics like logins, video completions, and quiz attempts, which may not fully capture the depth or quality of learning. Additionally, qualitative data on student perceptions or motivations for platform use were not collected, limiting interpretation of the engagement patterns observed. The regression model's limited predictive value (R\u0026sup2; = 0.114) also suggests that other important contributors to academic success were not accounted for. Future studies would benefit from a mixed-methods approach and longer-term follow-up to assess impacts on clinical competence and professional development.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eAs digital education continues to evolve, understanding how e-learning platforms integrate into broader educational models is crucial, particularly with the growing role of artificial intelligence (AI). AI-driven education tools are increasingly transforming how students interact with content, making learning experiences more adaptive and personalized (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). To maximize the benefits of these tools, it is essential to deeply understand how students engage with them and how they can be optimized for learning outcomes. This study provides key insights into the specific ways students interact with e-learning platforms, revealing patterns of engagement that are linked to academic success. By analyzing platform activity metrics, we gain a clearer understanding of how digital tools can be leveraged to enhance learning and support student achievement.\u003c/p\u003e \u003cp\u003eFurther research is needed to explore the long-term impact of e-learning platform engagement on academic performance and skill retention, particularly in relation to more emphasis on technology assisted CLTinspired teaching and learning underpinning contemporary eand future education on the health professions. Additionally, investigating the role of self-regulated learning strategies facilitated by these platforms could provide valuable insights into student adaptability and preparedness for clinical practice. Qualitative assessments of student experiences, motivation, and perceived effectiveness of e-learning tools would refine digital education strategies and optimize their integration into blended learning environments in medical education.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCMHS-College of Medicine and Health Sciences\u003c/p\u003e\n\u003cp\u003eUAEU- United Arab Emirates University\u003c/p\u003e\n\u003cp\u003eQuestion Bank-Q-bank\u003c/p\u003e\n\u003cp\u003eCoronavirus disease-COVID-19\u003c/p\u003e\n\u003cp\u003eSelf Directed Learning-SDL\u003c/p\u003e\n\u003cp\u003eRespiratory System module-RSM\u003c/p\u003e\n\u003cp\u003eProblem based Learning-PBL\u003c/p\u003e\n\u003cp\u003eTeam based learning-TBL\u003c/p\u003e\n\u003cp\u003eMultiple-choice questions-MCQs\u003c/p\u003e\n\u003cp\u003eUnited States Medical Licensing Examination-USMLE\u003c/p\u003e\n\u003cp\u003eNationa Medical Library-NML\u003c/p\u003e\n\u003cp\u003eArtificial Intelligence-AI\u003c/p\u003e\n\u003cp\u003eConstructivist Learning Theory (CLT)\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUAEU Social Science ethics committee reviewed, approved and assigned reference number ERSC_2022_787. The study was conducted in adherence to the principles of the Helsinki Declaration. Individual student consent was waived as the collection of data, LMS activity and assessment outcomes, is part of quality improvement and course review. On enrolment students consent to participate in quality improvement through program review. All data collected is collated before dissemination and no data is individually identifiable and hence the Social Sciences Committee of the UAEU waives further student consent for such low risk and quality improvement focused studies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor further data, please contact the first author Dr Mohammed Al-Houqani on [email protected]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAdonis Wazir receives a stipend from Lecturio for research and scholarship. The remaining authors declare that they have no conflict of interest to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable because this was a qualitative study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors and Affiliations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMohammed Al-Houqani\u003csup\u003e1*\u003c/sup\u003e, Adonis Wazir, \u003csup\u003e2\u0026nbsp;\u003c/sup\u003eSusan Waller \u003csup\u003e3, 4*\u003c/sup\u003e, \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eS.Ayhan \u0026Ccedil;alışkan \u003csup\u003e3,\u0026nbsp;\u003c/sup\u003eMohi Eldin Magzoub \u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e1. National Institute for Health Specialities, United Arab Emirates University, Al Ain, United Arab Emirates.\u003c/p\u003e\n\u003cp\u003e2. Surrey and Sussex Healthcare NHS Trust, Surrey, United Kingdom\u003c/p\u003e\n\u003cp\u003e3. Department of Medical Education, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates.\u003c/p\u003e\n\u003cp\u003e4. Monash Rural Health, Monash University, Warragul, Australia.\u003c/p\u003e\n\u003cp\u003e* Corresponding authors: Susan Waller https://orcid.org/0000-0002-6309-0360\u003c/p\u003e\n\u003cp\u003eMohammed Al-Houqani https://orcid.org/0000-0003-4481-3911\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMH: Conceptualization, Methodology, Data curation, Formal analysis, Project administration, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing. AW: Data curation, analysis, writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing. SW: \u0026nbsp;Analysis, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing. SC: \u0026nbsp;Analysis, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing. MM \u0026nbsp; Analysis, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to express their gratitude to the faculty and students of the CMHS, UAEU, for their participation and valuable feedback during the study. Special thanks to the National Medical Library staff for facilitating access to the Lecturio platform and supporting this research initiative.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDelungahawatta T, Dunne SS, Hyde S, Halpenny L, McGrath D, O'Regan A, et al. Advances in e-learning in undergraduate clinical medicine: a systematic review. BMC Med Educ. 2022;22(1):711.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEllaway R, Masters K. AMEE Guide 32: e-Learning in medical education Part 1: Learning, teaching and assessment. Med Teach. 2008;30(5):455\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWynter L, Burgess A, Kalman E, Heron JE, Bleasel J. Medical students: what educational resources are they using? BMC Med Educ. 2019;19(1):36.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVaona A, Banzi R, Kwag KH, Rigon G, Cereda D, Pecoraro V, et al. E-learning for health professionals. Cochrane Database Syst Rev. 2018;1(1):Cd011736.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlsoufi A, Alsuyihili A, Msherghi A, Elhadi A, Atiyah H, Ashini A, et al. Impact of the COVID-19 pandemic on medical education: Medical students' knowledge, attitudes, and practices regarding electronic learning. PLoS ONE. 2020;15(11):e0242905.