Exploring the Influence of Different Learning Activities on Medical Students' Psychological Pathways in Ultrasound Acquisition

Exploring the Influence of Different Learning Activities on Medical Students' Psychological Pathways in Ultrasound Acquisition | 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 Exploring the Influence of Different Learning Activities on Medical Students' Psychological Pathways in Ultrasound Acquisition Yu-Ting Huang, Enoch Yi-No Kang, Daniel Salcedo, Che-Wei Lin, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4650325/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Acquiring proficiency in medicine typically necessitates a combination of foundational knowledge and hands-on experience. However, how do lecture and hands-on practice affect the psychological learning process in ultrasound education remains unclear. The purpose of this research was to test how the different learning activities associated with the psychomotor domain, elucidating their connections with cognitive and affective domains in the context of ultrasound education. Method This study is originally based on the post-course survey of the Parallel Ultrasound Hands-on (PUSH) trial. The survey was done by 127 third-year medical students with information regarding attending times and learning self-efficacy scale that consisted of 12 items with adequate reliability (Cronbach's α =0.9). A partial least square structural equation modeling was used for analyzing the data. Results Attending times of lecture was positively associated with cognitive ( β =0.343; 95% CI: 0.093 to 0.567) and psychomotor domain ( β =0.252; 95% CI: 0.066 to 0.452), but hands-on practice was only significantly associated with psychomotor domain ( β =0.208; 95% CI: 0.043 to 0.376). Conclusion Lectures and hands-on practice exert varying impacts on medical students' psychological pathways involved in learning ultrasound. The combination learning design could be flexible at the time table of the course, but also be required with minimal attendance to the class. Bloom’s taxonomy Undergraduate Ultrasound education clinical skills Psychological Pathway Figures Figure 1 Figure 2 1. Introduction Ultrasound is used as the modern stethoscope for its efficacy, accuracy, and portability (Feilchenfeld et al., 2018). The applications of ultrasound present widely in cardiology, obstetrics and gynecology, and gastrointestinal medicine. Education of ultrasound is emerging at the preclinical level (Kameda et al., 2022). In America, it is reported that over 70 percent of the medical schools manage to integrate ultrasound into formal curricula (Nicholas et al., 2021). Although the learning of ultrasound presents high heterogeneity of contents and the learning time (Kameda et al., 2022), most learning modules consist of two kind of activities in terms of knowledge acquisition and skill practice (Finn et al., 2012; Knobe et al., 2012; Kondrashov et al., 2015; Rempell et al., 2016; Griksaitis et al., 2012; Dreher et al., 2014; Canty et al., 2015; Chen et al., 2022). It is reasonable to develop an ultrasound learning course that incorporates both theoretical understanding and practical application is essential (Bordage, 2009), given that proficiency in medicine relies on a balance between medical knowledge and hands-on experience. Both knowledge delivery and hand-on practice are important to learning ultrasound, and some studies have proven the benefits of the integrated learning module (Chen et al., 2022; Kondrashov et al., 2015; Rempell et al., 2016; Dreher et al., 2014); while insufficient empirical evidence exists to delineate the impact of diverse learning activities, such as lectures on fundamental knowledge and hands-on practice, on the psychological learning process. A comprehensive understanding of how these distinct learning modalities influence the psychological mechanisms of learning stands as a fundamental prerequisite for the enhancement of ultrasound courses within medical education. Moreover, such insights hold promise for mitigating the challenges encountered by medical students in effectively integrating theoretical knowledge into clinical practice during the crucial stages of their professional development. Early acquisition of ultrasound can not only increase medical students' skills by psychomotor training for clinical work, but it can also benefit the basic medical science in preclinical learning. For example, ultrasound features real-time images, which can help understanding physiology and building a three-dimensional picture of anatomy. While some students learn the ultrasound for its prominent strengths in clinical work, some learn it out of pure interest (Kameda et al., 2022). In other words, many medical students are motivated by self-growth needs rather than relatedness needs (Chen et al., 2022). The process of learning activity was explained by Bloom's taxonomy into three core domains: the cognitive domain, affective domain, and psychomotor domain (Krathwohl, 2002). More concretely, the cognitive domain is related to how students comprehend the content of knowledge; the affective domain is related to a student's value and commitment toward this learning; the psychomotor domain is the demonstration of a particular skill (clinical skills). However, in the preclinical medical education, there was so far only limited research based on Bloom's taxonomy. For clinical procedures including ultrasound, the psychomotor domain is often set as the training goal. However, the clinical applications and skills learning in medicine also hold professionality, which usually rely on the construction of knowledge and interests. In reference to the framework of affective domains in the Bloom's taxonomy, receiving and responding can be the base of valuing. In other words, through enhancing the understanding of medical knowledge, medical students evaluate the importance and decide their learning attitude, including paying more attention or active learning. When a student arouses one's interests, one can also increase one's motivation and engagement (Järvelä and Renninger, 2014). Briefly, it is worthy of further investigation that analyze the psychological learning process from learning activities, the cognitive domain, the affective domain, to the psychomotor domain. Thus, we would like to propose a mediation model based on the sequence of the psychological learning process in medical education. Also, researchers from medical education highlight the importance of self-efficacy in both skill acquisition and professional identity (Bulfone et al., 2016). In different educations, self-efficacy has been proved associated with motivation and achievements (Karatay and Bas, 2019), and in medical education, higher self-efficacy can lead to a better performance of skills with less anxiety and better preparedness (Chen et al., 2022). These findings might support an urgent need for early integration of ultrasound into undergraduate education. Among all the possible methods to evaluate students' learning activity, self-efficacy is assessed generally in medical education (Mavis, 2001), while most of the self-efficacy scales focus on the clinical skills and learning outcomes. Learning self-efficacy demonstrates the learning process of learners, especially the psychological domain. Learning self-efficacy scale (L-SES), built on three domains of Bloom's taxonomy, is widely used in different areas and even translated to other languages. The previous studies only presented scores from each domain but lacked interactions between different domains. Clarifying the relationships of cognitive, affective, and psychomotor domains can therefore assist educators to design an effective course for psychomotor training. The purpose of this research was to explore the path of participations of learning activities and psychological domains in ultrasound course. Accordingly, hypotheses had been raised before this study (Figure 1), and they were listed as follows: Hypothesis 1 Attending times of lecture and hands-on practice are positively associated with psychomotor domain (directly and indirectly). Hypothesis 2 Attending times of lecture and hands-on practice are positively associated with affective domain (directly and indirectly). Hypothesis 3 Attending times of lecture and hands-on practice are positively associated with cognitive domain (directly and indirectly). Hypothesis 4 Cognitive domain is positively associated with psychomotor domain (directly and indirectly). Hypothesis 5 Cognitive domain is positively associated with affective domain (directly). Hypothesis 6 Affective domain is positively associated with psychomotor domain (directly). 