Digital transformation in medical education: Factors that influence readiness

Abstract

Digital transformation has become inseparable from education, and its implementation has broadly increased due to the increased adoption of e-learning during the COVID-19 pandemic. The present study evaluated the levels and influence of computer anxiety and digital readiness for academic engagement among undergraduate students. A cross-sectional study was conducted on 272 medical students enrolled in a medicine program. Two previously validated instruments were adopted. We examined the association between students’ sociodemographic variables, internet use, and perceived academic performance during e-learning and their computer anxiety and digital readiness. The results show a significant effect of gender, age, and internet use on students’ computer anxiety and digital readiness. Males’ information-sharing behavior and skills outperformed those of females, and students’ computer anxiety decreased with increasing age. In addition, the results indicate that the greater the students’ internet use, the better their digital readiness for academic engagement. Furthermore, computer anxiety and digital readiness affect students’ perceptions of their academic performance in e-learning. The rapid rate of technological advancements and the integration of e-learning into education means that careful attention must be paid to student characteristics as well as their skills. This will allow educators to create a successful, personalized learning framework.

Keywords

Academic engagement academic performance computer anxiety digital readiness e-learning,medical students

Introduction

Higher education institutions are promoting digital transformation that allows the implementation of digital tools in all aspects of students’ academic experiences, and digital readiness and computer acceptance are vital to successfully incorporating these technologies into a curriculum.^1,2 Students who experience high levels of computer anxiety—that is, the fear and apprehension felt when considering the implications of using technology—may face barriers in their academic experience and performance.^3,4 Moreover, digital readiness for academic engagement, which is defined as “technology-related knowledge, skills, and attitudes and competencies for using digital technologies to meet educational aims and expectations in higher education,”⁵ is necessary for college students’ learning outcomes and experiences.⁶

Even though we live in a very technically advanced world, students’ willingness to adopt digital technology in education and academic literacy cannot be assumed, and not all students are equally prepared for sudden transformations in technology and digital learning.^7–10 Most governments around the world temporarily locked down educational institutions to control the spread of the COVID-19 pandemic. To maintain learning continuity during this situation, educational systems suddenly shifted to e-learning for distance learning, with entire courses delivered online. E-learning can be supported in diverse forms, including virtual classrooms, audio and video tape, and e-mails, to mention a few. A recent study evaluating the digital readiness of higher education students and its effect on socio-emotional experiences demonstrates that students need support to adapt to the challenges of e-learning.² Previous studies have shown that students’ sociodemographic characteristics (such as gender, age, and computer experience) are correlated with their levels of digital appetite.^11–14 The results of those studies, however, are contradictory and differ with education delivery formats as well as cultural and social factors.

Although extensive research has been conducted to address the factors that can enhance students’ academic experience in e-learning environments,^7,15–17 the correlation of computer anxiety and digital readiness to academic performance has yet to be discussed in the context of e-learning.¹⁸ A growing body of research has explored students’ digital readiness levels,^2,5,9 however, the extent to which these levels are affected by students characteristics is not fully explored.⁵

We believe that this exploration is also important in traditional settings. Universities around the world are increasingly adopting digital assignments, online discussion forums, and social media platforms for communication.¹⁹ The benefits of including such technological advancements are several. For example, students and instructors benefit from saved data or records of all communications and submitted assignments, which enables a system of monitoring students’ academic achievements and progress. In addition, many medical schools’ laboratories are equipped with smart screens, digitalized video, and bedside computer charting, enabling students to practice various skills and procedures and actively engage in learning through those practical skills. Evaluating the healthcare students’ computer anxiety and digital readiness is critical in such a learning environment and to provide an insight into the future workforce competency.^20,21

The present study aimed to provide an understanding of medical students’ readiness for e-learning and for computer technology in general. The objectives of the study were

• to evaluate computer anxiety and digital readiness among a sample of medical students,

• to investigate the relationship between computer anxiety and digital readiness,

• to determine the sociodemographic factors associated with students’ computer anxiety and digital readiness, and

• to determine whether there is a significant association between computer anxiety and digital readiness and students’ perceptions of academic performance in an e-learning environment.

