Skip to main content
Download PDF

Relationship between digital capabilities and academic performance: the mediating effect of self-efficacy

BMC Nursing volume 22, Article number: 434 (2023) Cite this article

Abstract

Aims

To assess digital capabilities and academic performance among nursing students and investigate the mediating role of students’ self-efficacy.

Background

In the context of education and technology, digital capabilities, self-efficacy, and academic performance among nursing students are interconnected concepts. Students who use their digital capabilities and competencies combined with their belief in the ability to efficiently perform learning tasks could improve their academic endeavors. Nevertheless, insufficient consideration has been placed on research understanding of the mediating roles and broad elements that influence their relationships.

Methods

A cross-sectional, correlational, descriptive, and quantitative study was established. During the 2022–23 academic year, data were gathered from 200 students. The Hayes Process Model 4 macro was employed to investigate the role of students’ self-efficacy mediating effect on the association between digital capabilities and academic performance.

Results

The digital capability level and self-efficacy level were high. Additionally, the academic performance level was moderate. The mediation analysis revealed that the direct effect of digital capabilities on student performance in the presence of the mediator was significant (b = 0.0063, p = 0.022). Hence, self-efficacy partially mediated the relationship between digital capabilities and student performance.

Conclusion

The study emphasized the importance of improving students’ digital capabilities that enhance their confidence and self-actualization. In addition, nursing students are encouraged to improve their sense of self-efficacy throughout their tenure in college because it is a predictor of future success.

Peer Review reports

Introduction

In the contemporary digital age, nursing students are required to possess a set of essential competencies referred to as digital capabilities [1]. Digital capabilities include a variety of skills and knowledge related to the use of digital technologies, tools, and resources [2]. These competencies encompass proficiencies in digital independent learning, digital information/data management, digital communication and collaboration, digital creation, and digital problem-solving [2, 3]. These capabilities empower nursing students to deliver proficient and effective care while also being able to adapt to the ever-changing digital environment within healthcare institutions [1, 3].

The concept of digital capabilities is strongly associated with self-efficacy, particularly in the field of learning [4]. The acquisition and application of digital capabilities are influenced by students’ self-efficacy beliefs. When students have a strong belief in their capacity to acquire the necessary skills to effectively use digital technologies, they actively participate in the educational experience with enthusiasm and tenacity [5]. Conversely, students with low self-efficacy features in digital abilities tend to lose their path and fail to accomplish tasks when confronted with digital activities [6].

Academic performance is influenced by digital self-efficacy [7]. Students’ academic performance improves when they have trust in their digital skills and abilities [8, 9] Academic performance measures the extent to which intended learning outcomes have been attained and is recorded in a student’s cumulative grade point average (CGPA) [10]. Farrington et al. [11] described academic performance as a “complex phenomenon” in which “cognitive and noncognitive elements continuously interact in crucial ways to generate learning”.

The literature provides an understanding of the elements that may influence the academic performance of learners [8, 12]. A recent study reported that students’ academic performance was negatively affected during COVID-19, as the majority of classes were held online because students had difficulty navigating the online learning platform [13]. The authors additionally suggested devoting special attention to students’ digital abilities to ensure that all students have access to an equitable education [13]. Academic performance should be studied from a multifactor viewpoint. This study will assess the effect of students’ self-efficacy on the relationship between digital capabilities and academic performance.

Background

Digital capabilities

Digital capabilities in the realm of educational organizations are defined as “the skills, talents, and abilities that enable an individual to live, study, and operate in a digital society” [14,15,16,17]. Additionally, the concept of digital capabilities refers to how effectively students can employ critical thinking and problem-solving skills when working with digital tools and resources [18, 19]. Digital literacy, on the other hand, is a concept frequently used in the digital health care literature, which is related to digitalization, but it focuses on the skills used to locate and cite information [20, 21]. According to Gilster [22], digital literacy is “the capacity to comprehend and utilize information in diverse formats from a variety of sources when it is displayed on a computer”. It is crucial to differentiate between concepts that are related to digitalization to be able to identify the needs of students [23].

Thus, digital capabilities frameworks have been established to empower students as well as faculty to cope with the demands of digital capabilities [3, 24]. Among these frameworks is the Joint Information System Committee (JISC), which is a framework designed to encourage the use of digital technology in education [23, 25]. The JISC framework incorporates capabilities and enablers into six domains [3, 26]. The model’s primary focus is on digital proficiency and productivity, with the other five categories listed as digital creation, problem-solving, and innovation; digital learning and development; digital identity and well-being; information, data, and media literacies; and digital communication, collaboration, and participation [27, 28].

College students with strong digital capabilities may find it easier to navigate online courses, interact with digital learning platforms, and engage in virtual classroom activities [27]. There is a common assumption that students master high digital skills because they are considered the digitalized generation [15, 29]. This poses the question of whether these students have the digital capabilities to be able to locate, evaluate and, more importantly, analyze information [20]. A research study of nursing students in the UAE was conducted to examine nursing students’ digital capabilities, and the authors reported that while participating students had excellent digital literacy skills, they did not appear to be competent in analyzing such information [30]. As a result, educational institutions must ensure that students use critical thinking skills in their interactions with the digital world [19]. This argument is also emphasized in a study that investigated the level of digital competences among nursing students [31]. Learning about digital skills is a topic of prime concern in university education, and its influence on student performance has recently sparked substantial attention [20, 30].