2. Methods This study is originally based on the Parallel Ultrasound Hands-on (PUSH) trial in 2022 (Chen et al., 2022 ). Due to the cross-over design, participants completed the PUSH course prior to engaging in the post-course survey. The present study serves as a post-hoc analysis of the PUSH trial, aimed at delving deeper into the relationship between course participation and the psychological learning process, leveraging attendance records and post-course survey data. 2.1. Course Design The PUSH trial was designed as a parallel course, while these participants were also in the regular anatomy classes. The course had seven 40-minute lectures in total, and there were also two additional hand-on practice workshops held between lectures. In the hand-on workshops, every workshop was held in 120-minute, and each student can practice for at least 15-minute and observe other students for 45-minute. Other details can be found in the previous study (Chen et al., 2022 ). 2.2. Participants In this trial, they enrolled 140 undergraduate third-year medical students from Taipei Medical University in Taiwan. All the participants joined the course with free will, and there were no drop-out within the whole trial. A total of 127 students out of the 140 PUSH course participants completed the post-course survey, which is the data source of the present study. 2.3. Instrument The post-course survey in the PUSH trial measured learning self-efficacy using a developed and valid scale for the understanding of the psychological learning process (Chen et al., 2022 ). The learning self-efficacy scale (L-SES) was conducted to test the learners' confidence in the learning ''process'' based on Bloom's taxonomy of learning objectives. The scale consists of three dimensions, including cognitive, affective, and psychomotor domains. Each dimension consists of four items, and there were twelve items in total. The students can score the items from one to five points, one for the most disagreed and five for the most agreed. The internal consistency reliability was calculated via Cronbach’s α correlation coefficient. The analysis of Cronbach's α correlation coefficient showed α = 0.9 for the whole scale, α = 0.9 for the “cognitive” domain, α = 0.81 for the “affective” domain, and α = 0.84 for the “psychomotor” domain. 2.4. Analysis and Statistics Statistical analysis was carried out in R version 4.2.2, in which descriptive statistics was calculated using package “stats,” and Cronbach’s alpha was obtained using package “psych.” Main analysis was based on partial least square structural equation modeling (PLS-SEM) that was conducted using package “seminr.” Due to interests of this study and practical considerations, hand-on practice and lecture were defined as composite indicators in the PLS-SEM. Items of the L-SES were set as reflective indicators for the latent variables in terms of the three domains of learning self-efficacy. To assess the statistical significance, bootstrapping procedure with 1,000 times resampling was applied in both factor analysis and the mediation analysis. Given that PLS-SEM operates independently of covariance-based methodologies, it forgoes fit measurements, pivoting instead towards the evaluation of validity (Hair Jr et al., 2017 ; Ringle et al., 2015 ). We evaluated discriminant validity using both Fornell & Larcker criterion and heterotrait-monotrait ratio of correlations (HTMT) (Ringle et al., 2015 ; Ab Hamid et al., 2017 ; Afthanorhan et al., 2021 ). Convergent validity of this PLS-SEM was evaluated by both composite reliability (CR) and average variance extracted (AVE). Besides, we also evaluated multicollinearity in this model based on variance inflation factor (VIF) (MacKinnon et al., 2002 ; Taylor et al., 2008 ). 3. Results 3.1. Descriptive statistics and factor loading Table 1 represents the mean scores and factor loadings on the composite indicators (hand-on practice and lectures) in the structural equation model of learning process and different items in the learning self-efficacy scale. Table 1 Descriptive statistics of mean scores and factor loadings on the composite indicators (hand-on practice and lectures) in the structural equation model of learning process Observation Original Bootstrapping item Mean SD Construct factor loading Factor loading LCI UCI Lecture 5.760 1.560 Lecture 1 1 1 1 Practice 1.070 0.640 Practice 1 1 1 1 LSES 3.860 0.560 LSESC 3.910 0.670 LSESC1 3.870 0.770 Cognitive domain 0.824 0.824 0.707 0.911 LSESC2 3.960 0.750 Cognitive domain 0.804 0.799 0.661 0.897 LSESC3 4.090 0.660 Cognitive domain 0.886 0.872 0.746 0.955 LSESC4 3.720 0.890 Cognitive domain 0.794 0.795 0.68 0.893 LSESA 3.330 0.660 LSESA1 2.300 0.910 Affective domain 0.509 0.506 0.145 0.785 LSESA2 3.790 0.850 Affective domain 0.836 0.823 0.665 0.977 LSESA3 3.760 0.780 Affective domain 0.767 0.755 0.531 0.912 LSESA4 3.470 0.770 Affective domain 0.794 0.78 0.583 0.927 LSESP 3.790 0.690 LSESP1 3.510 0.930 Psychomotor domain 0.62 0.621 0.446 0.751 LSESP2 3.460 0.920 Psychomotor domain 0.727 0.729 0.569 0.85 LSESP3 4.130 0.760 Psychomotor domain 0.862 0.848 0.715 0.945 LSESP4 4.050 0.750 Psychomotor domain 0.816 0.804 0.638 0.922 LCI, lower limit of 95% confidence interval; UCI, upper limit of 95% confidence interval; SD, standard deviation. Students can decide autonomously how many courses they want to take once they join the project. Within seven lectures, the students participated in the class for 5.76 times on average (SD = 1.56). Most of the students constantly went to the class seven times ( n = 58), but some students joined less than half of the course (n = 15). Within 2 times of hand-on practices, the mean participation was 1.07 times (SD = 0.64). There were around 24% of students ( n = 31) did hand-on practice twice or more times, half of students only went to hand-on practice for one time ( n = 74; 58%), and a few students did not go to hand-on practice ( n = 22; 17%). The mean score of the total learning self-efficacy scale was 3.86 (SD = 0.56), and the mean scores of all the items ranged from 2.30 (SD = 0.91) to 4.13 (SD = 0.76). Among all, the mean score of cognitive domains of the learning self-efficacy scale was 3.91 (SD = 0.67); that of the affective domain was 3.33 (SD = 0.66); that of the psychomotor domain was 3.79 (0.69). The variance between each item and the three domains were determined by factor loadings, and all of the factor loadings were above 0.5 which achieves expected threshold (Hair et al., 2010). The factor loadings of the cognitive domains ranged from 0.794 to 0.886; those of the affective domains were from 0.509 to 0.836, and those of the psychomotor domains were from 0.62 to 0.862. Notably, both the lowest mean scores and the factor loadings sat in the item A1. The results showed significant differences under assessment of bootstrapping technique. 4.1. Structural equation modeling Figure 2 presented the structural equation modeling of the mutual effect of lecture and hand-on practice on the cognitive domain, affective domain, and psychomotor domain of self-efficacy. While hand-on practice improved the psychomotor domain of self-efficacy, lecture had a relatively direct effect on the cognitive domain of self-efficacy. Table 2 showed the path coefficient of the structural equation modeling of learning self-efficacy scale was assessed both directly and indirectly. The first path presented the association between the times of lectures and learning self-efficacy. The beta coefficient of path from lecture to cognitive domain was 0.36 (bootstrapped β = 0.343; 95% CI: 0.093 to 0.567), which was the only path that reached statistical significance; nevertheless, the path from lecture to affective domain and psychomotor domain showed no significant effects. The path from lecture to psychomotor domain was also examined indirectly via cognitive domain and via affective domain. While the results indirectly from lecture to psychomotor domain via affective domain remained insignificant, the beta coefficient of path of lecture to psychomotor domain via cognitive domain was 0.265 (bootstrapped β = 0.252; 95% CI: 0.066 to 0.452), which also met statistical significance. Table 2 Direct and indirect path coefficient of the structural equation modeling of learning self-efficacy scale Original Bootstrapping Path coefficient Coefficient LCI UCI Lecture to cognitive domain 0.360 0.343 0.093 0.567 Lecture to affective domain -0.051 -0.059 -0.284 0.169 Lecture to psychomotor domain Direct -0.051 -0.057 -0.185 0.087 Indirect Via cognitive domain 0.265 0.252 0.066 0.452 Via affective domain -0.004 -0.005 -0.054 0.030 Practice to cognitive domain 0.030 0.034 -0.152 0.209 Practice to affective domain 0.066 0.070 -0.130 0.274 Practice to psychomotor domain Direct 0.208 0.213 0.043 0.376 Indirect Via cognitive domain 0.022 0.025 -0.111 0.152 Via affective domain 0.006 0.006 -0.026 0.050 LCI, lower limit of 95% confidence interval; UCI, upper limit of 95% confidence interval. The second path presented the association between the times of hand-on practice and learning self-efficacy. The beta coefficient of path from practice to both cognitive domain (β = 0.030) and affective domain (β = 0.066) were insignificant. However, the path coefficient from practice to psychomotor domain directly was 0.208 (bootstrapped β = 0.213; 95% CI: 0.043 to 0.376), which also indicates statistical significance. On the contrary, the indirect path from practice to psychomotor domain via neither cognitive domain nor affective domain showed significant results. 