Materials and methods

This cross-sectional study was conducted at the College of Medicine, King Saud Bin Abdulaziz University for Health Sciences, Saudi Arabia at the time when e-learning was employed during the COVID-19 pandemic. The college adopted a common approach to implement e-learning, using diverse platforms such as Blackboard Collaborate and Microsoft Teams. Through these platforms, students attended and participated in classes and had presentations, discussions, and meeting with one another to accomplish the required educational tasks, such as problem-based learning sessions, and personal and professional development presentations as well as course lectures. Additionally, video-based sessions were conducted in place of the required physical presence during practical lectures. A total of 272 of the 781 students enrolled in the second to sixth year of the medical program completed the survey. Students who failed to attend at least one exam for the enrolled blocks were excluded from this study. The participants’ mean age of was 21.2 (SD = 1.5) years, and their overall academic performance was excellent, with 199 (73.2%) students having an excellent grade point average (GPA) (4.5 or above) and 73 (26.8%) having a GPA below excellent (below 4.5). GPA was reported on a scale of 5.

The data collection tool in our study was a survey comprising two previously validated measuring scales: the Computer Anxiety Scale (CAS) and Digital Readiness for Academic Engagement (DRAE) scale. The scales’ reliability was measured using Cronbach’s alpha. The 16-item CAS²² measured computer anxiety among the study subjects, who responded on a 5-point Likert scale ranging from “strongly disagree” to “strongly agree.” The scale includes items such as “I feel anxious whenever I am using computers” and “I am confident in my ability to use computers” (reversed item), with higher scores indicating greater computer anxiety. The minimum and maximum scores range from 16 to 80. The Cronbach’s alpha reliability coefficient of the CAS in this study was 0.89, and the validity of the CAS was ensured by the counsel and evaluation of three experts before application. To measure the students’ digital readiness, the DRAE scale⁵ was adopted, which was developed to understand the collective influence of digital competencies in various academic learning activities on students’ engagement. All 17 items are measured on a 5-point Likert scale. The DRAE comprises five subscales: digital tool application (DTA), information-seeking skills (ISS), information-sharing behavior (ISB), digital media awareness (DMA), and digital application usage (DAU). The scores of the 17 items can be calculated individually for each subscale, with higher scores indicating a higher level of readiness. Each of these subscales is important in improving college students’ digital competencies. All the scales were internally consistent (the Cronbach’s alpha coefficient ranged from 0.68 to 0.89). The data were collected more than 20 weeks after the start of e-learning during the COVID-19 pandemic via an online survey (i.e., Google Forms) with three reminders over the course of 12 weeks.

Variables

In addition to the CAS and DRAE scales, sociodemographic variables were included in the research instrument. The choice of variables was based on the literature review.^14,23,24 The student characteristic variables—gender, age, prior undergraduate computer experience, and number of hours on the internet per week—were used to investigate their influence on the student’s computer anxiety and digital readiness for academic engagement. The students’ prior experience was assessed by their earlier computer science course grades at a college preparatory school before joining the medical program. The course is mandatory for all students entering the medical program. As the grades were predominately high, the variable was classified into two categories: excellent (a grade of A+ or A) or below excellent (B+ and below). Self-reported time online per week was assessed by the question “How many hours a week do you spend using the internet?”

Other factors that were examined in previous studies and found to be correlated with computer anxiety and digital readiness were not examined here, as they were irrelevant. For example, English proficiency¹ is a requirement of entry to the medical program, and the English proficiency level was high based on grades in an English course taken by the students before entering the college of medicine.

The student’s perceived academic performance during e-learning was assessed by the question “How would you describe your overall academic performance during e-learning?” There were three response options: “overall performance enhanced,” “overall performance decreased,” and “no change in overall academic performance.” The academic performance during e-learning was considered an outcome variable and the computer anxiety and digital readiness levels of students as factors of performance.