Self-efficacy

Bandura [32] defined self-efficacy as “the belief in one’s capacity to organize and execute the courses of action necessary to achieve specified goals” [32, 33]. Self-efficacy was described by Akhtar [34] as the confidence we have in our talent, particularly our capacity to face challenges and finish a task effectively [35]. It enables students to excel through their dedication and perseverance to complete their learning-related responsibilities [36, 37].

Self-efficacy is one of the most important determinants of academic achievement among nursing students [38]. Academic self-efficacy (ASE) pertains to students’ views and attitudes on their capacity to achieve academic achievement, as well as their ability to successfully complete academic activities [39,40,41]. It has been postulated that students who demonstrate high self-efficacy make efforts to find creative ways to approach challenges and solve problems; thus, self-efficacy could impact task selection and achievement [33, 42, 43].

Self-efficacy in relation to academic performance has received much attention because it appears to be the most influential factor in academic achievement [37]. Several researchers have examined self-efficacy and revealed that it has a major influence on student learning, motivation, and academic functioning [41, 44, 45]. In more recent research performed at three Chinese institutions, researchers investigated the learning situation of preservice special education teachers and observed that their ASE was only at a medium level [41].

The self-efficacy of nursing college students was linked to their digital skills, which was an important variable in academic learning [46]. For example, a study assessed the relationship between self-efficacy toward online eHealth literacy levels among nursing students [47]. The author concluded that there was a significant relationship between self-efficacy and the students’ use of eHealth [47]. Another recent study conducted at three nursing colleges in Korea to identify the relationship and direction of factors affecting nursing students’ Ehealth literacy in an online learning environment indicated that digital literacy and self-efficacy were found to be associated with Ehealth literacy and mediate the relationship between online learning attitude [37].

Academic performance (AP)

Academic performance could be improved by integrating digital capabilities and fostering self-efficacy [48]. The interplay of these factors can significantly influence a student’s academic success. Students’ grades are generally used as a proxy for their academic progress when examining the effects of educational methods [37]. A high-grade point average in college is a reliable sign of academic success [49].

Some studies have found evidence of a positive relationship between technology and student learning in terms of digital abilities [49, 50]. Students who possess varied sets of digital abilities and are skilled with technology often demonstrate good academic performance [51, 52]. A study conducted at a Korean college with a total of 614 undergraduates to determine the relationship between university students’ experience in e-learning and academic achievement (GPA) revealed that university e-learning settings need students to have strong digital skills to perform academic work and a commitment to active participation in the environment of academic learning [49].

A close association between self-efficacy and student academic (GPA) performance has been noted in educational settings since self-efficacy increases students’ perception of accomplishment and thus helps them achieve superior academic performance [53,54,55]. In the nursing literature, current research has shown that students who believe in their own abilities to successfully complete academic tasks perform better academically [37, 54, 56]. In 2021, Reynolds et al. conducted a systematic review that aimed to explore the relationship between noncognitive factors, including self-efficacy. It was found that high self-efficacy is associated with better performance [57].

Considering these previous findings, it would be beneficial to understand the effect of digital capabilities and self-efficacy on academic performance among nursing students. This is especially important when designing courses designed to boost students’ academic performance and empower them to apply their skills and competences effectively. Furthermore, it is hoped that this study will untangle the intricate threads of digital capabilities and self-efficacy, resulting in more effective use of students’ capabilities and an improvement in nursing students’ efficacious activities in the future. Figure 1 depicts the anticipated reciprocal relationship between digital capabilities and academic performance as mediated by students’ self-efficacy. Therefore, present study was conducted to assess digital capabilities and academic performance among nursing students and investigate the mediating role of students’ self-efficacy.

Fig. 1
figure 1

Self-efficacy mediates the reciprocal relationship between digital capabilities and academic performance

Full size image

Materials and methods

Research design, setting, and sample

This cross-sectional, correlational, descriptive, and quantitative study was conducted at the Nursing Department of Fatima College of Health Sciences, United Arab Emirates, in the academic year of 2022–2023, where the student intake is exclusively female. Nursing students from levels 2–4 at Fatima College of Health Sciences were included in the study population. These students started to be enrolled in the nursing program from level 2. The student’s grade point average (GPA) was used to measure their academic achievement. The number of participating nursing students was calculated using Epi-Info 7 to account for a 5% variation, 95% confidence, and 0.80 power at a 0.5 significance level by considering a 5% nonresponse rate. The final sample size was 200 convenient nursing students who were available and agreed to participate at that time.

Instruments

Among the collected demographic data were gender, age, marital status, and student level of education. Digital capabilities were assessed using the digital capabilities and self-efficacy (DCSE) scale, which is a structured self-administered questionnaire from the ZENODO repository that was used to collect data [58]. This scale contained 19 items organized into five dimensions: digital independent learning (5 items; α = 0.840), digital information/data management (4 items; α = 0.857), digital communication and collaboration (6 items; α = .0.858), digital creation (2 items; α = .0.759), and digital identity (2 items; α = .774).

Variables on the digital independent learning subscale include learning resource accessibility, augmenting lecture notes, accessing course information, studying instructional materials given by instructors, and exploring lecture-related web pages. Elements on the digital information/data management list included database search tools, browsing and watching lecture-related videos, completing assignments, and participating in online learning activities. Items on the Digital Communication and Collaboration Subscale included making my own digital materials for the module, talking to my instructors, communicating with classmates, and offering feedback on their work using various online tools. The items featured on Digital Creation” stand for participating in online group projects with other students and keeping my own blog. Finally, the Digital Identity subscale had indicators for online community involvement: participating actively in my professional community.