5. Discussion Based on Bloom's taxonomy, the present findings from the mediation model demonstrate the associations between learning activities and self-efficacy on cognitive and psychomotor domains. Unfortunately, non-significant association could be found between learning activities and learning self-efficacy of affective domain. Lecture is significantly associated with cognitive learning self-efficacy, while there seems to be no direct effects on the psychomotor learning self-efficacy. On the other hand, hands-on practice affects psychomotor learning self-efficacy significantly, but it has no effect on cognitive learning self-efficacy. From perspectives of psychological processes, briefly, our study confirms that different types of learning activities have their own advantages in enhancing ultrasound education. Besides, the current research also confirms the importance of cognitive learning self-efficacy between learning activities (i.e. lectures and hands-on practices) and psychomotor. In this mediation model of learning self-efficacy, composite indicators like lecture and hands-on practice take an important role in the learning process. In the process of constructing medical professions, many medical students find it a challenge to bridge the preclinical learning and clinical work. In recent years, a simulation workshop, which builds a relatively authentic situation for medical students to experience, is praised for its advantages of effective learning and bridging theoretical knowledge in a clinical setting (Weller, 2004 ). Simulation has been proven to be an effective approach to build a relatively authentic situation for health professional trainees to experience (Okuda et al., 2009 ), and has advantages of effective learning and bridging theoretical knowledge in clinical settings (Weller, 2004 ). The PUSH trial we used had been designed in accordance with the concepts of simulation, as it provided repeated practice without impacting patients' safety. From the analysis in our research, it should be noted that the both lecture and hand-on practice influence psychomotor domains, but they appear to impact psychomotor via different psychological mechanism. Both lecture and hand-on practice can establish knowledge to perform the clinical procedure (e.g. ultrasound) correctly, and with the profound knowledge of techniques, students can cultivate their abilities of self-error-correction. With self-error-correction, the simulation process became one's own ''learning experience'', and it further led to better performance on psychomotor skills (Chickering, 1977 ). Overall, we thus suggest the lecture design of medical education should combine adequate practicing amounts. However, our finding shows a relatively weaker role of the affective domains of self-efficacy. Theoretically, the evaluating process in the affective domains demonstrates the importance of the learning in clinical work or personal interests, it can also be affected by pressure. For medical students in the preclinical stage, the existence-pressure of being an independent doctor are also their main learning motivations (Chen et al., 2022 ), and the pressure can affect the learning process. Nevertheless, the PUSH trial was designed as additional parallel learning activities, in which the students' can decide whether they should attend the lecture and practice out of their interests (Chen et al., 2022 ). Seeing that, the participants might still own certain degrees of affection. In this trial, while their pressure from clinical expectation in the future was uneasy to be assessed, we attempted to demonstrate their affective domains of self-efficacy by research design. One possible reason for the weaker role of affective domains is measurement. The PUSH trial measured affective domain using reflective indicators rather than direct item (e.g. “Do you like to learn ultrasound…?”). Besides, some of the questions regarding the affective domains might also be correlated to the cognitive and psychomotor domains. For example, item A6 presents the ''gain'' in the learning process, which can also be thought of as gaining knowledge and related to the cognitive domains. In fact, when the learning self-efficacy scale was translated into another language, item A5 showed lower factor loadings, which could be explained that spending more time not only presented one's interests but also showed difficult learning (Bayazit et al., 2022 ). The role of affective domain in the present model remains unclear, and further studies are necessary to develop a more general model. For those who would like to design courses for psychomotor training in medical education, from our analysis, we suggest the course-design should combine both lecture and sufficient hand-on practice. Also, in the PUSH trial, both lectures and additional practice were conducted optionally, and the combination learning module brought effective learning outcomes. Thus, we encourage teachers to adequately integrate self-directed learning designs in clinical skill courses. The self-directed learning curriculum should be flexible at the time table of the course, but also be required with minimal attendance to the class. A certain degree of orientation is also recommended in the beginning of this sort of self-directed learning, so that the students can have a blue-print for their learning. 6. Limitations There are some limitations in this study. As this study is built on the PUSH trial, there are similar self-selection biases and restriction of course-design. However, the findings of this study remain valuable due to the variability in participation across learning activities, whether in lectures or hands-on practice. In essence, the diversity in participation levels offers insights into the psychological learning process, even among medical students with high motivation to learn ultrasound. Second, this study inherits a limitation from the PUSH trial, namely, the relatively small sample size. Typically, a psychological modeling analysis would aim to recruit at least 200 participants. Recognizing this constraint, we proactively considered it when proposing this post-hoc analysis. To ensure the robustness of the study, we employed advanced statistical techniques, specifically partial least squares structural equation modeling (Hair Jr et al., 2017 ; Reinartz et al., 2009 ). This method is less constrained by sample size requirements compared to covariance-based structural equation modeling, making it suitable for studies with limited sample sizes or complex models. Unlike covariance-based structural equation modeling, partial least square structural equation modeling does not rely on strict assumptions about the distribution of data, such as multivariate normality, making it more flexible and robust. In practice, maintaining extra learning activities proves challenging without additional resources like the PUSH course to increase sample size. The study's value lies in furnishing stakeholders with compelling evidence to support the incorporation of such course designs into contemporary educational practices. Third, although we emphasize both quality and quantity of hand-on practice, we could not manage to analyze the plateau of effects of the amount of practice due to the limited hand-on practice times in the original trial. Hence, we recommend that researchers endeavor to circumvent these limitations when applying this meditation model to other clinical skills learning contexts. 7. Conclusions This study explores the impact of lectures and hands-on practice on the psychological pathways of ultrasound skills acquisition among medical students. It demonstrates that both forms of learning activities influence psychomotor skills but operate through distinct psychological mechanisms. For learning ultrasound, an optimal design would be a combination of lectures and sufficient hands-on practice. The role of affective domain remains unclear in the present study, but the results might show a different pattern in learning other clinical skills by using this mediation model for exploring the psychological learning process. Declarations Ethical Approval: The study was approved by Taipei Medical University Joint Institutional Review Board (TMU-JIRB) for Human Experimentation. The number of IRB was TMU-JIRB N201909012. Consent for publication : Not applicable. Availability of data and materials : All data generated or analyzed during this study are included in this article. Competing Interest : The authors declare no competing financial interest. Funding : The present study is funded by Wan Fang Hospital, Taipei Medical University Research Grant 110-wf-eva-23, 110-wf-eva-21 Author contributions : YTH, YNK, DS, WCH contributed to the design of the study, collection and analysis of data and drafting the manuscript. CWL, KCH, CWH, CYC participated in the critical review of the manuscript, and contributed analysis tools, and made substantial contributions to interpretation of data. All authors read and approved the final manuscript. Acknowledgements : Special thanks to Miss Hao-Yu Chen who is the administrator in Center for Education in Medical Simulation (CEMS), Taipei Medical University, Taiwan for collections of students’ data. References Ab Hamid, M., Sami, W., & Sidek, M. M. (2017). 