Statistical analysis

Continuous and categorical variables are expressed as mean (SD) and percentages, respectively. The associations between the categorical variables were tested using the chi-square test, and the correlations between quantitative variables were estimated using Spearman’s coefficient. In this study, the multivariate general linear model (GLM) was used to assess the effects of categorical factors and age as covariates on computer anxiety and digital readiness. The GLM was used because the dependent variables (the CAS and DRAE scales) were correlated. Before the analyses, the assumptions of parametric statistics (i.e., normality, homogeneity of variances, linearity, and multicollinearity) were tested. The effect of each variable on the combined scales was evaluated by Pillai’s trace. Univariate and multivariate outliers were identified and eliminated from the analysis, resulting in a final analysis sample of 272, with six observations removed. When a main effect or an interaction was significant for scores on the subscales in a multivariate test, a univariate analysis of variance test was performed separately for each subscale to locate the sources of the differences. Bonferroni post hoc pairwise comparisons were used. Partial η² was used as an estimate of effect size (partial η²: small = 0.01, medium = 0.06, large = 0.14).²⁵ First, all the factors and their interactions were tested, and nonsignificant interactions were removed from the final model. R-squared values are reported, which represent the percentage of the variance explained in each dependent variable accounted for by the factors in the model. To examine the association between computer anxiety and the DRAE subscales and academic performance, a multinomial regression analysis was conducted, as the outcome here is a categorial (three-level) variable. A p-value of <.05 was considered statistically significant. The data were analyzed using the JMP software package (JMP 14, SAS Institute, Cary, North Carolina).

Ethical consideration

The students were informed (by consent forms) of the aim of the study and were promised anonymity and confidentiality. Participation in the study was voluntary. After ethical review, the study was approved by the ethics committee at King Abdullah International Medical Research Centre (Study Protocol SP20/495/J).

Results

Table 1 shows the students’ characteristics. Of the students who completed the survey, 147 (54%) were females and 125 (46%) were males. Most of the students (58.1%) spent more than 20 h per week online, followed by 58 (21.3%) students who spent 11–20 h online. Regarding previous performance in a computer science course, most of the students (74%) had an excellent score. Seventy-seven students (28.3%) said that they had experienced a decrease in their overall performance during e-learning, while 131 (48.2%) reported an overall enhancement and 64 (23.5%) noticed no change in their academic performance. Table 2 presents the Spearman’s correlation between the CAS and DRAE scales. All the variables were significantly and moderately correlated to each other.

Table 1.

Survey respondents’ sociodemographic characteristics, internet use, and perceived academic performance in e-learning (n = 272).

Variables	Frequency	Percentage
Gender
Male	125	46
Female	147	54
Previous performance in a computer science course
Excellent	196	74
Below excellent	69	26
Weekly time spent online, hours
<11	56	20.6
11–20	58	21.3
>20	158	58.1
Perceived academic performance in e-learning
Overall performance enhanced	131	48.2
Overall performance decreased	77	28.3
No change in overall performance	64	23.5

Table 2.

Descriptive statistics, bivariate correlations, and internal consistency (alpha) for computer anxiety scale and digital readiness for academic engagement subscales.

	CAS	DTA	DAU	DMA	ISS	ISB
CAS	—
DTA	−0.65	—
DAU	−0.56	0.67	—
DMA	−0.57	0.55	0.54	—
ISS	−0.54	0.65	0.64	0.61	—
ISB	−0.64	0.58	0.60	0.58	0.68	—
Alpha	0.89	0.71	0.78	0.85	0.68	0.78
Mean(SD)	31.4(12.4)	14.4(3.7)	12.1(2.9)	10.7(2.8)	10.8(2.9)	16.6(3.9)
Minimum-maximum	26–72	4–20	3–15	3–15	3–15	4–20

SD: standard deviation; CAS: computer anxiety scale; DTA: digital tool application; ISS: information-seeking skills; ISB: information-sharing behavior; DMA: digital media awareness; DAU: digital application usage. All correlation coefficients are significant at p < 0.01.

Gender, previous computer science score, internet use, and age

The assumption of the homogeneity of covariances was not met (Box’s M = 651.5, F = 4.6, p = .003). However, the GLM is relatively robust against the violation of this assumption when the sample size is adequate.²⁶ Therefore, Pillai’s trace was used to examine the data.

Performance in a previous computer course had no significant effect on the dependent variables, although the p-value was very close to significance (Pillai’s trace = 0.05, p = .051, partial η² = 0.04). The results show significant strong main effects of gender (Pillai’s trace = 0.11, p < .001, partial η² = 0.11), time spent on the internet (Pillai’s trace = 0.20, p < .001, partial η² = 0.10), and age (Pillai’s trace = 0.09, p < .001, partial η² = 0.09) on the combined dependent variables.

