




































BANGLADESH JOURNAL OF MULTIDISCIPLINARY SCIENTIFIC RESEARCH 10(6) (2025), 73-86 

73 

        MULTIDISCIPLINARY SCIENTIFIC RESEARCH 
          BJMSR VOL 10 NO 6 (2025) P-ISSN 2687-850X E-ISSN 2687-8518 

         Available online at https://www.cribfb.com 

     Journal homepage: https://www.cribfb.com/journal/index.php/BJMSR 

                                                                                                                                                                                                    Published by CRIBFB, USA 
                                                                                                                              

THE IMPACT OF DIGITAL TOOLS ON NURSING STUDENTS’ 

CLINICAL PERFORMANCE: A MEDIATING ROLE OF 

COGNITIVE LOAD               
                      

 Yuan Jiang  (a)1   Cuiping Chen  (b)  Xiaomin Huang (c)   Jian Zhang (d)   Liyu Ding (e) 
 

(a) Lecturer, School of Medicine, Tongji University, Shanghai, China; E-mail: syekjy@163.com 
(b) Lecturer, School of Medicine, Tongji University, Shanghai, China; E-mail: cui182000@163.com 
(c) Lecturer, School of Medicine, Tongji University, Shanghai, China; E-mail: 21666131@tongji.edu.cn 
(d) Lecturer, School of Medicine, Tongji University, Shanghai, China; E-mail: zlhjp@aliyun.com 
(e) Lecturer, Shanghai Tenth People’s Hospital, Tongji University, Shanghai, China; E-mail: dingliyu2017@163.com 

 

 
A R T I C L E I N F O 

 
 

Article History: 

 
Received: 18th June 2025 

Reviewed & Revised: 18th June 2025 

to 10th September 2025 

Accepted: 15th September 2025 

Published: 20th September 2025 

 
Keywords: 

 

Clinical Performance, Nursing Students, 

Cognitive Load, Technology Anxiety, 

Instructor Support 

 
JEL Classification Codes: 

 

      C12, C20, C31 

 
      Peer-Review Model:  

 

      External peer review was done through  

      double-blind method.        

 
A B S T R A C T      

 

The clinical performance of nursing students is a big problem in the era of modern technological 
advancement. With advancements in artificial intelligence and technology, clinical performance 

strategies with modern technology are required for nursing students. However, in the contemporary 

clinical environment of competition, a high level of performance is needed from nursing students. This 

research was conducted to investigate the impact of digital literacy level, instructor support for digital 

learning, and lack of technology anxiety on cognitive load and clinical performance. Furthermore, the 

study also investigated the direct impact of a lack of cognitive load on clinical performance. The 

mediating role of cognitive load in the relationship between digital literacy level, instructor support for 

digital learning, technology anxiety, and clinical performance was also investigated. A sample of 305 
nursing students was collected from China using purposive sampling. This study used a Partial Least 

Squares – Structural Equation Model (PLS-SEM) to investigate the complex relationships presented in 

the framework. The study found that digital literacy level, instructor support for digital learning, and 

lack of technology anxiety have a significant impact on cognitive load and clinical performance. At the 

same time, the mediating role of lack of cognitive load between digital literacy level, instructor support 

for digital learning, lack of technology anxiety, and clinical performance was also accepted. The findings 

of this research provide new insights into clinical performance and nursing literature and recommend 

actionable practices for advancing the students' clinical performance in China.  
 

 

© 2025 by the authors. Licensee CRIBFB, USA. This open-access article is distributed under the 
terms and conditions of the Creative Commons Attribution (CC BY) license 
(http://creativecommons.org/licenses/by/4.0).  

            

       

INTRODUCTION 

China’s health professional education faces a dual challenge, elevating students’ clinical performance while preventing 

excessive cognitive load in increasingly digital learning environments (Sun et al., 2021). As hospitals and universities 

expand electronic health records, high-fidelity simulation centers, and AI-enabled decision support, students must master 

unfamiliar interfaces alongside complex clinical reasoning (Pawar & Vispute, 2024). This overlay of technological demands 

on already intensive curricula can saturate working memory and depress performance during bedside care. Moreover, the 

rapid nationwide expansion of online and blended instruction since the pandemic has widened variability in students' digital 

readiness and access to high-quality instructional support (Werfhorst et al., 2022). Clinical placements, often in high-volume 

urban settings, add time pressure and documentation burdens that intensify cognitive demands. The practical problem is 

thus not merely technological adoption but aligning instructional design and faculty support with learners’ digital literacy 

so that technology reduces, rather than amplifies, mental load (Yu, 2022). When digital ecosystems, instructor practices, 

and student competencies are misaligned, learners experience disorientation, anxiety, and inefficiency at precisely the 

moment when accuracy, speed, and patient-centered judgment are required (Tammets et al., 2022). Addressing this 

misalignment is essential to secure safe, effective clinical learning and to translate China’s substantial investment in digital 

health education into consistent improvements in learners’ clinical performance. 

Unresolved misalignment between digital demands and learner capacity produces consequences that reverberate 

across students, patients, and institutions (Lattouf, 2022). At the student level, excessive cognitive load increases error 

                                                      
1Corresponding author: ORCID ID: 0009-0000-3442-7924 
© 2025 by the authors. Hosting by CRIBFB. Peer review under responsibility of CRIBFB, USA.  

https://doi.org/10.46281/bjmsr.v10i6.2595 

 
To cite this article: Jiang, Y., Chen, C., Huang, X., Zhang, J., & Ding, L. (2025). THE IMPACT OF DIGITAL TOOLS ON NURSING STUDENTS’ 

CLINICAL PERFORMANCE: A MEDIATING ROLE OF COGNITIVE LOAD. Bangladesh Journal of Multidisciplinary Scientific Research, 10(6), 73-

86. https://doi.org/10.46281/bjmsr.v10i6.2595 

http://creativecommons.org/licenses/by/4.0/)
http://creativecommons.org/licenses/by/4.0/)
https://www.openaccess.nl/en
https://doi.org/10.46281/bjmsr.v10i6.2595
https://orcid.org/0009-0000-3442-7924
https://orcid.org/0009-0006-0777-2056
https://orcid.org/0009-0000-5779-564X
https://orcid.org/0009-0005-5194-6888
https://orcid.org/0009-0004-7751-367X


Jiang et al., Bangladesh Journal of Multidisciplinary Scientific Research 10(6) (2025), 73-86

 

74 

proneness, slows clinical reasoning, and hinders the transfer of classroom knowledge to practice (Thornby et al., 2023). 