The participants rated each item on a seven-point scale (1 = not at all, 7 = to a very great extent). The researchers computed the average score for each component and the overall scale score (α = 0.936), which was the average of the five dimensions; higher scores indicated a greater degree of digital skills. The total score, which ranges from 19 to 133 points, can be divided into limited capabilities (19–56 points), intermediate capabilities (57–94 points), or acceptable capabilities (95–133 points). The scale was identified to be satisfactory in terms of its concept validity, criteria validity, and internal consistency reliability (0.975).

Pintrich et al. created the Motivated Learning Strategies Questionnaire (MLSQ) in 1993 [59, 60]. The MSLQ consists of 6 motivational and 9 learning strategies subscales. Self-efficacy for learning and performance is one of the six motivational subscales of the MLSQ [59]. The self-efficacy subscale is an 8-item instrument (α 0.955) that measures a student’s perceptions of their talents and capacity to complete a given activity successfully. Using a seven-point Likert scale ranging from (1 = not at all true of me to 7 = very true of me), participants scored each item. Students were instructed to determine which number between 1 and 7 best represents them. This instrument’s validity and reliability have been confirmed, and it has undergone comprehensive validation in multiple investigations [61, 62].

The total score was obtained by adding and averaging the scores of eight items, with potential averages ranging from 8 to 56, with higher scores indicating more self-efficacy. The total score can be regarded as low efficacy level (8–23 points), moderately efficacious (24–39 points), or highly efficacious (40–56 points).

The academic performance of students is determined by their grade point average; a cumulative grade point average is classed as acceptable (2.0–2.74), good (2.75–3.74) very good (3.75–4.49), and Excellent (4.5–5). The statistical scoring system of the digital capabilities was as follows: high digital capabilities: (66.7–100%), moderate digital capabilities: (33.4–66.6%), and low digital capabilities: (0–33.3%).

Validity and reliability

Cronbach’s alpha coefficient test was used to measure the internal consistency of items to check the reliability of the study tools. Both tools were found to be reliable at a statistical significance level of P < .05, with 0.936 for digital capabilities and self-efficacy (DCSE) tool and 0.955 for Self-efficacy for learning and performance tool.

Ten percent of the participants (n = 20) from the context specified above participated in the pilot study to evaluate the clarity and utility of the instruments, identify potential obstacles during data collection, and determine the length of time required to complete the tools. The pilot study participants were not included in the study sample.

Data collection

The survey was sent to all FCHS students through the students’ service center, which has access to all students’ email addresses. In the email, the letter of information that includes the study purpose, benefits, and participants’ rights was attached. Additionally, it was clearly stated that students have the option to take part in the study or not. The survey requires approximately 15 minutes to be completed. Data were collected over a one-month period. There were no missing data because the data collection was completed after the calculated sample size (n = 200) was attained. The researcher’s contact information was supplied. All participant queries were addressed and elucidated.

Ethical considerations

After submitting the study protocol, instrument, and consent to members of the committee, the Research Ethics Committee of Fatima College of Health Sciences, UAE, sanctioned ethical approval for this study [IRB approval number: FECE-03-20-23-NUR-Rasha]. Subjects volunteered to participate after being informed of the precautions implemented to guarantee data confidentiality. The student’s response was 100% anonymous; therefore, no private information was stored. By disseminating the information sheet and consent form, the nursing students who participated in this study were made aware of the nature and goal of the research endeavor.

Statistical analysis

The data were analyzed using SPSS 23. Descriptive statistics (frequency, means, standard deviations, and percentages) were deployed to quantify demographic variables. Pearson’s coefficient correlation was used to assess the relationship between the variables in the study. To predict self-efficacy scores and student performance in response to digital capabilities, a multiple regression analysis was undertaken. The variables encompassed as independent variable in the multiple regression models was overall digital capabilities and dependent variables were self-efficacy and cumulative grade point average. Self-efficacy was included to investigate its role as a mediator in the relationship between digital capabilities and student performance. The mediating effect of self-efficacy was investigated using the Hayes Process Model 4 macro software, with digital capabilities as the independent variable, student performance as the dependent variable, and self-efficacy as the mediating variable [63].

Results

The response rate was 100% after tackling every single participant. Sixty-four percent of the research participants were between 20 and 22 years old. The contestants were all female. Regarding marital status, all undergraduates were single. Considerably more than half (n = 107; 53.5%) of the student responders were in level 2 (Table 1). According to Table 2, the overall digital capability level was high (65%), with a mean score of 103.3 ± 19.54. In terms of individual subscales, the “ Digital communication and collaboration “ subscale had the highest mean score (31.12 ± 7.93), while the “Digital creation” subscale had the lowest (8.98 ± 3.73). In addition, the overall self-efficacy was high (67.5%), with a mean score of 43.59 ± 10.13. Table 2 reveals that the overall academic performance of students was moderate (50.5%), with a mean score of 2.97 ± 0.54.

