Digital learning effectiveness and strategies in medical training: perspectives on motivation and attitude among interns and PGY trainees

Abstract

Background

The digitalization of medical education has accelerated beyond pandemic-driven adaptations, yet learners’ motivation and attitudes toward digital platforms remain inadequately characterized across training stages. This study explores the perceived effectiveness and strategic integration of digital learning in undergraduate and postgraduate medical education, focusing on learners’ motivation and attitudes.

Methods

A cross-sectional survey using purposive sampling was conducted in Taiwan between 2021 and 2022, enrolling 214 participants, including 104 medical interns and 110 Post-Graduate Year (PGY) trainees. Data were collected using validated instruments assessing digital learning perception (15 items), learning attitude (21 items), and learning motivation (35 items). Pearson correlation analysis was performed to examine relationships among key variables.

Results

Medical interns reported significantly higher levels of motivation (M = 3.74 vs. M = 3.53, p < .01) and more favorable attitudes (M = 3.68 vs. M = 3.42, p < .01) toward digital learning compared to PGY trainees. A strong positive correlation was observed between learning environment incentives and learners’ attitudes (r = .624, p < .01). While digital modalities were valued for enhancing accessibility and flexibility, participants indicated they were insufficient to replace hands-on clinical training, especially in skill-based competencies.

Conclusions

These findings highlight the importance of a balanced, hybrid educational model wherein digital learning serves to complement, rather than substitute, traditional clinical instruction. Future curriculum design should emphasize pedagogical alignment between digital content and clinical competencies, with tailored approaches for different training stages to optimize educational outcomes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12909-025-08294-1.

Introduction

The digitalization of medical education has progressed beyond pandemic contingencies into a sustained transformation of training. Emerging technologies (e.g., virtual reality, artificial intelligence, big-data–enabled platforms) expand flexible and interactive learning while demanding faculty development and greater learner self-regulation [1–3]. Accordingly, institutions must strengthen engagement with digital platforms and cultivate independent learning habits as core competencies for sustainable integration [4, 5].

In Taiwan, medical education comprises a six-year undergraduate curriculum followed by a two-year Post-Graduate Year (PGY) program before specialization. During the COVID-19 pandemic, medical schools and teaching hospitals rapidly adopted institutional platforms for lectures and clinical modules delivered to both interns and PGY trainees. Given their differing roles and responsibilities, these groups may engage with digital learning in systematically different ways.

Traditional models—didactic teaching, hands-on clinical training, and apprenticeship—remain effective yet constrained by rigidity, limited personalization, and uneven clinical exposure, making standardized assessment difficult across settings [4, 6–8]. As a complement to traditional training, digital modalities enable standardized simulation, repetitive practice in risk-free settings, and individualized learning pathways [9, 10], yet their effective use still hinges on faculty preparedness and learners’ self-management skills [11, 12].

Digital learning broadens knowledge dissemination and collaboration across institutions; effective transfer is shaped by interactional factors such as frequency, trust, and professional recognition [4, 13]. Digital learning can reduce institutional costs and expand reach [14], supports asynchronous access and flexibility [15, 16], and benefits learners in remote or resource-limited settings [17]. Adoption depends on user experience and perceived benefits [18, 19], together with platform accessibility and alignment with professional development [20, 21]. Consequently, optimizing technical design and content relevance remains essential [22, 23].

Learning environment, attitude, and motivation form an interrelated system in the shift to digital platforms. While supportive environments are known to enhance attitudes and motivation in traditional contexts [24, 25], how specific features of digital environments (e.g., self-efficacy demands, system interactivity) shape these constructs is less clear [26]. Clarifying these relationships is essential for designing virtual learning that sustains engagement and self-regulation without compromising clinical competency development [27]. Notably, potential differences across training stages remain insufficiently described and warrant targeted comparison.