Furthermore, heightened technology anxiety magnifies avoidance, undermines self-efficacy, and increases attrition risk 

during demanding clinical rotations. Over time, these strains accumulate as fatigue and burnout, eroding professional 

identity and willingness to engage in reflective learning (Kong et al., 2023). For patients, reduced clinical performance can 

manifest as delayed interventions, documentation inaccuracies, and weaker continuity of care, particularly in fast-paced 

wards where trainees shoulder heavy information management tasks (Lustberg et al., 2023). Institutionally, variable 

performance undermines confidence in costly digital learning infrastructure and simulation investments, while widening 

disparities between well-resourced urban centers and less well-resourced rural areas. At the same time, faculty face heavier 

supervisory burdens and must remediate basic digital competencies rather than coach higher-order clinical judgment (Lewis, 

2023). At the system scale, uneven readiness slows health information integration and constrains the value of nationwide 

digitization initiatives, limiting data quality, interoperability, and analytics readiness (Goodacre et al., 2023). The cumulative 

impact is a vicious cycle: technology intended to streamline practice inadvertently increases mental load, which depresses 

performance, which in turn reduces the perceived utility of digital tools and discourages further pedagogical innovation for 

trainees alike. 

On the other hand, timely action is imperative because the forces amplifying cognitive load and performance 

variability are accelerating, not abating. China’s population is aging, the burden of chronic disease is increasing, and care 

models are shifting toward data-intensive, team-based practice (Su et al., 2023). Simultaneously, hospitals are deepening 

adoption of electronic records, clinical decision support, and AI-enabled triage, expanding the quantity, velocity, and 

complexity of information that trainees must process (Liang et al., 2021). If educational design does not proactively cultivate 

digital literacy, reduce technology anxiety, and provide consistent instructor support, the gap between curricular preparation 

and workplace reality will widen. Post-pandemic reforms have opened a narrow window to re-architect digital pedagogy, 

refresh assessment, and institutionalize simulation; missing this window risks path dependence on suboptimal practices that 

normalize high mental load (Vallo Hult et al., 2023). According to scholars, disparities in students’ digital readiness and 

access threaten equity goals, as learners from less-resourced settings may encounter disproportionately higher cognitive 

barriers. Addressing the problem now can prevent a productivity penalty in the future workforce, protect patient safety, and 

accelerate return on investment in national digitization. It can also ensure that emerging AI tools augment, rather than 

distract from, core clinical reasoning. Therefore, without timely remediation, today’s instructional frictions could harden 

into structural deficits tomorrow. 

This study aims to address these challenges by empirically modeling how digital literacy, instructor support for 

digital learning, and a lack of technology anxiety influence students' clinical performance, both directly and indirectly 

through reduced cognitive load. Based on the comprehensive and novel framework, the study tested a set of hypotheses 

specifying: (a) direct associations between digital literacy, instructor support, and lack of technology anxiety with reduced 

mental burden and higher performance; and (b) the mediating role of lack of cognitive load linking these antecedents to 

clinical outcomes. The study objective was twofold: to quantify effect sizes among these constructs within Chinese clinical 

education settings, and to identify leverage points for instructional and organizational design. Conceptually, we contribute 

by reframing the lack of cognitive load as a desirable instructional state that can be systematically engineered through the 

alignment of learner competencies and pedagogical supports, rather than treating it as a passive by-product. Empirically, 

the study extends the literature by examining these relationships in a digitally transforming healthcare education ecosystem, 

where contextual pressures are acute and policy ambitions high. Practically, the study translates findings into design 

principles for faculty development, curriculum sequencing, and technology procurement that target reductions in mental 

load while enhancing meaningful performance. For this purpose, this study used quantitative data collected from nursing 

students to analyze the relationships between variables. 

However, the study's novelty lies in jointly foregrounding a lack of technology anxiety and a lack of cognitive load 

as positive, engineerable states, and then embedding them within a coherent model that links learner competencies, 

instructional support, and clinical performance in China's digitizing education system. Rather than treating anxiety and 

cognitive load as incidental symptoms, this study conceptualizes them as design targets that administrators and faculty can 

influence through policies, infrastructures, and pedagogies. By testing mediation, the study identified the mechanism 

through which digital literacy and instructor support translate into performance, offering a diagnostic lens for where to 

intervene when outcomes stall. This mechanism-focused approach yields broader implications. For institutions, results can 

inform investments in learning technologies, faculty capability building, and simulation design that reduce mental load 

while improving bedside competence. For policymakers, evidence can guide digital education standards that emphasize 

usability, support, and learner preparedness alongside content coverage. For technology vendors, findings highlight 

requirements for workflow-aligned interfaces and just-in-time guidance in student systems. Beyond China, the framework 

is portable to other health education systems navigating rapid digitization and workforce pressure, especially in emerging 

economies. By centering low-anxiety, low-load learning as a strategic objective, the study reframes digital transformation 

as a human-performance initiative with scalable impact. The second section is based on a review of the literature and the 

development of hypotheses. The third section is based on methodology, while the fourth section is based on analysis and 

presentation of data. The fifth section of this study is about discussion, followed by conclusion, implications, and future 

directions. 

 

LITERATURE REVIEW 

Digital literacy is becoming increasingly recognized as an essential competence for students engaging with technology-

related education. Digital literacy is defined as the ability to effectively access, evaluate, and use digital technologies for 

academic and professional purposes (Rehman et al., 2024). More specifically, digitally literate students can access relevant 



Jiang et al., Bangladesh Journal of Multidisciplinary Scientific Research 10(6) (2025), 73-86

 

75 

content on online platforms, clinical technologies, or digital resources to enable them to complete a learning task, and are 

likely to require less cognitive and physical load (Skulmowski & Xu, 2022). Peng and Yu (2022) acknowledge that 

extraneous load occurs when learners must use mental energy to learn irrelevant things. In situations where students do not 

have digital literacy, they could spend inordinate amounts of time attempting to troubleshoot their technical problems, 

redirecting their attention away from learning and cognitive burden (Kabakus et al., 2023). Furthermore, digital competence 

has also been related to decreased cognitive load and increased engagement with simulation-based learning and electronic 

health records in health professions education (Holm, 2025). In essence, digital literacy is a cognitive enabler, reduces 

uninformative processing, and frees cognitive resources to do something meaningful with knowledge. 