890 'Discriminant validity assessment: Use of Fornell & Larcker criterion versus HTMT criterion' Journal of Physics: Conference Series . IOP Publishing, p. 012163 1. 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S., Saldana, F., DiSalvo, D., Kumar, N., Stone, M. B., et al. (2016). Pilot Point-of-Care Ultrasound Curriculum at Harvard Medical School: Early Experience. West J Emerg Med , 17 (6), 734–740. 10.5811/westjem.2016.8.31387 . Ringle, C. M., Wende, S., & Becker, J. M. (2015). SmartPLS 3. Retrieved Aug. 13, from https://www.smartpls.com . Taylor, A. B., MacKinnon, D. P., & Tein, J. Y. (2008). Tests of the three-path mediated effect. Organizational research methods , 11 (2), 241–269. Weller, J. M. (2004). Simulation in undergraduate medical education: bridging the gap between theory and practice. Medical Education , 38 (1), 32–38. 10.1111/j.1365-2923.2004.01739.x . Additional Declarations No competing interests reported. Supplementary Files TableS1.xlsx Table S1 Summary of discriminant validity assessment. TableS2.xlsx Table S2 Summary of convergent validity assessment TableS3.xlsx Table S3 Summary of multicollinearity assessment Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4650325","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":328110734,"identity":"fae6dfab-e5b1-4ebe-89fd-8592f3e07316","order_by":0,"name":"Yu-Ting Huang","email":"","orcid":"","institution":"Taipei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yu-Ting","middleName":"","lastName":"Huang","suffix":""},{"id":328110741,"identity":"48783d90-8079-4c68-b4c2-750f55e99d33","order_by":1,"name":"Enoch Yi-No Kang","email":"","orcid":"","institution":"Wan Fang Hospital","correspondingAuthor":false,"prefix":"","firstName":"Enoch","middleName":"Yi-No","lastName":"Kang","suffix":""},{"id":328110744,"identity":"6eb06708-bb74-4d14-b1a5-7b370f9762e1","order_by":2,"name":"Daniel Salcedo","email":"","orcid":"","institution":"Case Western Reserve University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"","lastName":"Salcedo","suffix":""},{"id":328110745,"identity":"f98e26f8-4752-4df7-97b9-6fbb02784003","order_by":3,"name":"Che-Wei Lin","email":"","orcid":"","institution":"Taipei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Che-Wei","middleName":"","lastName":"Lin","suffix":""},{"id":328110746,"identity":"9433125b-5c53-44f3-9648-5193c1cfbd8a","order_by":4,"name":"Kai-Chun Hu","email":"","orcid":"","institution":"Wan Fang 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Huang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYBACA2bGhgMJFTY8/OzNByBCBwhpYW8+eODDmTQ5yZ5jiQ1QLYwNeLXwHEs+OLPtsLHBjBxD4rSYS+QYHOY5czhxA8+Z749utjHI8d1IYH/Mg0eL5QyQlor0xO3svRubc9sYjCVvJDA249NicANsi3Xizp6zYC2JG0Bacghp4W1jBqrMeQjSUk9Yy5ljCUDvOxsD9TKCtCQYENJi2d58ABbIhrNzzkkYzjzzsHH2HzxazJkZmz9Ao/LB55wyG3m+48kHPs7AowUdSAAx/mgZBaNgFIyCUUAEAADYNV/xmQW0dgAAAABJRU5ErkJggg==","orcid":"","institution":"Wan Fang Hospital","correspondingAuthor":true,"prefix":"","firstName":"Wen-Cheng","middleName":"","lastName":"Huang","suffix":""}],"badges":[],"createdAt":"2024-06-27 17:11:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4650325/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4650325/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60692000,"identity":"a6c4f944-4961-4d39-a759-320b8c6b982b","added_by":"auto","created_at":"2024-07-19 15:15:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":441646,"visible":true,"origin":"","legend":"\u003cp\u003eHypothesized model of a path analysis\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-4650325/v1/4b0a0073815903106f66d5e7.png"},{"id":60691998,"identity":"ff8d2318-925b-44dc-b756-4e4dc9b973f6","added_by":"auto","created_at":"2024-07-19 15:15:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1024014,"visible":true,"origin":"","legend":"\u003cp\u003eStructural equation modeling model of the pathway from L-SES (p\u0026lt;0.05)\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-4650325/v1/022cb2c1ccf5d2fa69b07700.png"},{"id":70285835,"identity":"fe4504fe-240c-4667-a855-282187941bc4","added_by":"auto","created_at":"2024-12-01 16:23:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1965090,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4650325/v1/c3036766-1053-473c-896e-b72c1b10c019.pdf"},{"id":60692002,"identity":"9fb86cca-5062-4ba6-9e3c-998d767144fb","added_by":"auto","created_at":"2024-07-19 15:15:43","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":11265,"visible":true,"origin":"","legend":"\u003cp\u003eTable S1 Summary of discriminant validity assessment.\u003c/p\u003e","description":"","filename":"TableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4650325/v1/91396d52801822d70a7db4fd.xlsx"},{"id":60692003,"identity":"5c4d9ac6-52d1-4759-af3e-5dbda9966d41","added_by":"auto","created_at":"2024-07-19 15:15:43","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":10263,"visible":true,"origin":"","legend":"\u003cp\u003eTable S2 Summary of convergent validity assessment\u003c/p\u003e","description":"","filename":"TableS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4650325/v1/d2bc666691a50664edae8692.xlsx"},{"id":60692626,"identity":"e276a30d-2adc-4d05-b8ba-783bbfcfd221","added_by":"auto","created_at":"2024-07-19 15:23:43","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":10068,"visible":true,"origin":"","legend":"\u003cp\u003eTable S3 Summary of multicollinearity assessment\u003c/p\u003e","description":"","filename":"TableS3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4650325/v1/4060e9b2f83aefacf0ecb201.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring the Influence of Different Learning Activities on Medical Students' Psychological Pathways in Ultrasound Acquisition","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eUltrasound is used as the modern stethoscope for its efficacy, accuracy, and portability (Feilchenfeld et al., 2018). The applications of ultrasound present widely in cardiology, obstetrics and gynecology, and gastrointestinal medicine. Education of ultrasound is emerging at the preclinical level (Kameda et al., 2022). In America, it is reported that over 70 percent of the medical schools manage to integrate ultrasound into formal curricula (Nicholas et al., 2021). Although the learning of ultrasound presents high heterogeneity of contents and the learning time (Kameda et al., 2022), most learning modules consist of two kind of activities in terms of knowledge acquisition and skill practice (Finn et al., 2012; Knobe et al., 2012; Kondrashov et al., 2015; Rempell et al., 2016; Griksaitis et al., 2012; Dreher et al., 2014; Canty et al., 2015; Chen et al., 2022). It is reasonable to develop an ultrasound learning course that incorporates both theoretical understanding and practical application is essential (Bordage, 2009), given that proficiency in medicine relies on a balance between medical knowledge and hands-on experience. Both knowledge delivery and hand-on practice are important to learning ultrasound, and some studies have proven the benefits of the integrated learning module (Chen et al., 2022; Kondrashov et al., 2015; Rempell et al., 2016; Dreher et al., 2014); while insufficient empirical evidence exists to delineate the impact of diverse learning activities, such as lectures on fundamental knowledge and hands-on practice, on the psychological learning process. A comprehensive understanding of how these distinct learning modalities influence the psychological mechanisms of learning stands as a fundamental prerequisite for the enhancement of ultrasound courses within medical education. Moreover, such insights hold promise for mitigating the challenges encountered by medical students in effectively integrating theoretical knowledge into clinical practice during the crucial stages of their professional development.\u003c/p\u003e\n\u003cp\u003eEarly acquisition of ultrasound can not only increase medical students\u0026apos; skills by psychomotor training for clinical work, but it can also benefit the basic medical science in preclinical learning. For example, ultrasound features real-time images, which can help understanding physiology and building a three-dimensional picture of anatomy. While some students learn the ultrasound for its prominent strengths in clinical work, some learn it out of pure interest (Kameda et al., 2022). In other words, many medical students are motivated by self-growth needs rather than relatedness needs (Chen et al., 2022). The process of learning activity was explained by Bloom\u0026apos;s taxonomy into three core domains: the cognitive domain, affective domain, and psychomotor domain (Krathwohl, 2002). More concretely, the cognitive domain is related to how students comprehend the content of knowledge; the affective domain is related to a student\u0026apos;s value and commitment toward this learning; the psychomotor domain is the demonstration of a particular skill (clinical skills). However, in the preclinical medical education, there was so far only limited research based on Bloom\u0026apos;s taxonomy.