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTabatabai S. COVID-19 impact and virtual medical education. J Adv Med Educ Prof. 2020;8(3):140\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDevi B, Sharma C. Blended learning-A global solution in the age of COVID-19. J Pharm Res Int. 2021;33(41B):125\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKedia P, Mishra L. Exploring the factors influencing the effectiveness of online learning: A study on college students. 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Online and Remote Learning in Higher Education Institutes: A Necessity in light of COVID-19 Pandemic. High Educ Stud. 2020;10:16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHodges C, Moore S, Lockee B, Trust T, Bond A. The difference between emergency remote teaching and online learning. Educause Rev [Internet]. 2020.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMartin F, Polly D, Coles S, Wang C. Examining Higher Education Faculty Use of Current Digital Technologies: Importance, Competence, and Motivation. Int J Teach Learn High Educ. 2020;32:73\u0026ndash;86.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang X, Chen W, Qiu H, Eldurssi A, Xie F, Shen J, editors. A Survey on the E-learning platforms used during COVID-19. 2020 11th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON); 2020: IEEE.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSutoi D, Bazavan CO, Sutoi M, Petrica A, Marza AM, Trebuian CI, et al. The Learning Experience of Romanian Medical Students During the Online Teaching Imposed by the COVID-19 Pandemic. Adv Med Educ Pract. 2023;14:1077\u0026ndash;86.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingaram VS, Naidoo KL, Singh S. Self-Directed Learning During the COVID-19 Pandemic: Perspectives of South African Final-Year Health Professions Students. Adv Med Educ Pract. 2022;13:1\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXia Y, Hu Y, Wu C, Yang L, Lei M. Challenges of online learning amid the COVID-19: College students' perspective. Front Psychol. 2022;13:1037311.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMartin P, McGrail M, Fox J, Ostini R, Doyle Z, Playford D, et al. Impact of the COVID-19 pandemic on student experiences during rural placements in Australia: findings from a national multi-centre survey. BMC Med Educ. 2022;22(1):852.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKibble JD. Best practices in summative assessment. Adv Physiol Educ. 2017;41(1):110\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBroadbent J, Poon WL. Self-regulated learning strategies \u0026amp; academic achievement in online higher education learning environments: A systematic review. Internet High Educ. 2015;27:1\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeem B-H, Leem B-H. Impact of interactivity on learning outcome in online learning settings: Ordinal logit model. Int J Eng Bus Manage. 2023-09;15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoediger H, Butler A. The critical role of retrieval practice in long-term retention. Trends Cogn Sci. 2011;15(1):20\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eK\u0026uuml;hbeck F, Berberat PO, Engelhardt S, Sarikas A, K\u0026uuml;hbeck F, Berberat PO, et al. Correlation of online assessment parameters with summative exam performance in undergraduate medical education of pharmacology: a prospective cohort study. BMC Med Educ. 2019;19:1. 2019-11-08;19(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMishra S. Transformative Impact of AI on Education: Personalizing Learning and Addressing Challenges. Int J Res Appl Sci Eng Technol. 2024;12:1183\u0026ndash;9.\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":"E-Learning, Online Learning, Engagement, Medical Education, Self-Directed Learning","lastPublishedDoi":"10.21203/rs.3.rs-8121618/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8121618/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThis study investigates how medical student engagement with an e-learning platform (Lecturio) correlates with academic performance in a respiratory system module at the College of Medicine and Health Sciences (CMHS), United Arab Emirates University (UAEU).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA retrospective cohort study analyzed six student cohorts (2018\u0026ndash;2024) using engagement metrics from Lecturio and academic outcomes from internal assessments. Metrics included videos watched, logins, quiz and Question-bank (Q-bank) activity, and quiz accuracy.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003ePlatform engagement was highest in the first year and varied across subsequent cohorts. Students who actively used the platform achieved significantly higher final scores than those with minimal engagement (85.50 vs. 82.04, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Key engagement metrics\u0026mdash;particularly quiz questions attempted and accuracy on first attempt\u0026mdash;were significantly associated with academic performance.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eStudent interaction with e-learning tools is positively associated with academic success. This study highlights specific engagement patterns that correlate with improved outcomes, supporting the integration of digital platforms into blended medical curricula. Future research should explore long-term effects, qualitative student experiences, and the role of e-learning in professional skill development and lifelong learning.\u003c/p\u003e","manuscriptTitle":"A multicohort study using an eLMS in the UAE The Impact of an eLearning platform on Medical Student Performance in Respiratory System Module: A Multi-Cohort Study in the United Arab Emirates","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-10 15:11:43","doi":"10.21203/rs.3.rs-8121618/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-02T03:52:26+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-26T04:05:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"47696073875835288266413608150323904303","date":"2026-02-26T00:43:18+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-15T11:00:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"159949725488584614766171713421460878086","date":"2026-02-09T13:49:21+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-05T19:50:02+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-03T11:49:39+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-08T12:59:13+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-06T04:36:21+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Education","date":"2025-12-06T04:30:34+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":"8fe619c3-d42b-4943-9324-83ef7d9c1a22","owner":[],"postedDate":"February 10th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-03-02T04:09:30+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-10 15:11:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8121618","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8121618","identity":"rs-8121618","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}