Instructor support is one of the most important factors in the adjustment of learners in a digital learning environment 

(Susan, 2023). Supportive teaching behaviours, which include the provision of information, technical help, and feedback, 

can lower the cognitive load for students (Kang et al., 2021). Ha and Lim (2023) found that students who believed they 

received high levels of instructor support reported lower levels of frustration and disorientation in their online courses. By 

providing scaffolds for student learning, instructors help to manage the extraneous cognitive load for the student and create 

a space to allow the learner to focus on more meaningful learning (Arrogante et al., 2021). In digital healthcare education, 

which includes clinical simulations and dynamic, complex systems, instructors play a crucial role in reducing cognitive load 

by managing expectations and overcoming technical difficulties (Imanipour et al., 2022). Without the support from an 

instructor, students could be using much energy problem-solving significant technical problems and not spending that same 

energy to learn meaningfully (Fooladi et al., 2022). Therefore, instructor support acts as a motivational and cognitive 

resource that can improve the overall digital experience but lessen the imposed or extraneous load on the learner's cognitive 

load. 

Technology anxiety refers to the mental uneasiness or dread experienced by learners when using digital tools 

(Theobald et al., 2021). Chang et al. (2021) indicate that the presence of high technology anxiety increases a learner's 

extraneous cognitive load because the learners now must engage cognitive resources to manage their anxiety instead of 

utilizing those resources toward performing the task at hand (Rogers & Franklin, 2021). Learners with less anxiety would 

feel more confident with the digital platforms and therefore would have less extraneous processing. Azher et al. (2023) 

indicate that confidence in their ability to use technologies alleviates the learners' anxiety, thus allowing them to focus on 

the task they are required to complete. When a learner is engaged in a digital learning context, the decreased cognitive load 

leads to a greater degree of engagement with less emotional response. In learner populations with low technology anxiety, 

the literature shows that they possess better problem-solving approaches and improved focus in learning situations (Bolatli 

& Kizil, 2022). In terms of healthcare education, decreased anxiety allows the learners to engage with simulation 

technologies and electronic patient records without some of the cognitive overloads that may undermine their learning 

(Wilson et al., 2022). When affective barriers are lessened, a lack of technology anxiety provides a more straightforward 

cognitive pathway for more efficient cognitive processing and improved learning. 

Digital literacy not only removes cognitive barriers but is an essential component of clinical competence. Within 

healthcare education, digital technologies such as simulation, electronic health records, and diagnostic tools are ubiquitous; 

therefore, the inclusion of digital competence is critical (Kim & Shin, 2021). Specifically, students with appropriate digital 

literacy have more capacity for integrating the technology into their clinical practice, which will lead to enhanced diagnostic 

reasoning and decision making (Yeşilyurt & Vezne, 2023). Previous research suggests that digital dexterity enhances 

learners' confidence and ability to interact with complex environments (Kabakus et al., 2023). Furthermore, confidence 

affects performance through practical tasks where technology is embedded. Bahari (2023) suggests that if the learner does 

not struggle with the technological tools, they can allocate the cognitive demand to higher-order problem solving, improving 

the clinical outcome. In previous studies in nursing and medical education, students with significantly greater digital literacy 

have demonstrated greater efficiency and accuracy in simulation-based assessments (Giudice da Silva Cezar & Maçada, 

2021). Overall, digital literacy offers cognitive and practical benefits, enabling students to transition from digital competence 

to clinical competence and ultimately achieve professional readiness. 

Instructor support is widely recognized as the foremost contributor to student success in online learning 

environments. Pawar and Vispute (2024) emphasize that support through well-structured scaffolds, timely feedback, and 

technical assistance fosters a productive learning environment where learning and skill development thrive. In healthcare 

disciplines, instructors also help students transition into practice by helping bridge the digital tools into clinical application 

(Sun et al., 2021). Instructor support can help students transition from education to practice and provide a smoother 

integration of the skills and digital tools learned (Hawes & Arya, 2023). Learners who receive high levels of instructor 

support experienced low levels of uncertainty, increased motivation to engage in practice, and greater self-efficacy (Na & 

Roh, 2021). This supportive educational climate can help students engage with their learning about digital content and also 

provide support for practical clinical activities. Furthermore, instructor feedback provides spaces for reflective learning, 

where students can integrate their digital competencies while refining their clinical skills (Sim et al., 2022). Werfhorst et al. 

(2022) illustrate that students whom supportive instructors guide do better in clinical simulation and assessment 

environments. Supporting students with scaffolding and guidance helps learners distinguish content learning from 

knowledge about the tools they are using, mitigating technology barriers. Therefore, instructor support is not only a 

moderator of cognitive load, but also an enabler of clinical competence. 

Complications associated with anxiety toward technology can inhibit students' application of knowledge at the 

clinical level (Guerrero et al., 2021). High levels of anxiety toward technology tools likely cause avoidance behaviours, 

decrease motivation, and diminish concentration (Yin et al., 2024). In the context of healthcare education, where simulation 

activities and electronic health records are increasingly prevalent, anxiety related to technology can impact a student's ability 

to perform (Tzafilkou et al., 2021). When learners experience a reduced level of anxiety, they approach clinical tasks with 



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confidence, which allows them to apply their knowledge and skills more effectively. Mauriz et al. (2021) suggest that anxiety 

reduction produces competence, which correlates with an improved performance outcome. Evidence suggests that students 

who experience decreased levels of technology anxiety tend to demonstrate increased competence when using simulation-

based training and more accurate diagnostic reasoning (McVeigh et al., 2021). Less anxious students can take feedback fully 

from their instructor, which contributes to a higher sense of competence (Nooijen et al., 2024). 

The study Yu (2022) maintains that the amount of information the average human working memory can hold is 

finite; when surpassed, performance and learning are impaired. In clinical education, cognitive load is critical because 

students must learn to combine and fluidly integrate theoretical knowledge, skills, and behaviours with time restrictions 

(Jallad & Işık, 2022). When cognitive load is not a concern, learners can allocate their cognitive resources toward clinical 

reasoning and procedure tasks essential to their learning, rather than on useless or extraneous demands (Zhao et al., 2023). 