\u003c/p\u003e\n\u003cp\u003eFor clinical procedures including ultrasound, the psychomotor domain is often set as the training goal. However, the clinical applications and skills learning in medicine also hold professionality, which usually rely on the construction of knowledge and interests. In reference to the framework of affective domains in the Bloom\u0026apos;s taxonomy, receiving and responding can be the base of valuing. In other words, through enhancing the understanding of medical knowledge, medical students evaluate the importance and decide their learning attitude, including paying more attention or active learning. When a student arouses one\u0026apos;s interests, one can also increase one\u0026apos;s motivation and engagement (J\u0026auml;rvel\u0026auml; and Renninger, 2014). Briefly, it is worthy of further investigation that analyze the psychological learning process from learning activities, the cognitive domain, the affective domain, to the psychomotor domain. Thus, we would like to propose a mediation model based on the sequence of the psychological learning process in medical education.\u003c/p\u003e\n\u003cp\u003eAlso, researchers from medical education highlight the importance of self-efficacy in both skill acquisition and professional identity (Bulfone et al., 2016). In different educations, self-efficacy has been proved associated with motivation and achievements (Karatay and Bas, 2019), and in medical education, higher self-efficacy can lead to a better performance of skills with less anxiety and better preparedness (Chen et al., 2022). These findings might support an urgent need for early integration of ultrasound into undergraduate education. Among all the possible methods to evaluate students\u0026apos; learning activity, self-efficacy is assessed generally in medical education (Mavis, 2001), while most of the self-efficacy scales focus on the clinical skills and learning outcomes. Learning self-efficacy demonstrates the learning process of learners, especially the psychological domain. Learning self-efficacy scale (L-SES), built on three domains of Bloom\u0026apos;s taxonomy, is widely used in different areas and even translated to other languages. The previous studies only presented scores from each domain but lacked interactions between different domains. Clarifying the relationships of cognitive, affective, and psychomotor domains can therefore assist educators to design an effective course for psychomotor training. The purpose of this research was to explore the path of participations of learning activities and psychological domains in ultrasound course. Accordingly, hypotheses had been raised before this study (Figure 1), and they were listed as follows:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eHypothesis 1\u003c/em\u003e Attending times of lecture and hands-on practice are positively associated with psychomotor domain (directly and indirectly).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eHypothesis 2\u003c/em\u003e Attending times of lecture and hands-on practice are positively associated with affective domain (directly and indirectly).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eHypothesis 3\u003c/em\u003e Attending times of lecture and hands-on practice are positively associated with cognitive domain (directly and indirectly).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eHypothesis 4\u003c/em\u003e Cognitive domain is positively associated with psychomotor domain (directly and indirectly).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eHypothesis 5\u003c/em\u003e Cognitive domain is positively associated with affective domain (directly).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eHypothesis 6\u0026nbsp;\u003c/em\u003eAffective domain is positively associated with psychomotor domain (directly).\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003eThis study is originally based on the Parallel Ultrasound Hands-on (PUSH) trial in 2022 (Chen et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Due to the cross-over design, participants completed the PUSH course prior to engaging in the post-course survey. The present study serves as a post-hoc analysis of the PUSH trial, aimed at delving deeper into the relationship between course participation and the psychological learning process, leveraging attendance records and post-course survey data.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Course Design\u003c/h2\u003e \u003cp\u003eThe PUSH trial was designed as a parallel course, while these participants were also in the regular anatomy classes. The course had seven 40-minute lectures in total, and there were also two additional hand-on practice workshops held between lectures. In the hand-on workshops, every workshop was held in 120-minute, and each student can practice for at least 15-minute and observe other students for 45-minute. Other details can be found in the previous study (Chen et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Participants\u003c/h2\u003e \u003cp\u003eIn this trial, they enrolled 140 undergraduate third-year medical students from Taipei Medical University in Taiwan. All the participants joined the course with free will, and there were no drop-out within the whole trial. A total of 127 students out of the 140 PUSH course participants completed the post-course survey, which is the data source of the present study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Instrument\u003c/h2\u003e \u003cp\u003eThe post-course survey in the PUSH trial measured learning self-efficacy using a developed and valid scale for the understanding of the psychological learning process (Chen et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The learning self-efficacy scale (L-SES) was conducted to test the learners' confidence in the learning ''process'' based on Bloom's taxonomy of learning objectives. The scale consists of three dimensions, including cognitive, affective, and psychomotor domains. Each dimension consists of four items, and there were twelve items in total. The students can score the items from one to five points, one for the most disagreed and five for the most agreed.\u003c/p\u003e \u003cp\u003eThe internal consistency reliability was calculated via Cronbach\u0026rsquo;s α correlation coefficient. The analysis of Cronbach's α correlation coefficient showed α\u0026thinsp;=\u0026thinsp;0.9 for the whole scale, α\u0026thinsp;=\u0026thinsp;0.9 for the \u0026ldquo;cognitive\u0026rdquo; domain, α\u0026thinsp;=\u0026thinsp;0.81 for the \u0026ldquo;affective\u0026rdquo; domain, and α\u0026thinsp;=\u0026thinsp;0.84 for the \u0026ldquo;psychomotor\u0026rdquo; domain.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Analysis and Statistics\u003c/h2\u003e \u003cp\u003e Statistical analysis was carried out in R version 4.2.2, in which descriptive statistics was calculated using package \u0026ldquo;stats,\u0026rdquo; and Cronbach\u0026rsquo;s alpha was obtained using package \u0026ldquo;psych.\u0026rdquo; Main analysis was based on partial least square structural equation modeling (PLS-SEM) that was conducted using package \u0026ldquo;seminr.\u0026rdquo; Due to interests of this study and practical considerations, hand-on practice and lecture were defined as composite indicators in the PLS-SEM. Items of the L-SES were set as reflective indicators for the latent variables in terms of the three domains of learning self-efficacy. To assess the statistical significance, bootstrapping procedure with 1,000 times resampling was applied in both factor analysis and the mediation analysis. Given that PLS-SEM operates independently of covariance-based methodologies, it forgoes fit measurements, pivoting instead towards the evaluation of validity (Hair Jr et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Ringle et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). We evaluated discriminant validity using both Fornell \u0026amp; Larcker criterion and heterotrait-monotrait ratio of correlations (HTMT) (Ringle et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Ab Hamid et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Afthanorhan et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Convergent validity of this PLS-SEM was evaluated by both composite reliability (CR) and average variance extracted (AVE). Besides, we also evaluated multicollinearity in this model based on variance inflation factor (VIF) (MacKinnon et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Taylor et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1. Descriptive statistics and factor loading\u003c/h2\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e represents the mean scores and factor loadings on the composite indicators (hand-on practice and lectures) in the structural equation model of learning process and different items in the learning self-efficacy scale.