Table 1 Socio-demographic facets of research participants (n = 200)
Full size table
Table 2 Mean scores of Digital capabilities, Academic Performance, and Self Efficacy (n = 200)
Full size table

In terms of the correlation analysis in Table 3, a strong, positive, and significant correlation was noted between academic performance and the overall self-efficacy scale r (198) = 0.308, p = 0.001. The previously mentioned correlation was detected not only between academic performance and overall digital capabilities r (198) = 0.315, p < 0.001 but also with all subscales of digital capabilities, which were digital independent learning r (198) = 0.223, p = 0.002, digital information/data management r (198) = 0.249, p < 0.001, digital communication and collaboration r (198) = 0.255, p < 0.001, digital creation r (198) = 0.181, p = 0.010), and digital identity r (198) = 0.166, p = 0.019.

Table 3 Matrix of Correlation Between the Variables of the Study (n = 200)
Full size table

Not only was there a substantial positive correlation between the self-efficacy scale and overall digital capabilities r (198) = 0.634, p < 0.001, but there was also a substantial positive correlation between the self-efficacy scale and all subscales of digital capabilities, which were digital independent learning r (198) = 0.620, p < 0.001, digital information/data management r (198) = 0.522, p < 0.001, digital communication and collaboration r (198) = 0.587, p < 0.001, digital creation r (198) = 0.525, p < 0.001, and digital identity r (198) = 0.261, p < 0.001.

To validate the relationship between digital capabilities and self-efficacy, a regression analysis was performed, with digital capabilities as the independent variable and self-efficacy as the dependent variable (Table 4). According to the regression analysis, students’ perception of their digital capabilities could predict their self-efficacy (F (1, 198) = 132.757, p < .001, adj. R2 = 0.401).

Table 4 Linear regression analysis for Overall Self-Efficacy Scale
Full size table

To validate the relationship between digital capabilities and academic performance, a regression analysis was performed, with digital capabilities as the independent variable and academic performance as the dependent variable (Table 5). According to the regression analysis, students’ perception of their digital capabilities could predict their academic performance (F (1, 198) = 15.049, p < .001, adj. R2 = 0.071).

Table 5 Linear regression analysis for Academic Performance
Full size table

The study assessed the mediating role of self-efficacy on the relationship between digital capabilities and student performance (Table 6). The results revealed a significant indirect effect of the impact of digital capabilities on student performance (b = 0.0036, t = 2). Furthermore, the direct effect of digital capabilities on student performance in the presence of the mediator was also found to be significant (b = 0.0063, p = 0.022). Hence, self-efficacy partially mediated the relationship between digital capabilities and student performance. This means that in the model (Fig. 2), there is a significant positive correlation in paths a–c.

Table 6 Path analysis of direct and indirect effects of digital capabilities on students’ performance mediated by self-efficacy
Full size table
Fig. 2
figure 2

Direct and Indirect effect of Digital Capabilities and Self Efficacy on Students GPA

Full size image

Discussion

This study investigated how self-efficacy mediates the relationship between nursing students’ digital capabilities and their academic performance (GPA). First, the study revealed that digital capabilities positively affect nursing students’ academic performance. These findings align with several studies that aimed to study the relationship between digital capabilities and academic performance. For example, a recent study by Chen et al. [41] investigated the relationship between digital competence and academic performance among nursing students in China. The study found that nursing students with higher levels of digital competence had better academic performance, including higher exam scores and better grades [41]. These results coincided with those presented among nursing students in Saudi Arabia and Jordan, who found that nursing students with higher levels of digital literacy had better academic performance and better scores on clinical evaluations [48, 64]. Recent studies in South Korea and Pakistan have examined the impact of a digital nursing education program on academic performance among nursing students. These studies found that nursing students who participated in the program had significantly better academic performance, including higher GPAs and better scores on clinical evaluations [37, 65, 66]. On the basis of these findings, it is critical for educational institutions to equip students with suitable technological infrastructures and digital resources to connect their learning with digital learning environments, resulting in good digital education. For nursing students to succeed in healthcare facilities, there is a demand to be involved earlier in the digital world [2].

Second, the current study revealed that self-efficacy had a statistically significant positive correlation with nursing students’ academic performance. Moreover, it had a mediating effect on the relationship between digital capabilities and nursing students’ academic performance. Academic achievement increased among students who reported high levels of self-efficacy. Self-efficacy is described as a person’s confidence in their own ability to plan and carry out a series of activities to achieve a desired result [33]. It has been investigated as a possible factor in integrating theory and practice [37]. The significant relationship between study variables confirms the literature’s claims that these concepts may play a vital role in overcoming the barriers of nursing education.

These findings were consistent with many studies conducted to assess the relationship between self-efficacy and academic performance [67,68,69]. In a systematic review that analyzed 12 years of research on the relationship between academic self-efficacy and the academic performance of university students, researchers determined that fifty-nine studies found a moderate correlation between academic self-efficacy and academic performance [70]. In contrast, a study was conducted to evaluate self-efficacy on students’ performance among nursing students. The results of the 296 participants’ study concluded that there is no relationship between self-efficacy and students’ performance, which means that students with high self-efficacy do not necessarily improve students’ performance in clinical settings [71].

Last, given the widespread incorporation of technological advances in the field of education, the acquisition of digital self-efficacy is deemed an essential competency. Put differently, digital self-efficacy enables students to obtain access to unlimited educational resources and enhances their interaction in the learning environment. Recent researchers found that nursing students with high levels of technologic self-efficacy were more likely to apply it in their academic work [67,68,69].

Hence, self-efficacy reinforcement may be an effective way to foster nursing students’ digital capabilities and prepare them for the increasingly digital healthcare environment. These results suggested including digital technologies and self-efficacy in nursing education by identifying factors to improve their digital capabilities in a tech environment. It is necessary to develop a strategy to improve nursing students’ digital capabilities and self-efficacy.