Table 3 presents the results of the general linear model. When the results for the dependent variables were considered separately in relation to gender, significant differences between female and male students were found in the ISB (F(1,259) = 8.04, p = .005, partial η² = 0.03) and ISS subscales (F(1,259) = 4.61, p = .033, partial η² = 0.02). Table 4 shows the mean scores reported as estimated marginal means with standard errors of the dependent variables for gender, previous computer score, and time spent on the internet after accounting for the factors and the covariate of age. Male students had a significantly higher ISB (EMM = 16.9, SE = 0.39) than females (EMM = 15.5, SE = 0.37). The ISS was slightly higher among males (EMM = 10.8, SE = 0.29) than among females (EMM = 10.0, SE = 0.27).

Table 3.

General linear model of computer anxiety scale and digital readiness for academic engagement subscales (n = 272).

Source	Dependent variable	df	F	p-value	Partial η²
Gender	CAS	1	0.70	.40	0.003
	ISB	1	8.04	.005	0.03
	ISS	1	4.61	.033	0.02
	DMA	1	1.12	.29	0.004
	DAU	1	0.72	.40	0.003
	DTA	1	1.07	.30	0.004
Previous performance in a computer course	CAS	1	1.52	.21	0.01
	ISB	1	0.90	.34	0.003
	ISS	1	3.56	.06	0.01
	DMA	1	0.13	.71	0.001
	DAU	1	2.42	.12	0.01
	DTA	1	0.11	.74	0.00
Weekly time spent online, hours	CAS	2	0.64	.53	0.01
	ISB	2	4.58	.011	0.03
	ISS	2	4.03	.019	0.03
	DMA	2	1.80	.16	0.01
	DAU	2	10.8	<.001	0.08
	DTA	2	6.51	.002	0.05
Age, years	CAS	1	10.1	.002	0.04
	ISB	1	1.77	.18	0.01
	ISS	1	11.1	.001	0.04
	DMA	1	11.6	.001	0.04
	DAU	1	7.18	.008	0.03
	DTA	1	4.55	.034	0.02
Error		259

df: degrees of freedom; CAS: computer anxiety scale ; DTA: digital tool application; ISS: information-seeking skills; ISB: information-sharing behavior; DMA: digital media awareness; DAU: digital application usage.

Table 4.

Estimated marginal means (EMM) and standard errors (SE) for the scores of computer anxiety and digital readiness by gender, previous performance in computer science, and internet use with age as covariate.

	Gender		Previous performance in computer course		Weekly time spent online, hours
	Female	Male	Excellent	Below excellent	<11	11–20	>20
CAS	32.9(1.2)	31.6(1.2)	31.2(1.0)	33.3(1.5)	32.2(1.71)	33.4(1.71)	31.2(1.12)
ISB	15.5(0.37)	16.9(0.39)	16.5(0.31)	16.0(0.48)	15.3(0.54)	16.4(0.54)	17.1(0.35)
ISS	10.0(0.27)	10.8(0.29)	10.8(0.23)	10.1(0.35)	9.90(0.39)	10.2(0.39)	11.1(0.26)
DMA	10.4(0.27)	10.8(0.29)	10.7(0.23)	10.5(0.35)	10.9(0.39)	10.0(0.39)	10.9(0.26)
DAU	11.6(0.27)	11.3(0.29)	11.8(0.23)	11.2(0.35)	11.2(0.39)	10.6(0.39)	12.6(0.26)
DTA	13.8(0.35)	14.3(0.38)	14.4(0.30)	14.1(0.46)	14.0(0.52)	13.0(0.52)	15.0(0.34)

CAS: computer anxiety scale; DTA: digital tool application; ISS: information-seeking skills; ISB: information-sharing behavior; DMA: digital media awareness; DAU: digital application usage. Age covariate appearing in the model is evaluated at the 21.1 years. EMM(SE) are reported.