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDescriptive statistics of mean scores and factor loadings on the composite indicators (hand-on practice and lectures) in the structural equation model of learning process\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eObservation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOriginal\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBootstrapping\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eitem\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eConstruct\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003efactor loading\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFactor loading\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLCI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUCI\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLecture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.760\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.560\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLecture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePractice\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.640\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePractice\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLSES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.860\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.560\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLSESC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.910\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.670\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLSESC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.870\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.770\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCognitive domain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.824\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.824\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.707\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.911\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLSESC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.960\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.750\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCognitive domain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.804\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.799\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.661\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.897\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLSESC3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.660\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCognitive domain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.886\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.872\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.746\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.955\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLSESC4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.720\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.890\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCognitive domain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.794\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.795\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.893\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLSESA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.660\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLSESA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.910\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAffective domain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.506\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.785\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLSESA2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.790\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.850\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAffective domain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.836\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.823\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.665\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.977\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLSESA3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.760\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.780\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAffective domain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.755\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.531\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.912\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLSESA4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.470\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.770\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAffective domain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.794\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.583\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.927\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLSESP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.790\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.690\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLSESP1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.510\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.930\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePsychomotor domain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.621\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.446\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.751\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLSESP2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.460\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.920\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePsychomotor domain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.727\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.729\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.569\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLSESP3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.760\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePsychomotor domain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.862\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.848\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.715\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.945\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLSESP4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.750\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePsychomotor domain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.816\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.804\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.638\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.922\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003eLCI, lower limit of 95% confidence interval; UCI, upper limit of 95% confidence interval; SD, standard deviation.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eStudents can decide autonomously how many courses they want to take once they join the project. Within seven lectures, the students participated in the class for 5.76 times on average (SD\u0026thinsp;=\u0026thinsp;1.56). Most of the students constantly went to the class seven times (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;58), but some students joined less than half of the course (n\u0026thinsp;=\u0026thinsp;15). Within 2 times of hand-on practices, the mean participation was 1.07 times (SD\u0026thinsp;=\u0026thinsp;0.64). There were around 24% of students (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;31) did hand-on practice twice or more times, half of students only went to hand-on practice for one time (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;74; 58%), and a few students did not go to hand-on practice (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;22; 17%).\u003c/p\u003e\n \u003cp\u003eThe mean score of the total learning self-efficacy scale was 3.86 (SD\u0026thinsp;=\u0026thinsp;0.56), and the mean scores of all the items ranged from 2.30 (SD\u0026thinsp;=\u0026thinsp;0.91) to 4.13 (SD\u0026thinsp;=\u0026thinsp;0.76). Among all, the mean score of cognitive domains of the learning self-efficacy scale was 3.91 (SD\u0026thinsp;=\u0026thinsp;0.67); that of the affective domain was 3.33 (SD\u0026thinsp;=\u0026thinsp;0.66); that of the psychomotor domain was 3.79 (0.69). The variance between each item and the three domains were determined by factor loadings, and all of the factor loadings were above 0.5 which achieves expected threshold (Hair et al., 2010). The factor loadings of the cognitive domains ranged from 0.794 to 0.886; those of the affective domains were from 0.509 to 0.836, and those of the psychomotor domains were from 0.62 to 0.862. Notably, both the lowest mean scores and the factor loadings sat in the item A1. The results showed significant differences under assessment of bootstrapping technique.