Implications and limitations

The study’s findings have significant consequences for nursing educators, legislators, and students. To improve students’ digital capabilities and overall performance, instructors should assess their curriculum and thoroughly think about incorporating digital technologies into the designs they produce. Implementing novel approaches to teaching, expanding access to a broader range of information and resources, and developing new skills for the digital era are all possibilities to improve the pedagogical system. Guidelines should specifically recognize the promise for digital capabilities to boost student outcomes. Enhancing nursing students’ digital capabilities can be a useful approach for improving both academic achievement and self-efficacy. The relationship between self-efficacy and academic success has implications for practice.

According to the research, nursing students should strive to develop their self-efficacy throughout their undergraduate careers since it predicts their success in the workforce after graduation. They must also dedicate time to their education and strive to improve themselves. According to the study’s findings, digital capabilities and self-efficacy were extremely advantageous in the curriculum, which could give rise to greater job possibilities.

Although the study provides fresh viewpoints for theory and future investigations, it does have limitations. An online survey of undergraduate nursing students was used in the study. This may reduce the findings’ applicability in other settings. Another limitation is the possibility of bias created by self-reporting. In the context of this study, self-report was used to assess digital capabilities, self-efficacy, and self-reported GPAs. Furthermore, because of the wide range of programs, the relatively small number of participants, and the inclusion of only undergraduate nursing students, the results should be cautiously generalized to a broader population.

Conclusion

Overall, we believe our findings provide a solid foundation for future research on digital competence and self-efficacy. Students who are well-versed and skilled are more capable of absorbing and using what they have learned. Academic advancement and self-confidence in one’s own capacity to succeed are both considered to be rooted in the growth of DC skills among undergraduates. Therefore, digitization is critical to the evolution of any modern civilization, as a shortage of competent employees would hinder society’s future development. Based on the findings of this study, we can conclude that performance prediction will benefit educators and the educational institution as a whole by allowing them to modify their pedagogical techniques and assist students in optimizing their own learning strategies. Students with a low level of self-efficacy and limited digital capabilities should be looked at frequently and provided with further support to reduce the probability that they will drop prematurely from college.

Availability of data and materials

The dataset gathered and analyzed in the current work is not accessible to the general public; however, it is obtainable from the corresponding author given adequate justification upon request.

Abbreviations

CGPA:

Cumulative Grade Point Average

JISC:

Joint Information System Committee

ASE:

Academic Self-Efficacy

DCSE:

Digital Capabilities and Self-efficacy

References

  1. Clarke-Darrington J, McDonald T, Ali P. Digital capability: an essential nursing skill for proficiency in a post-COVID-19 world. Int Nurs Rev. 2023;70(3):291–6.

    Article  PubMed  Google Scholar 

  2. Booth RG, Strudwick G, McBride S, O’Connor S, Solano López AL. How the nursing profession should adapt for a digital future. BMJ. 2021;373:n1190.

  3. Elphick M. The impact of embedded iPad use on student perceptions of their digital capabilities. Educ Sci. 2018;8(3):102.

    Article  Google Scholar 

  4. Karunaweera AS, Lee KW. Measuring digital competence: an exploratory study mapping DigitalCompetence profiles of Sri Lankan English language teachers. APJEE. 2021;36(1):93–112.

    Article  Google Scholar 

  5. Blondeel E, Everaert PC, Opdecam E. Does Practice Make Perfect? The Effect of Online Formative Assessments on Students’ Self-Efficacy and Test Anxiety. Rochester, NY; 2023 [cited 2023 Oct 12]. Available from: https://papers.ssrn.com/abstract=4357056.

  6. Brew EA, Nketiah B, Koranteng R. A literature review of academic performance, an insight into factors and their influences on academic outcomes of students at senior high schools. Open Access Libr J. 2021;8(6):1–14.

    Google Scholar 

  7. Ulfert-Blank AS, Schmidt I. Assessing digital self-efficacy: review and scale development. Comput Educ. 2022;191:104626.

    Article  Google Scholar 

  8. De Clercq M, Galand B, DuPont S, Frenay M. Achievement among first-year university students: an integrated and contextualized approach. Eur J Psychol Educ. 2013;28(3):641–62.

    Article  Google Scholar 

  9. Singh DS. Study of factors affecting academic achievement in medical students. jmscr. 2016;04(12):1968–14972.

    Article  Google Scholar 

  10. Hayat AA, Shateri K, Amini M, Shokrpour N. Relationships between academic self-efficacy, learning-related emotions, and metacognitive learning strategies with academic performance in medical students: a structural equation model. BMC Med Educ. 2020;20(1):76.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Farrington CA, Roderick M, Allensworth E, Nagaoka J, Keyes TS, Johnson DW, et al. Teaching Adolescents to Become Learners: The Role of Noncognitive Factors in Shaping School Performance--A Critical Literature Review. Consortium on Chicago School Research. Consortium on Chicago School Research; 2012 [cited 2023 Oct 12]. Available from: https://eric.ed.gov/?id=ED542543.

  12. Masud S, Mufarrih SH, Qureshi NQ, Khan F, Khan S, Khan MN. Academic performance in adolescent students: the role of parenting styles and socio-demographic factors – a cross sectional study from Peshawar, Pakistan. Front Psychol. 2019;10 https://doi.org/10.3389/fpsyg.2019.02497.