Sustaining engagement and motivation without face-to-face interaction remains a central challenge; instructional content, delivery, and presentation style are pivotal determinants of participation in online settings [28]. Against this backdrop—and given role-related contrasts between interns and PGY trainees—this study compares these groups’ motivation, attitudes, and strategies toward digital learning to inform stage-appropriate curriculum design.

Methods

A cross-sectional questionnaire-based study was conducted in Taiwan between 2021 and 2022 to explore the relationships among digital learning motivation, attitude, and perception among medical trainees. Purposive sampling was used to recruit participants because medical interns and PGY trainees represent defined stages within Taiwan’s medical training pathway, allowing for meaningful comparison of their experiences with institutional e-learning systems. Inclusion criteria included: (1) medical interns in their final undergraduate year (6th year) or PGY trainees in their first postgraduate year; (2) currently enrolled in or having completed digital learning modules through institutional platforms during 2021–2022; and (3) able to provide informed consent. Exclusion criteria included: (1) incomplete demographic data or questionnaire responses; (2) duplicate submissions identified by timestamp or patterned responses; and (3) participants who did not engage with digital learning platforms during the study period.

The questionnaire employed in this study was adapted from three well-established instruments designed to evaluate digital learning perception, learning attitude, and learning motivation among medical interns and PGY trainees. The perception scale comprised 15 items adapted from Gherheș et al. [29], capturing both the advantages and disadvantages of digital learning. The learning attitude scale included 21 items derived from Wu Mingda’s Gaozhan Project Student Learning Attitude Questionnaire [30], which covers the domains of course, teacher, and peers (original α = 0.94). The learning motivation scale consisted of 35 items adapted from Tuan and Shieh’s SMTSL questionnaire [31], encompassing six dimensions such as self-efficacy, active learning strategies, and learning value (original α = 0.89).To minimize response bias, four negatively worded items were incorporated and reverse-coded before analysis, and all items were rated on a five-point Likert scale. Reliability testing in the present study demonstrated excellent internal consistency across the adapted scales: perception (α = 0.88), learning attitude (α = 0.93), and learning motivation (α = 0.91). A rigorous forward–backward translation based on the Brislin model ensured linguistic accuracy. Five medical education and psychometric experts reviewed the adapted version for clarity and relevance, resulting in a mean content validity index above 0.90. A pilot test involving 30 interns and PGY trainees confirmed feasibility, with an average completion time of 15 min. Only minor rewording was required for three items.

The finalized questionnaire was administered online via SurveyCake. Recruitment occurred through (1) social media groups for medical interns and PGY trainees and (2) announcements during institutional online training sessions across multiple teaching hospitals. In total, 224 responses were received, and after applying exclusion criteria, 214 valid responses (104 interns, 110 PGY trainees) were included for analysis.

Ethical approval was obtained from the Chang Gung Medical Foundation Institutional Review Board (IRB No. 202100780B0A3). Participation was voluntary and anonymous, and electronic informed consent was obtained from all participants prior to survey completion. No identifiable personal information was collected, and data were stored on password-protected institutional servers accessible only to the research team.

Data analysis was performed using SPSS version 20.0 (IBM Corp., Armonk, NY, USA). Descriptive statistics summarized demographic and learning variables. Pearson correlation analyses examined associations among motivation, attitude, and perception scores, with statistical significance set at p <.05 (two-tailed).

Results

Overview of key findings

Three key findings emerged from this study: (1) Medical interns demonstrated significantly higher motivation and more positive attitudes toward digital learning compared to PGY trainees; (2) Learning environment incentives showed the strongest correlation with learning attitude, exceeding all other motivational dimensions; (3) PGY trainees reported significantly less daily self-directed learning time, suggesting time constraints and workload as key barriers to digital learning engagement.