The literature in health education training indicates that students who experience a lower cognitive load can perform more 

effectively in simulations and assessments, and that cognitive load influences their cognitive resources away from learning 

and toward problem-solving and decision-making (Bolatli & Kizil, 2022). Furthermore, cognitive overload has been shown 

to increase error rates, decrease retention, and decrease diagnostic accuracy (Kwak et al., 2022). On the other hand, when 

instructional designs and any supporting mechanisms alleviate unnecessary load, students' clinical competence significantly 

improves. This suggests that a lack of cognitive load allows learners to allocate their limited cognitive resources better, 

improving their performance in clinical practice. 

Digital literacy and clinical performance are not only directly related but also indirectly related through the 

cognitive load (Berdida, 2023). The more digitally literate learners can better utilize educational technologies, lessening 

extraneous cognitive load (Chang et al., 2021), freeing up attention to focus on clinical and deeper learning, which enhances 

performance (Tammets et al., 2022). Empirical evidence demonstrates that digital competence leads to less frustration and 

more engagement in clinical-related tasks, particularly when dealing with complicated simulation media and electronic 

patient records in clinical placements (Theobald et al., 2021). However, with high cognitive load, even digitally literate 

learners may have compromised efficiency because of the mental resources consumed with technology management 

(Lattouf, 2022). As a result, the ability of digital literacy to enhance clinical performance is dependent on levels of cognitive 

load. In cases of minimal cognitive load, digital literacy acts on clinical competence without the limiting effects of the 

cognitive load. Thornby et al. (2023) report that cognitive processing efficiency acts as the link between the technological 

abilities of learners and actual applied practice performance. 

Instructor support is critical in both directly changing clinical performance and facilitating the cognitive processes 

behind performance. As Kwak et al. (2022) note, adequate instructor support reduces cognitive load as instructors can assist 

with expectations, troubleshoot problems, and provide feedback in a structured way. This support allows students to allocate 

their cognitive resources to the clinical tasks rather than dealing with an excess of information and potential complexity 

from technology or instructor directions (Huai et al., 2024). The mediating effect of cognitive load can be illustrated by 

problems where instructor support allowed students to engage the digital interface with minimal examples of cognitive load 

(Lavoie-Tremblay et al., 2021). When supporting blended learning activities, learner outcomes should depend more on 

improved clinical competence than on the manner of digital interface delivery (Lapierre et al., 2022). Both nursing and 

medical education literature have highlighted that learners who identified higher instructor support reported reduced 

cognitive load, and the decreased cognitive load leads to improved diagnostic accuracy and clinical reasoning. 

According to Chen et al. (2022), finding technology anxiety has been targeted to the increase in cognitive load, as 

learners who are anxious devote cognitive energy to manage their anxiety, as opposed to focusing on a learning task. As 

cognitive load increases, student performance can deteriorate in both digital and clinical learning environments (Hawes & 

Arya, 2023). On the other hand, if students experience little technology anxiety, they engage with digital tasks with more 

confidence, resulting in lower mental load and improved clinical performance (Wilson et al., 2022). According to Sim et al. 

(2022), students who have confidence in managing technology spend less time in extraneous processing, which allows them 

to engage fully in the particulars of clinical reasoning. Research also indicates that students who have little technology 

anxiety perform better in simulation-based training and demonstrate more situation shifting in clinical decision-making 

(Azher et al., 2023). The effects of cognitive load are apparent, as decreased anxiety results in fewer cognitive demands, 

which ultimately allows for better performance (Jallad & Işık, 2022). Cognitive load works to mediate how lack of 

technology anxiety contributes to developing students' clinical skills and professional readiness. Based on a review of the 

literature, inconsistencies exist in the relationships between variables across different contexts. Hence, the following 

hypotheses are developed in this study. Furthermore, this study aims to investigate the empirical evidence for testing the 

following hypotheses. 

H1: Digital literacy level is positively associated with a lack of cognitive load. 

H2: Instructor support for digital learning is positively associated with reduced cognitive load. 

H3: Lack of technology anxiety is positively associated with lack of cognitive load. 

H4: Digital literacy level is positively associated with clinical performance. 

H5: Instructor support for digital learning is positively associated with clinical performance. 

H6: Lack of technology anxiety is positively associated with clinical performance. 

H7: Lack of cognitive load is positively associated with clinical performance. 

H8: Lack of cognitive load mediates the relationship between digital literacy level and clinical performance. 

H9: Lack of cognitive load mediates the relationship between instructor support for digital learning and clinical 

performance. 

H10: Lack of cognitive load mediates the relationship between technology anxiety and clinical performance. 

 



Jiang et al., Bangladesh Journal of Multidisciplinary Scientific Research 10(6) (2025), 73-86

 

77 

The model used in this study, illustrating the relationship between variables, is presented in Figure 1. 

 

 
Figure 1. Research Framework 

 

MATERIALS AND METHODS 
This research was based on quantitative data, where respondents were asked to provide information on a survey-based 

questionnaire. The variables of this study were also previously investigated using quantitative data, which is necessary to 

analyze the relationship between variables. This study employed quantitative data, as previous studies in the nursing 

literature have also utilized quantitative instruments. In this study, data were collected through a rigorous methodology, 

targeting nursing students enrolled at multiple universities in Shanghai, China, as the study population. In this study, nursing 

students were selected for data collection, provided they had some experience in clinical practice. It was ensured that the 

students had an appropriate experience of clinical practice, and they were able to measure clinical performance. 

This study used a purposive sampling method where the data were collected from the students based on the way 

they were available. The respondents were approached with informed consent, and the purpose of the study was explained 

to them. It was ensured that these students had appropriate knowledge about the nursing practices and agreed to provide the 

data. They were assured that their personal information would not be shared with any third party, but would be used solely 

for this study. The respondents agreed to provide the data, and a self-administered questionnaire was distributed to them. 

The questionnaire was based on two sections: demographics and Likert scale instruments. The demographic data was 

collected to understand the nature of the participants. At the same time, the Likert scale was adapted to collect data to 

measure the instruments of the study. The instruments used in this study are reported in Appendix A. To collect the data, 

this study distributed 500 questionnaires, assuming a maximum sample size of 300. However, a total of 317 responses were 

collected. During the preliminary analysis, this study removed the responses that were not reliable and outliers. The 

remaining sample of 305 responses was considered final for data analysis. This study used Partial Least Squares – Structural 

Equation Model (PLS-SEM) for the analysis of data. For this purpose, the software IBM SPSS 29 was used for demographics 

analysis, and Smart PLS 4 was used for inferential statistics. 