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e4.1. Structural equation modeling\u003c/h2\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e presented the structural equation modeling of the mutual effect of lecture and hand-on practice on the cognitive domain, affective domain, and psychomotor domain of self-efficacy. While hand-on practice improved the psychomotor domain of self-efficacy, lecture had a relatively direct effect on the cognitive domain of self-efficacy.\u003c/p\u003e\n \u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e showed the path coefficient of the structural equation modeling of learning self-efficacy scale was assessed both directly and indirectly. The first path presented the association between the times of lectures and learning self-efficacy. The beta coefficient of path from lecture to cognitive domain was 0.36 (bootstrapped \u0026beta;\u0026thinsp;=\u0026thinsp;0.343; 95% CI: 0.093 to 0.567), which was the only path that reached statistical significance; nevertheless, the path from lecture to affective domain and psychomotor domain showed no significant effects. The path from lecture to psychomotor domain was also examined indirectly via cognitive domain and via affective domain. While the results indirectly from lecture to psychomotor domain via affective domain remained insignificant, the beta coefficient of path of lecture to psychomotor domain via cognitive domain was 0.265 (bootstrapped \u0026beta;\u0026thinsp;=\u0026thinsp;0.252; 95% CI: 0.066 to 0.452), which also met statistical significance.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDirect and indirect path coefficient of the structural equation modeling of learning self-efficacy scale\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOriginal\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBootstrapping\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePath\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ecoefficient\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLCI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUCI\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLecture to cognitive domain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.343\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.567\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLecture to affective domain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.169\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLecture to psychomotor domain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDirect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndirect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVia cognitive domain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.452\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVia affective domain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePractice to cognitive domain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.209\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePractice to affective domain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.274\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePractice to psychomotor domain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDirect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.376\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndirect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVia cognitive domain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.152\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVia affective domain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eLCI, lower limit of 95% confidence interval; UCI, upper limit of 95% confidence interval.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThe second path presented the association between the times of hand-on practice and learning self-efficacy. The beta coefficient of path from practice to both cognitive domain (\u0026beta;\u0026thinsp;=\u0026thinsp;0.030) and affective domain (\u0026beta;\u0026thinsp;=\u0026thinsp;0.066) were insignificant. However, the path coefficient from practice to psychomotor domain directly was 0.208 (bootstrapped \u0026beta;\u0026thinsp;=\u0026thinsp;0.213; 95% CI: 0.043 to 0.376), which also indicates statistical significance. On the contrary, the indirect path from practice to psychomotor domain via neither cognitive domain nor affective domain showed significant results.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eBased on Bloom's taxonomy, the present findings from the mediation model demonstrate the associations between learning activities and self-efficacy on cognitive and psychomotor domains. Unfortunately, non-significant association could be found between learning activities and learning self-efficacy of affective domain. Lecture is significantly associated with cognitive learning self-efficacy, while there seems to be no direct effects on the psychomotor learning self-efficacy. On the other hand, hands-on practice affects psychomotor learning self-efficacy significantly, but it has no effect on cognitive learning self-efficacy. From perspectives of psychological processes, briefly, our study confirms that different types of learning activities have their own advantages in enhancing ultrasound education. Besides, the current research also confirms the importance of cognitive learning self-efficacy between learning activities (i.e. lectures and hands-on practices) and psychomotor.\u003c/p\u003e \u003cp\u003eIn this mediation model of learning self-efficacy, composite indicators like lecture and hands-on practice take an important role in the learning process. In the process of constructing medical professions, many medical students find it a challenge to bridge the preclinical learning and clinical work. In recent years, a simulation workshop, which builds a relatively authentic situation for medical students to experience, is praised for its advantages of effective learning and bridging theoretical knowledge in a clinical setting (Weller, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Simulation has been proven to be an effective approach to build a relatively authentic situation for health professional trainees to experience (Okuda et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), and has advantages of effective learning and bridging theoretical knowledge in clinical settings (Weller, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). The PUSH trial we used had been designed in accordance with the concepts of simulation, as it provided repeated practice without impacting patients' safety. From the analysis in our research, it should be noted that the both lecture and hand-on practice influence psychomotor domains, but they appear to impact psychomotor via different psychological mechanism. Both lecture and hand-on practice can establish knowledge to perform the clinical procedure (e.g. ultrasound) correctly, and with the profound knowledge of techniques, students can cultivate their abilities of self-error-correction. With self-error-correction, the simulation process became one's own ''learning experience'', and it further led to better performance on psychomotor skills (Chickering, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1977\u003c/span\u003e). Overall, we thus suggest the lecture design of medical education should combine adequate practicing amounts.\u003c/p\u003e \u003cp\u003eHowever, our finding shows a relatively weaker role of the affective domains of self-efficacy. Theoretically, the evaluating process in the affective domains demonstrates the importance of the learning in clinical work or personal interests, it can also be affected by pressure. For medical students in the preclinical stage, the existence-pressure of being an independent doctor are also their main learning motivations (Chen et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and the pressure can affect the learning process. Nevertheless, the PUSH trial was designed as additional parallel learning activities, in which the students' can decide whether they should attend the lecture and practice out of their interests (Chen et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Seeing that, the participants might still own certain degrees of affection. In this trial, while their pressure from clinical expectation in the future was uneasy to be assessed, we attempted to demonstrate their affective domains of self-efficacy by research design. One possible reason for the weaker role of affective domains is measurement. The PUSH trial measured affective domain using reflective indicators rather than direct item (e.g. \u0026ldquo;Do you like to learn ultrasound\u0026hellip;?