Participant characteristics and homogeneity testing

This study utilized purposive sampling to recruit medical interns and Postgraduate Year (PGY) trainees who participated in internship and training programs within a designated administrative district during 2021–2022. The study aimed to explore learners’ perceptions of digital learning tools and examine relationships among motivation, attitudes, and behaviors toward digital learning platforms. Data were collected through an online questionnaire, yielding a total of 214 valid responses (104 medical interns and 110 PGY trainees).

Table 1 presents the results of Chi-square and Fisher’s exact tests comparing demographic characteristics and learning behaviors between groups. Most participants were aged 20–25 years (66.4%), and a majority were female (57.9%) and graduates of private universities (57.5%). Significant group differences were found in age distribution, internship hospital level, and average daily self-directed learning time after work (p <.01). Medical interns were more likely to train in medical centers, and spent more time in self-directed learning compared with PGY trainees. These findings indicate that medical interns demonstrated greater engagement in digital learning activities outside of working hours.

Table 1.

Homogeneity test results of background variables and usage behavior by groups

Variable	All participants (N = 214)	Medical interns (n = 104)	PGY (n = 110)	p value
Age group				< 0.01**
20–25 yrs	142 (66.4%)	54 (51.9%)	88 (80.0%)
26–30 yrs	67 (31.3%)	47 (45.2%)	20 (18.2%)
≥ 31 yrs	5 (2.3%)	3 (2.9%)	2 (1.8%)
Gender				0.84
Male	90 (42.1%)	43 (41.3%)	47 (42.7%)
Female	124 (57.9%)	61 (58.7%)	63 (57.3%)
Type of university graduated				0.30
Public	91 (42.5%)	48 (46.2%)	43 (39.1%)
Private	123 (57.5%)	56 (53.8%)	67 (60.9%)
Internship hospital level				< 0.01**
Medical center	74 (34.6%)	45 (43.3%)	29 (26.4%)
Regional hospital	140 (65.4%)	59 (56.7%)	81 (73.6%)
Average daily self-directed learning time after work (past 3 months)				< 0.01**
> 3 h	54 (25.2%)	36 (34.6%)	18 (16.4%)
1–3 h	124 (57.9%)	60 (57.7%)	64 (58.2%)
< 1 h	36 (16.8%)	8 (7.7%)	28 (25.5%)

*p <.05, **p <.01

Learning attitude and motivation: group comparisons

According to Table 2, the overall mean score for learning attitude was 3.54 (SD = 0.63), with medical interns reporting an average of 3.68 (SD = 0.61) and PGY trainees reporting an average of 3.42 (SD = 0.63). Additionally, the overall mean score for learning motivation was 3.63 (SD = 0.48), with medical interns and PGY trainees reporting average scores of 3.74 (SD = 0.43) and 3.53 (SD = 0.50), respectively. These findings suggest a generally positive inclination toward both learning attitude and learning motivation among the participants. Notably, medical interns consistently reported higher scores than PGY trainees across both learning attitude and motivation scales. This suggests that interns, who are closer to the academic learning environment, may retain stronger study habits and greater receptivity to structured digital resources. In contrast, PGY trainees, who bear heavier clinical responsibilities, might experience reduced available time and cognitive resources for engaging in digital learning. These findings align with previous studies reporting that transition into full clinical duties often attenuates learners’ motivation and shifts their preference toward clinically oriented, case-based resources rather than general digital modules.

Table 2.

Descriptive statistics of learning attitude and learning motivation of online course usage

Scale and dimensions		Mean (SD)
	All participants (standard deviation)	Medical interns (standard deviation)	PGY (standard deviation)
Learning attitude	3.54(0.63)	3.68(0.61)	3.42(0.63)
Course	3.47(0.80)	3.66(0.74)	3.28(0.82)
Teacher	3.78(0.60)	3.84(0.59)	3.72(0.61)
Classmate	3.37(0.73)	3.51(0.70)	3.24(0.74)
Learning motivation	3.63(0.48)	3.74(0.43)	3.53(0.50)
Self-efficacy	3.30(0.58)	3.30(0.53)	3.30(0.63)
Active learning strategies	3.99(0.63)	4.10(0.61)	3.88(0.63)
Learning value	3.97(0.67)	4.14(0.66)	3.81(0.64)
Performance goal	2.95(0.89)	3.06(0.96)	2.84(0.81)
Achievement goal	3.78(0.67)	3.84(0.66)	3.72(0.67)
learning environment incentives	3.61(0.62)	3.77(0.57)	3.46(0.64)