  13. Benoza MS, Palaoag T. Exploring college academic performance of K to12 IT and non-IT-related strands to reduce academic deficiencies. Int J Adv Comp Sci Appl. 2023;14(1). https://thesai.org/Publications/ViewPaper?Volume=14&Issue=1&Code=IJACSA&SerialNo=37. Accessed 13 Mar 2023.

  14. Idris F, Hassan Z, Ya’acob A, Gill SK, Awal NAM. The role of education in shaping Youth’s National Identity. Procedia Soc Behav Sci. 2012;59:443–50.

    Article  Google Scholar 

  15. Bullen M, Morgan T, Qayyum A. Digital learners in higher education: generation is not the issue. Can J Learn Technol. 2011. https://cjlt.ca/index.php/cjlt/article/view/26364. Accessed 20 Apr 2023.

  16. Brown J, Pope N, Bosco AM, Mason J, Morgan A. Issues affecting nurses’ capability to use digital technology at work: an integrative review. J Clin Nurs. 2020;29(15–16):2801–19.

    Article  PubMed  Google Scholar 

  17. Rahmat A, Mohd I, Omar M. Digital capabilities in the digital environment: employers’ view on Malaysian youth capabilities. Intelligent Systems in Accounting Finance & Management; 2021. p. 199–207.

    Google Scholar 

  18. Austen L, Parkin H, Jones-Devitt S, McDonald K, Irwin B. Digital capability and teaching excellence: an integrative systematic review exploring what infrastructure and strategies are necessary to support effective use of technology enabled learning (TEL); 2016.

    Google Scholar 

  19. Akour M, Alenezi M. Higher education future in the era of digital transformation. Educ Sci. 2022;12(11):784.

    Article  Google Scholar 

  20. Nes AAG, Steindal SA, Larsen MH, Heer HC, Lærum-Onsager E, Gjevjon ER. Technological literacy in nursing education: a scoping review. J Prof Nurs. 2021;37(2):320–34.

    Article  PubMed  Google Scholar 

  21. Audrin C, Audrin B. Key factors in digital literacy in learning and education: a systematic literature review using text mining. Educ Inf Technol. 2022;27(6):7395–419.

    Article  Google Scholar 

  22. Gilster P. Digital literacy. New York: Wiley Computer Pub; 1997. xii. p. 276.

    Google Scholar 

  23. Lee YT, Fanea-Ivanovici M. An Exploratory Study of Digital Literacy Frameworks: A Comparative Analysis. Rochester, NY; 2022 [cited 2023 Oct 12]. Available from: https://papers.ssrn.com/abstract=4088293.

  24. Martínez MC, Sádaba C, Serrano-Puche J. Meta-framework of digital literacy: a comparative analysis of 21st-century skills frameworks. Rev Lat Commun Soc. 2021;79:76–110.

    Google Scholar 

  25. Digital discovery tool. Jisc Building Digital Capability Blog. [cited 2023 Oct 12]. Available from: https://digitalcapability.jiscinvolve.org/wp/digital-capability-discovery-tool/.

  26. Jisc. [cited 2023 Oct 12]. Building digital capability. Available from: https://beta.jisc.ac.uk/building-digital-capability.

  27. Johnston B, MacNeill S, Smyth K. The myth of digital transformation. In: Johnston B, MacNeill S, Smyth K, editors. Conceptualizing the Digital University: the intersection of policy, pedagogy and practice. Cham: Springer International Publishing; 2018. p. 63–84. (Digital Education and Learning). https://doi.org/10.1007/978-3-319-99160-3_4.

    Chapter  Google Scholar 

  28. Sandhu G. The Role of Academic Libraries in the Digital Transformation of the Universities. In: 2018 5th International Symposium on Emerging Trends and Technologies in Libraries and Information Services (ETTLIS). 2018 [cited 2023 Oct 12]. p. 292–6. Available from: https://ieeexplore.ieee.org/document/8485258.

  29. Kennedy G, Judd T, Dalgarno B, Waycott J. Beyond natives and immigrants: exploring types of net generation students. J Comput Assist Learn. 2010;26(5):332–43.

    Article  Google Scholar 

  30. Alaleeli S, Alnajjar A. The Arab digital generation’s engagement with technology: the case of high school students in the UAE. Journal of Technology and Science Education. 2020;10(1):159–78.

    Article  Google Scholar 

  31. Kaçar H, McCullough F. Nutrition messaging by healthcare students - a mixed method study exploring social media usage and digital competence; 2022.

    Book  Google Scholar 

  32. Bandura A, Freeman WH, Lightsey R. Self-efficacy the exercise of control. J Cogn Psychother. 1997;13:158–66. References - Scientific Research Publishing. [cited 2023 Oct 12]. Available from: https://www.scirp.org/%28S%28lz5mqp453edsnp55rrgjct55%29%29/reference/referencespapers.aspx?referenceid=3088022.

  33. Bandura A, Bandura A. Guide for constructing self-efficacy scales.

  34. Akhtar M. What is self-efficacy? Bandura’s 4 sources of efficacy beliefs. United Kingdom: Positive Psychology; 2008. Accesssed 25 Apr 2023.

  35. What is Self-Efficacy? Bandura’s 4 Sources of Efficacy Beliefs. PositivePsychology.org.uk. 2008 [cited 2023 Oct 13]. Available from: http://positivepsychology.org.uk/self-efficacy-definition-bandura-meaning/.