\u0026rdquo;). Besides, some of the questions regarding the affective domains might also be correlated to the cognitive and psychomotor domains. For example, item A6 presents the ''gain'' in the learning process, which can also be thought of as gaining knowledge and related to the cognitive domains. In fact, when the learning self-efficacy scale was translated into another language, item A5 showed lower factor loadings, which could be explained that spending more time not only presented one's interests but also showed difficult learning (Bayazit et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The role of affective domain in the present model remains unclear, and further studies are necessary to develop a more general model.\u003c/p\u003e \u003cp\u003eFor those who would like to design courses for psychomotor training in medical education, from our analysis, we suggest the course-design should combine both lecture and sufficient hand-on practice. Also, in the PUSH trial, both lectures and additional practice were conducted optionally, and the combination learning module brought effective learning outcomes. Thus, we encourage teachers to adequately integrate self-directed learning designs in clinical skill courses. The self-directed learning curriculum should be flexible at the time table of the course, but also be required with minimal attendance to the class. A certain degree of orientation is also recommended in the beginning of this sort of self-directed learning, so that the students can have a blue-print for their learning.\u003c/p\u003e"},{"header":"6. Limitations","content":"\u003cp\u003eThere are some limitations in this study. As this study is built on the PUSH trial, there are similar self-selection biases and restriction of course-design. However, the findings of this study remain valuable due to the variability in participation across learning activities, whether in lectures or hands-on practice. In essence, the diversity in participation levels offers insights into the psychological learning process, even among medical students with high motivation to learn ultrasound. Second, this study inherits a limitation from the PUSH trial, namely, the relatively small sample size. Typically, a psychological modeling analysis would aim to recruit at least 200 participants. Recognizing this constraint, we proactively considered it when proposing this post-hoc analysis. To ensure the robustness of the study, we employed advanced statistical techniques, specifically partial least squares structural equation modeling (Hair Jr et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Reinartz et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). This method is less constrained by sample size requirements compared to covariance-based structural equation modeling, making it suitable for studies with limited sample sizes or complex models. Unlike covariance-based structural equation modeling, partial least square structural equation modeling does not rely on strict assumptions about the distribution of data, such as multivariate normality, making it more flexible and robust. In practice, maintaining extra learning activities proves challenging without additional resources like the PUSH course to increase sample size. The study's value lies in furnishing stakeholders with compelling evidence to support the incorporation of such course designs into contemporary educational practices. Third, although we emphasize both quality and quantity of hand-on practice, we could not manage to analyze the plateau of effects of the amount of practice due to the limited hand-on practice times in the original trial. Hence, we recommend that researchers endeavor to circumvent these limitations when applying this meditation model to other clinical skills learning contexts.\u003c/p\u003e"},{"header":"7. Conclusions","content":"\u003cp\u003eThis study explores the impact of lectures and hands-on practice on the psychological pathways of ultrasound skills acquisition among medical students. It demonstrates that both forms of learning activities influence psychomotor skills but operate through distinct psychological mechanisms. For learning ultrasound, an optimal design would be a combination of lectures and sufficient hands-on practice. The role of affective domain remains unclear in the present study, but the results might show a different pattern in learning other clinical skills by using this mediation model for exploring the psychological learning process.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval: \u0026nbsp;\u0026nbsp;\u003c/strong\u003eThe study was approved by Taipei Medical University Joint Institutional Review Board (TMU-JIRB) for Human Experimentation. The number of IRB was TMU-JIRB\u0026nbsp;N201909012.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e: Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e: All data generated or analyzed during this study are included in this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interest\u003c/strong\u003e: The authors declare no competing financial interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e: The present study is funded by Wan Fang Hospital, Taipei Medical University Research Grant 110-wf-eva-23, 110-wf-eva-21\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e: YTH, YNK, DS, WCH contributed to the design of the study, collection and analysis of data and drafting the manuscript. CWL, KCH, CWH, CYC participated in the critical review of the manuscript, and contributed analysis tools, and made substantial contributions to interpretation of data. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e: Special thanks to Miss Hao-Yu Chen who is the administrator in Center for Education in Medical Simulation (CEMS), Taipei Medical University, Taiwan for collections of students\u0026rsquo; data.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAb Hamid, M., Sami, W., \u0026amp; Sidek, M. M. (2017). 890 'Discriminant validity assessment: Use of Fornell \u0026amp; Larcker criterion versus HTMT criterion' \u003cem\u003eJournal of Physics: Conference Series\u003c/em\u003e. IOP Publishing, p. 012163 1.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAfthanorhan, A., Ghazali, P. L., \u0026amp; Rashid, N. 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Simulation in undergraduate medical education: bridging the gap between theory and practice. \u003cem\u003eMedical Education\u003c/em\u003e, \u003cem\u003e38\u003c/em\u003e(1), 32\u0026ndash;38. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/j.1365-2923.2004.01739.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1365-2923.2004.01739.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Bloom’s taxonomy, Undergraduate, Ultrasound education, clinical skills, Psychological Pathway","lastPublishedDoi":"10.21203/rs.3.rs-4650325/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4650325/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAcquiring proficiency in medicine typically necessitates a combination of foundational knowledge and hands-on experience. However, how do lecture and hands-on practice affect the psychological learning process in ultrasound education remains unclear. The purpose of this research was to test how the different learning activities associated with the psychomotor domain, elucidating their connections with cognitive and affective domains in the context of ultrasound education.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethod\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is originally based on the post-course survey of the Parallel Ultrasound Hands-on (PUSH) trial. The survey was done by 127 third-year medical students with information regarding attending times and learning self-efficacy scale that consisted of 12 items with adequate reliability (Cronbach's \u003cem\u003eα\u003c/em\u003e=0.9). A partial least square structural equation modeling was used for analyzing the data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAttending times of lecture was positively associated with cognitive (\u003cem\u003eβ\u003c/em\u003e=0.343; 95% CI: 0.093 to 0.567) and psychomotor domain (\u003cem\u003eβ\u003c/em\u003e=0.252; 95% CI: 0.066 to 0.452), but hands-on practice was only significantly associated with psychomotor domain (\u003cem\u003eβ\u003c/em\u003e=0.208; 95% CI: 0.043 to 0.376).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLectures and hands-on practice exert varying impacts on medical students' psychological pathways involved in learning ultrasound. The combination learning design could be flexible at the time table of the course, but also be required with minimal attendance to the class.\u003c/p\u003e","manuscriptTitle":"Exploring the Influence of Different Learning Activities on Medical Students' Psychological Pathways in Ultrasound Acquisition","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-19 15:15:38","doi":"10.21203/rs.3.rs-4650325/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5ef9dec8-d281-45e3-ac85-5076f22a20dc","owner":[],"postedDate":"July 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-12-01T16:23:13+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-19 15:15:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4650325","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4650325","identity":"rs-4650325","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}