For the time spent on the internet per week, analysis of the dependent variables separately revealed significant differences on the level of ISB (F(2,259) = 4.58, p = .011, partial η² = 0.03), ISS (F(2,259) = 4.03, p = .019, partial η² = 0.03), DAU (F(2,259) = 10.8, p < .001, partial η² = 0.08), and DTA (F(2,259) = 6.51, p = .002, partial η² = 0.05). Pairwise comparisons revealed significant differences in ISB, ISS, and DAU scores between students who spent less than 11 h on the internet and those spending more than 20 h (p = .008, p = .033, and p = .008, respectively). In addition, the total DAU score for those spending 11–20 h compared to those spending more than 20 h was significantly different (p < .001). The total DTA score for those spending 11–20 h was significantly lower than for those spending more than 20 h (p = .002).

In regard to the students’ age, univariate analysis showed that age effect was significant on computer anxiety (F(1,259) = 10.1, p = .002, partial η²=0.04), ISS (F(1,259) = 11.1, p = .001, partial η² = .04), DMA (F(1,259) = 11.6, p = .001, partial η² = 0.04), DAU (F(1,259) = 7.18, p = .008, partial η² = 0.03), and DTA (F(1,259) = 4.55, p = .034, partial η² = 0.02). The final model, including the significant variables, is shown in Table 5. The R-squared values ranged from 8% to 15%. The score in the previous computer course was removed because of statistical insignificance. The results in Table 5 show that a 1-year increase in age (regardless of gender or time spent on the internet) is associated with a decrease of approximately two points of computer anxiety (B=−1.78, p = .002). In addition, older students acquired higher ISS, DMA, DAU, and DTA scores (all p-values < .05).

Table 5.

Estimate parameters of the general linear model with significant variables affecting the total computer anxiety scale and digital readiness for academic engagement subscales.

Variables	CAS		ISB		ISS		DMA		DAU		DTA
Variables	B (SE)	p-value	B (SE)	p-value	B (SE)	p-value	B (SE)	p-value	B (SE)	p-value	B (SE)	p-value
Female, gender	1.82 (1.5)	0.40	−1.39 (0.49)	.005	−0.77 (0.35)	.033	−0.38 (0.35)	.29	0.31 (0.36)	.40	−0.49 (0.47)	.30
Weekly time spent online, hours
<11	0.92 (1.9)	0.63	−1.81 (0.60)	.003	−1.1 (0.44)	.010	0.01 (0.43)	.98	−1.41 (0.45)	.002	−1.22 (0.58)	.051
11–20	2.63 (1.9)	0.16	−0.69 (0.56)	.27	−0.83 (0.46)	.08	−0.85 (0.46)	.07	−1.95 (0.47)	<.001	−2.11 (0.61)	.001
Age, years	−1.78 (0.56)	0.002	0.24 (0.18)	.18	0.43 (0.12)	.001	0.44 (0.13)	.001	0.35 (0.13)	.008	0.36 (0.17)	.034
R-squared	0.08		0.14		0.15		0.14		0.13		0.14

B: unstandardized coefficient; SE: standard error; CAS: computer anxiety scale; DTA: digital tool application; ISS: information-seeking skills; ISB: information-sharing behavior; DMA: digital media awareness; DAU: digital application usage . The coefficients for age show the change in scales corresponding to a change of 1 year in age. Reference categories for gender is “male,” and for weekly time spent online “>20 h,”

Perception of academic performance in e-learning, computer anxiety, and digital readiness for academic engagement

We first examined the bivariate associations between perceived academic performance in e-learning and the factors of interest in this study to account for any confounding effect of those factors on performance. No significant associations were found between academic performance in e-learning and gender (χ² = 3.1, p = .21), previous computer score (χ² = 1.71, p = .42), or weekly time spent online (χ² = 2.7, p = 0.59). The results of multinomial regression analysis show that the model was statistically significant (χ²= 95.2, p <.001), indicating that levels of computer anxiety and digital readiness have an impact on students’ academic performance. Table 6 provides a summary of the results with significant p-values. Students with a perception of enhanced performance were more likely to have higher DMAs and DAUs than students with no change in performance. In addition, students with a perception of decreased performance were more likely to have higher computer anxiety than students with no change in performance.

Table 6.

Results of multinomial regression analysis.