  36. Pfitzner-Eden F. Why do I feel more confident? Bandura’s sources predict preservice teachers’ latent changes in teacher self-efficacy. Front Psychol. 2016;7 https://doi.org/10.3389/fpsyg.2016.01486.

  37. Jeon J, Kim S. The mediating effects of digital literacy and self-efficacy on the relationship between learning attitudes and Ehealth literacy in nursing students: a cross-sectional study. Nurse Educ Today. 2022;113:105378.

    Article  PubMed  Google Scholar 

  38. McCleary-Jones V. A systematic review of the literature on health literacy in nursing education. Nurse Educ. 2016;41(2):93.

    Article  PubMed  Google Scholar 

  39. Ferla J, Valcke M, Cai Y. Academic self-efficacy and academic self-concept: reconsidering structural relationships. Learn Individ Differ. 2009;19(4):499–505.

    Article  Google Scholar 

  40. Elias SM, MacDonald S. Using past performance, proxy efficacy, and academic self-efficacy to predict college performance. J Appl Soc Psychol. 2007;37(11):2518–31.

    Article  Google Scholar 

  41. Chen X, Zhong J, Luo M, Lu M. Academic self-efficacy, social support, and professional identity among preservice special education teachers in China. Front Psychol. 2020;11 https://doi.org/10.3389/fpsyg.2020.00374.

  42. Chang CS, Liu EZF, Sung HY, Lin CH, Chen NS, Cheng SS. Effects of online college student’s internet self-efficacy on learning motivation and performance. Innov Educ Teach Int. 2014;51(4):366–77.

    Article  Google Scholar 

  43. Adler-Constantinescu C, Beşu EC, Negovan V. Perceived social support and perceived self-efficacy during adolescence. Procedia Soc Behav Sci. 2013;78:275–9.

    Article  Google Scholar 

  44. Elstad E, Christophersen KA. Perceptions of digital competency among student teachers: contributing to the development of student teachers’ instructional self-efficacy in technology-rich classrooms. Educ Sci. 2017;7. https://eric.ed.gov/?id=EJ1135107. Accessed 25 Apr 2023.

  45. Siddiq F, Gochyyev P, Wilson M. Learning in digital networks – ICT literacy: a novel assessment of students’ 21st century skills. Comput Educ. 2017;109:11–37.

    Article  Google Scholar 

  46. Factors influencing eHealth literacy among Korean nursing students: A cross-sectional study - Kim - 2020 - Nursing & Health Sciences - Wiley Online Library. [cited 2023 Oct 13]. Available from: https://doi.org/10.1111/nhs.12711.

  47. Sögüt S, Cangöl E, Dolu İ. The relationship between eHealth literacy and self-efficacy levels in midwifery students receiving distance education during the COVID-19 pandemic. J Nurs Res. 2022;30(2):e203.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Alghamdi A, Karpinski AC, Lepp A, Barkley J. Online and face-to-face classroom multitasking and academic performance: moderated mediation with self-efficacy for self-regulated learning and gender. Comput Hum Behav. 2020;102:214–22.

    Article  Google Scholar 

  49. Kim HJ, Hong AJ, Song HD. The roles of academic engagement and digital readiness in students’ achievements in university e-learning environments. Int J Educ Technol High Educ. 2019;16(1):21.

    Article  Google Scholar 

  50. Digital Competence of Higher Education Students as a Predictor of Academic Success | SpringerLink. [cited 2023 Oct 13]. Available from: https://doi.org/10.1007/s10758-022-09624-8.

  51. Disengagement, engagement and digital skills in technology-enhanced learning | Education and Information Technologies. [cited 2023 Oct 13]. Available from: https://doi.org/10.1007/s10639-019-09998-w.

  52. Cabero-Almenara J, Gutiérrez-Castillo JJ, Guillén-Gámez FD, Gaete-Bravo AF. Digital competence of higher education students as a predictor of academic success. Tech Know Learn. 2023;28(2):683–702.

    Article  Google Scholar 

  53. Luo Q, Chen L, Yu D, Zhang K. The mediating role of learning engagement between self-efficacy and academic achievement among Chinese college students. Psychol Res Behav Manag. 2023;16:1533–43.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Beauvais AM, Stewart JG, DeNisco S, Beauvais JE. Factors related to academic success among nursing students: a descriptive correlational research study. Nurse Educ Today. 2014;34(6):918–23.

    Article  PubMed  Google Scholar 

  55. Hayden LJ, Jeong SY, Norton CA. An analysis of factors affecting mature age students’ academic success in undergraduate nursing programs: a critical literature review. Int J Nurs Educ Scholarsh. 2016;13(1):127–38.

    Article  PubMed  Google Scholar 

  56. Gregory L, Villarosa AR, Ramjan LM, Hughes M, O’Reilly R, Stunden A, et al. The influence of mathematics self-efficacy on numeracy performance in first-year nursing students: a quasiexperimental study. J Clin Nurs. 2019;28(19–20):3651–9.

    Article  PubMed  Google Scholar 

  57. Reynolds K, Bazemore C, Hanebuth C, Hendren S, Horn M. The relationship of noncognitive factors to academic and clinical performance in graduate rehabilitation science students in the United States: a systematic review. J Educ Eval Health Prof. 2021;18:31.