 

RESULTS 

The demographic profile highlights the characteristics of the study participants (see Table 1). The majority of respondents 

(69.84%) were pursuing a Bachelor's degree in Nursing, with smaller proportions holding a diploma (22.3%) or postgraduate 

qualification (7.87%). Most participants were young, with 60% aged 20–24 years, followed by 18.36% in the 25–29 age 

group. Only a small fraction were above 35 years (4.26%), indicating that the sample essentially represents early-career 

nursing students. In terms of clinical practice experience, nearly one-third (33.44%) had less than six months' exposure, 

while 29.84% reported six months to one year, suggesting that many were still in the early stages of practical training. 

Gender distribution was skewed towards females (81.31%), consistent with global nursing trends. 

Regarding the institutional background, 41.97% were enrolled in public universities, followed by 31.48% in private 

universities. Fewer participants came from community colleges (17.38%) and nursing schools (9.18%). Access to the 

internet at clinical sites varied, though more than half (56.07%) always had access, while 29.84% sometimes had access, 

pointing to disparities in digital connectivity. Finally, technology familiarity was moderate among most respondents 

(39.67%), and study years were relatively balanced across cohorts, with the largest group in the third year (27.21%). 

 

Table 1. Demographics Profile 

 
Variable Category n Percentage (%) 

Academic Level Bachelor's in Nursing 213 69.84 

Diploma in Nursing 68 22.3 

Postgraduate 24 7.87 

Age 20–24 183 60 

25–29 56 18.36 

30–34 35 11.48 

35 and above 13 4.26 

Under 20 18 5.9 



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Clinical Practice Experience 1–2yr 62 20.33 

6mo–1yr 91 29.84 

<6 months 102 33.44 

>2yr 50 16.39 

Gender Female 248 81.31 

Male 57 18.69 

Institution Type Community College 53 17.38 

Nursing School 28 9.18 

Private University 96 31.48 

Public University 128 41.97 

Internet Access at Clinical Site Always 171 56.07 

Never 13 4.26 

Rarely 30 9.84 

Sometimes 91 29.84 

Tech Familiarity High 92 30.16 

Low 41 13.44 

Moderate 121 39.67 

Very High 39 12.79 

Very Low 12 3.93 

Year of Study 1st Year 76 24.92 

2nd Year 77 25.25 

3rd Year 83 27.21 

Final Year 69 22.62 

 
The findings of the measurement model assessment were used at the initial stage of PLS-SEM (see Figure 2). In 

the assessment of the measurement model, the study investigated the convergent validity first. The convergent validity was 

assessed using the findings of individual item reliability, which was checked with outer loadings. The findings of outer 

loadings above 0.70 are accepted as significant and acceptable (Hair et al., 2019). The study confirmed that all values for 

factor loadings were above the recommended threshold. Furthermore, the study tested the findings of average variance 

extracted to confirm the variance in the data. The findings of average variance extracted above 0.50 are considered 

significant, and all variables of this study achieved this threshold (Hair et al., 2019). Meanwhile, Cronbach's alpha and 

composite reliability were also tested in this study. The findings of both factors were above 0.70 for all variables (Hair et 

al., 2019). Hence, the reliability and validity of data were established, which is reported in Table 2. 

 

 

Figure 2. Measurement Model 

 

Table 2. Convergent Validity 

 
Variables Items Loadings Cronbach's 

alpha 

Composite 

reliability 

Average variance 

extracted 

Lack of Cognitive Load CL1 0.878 0.925 0.943 0.769 

 CL2 0.884    

 CL3 0.909    

 CL4 0.850    



Jiang et al., Bangladesh Journal of Multidisciplinary Scientific Research 10(6) (2025), 73-86

 

79 

 CL5 0.861    

Clinical Performance CP1 0.792 0.923 0.943 0.768 

 CP2 0.928    

 CP3 0.929    

 CP4 0.930    

 CP5 0.789    

Digital Literacy Level DL1 0.856 0.906 0.930 0.728 

 DL2 0.844    

 DL3 0.885    

 DL4 0.809    

 DL5 0.868    

Instructor Support for Digital Learning ISDL1 0.926 0.955 0.965 0.847 

 ISDL2 0.892    

 ISDL3 0.928    

 ISDL4 0.917    

 ISDL5 0.937    

Lack of Technology Anxiety TA1 0.889 0.921 0.941 0.760 

 TA2 0.891    

 TA3 0.830    

 TA4 0.861    

  TA5 0.887       

 
The study further tested the discriminant validity in the measurement model assessment. Heteritrait-Monotrait 

(HTMT) is a significant method to confirm the discriminant validity in the data. It confirms that the variables of the study 

have different data from one another, and the data do not overlap. Scholars recommended that an HTMT value less than 

0.85 is significant to confirm no discriminant validity in the data (Henseler et al., 2015). The findings reported in Table 3 

highlighted that discriminant validity was significantly achieved, as all HTMT values for all variables were less than 0.85. 

 

Table 3. Discriminant Validity 

 
Variables Clinical 

Performance 

Digital 

Literacy 
Level 

Instructor 

Support for 
Digital 

Learning 

Lack of 

Cognitive 
Load 

Lack of Technology 

Anxiety 

Clinical Performance 1.000     

Digital Literacy Level 0.647 1.000    

Instructor Support for Digital Learning 0.648 0.645 1.000   

Lack of Cognitive Load 0.656 0.523 0.590 1.000  

Lack of Technology Anxiety 0.530 0.383 0.492 0.520 1.000 

 
In primary data collection studies, when a single source is used to collect the data, there can be multicollinearity in 

the data. The multicollinearity of data refers to the similarity of data and the collinearity of data for different variables. The 

study examined multicollinearity using the variance inflation factor (VIF), with findings ideally less than 3.3 (Hair et al., 

2019). The assessment of VIF reported in Table 4 confirmed that all findings were less than 3.3. Hence, no multicollinearity 

issues were found in the data. 