		Odds ratio	95% CI	p-value
Overall performance enhanced	DMA	1.28	1.07–1.52	.007
Overall performance enhanced	DAU	1.38	1.13–1.67	.001
Overall performance decreased	CAS	1.12	1.07–1.18	<.001

CI: confidence interval; DMA: digital media awareness; DAU: digital application usage; CAS: computer anxiety scale. Reference category for perceived academic performance is “no change.”

Discussion

This study investigated computer anxiety and digital readiness for academic engagement among undergraduate medical students. To the best of our knowledge, this is the first study in a medical school with that objective. It sought to determine the factors that affect students’ academic experiences, especially during the shift to an e-learning environment, by exploring the relationships between computer anxiety, digital readiness, gender, age, previous computer experience, and internet use.

Significant differences were found between female and male students in the ISB and ISS subscales. In recently published studies investigating university students’ information-sharing behavior and social media use, male students are more likely to share learning resources (e.g., class notes), and they use social media for educational purposes more frequently than their female counterparts.²⁷ Future research may investigate whether these differences in gender have an impact on the actual academic performance. In addition, course educators may foster different learning paths with the target to enhance course satisfaction. Here, gender was not significantly related to computer anxiety. Prior studies on the relationship between gender and computer anxiety have yielded inconsistent results.^22,28–33 Further research is needed to determine the accuracy for the correlation and to clarify the differences between the various findings across cultures.

Age was negatively associated with computer anxiety and positively associated with students’ digital readiness. This suggests that older students may have more experience in using computers and feel less anxious about them, whereas younger students with less experience are more anxious. Thus, one might assume that older students were more ready for the shift to digital learning than younger students during the shift to e-learning in the COVID-19 pandemic. The relationship between computer experience and students’ anxiety is well established in the literature; the more that people are experienced and knowledgeable in using computers, the less anxious they are about computers.^14,34

Most of the digital readiness subscales were further positively and significantly associated with internet use, showing that greater internet use leads to better digital readiness for academic engagement among students. This supports the results of a previous study.³³ A greater provision of digital technology sessions and resources to students is recommended to enhance their digital readiness.

Perceived academic performance was found to be associated with computer anxiety, digital media awareness, and digital application usage. With the development of technology and the rapid shift from traditional to online learning, this research offers a potential path to understand how students’ computer anxiety and digital readiness affect their learning outcomes. Our results show that students with a perceived decreased performance in e-learning were likely to have higher computer anxiety. By contrast, those with a perceived increase in performance were more likely to have higher digital media awareness and digital application usage scores. Previous research has shown that students with digital readiness—that is, who are confident in using their digital skills in their academic work—have more opportunities for academic achievement in e-learning environments.⁹ In addition, the students here may have perceived that strong digital skills are necessary for academic success.³⁵ However, in this study, the learners’ perceived academic performance in e-learning was evaluated by a single-question measurement. In future work, our measured academic performance should be validated and compared to other valid measures, such as actual academic performance.

Limitations

Some limitations may affect the interpretation of our findings. The study had a low response rate, with only 272 of 781 students enrolled in the medical program participating. However, our response rate is considered acceptable in online surveys.³⁶ In addition, the sample is not representative of all medical students, as it was collected from only one university. For this reason, another study could be conducted to expand the sample and include students from different academic institutions and specialties. Moreover, our study did not examine whether the level of student online course satisfaction was associated with computer anxiety and digital readiness. This highlights the need for future studies to address any possible association. This study analyzed only certain variables and identified their associations with computer anxiety and digital readiness. Consequently, further research should identify new, unrecognized factors. For instance, race and ethnic background, socioeconomic status, physical and/or mental disabilities, learning difficulties (e.g., dyslexia), as well as the digital readiness of the course educator and his/her willingness to use e-learning may or may not affect apprehension and anxiety of students. Finally, the study used a self-report instrument for data collection, which may have introduced response bias.³⁷

Conclusion

Using e-learning and digital technologies in delivering the curriculum provides several potential benefits. In order for students to benefit from such a learning environment, digital readiness and low computer anxieties are essential. Course educators are, therefore, encouraged to assess the digital readiness of their students before incorporating said tools in their learning activities. These assessments would support educators in adequately meeting their students’ learning requirements by employing more helpful resources in view of individual learners’ profiles. Moreover, the integration of technology courses into continuing education and enhancement workshops is recommended to support learners’ academic engagement.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Alaa Althubaiti

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