    Article  PubMed  PubMed Central  Google Scholar 

  58. Digital Capabilities and Self-efficacy (DCSE) Questionnaire | Zenodo. [cited 2023 Oct 13]. Available from: https://zenodo.org/record/3900607#.Y8uvjnZBw2w.

  59. Pintrich PR, Smith DAF, Garcia T, Mckeachie WJ. Reliability and predictive validity of the motivated strategies for learning questionnaire (Mslq). Educ Psychol Meas. 1993;53(3):801–13.

    Article  Google Scholar 

  60. Ngwira F, Gu C, Mapoma H, Kondowe W. The role of academic emotions on medical and allied health students and #8217; motivated self-regulated learning strategies. J Contemp Med EdU. 2017;5(1):1.

    Article  Google Scholar 

  61. Zurita Ortega F, Martinez Martinez A, Chacon Cuberos R, Ubago Jiménez JL. Analysis of the psychometric properties of the motivation and strategies of learning questionnaire—short form (MSLQ-SF) in Spanish higher education students. Soc Sci. 2019;8(5):132.

    Article  Google Scholar 

  62. Soemantri D, Mccoll G, Dodds A. Measuring medical students’ reflection on their learning: modification and validation of the motivated strategies for learning questionnaire (MSLQ). BMC Med Educ. 2018;18(1):274.

    Article  PubMed  PubMed Central  Google Scholar 

  63. Akoglu H. User’s guide to correlation coefficients. Turk J Emerg Med. 2018;18(3):91–3.

    Article  PubMed  PubMed Central  Google Scholar 

  64. Al-Salman S, Haider AS. Jordanian University students’ views on emergency online learning during COVID-19. OLJ. 2021;25(1). https://olj.onlinelearningconsortium.org/index.php/olj/article/view/2470. Accessed 28 Apr 2023.

  65. Cho M, Kim KM. Exploring the disparity in tangible outcomes of internet use between persons with disabilities and persons without disabilities in South Korea. Disabil Health J. 2021;14(3):101101.

    Article  PubMed  Google Scholar 

  66. Marks A, AL-Ali M, Atassi R, Elkishk AA, Rezgui Y. Digital transformation in higher education: maturity and challenges post COVID-19. In: Rocha Á, Ferrás C, López-López PC, Guarda T, editors. Information technology and systems. Cham: Springer International Publishing; 2021. p. 53–70. (Advances in Intelligent Systems and Computing).

    Chapter  Google Scholar 

  67. Choi N. Self-efficacy and self-concept as predictors of college students’ academic performance. Psychol Sch. 2005;42:197–205.

    Article  Google Scholar 

  68. Andrew S, Salamonson Y, Halcomb EJ. Nursing students’ confidence in medication calculations predicts math exam performance. Nurse Educ Today. 2009;29(2):217–23.

    Article  PubMed  Google Scholar 

  69. Taylor H, Reyes H. Self-efficacy and resilience in baccalaureate nursing students. Int J Nurs Educ Scholarsh. 2012;9:2.

    Article  PubMed  Google Scholar 

  70. Honicke T, Broadbent J. The influence of academic self-efficacy on academic performance: a systematic review. Educ Res Rev. 2016;17:63–84.

    Article  Google Scholar 

  71. Learning Self-Efficacy as Predictor of Nursing Students’ Performance of Clinical Skills - ProQuest. [cited 2023 Oct 13]. Available from: https://www.proquest.com/openview/b354c99c0bf2c45e40d9250d397fbade/1?pq-origsite=gscholar&cbl=28575.

Download references

Acknowledgments

The authors would like to thank all the participants for agreeing to participate in the study.

Funding

This research was not funded by any institution.

Author information

Authors and Affiliations

  1. Nursing Department, Fatima College of Health Sciences, Baynunah Complex, Al Dhafra Region, Madinat Zayed, UAE

    Rasha Kadri Ibrahim

  2. Nursing Department, Faculty of Nursing, Damnhour University, Damanhour, Egypt

    Rasha Kadri Ibrahim

  3. Nursing Department, Fatima College of Health Sciences, Al Ain, UAE

    Aisha Namshan Aldawsari

Authors
  1. Rasha Kadri Ibrahim
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Aisha Namshan Aldawsari
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

R.K.I.: The principal investigator, Conceptualization, Methodology, Validation, Investigation, Visualization, Supervision, Results preparation, writing, Manuscript review, Editing. A.N.D. Conceptualization, Methodology, Visualization, Supervision, Results revision, Writing original draft preparation, Reviewing the manuscript, Editing. The authors read and approved the final manuscript.

Corresponding author

Correspondence to Rasha Kadri Ibrahim.

Ethics declarations

Ethics approval and consent to participate

The Institutional Review Board (IRB) of the Fatima College of Health Sciences, UAE approved the research (No. FECE-03-20-23-NUR-Rasha). The participants gave informed consent after being apprised of the purpose of the study. Subjects volunteered to participate after being informed of the precautions implemented to guarantee data confidentiality. The student’s response was 100% anonymous; therefore, no private information was stored. Informed consent was obtained from all participants included in the study. All methods were performed in accordance with relevant guidelines and regulations.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ibrahim, R.K., Aldawsari, A.N. Relationship between digital capabilities and academic performance: the mediating effect of self-efficacy. BMC Nurs 22, 434 (2023). https://doi.org/10.1186/s12912-023-01593-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12912-023-01593-2

Keywords

BMC Nursing

ISSN: 1472-6955

Contact us