 

Table 4. Multicollinearity Assessment 

 
Variables Clinical Performance Lack of Cognitive Load 

Digital Literacy Level 1.649 1.584 

Instructor Support for Digital Learning 1.925 1.762 

Lack of Cognitive Load 1.665 - 

Lack of Technology Anxiety 1.404 1.284 

 
After the analysis of the measurement model, this study performed a structural model assessment test. In structural 

model assessment, the relationships between research variables were investigated (see Figure 3). For this purpose, scholars 

recommended that a t-value above 1.96 is necessary to accept the hypotheses with a probability level of 0.05 (Hair et al., 

2019). Regarding H1, the study found that digital literacy level is positively associated with a lack of cognitive load. 

Furthermore, the study found that instructor support for digital learning is positively associated with a lack of cognitive 

load, confirming H2. Concerning H3, the study highlighted that a lack of technology anxiety is positively associated with a 

lack of cognitive load. Moreover, H4 is supported by evidence that digital literacy level is positively associated with clinical 

performance. The findings of H5 also confirmed that instructor support for digital learning is positively associated with 

clinical performance. Meanwhile, H6 reported that a lack of technology anxiety is positively associated with clinical 

performance. The findings of H7 reported that a lack of cognitive load is positively associated with clinical performance. 

The findings of direct paths are shown in Table 5. 

 



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80 

 

Figure 3. Structural Model 

 

Table 5. Direct Path Findings 

 
Paths Original sample Standard deviation T statistics P values 

Digital Literacy Level -> Clinical Performance 0.275 0.068 4.039 0.000 

Digital Literacy Level -> Lack of Cognitive Load 0.199 0.074 2.698 0.007 

Instructor Support for Digital Learning -> Clinical Performance 0.220 0.068 3.255 0.001 

Instructor Support for Digital Learning -> Lack of Cognitive Load 0.313 0.073 4.258 0.000 

Lack of Cognitive Load -> Clinical Performance 0.276 0.064 4.338 0.000 

Lack of Technology Anxiety -> Clinical Performance 0.161 0.057 2.820 0.005 

Lack of Technology Anxiety -> Lack of Cognitive Load 0.268 0.062 4.329 0.000 

 
The study further analyzed the relationship, and the findings are reported in Table 6. The findings of H8 reported 

that the lack of cognitive load mediates the relationship between digital literacy level and clinical performance. Moreover, 

the findings of H9 confirmed that the lack of cognitive load mediates the relationship between instructor support for digital 

learning and clinical performance. Finally, H10 results reported that the lack of cognitive load mediates the relationship 

between technology anxiety and clinical performance. The findings of indirect paths are reported in Table 6. 

 

Table 6. Mediating Path Findings 

 
Paths Original 

sample 

Standard 

deviation 

T 

statistics 

P values 

Lack of Technology Anxiety -> Lack of Cognitive Load -> Clinical Performance 0.074 0.027 2.741 0.006 

Digital Literacy Level -> Lack of Cognitive Load -> Clinical Performance 0.055 0.025 2.168 0.030 

Instructor Support for Digital Learning -> Lack of Cognitive Load -> Clinical Performance 0.086 0.028 3.082 0.002 

 

The study further analyzed the findings of the effect size (see Table 7). Effect size is assessed to check the effect 

of exogenous variables on endogenous variables. According to scholars, an effect size of 0.02 is small, 0.15 is medium, and 

0.35 is large (Cohen, 1992). The findings of this study reported that the effect of digital literacy level is small on the lack of 

cognitive load and clinical performance. Furthermore, the study found that the effect of instructor support on digital learning 

is small, particularly in terms of reducing cognitive load and improving clinical performance. The study also found that the 

effect of a lack of cognitive control is small on clinical performance. The study also found that the effect of lack of 

technology anxiety is small on lack of cognitive load and clinical performance.  

 

Table 7. Effect Size 

 
Variables Clinical Performance Lack of Cognitive Load 

Digital Literacy Level 0.101 0.042 

Instructor Support for Digital Learning 0.055 0.092 

Lack of Cognitive Load 0.100 - 

Lack of Technology Anxiety 0.040 0.093 

 



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81 

The findings of the coefficient of determination were also tested to understand the proportion of variables in the 

dependent variable based on the independent variables. Scholars recommended that an R2 value of 0.25 is weak, 0.50 is 

moderate, and 0.75 is substantial (Hair et al., 2019). The findings of R2 reported in this study confirmed that the lack of 

cognitive load is weak, but clinical performance achieved a moderate proportion from the independent variables. The 

findings of the coefficient of determination are reported in Table 8. 

 

Table 8. Coefficient of Determination 

 
Variables R-square R-square adjusted 

Clinical Performance 0.546 0.540 

Lack of Cognitive Load 0.399 0.393 

 
Finally, the study assessed the findings of predictive relevance (see Figure 4 and Table 9). The findings of 

predictive relevance are used to assess the predictive power of the model. According to scholars, the value of predictive 

relevance (Q2) should be above 0, which confirms the model has significant predictive relevance (Hair et al., 2019). The 

findings of this study reported that Q2 was above the recommended threshold for lack of cognitive load and clinical 

performance. Hence, it was confirmed that the model has significant predictive power. 

 

 

Figure 4. Predictive Relevance 
 

Table 9. Predictive Relevance 

 
Variable SSO SSE Q² (=1-SSE/SSO) 

Clinical Performance 1525.000 897.494 0.411 

Digital Literacy Level 1525.000 1525.000 0.000 

Instructor Support for Digital Learning 1525.000 1525.000 0.000 

Lack of Cognitive Load 1525.000 1065.113 0.302 

Lack of Technology Anxiety 1525.000 1525.000 0.000 

 

DISCUSSIONS 

In this study, all hypotheses were supported. Regarding H1, the study found that digital literacy level is positively associated 

with a lack of cognitive load. Previous studies, such as those by Peng and Yu (2022), also reported that high digital literacy 

levels reduce cognitive load on individuals. Another research, Skulmowski and Xu (2022), also provided similar findings 

that digital literacy helps the students to deal with critical challenges related to their performance. In the era of digital 

literacy, Rehman et al. (2024) reported that students should focus on learning digital technology that can help them shape 

their learning and performance more effectively. Although the previous studies supported this relationship, in the context 

of clinical performance of Chinese nursing students, the findings are novel. 

Furthermore, the study found that instructor support for digital learning is positively associated with a lack of 

cognitive load, confirming H2. Susan (2023) highlighted that when the instructors provide appropriate support to the 

students regarding their digital learning, the performance of students is improved. At the same time, the study by Fooladi et 

al. (2022) emphasized that instructors provide appropriate mentorship to students regarding their learning of digital literacy, 

which can reduce the cognitive burden on them. At the same time, Imanipour et al. (2022) discussed how instructors' support 



Jiang et al., Bangladesh Journal of Multidisciplinary Scientific Research 10(6) (2025), 73-86

 

82 

significantly shapes students' learning approach and digital literacy behavior. 

Concerning H3, the study highlighted that a lack of technology anxiety is positively associated with a lack of 

cognitive load. The findings of this hypothesis are aligned with Ha and Lim (2023), who reported that a lack of anxiety 

about using technology can improve the digital learning of students, resulting in limited cognitive load. Furthermore, 

Tzafilkou et al. (2021) noted that reducing technology access can increase the workload of students, which should be 

minimized to control cognitive load. At the same time, the study by Na and Roh (2021) highlighted that students should be 

motivated to use technology that advances their behaviour for digital learning. However, in the context of Chinese nursing 

students, the findings are new in the discussion. 

Moreover, H4 is supported by evidence that digital literacy level is positively associated with clinical performance. 

Giudice da Silva Cezar and Maçada (2021) also highlighted that when there is appropriate digital literacy, the clinical 

performance is reduced, resulting in productivity. Furthermore, Yeşilyurt and Vezne (2023) stated that advancements in 

digital literacy learning improve work performance. Meanwhile, Bahari (2023) pointed out that nursing staff should have 

information about digital literacy and the use of technology that results in shaping their workflow in better directions. Even 

though the relationship between digital literacy and performance has been discussed before, it is still new to the discussion 

in the context of clinical practice. 

The findings of H5 also confirmed that instructor support for digital learning is positively associated with clinical 

performance. The findings are also aligned with those of Guerrero et al. (2021), who discussed how instructor support can 

shape student behavior and digital literacy levels. Furthermore, McVeigh et al. (2021) discussed that the clinical 

performance of nursing students is improved when they are appropriately trained for it by their instructors. Meanwhile, 

Arrogante et al. (2021) confirmed that reliable mentorship can improve the performance of clinical students. While existing 

studies support the findings, this study provides new insights into the knowledge. 

Meanwhile, H6 reported that a lack of technology anxiety is positively associated with clinical performance. Hawes 

and Arya (2023) also emphasized that if there is no technological anxiety, the performance of nursing students is improved. 

The study by Zhao et al. (2023) stated that when clinical students are motivated to work, their way of working and direction 

are improved, which has a significant impact on their performance. At the same time, Yin et al. (2024) stated that when 

students have reasonable experience using modern technology, their understanding improves, which in turn shapes their 

behavior and learning for clinical performance. 

The findings of H7 reported that a lack of cognitive load is positively associated with clinical performance. The 

study by Mauriz et al. (2021) stated that when there is limited cognitive load, the working performance of nursing students 

is increased. Another study, Kim and Shin (2021), also reported that limited cognitive burdens improve the performance of 

students, which is necessary for their appropriate behaviour and reasonable performance. While the study by Kang et al. 

(2021) stated that students with high motivation experience improved performance, as they have no cognitive load. 

The findings of H8 reported that the lack of cognitive load mediates the relationship between digital literacy level 

and clinical performance. Even though this mediating relationship contributes new insights into the knowledge, a few 

previous studies support this relationship. The study by Berdida (2023) highlights that digital literacy is a significant factor 

in reducing cognitive load. At the same time, Chang et al. (2021) noted that a limited cognitive load is beneficial for students 

to enhance their clinical performance. Hence, the findings of this study are supported by previous research. 

Moreover, the findings of H9 confirmed that the lack of cognitive load mediates the relationship between instructor 

support for digital learning and clinical performance. Although this mediating relationship is new, it has support from 

existing studies. Rogers and Franklin (2021) highlighted that when instructors play a significant role in students' learning, 

their cognitive load is reduced. On the other hand, Chen et al. (2022) stated that limited cognitive load is a significant and 

contributing factor for the clinical performance of the students. Therefore, the findings reported in this study are novel for 

the body of knowledge. 

Finally, H10 results reported that lack of cognitive load mediates the relationship between lack of technology 

anxiety and clinical performance. This mediating relationship is new in the literature, but the previous studies support the 

findings. The study by Huai et al. (2024) stated that a lack of technology anxiety reduces the cognitive burden on people. 

Furthermore, Lapierre et al. (2022) noted that the lack of burden on students enhances their clinical performance. Hence, 

the findings of this study are significantly supported by existing studies, yet they are new to the existing knowledge base. 

 

CONCLUSIONS  

This study aims to respond to these challenges by empirically modeling digital literacy and instructor support for digital 

learning. To conclude, this study validates a coherent model in which digital literacy, instructor support for digital learning, 

and lack of technology anxiety exert direct, positive effects on lack of cognitive load and clinical performance. All 

hypothesized relationships were accepted, and the mediating role of cognitive load is clarified by the lack of cognitive load, 

which explains the mechanism by which learner competencies and instructional conditions translate into observable 

performance in clinical contexts. The results extend the traditions by demonstrating the concurrent operation within China's 

rapidly digitizing health education. The developed model in this study demonstrates how reductions in mental burden are 

not incidental but engineerable outcomes that amplify the returns on digital investment in students, thereby improving their 

clinical performance. From a novelty perspective, the findings suggest that digitally literate students, supported by 

instructors and experiencing a lack of technology anxiety, can allocate scarce cognitive resources to core tasks, yielding 

stronger clinical reasoning and safer practice. The study offers an integrated explanation of performance variation. It 

provides a practical blueprint, including cultivating digital literacy, institutionalizing instructor support, and systematically 

reducing anxiety to sustain a state of cognitive load that enables high-quality clinical performance in Chinese settings. 



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83 

Theoretically, this study provides advances in knowledge in four ways and addresses potential gaps. Firstly, this 

study reframes reduced cognitive load as a positive engineerable state and places it as the proximal component linking 

digital literacy, instructor support for digital learning, and clinical performance. Therefore, it extends the knowledge to 

include variables such as technology anxiety and instructor support as part of the model. Secondly, this study connects these 

relationships to China as context, highlighting that this has been overlooked by scholars previously. Thirdly, this study 

provides operational definitions for anxiety reduction and cognitive load reduction and differentiates their possible role in 

the pipeline to performance. Fourthly, the study indicates that online learning has also given some evidence that 

competencies and the pedagogy students receive can affect performance more negatively through reduced cognitive load. 

Hence, the tested relationships in this study, including the mediating role of lack of cognitive load, are a new and significant 

contribution to the body of knowledge. 

These findings of this study lead directly to actionable recommendations for universities, teaching hospitals, 

instructors, and vendors, all seeking to manage cognitive load in order to improve clinical performance. Firstly, this study 

highlights the need for baseline digital literacy diagnostics of incoming students, the provision of graduated micro-

credentials in courses, and the integration of embedded opportunities to practice with the systems relied upon in placements 

over time. Secondly, this study provides information on faculty development in load-aware pedagogy-based segmentation, 

signaling, worked examples, pretraining, and just-in-time technical coaching to promote consistent instructor support for 

students. Thirdly, the study highlights the need to facilitate onboarding, low-stakes practice, peer support, and an in-system 

and simulation platform to help enable students to cope with technology anxiety. Fourthly, this study emphasizes 

considering usability and workflow fit as criteria for procurement decision-making, selecting interfaces with the least 

amount of gestural steps that can help students improve their clinical performance. To sum up, these steps make digital 

transformation a human-performance initiative rather than a technology deployment, producing more consistent clinical 

learning outcomes. 

Although the findings of this research are novel in the literature, the study has some limitations that open a gate for 

further researchers. Initially, this study tested a model using quantitative data, which was based on self-administered 

structural questionnaires. Hence, the respondents had no option but to provide their subjective response to the questionnaires. 

Future studies are recommended to use mixed methodology, where data collected should provide significant insights both 

quantitatively and qualitatively. Second, this study focused solely on the context of China, where the population consisted 

of nursing students. Future studies should target other hotspots, such as demographics other than China, which can contribute 

significantly to the body of knowledge. In this way, the findings of this study would provide a significant and broader 

contribution to the body of knowledge. 

 
 

Author Contributions: Conceptualization, Y.J. and C.C.; Methodology, Y.J.; Software, Y.J.; Validation, Y.J., X.H. and J.Z.; Formal Analysis, LD.; 

Investigation, Y.J.; Resources, C.C.; Data Curation, Y.J.; Writing – Original Draft Preparation, Y.J.; Writing – Review & Editing, Y.J.; Visualization, 
Y.J.; Supervision, Y.J.; Project Administration, Y.J.; Funding Acquisition, Y.J. Authors have read and agreed to the published version of the manuscript. 

Institutional Review Board Statement: Ethical review and approval were waived for this study, due to that the research does not deal with vulnerable 

groups or sensitive issues. 
Funding: This work was supported by the 2025 Undergraduate Education and Teaching Reform Project of Tongji University: Exploration and Practice of 

Undergraduate Nursing Teaching Model Based on the Concept of “Digitalization, Greenization, and Integration”. 

Acknowledgments: Our sincere appreciation goes out to all the participants who took part in this study. Shanghai Tenth People’s Hospital, Tongji 
University, Shanghai, China, is highly acknowledged. 

Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. 

Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available 
due to restrictions. 

Conflicts of Interest: The authors declare no conflict of interest.                                                                                                                                                                                                                                   

 

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APPENDICES 

Appendix A: Measurement Questionnaire 

 
Variables Item Statement 

Digital Literacy Level I can efficiently search for clinical information online. 

I am confident in using digital tools for clinical learning. 

I can evaluate the reliability of online nursing resources. 

I am comfortable using digital platforms for academic work. 

I can troubleshoot common technical issues on my own. 

Instructor Support for Digital Learning My instructors encourage the use of digital tools in clinical practice. 

I receive timely help from instructors when I face technical difficulties. 

Instructors integrate digital learning into clinical training effectively. 

My instructors provide clear instructions for using digital platforms. 

Instructors are open to using innovative technologies in teaching. 

Lack of Technology Anxiety I feel calm when starting a new digital tool required for learning. 

I feel relaxed using technology during clinical tasks. 

https://doi.org/10.1007/s10648-024-09848-3
https://doi.org/10.1080/08841241.2023.2239720
https://doi.org/https:/doi.org/10.1155/2022/2533413
https://doi.org/https:/doi.org/10.1016/j.actpsy.2024.104602
https://doi.org/https:/doi.org/10.1016/j.nedt.2021.104815
https://doi.org/https:/doi.org/10.1016/j.ecns.2022.05.006
https://doi.org/10.1007/s10648-021-09624-7
https://doi.org/10.1007/s10648-021-09624-7
https://doi.org/10.1186/s12992-021-00665-9
https://doi.org/10.1186/s12992-021-00665-9
https://doi.org/10.3928/01484834-20221109-03
https://doi.org/10.1109/TLT.2022.3198739
https://doi.org/https:/doi.org/10.1016/j.ajpe.2022.12.002
https://doi.org/10.1007/s10639-021-10604-1
https://doi.org/10.1186/s12909-023-04390-2
https://doi.org/https:/doi.org/10.1016/j.caeo.2022.100100
https://doi.org/https:/doi.org/10.1016/j.nedt.2022.105451
https://doi.org/10.1007/s10639-022-11311-1
https://doi.org/10.1109/TLT.2024.3364015
https://doi.org/10.3390/su14084388
https://doi.org/10.1080/10447318.2022.2041911


Jiang et al., Bangladesh Journal of Multidisciplinary Scientific Research 10(6) (2025), 73-86

 

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I remain at ease when a device or app behaves unexpectedly. 

I am comfortable exploring unfamiliar features in educational software. 

I am composed when troubleshooting basic technical issues. 

I am confident in handling updates or changes to digital platforms. 

Lack of Cognitive Load I can process digital learning content without feeling mentally strained. 

The digital tasks I do for clinical learning feel manageable in terms of mental effort. 

I rarely need to repeat steps to understand digital instructions. 

The layout of digital platforms makes it easy to find what I need with little effort. 

I can keep my attention on clinical content without feeling mentally fatigued. 

The amount of information presented at one time feels manageable. 

Clinical Performance I can apply theoretical knowledge effectively during clinical tasks. 

I make informed decisions using digital tools during clinical practice. 

I am confident in performing clinical procedures accurately. 

My clinical performance has improved with the help of digital learning. 

I can meet the clinical objectives set by my program. 

 

 
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