Correlation analysis: learning attitude and motivation

To investigate the relationship between learning attitude and learning motivation, Pearson Correlation Analysis was conducted, and the results are shown in Table 3. While most dimensions exhibited significant correlations, exceptions were noted between “Learning Motivation-Self-Efficacy” and “Learning Attitude-Course,” as well as between “Learning Motivation-Self-Efficacy” and “Learning Attitude.” Notably, “Learning Environment Incentives” emerged as having the strongest positive correlation with “Learning Attitude,” emphasizing its crucial influence on shaping participants’ learning attitudes. Beyond the general positive correlations, the particularly strong link between learning environment incentives and attitude underscores the role of contextual design features. Compared with other motivational dimensions, incentives such as interface usability, relevance of content, and immediacy of feedback appear to shape learners’ affective engagement more directly. Interestingly, self-efficacy demonstrated weak or even negative associations with certain attitude domains, suggesting that learners with lower confidence may actually value external environmental supports more strongly. This contrast highlights the dual importance of both intrinsic (confidence, active strategies) and extrinsic (platform features, social presence) factors in sustaining motivation within digital medical education.

Table 3.

Pearson correlation analysis of learning attitude and learning motivation

	Learning Attitude	p (two-tailed)	Course	p (two-tailed)	Teacher	p (two-tailed)	Classmates	p (two-tailed)
Learning Motivation	0.656	< 0.01**	0.598	< 0.01**	0.553	< 0.01**	0.521	< 0.01**
Learning Confidence	0.539	< 0.01**	0.530	< 0.01**	0.487	< 0.01**	0.405	< 0.01**
Self-Efficacy	−0.117	0.088	−0.120	0.081	−0.210	< 0.01**	−0.184	< 0.01**
Proactive Learning Strategies	0.445	< 0.01**	0.409	< 0.01**	0.459	< 0.01**	0.393	< 0.01**
Learning Value	0.528	< 0.01**	0.517	< 0.01**	0.505	< 0.01**	0.422	< 0.01**
Performance Goals	0.289	< 0.01**	0.333	< 0.01**	0.178	< 0.01**	0.194	< 0.01**
Achievement Goals	0.464	< 0.01**	0.376	< 0.01**	0.486	< 0.01**	0.322	< 0.01**
Learning Environment Incentives	0.624	< 0.01**	0.595	< 0.01**	0.488	< 0.01**	0.505	< 0.01**

*p <.05, **p <.01

Discussion

In pre-pandemic research, digital learning was generally viewed as having limited influence on overall knowledge and skill development among practicing clinicians, though several studies highlighted its potential benefits for medical interns [32, 33]. During the COVID-19 pandemic, digital learning rapidly transitioned from an exception to a mainstream approach across medical and allied health education. Prior studies have emphasized both its strengths and limitations. Among its benefits are improved knowledge acquisition and engagement through personalized and interactive platforms [34], as well as sustained access for trainees in remote or resource-limited regions [35].

Our study demonstrated that medical interns exhibited higher motivation, learning strategy use, and engagement than PGY trainees. While examination pressure may partially account for this difference, our data indicate that broader contextual and experiential factors play a substantial role. PGY trainees face heavier clinical responsibilities and longer duty hours, reducing opportunities for self-directed learning. In contrast, interns, having recently transitioned from undergraduate programs, often have greater familiarity with structured digital platforms introduced during the pandemic, which may enhance their confidence and willingness to use online resources. Prior research similarly shows that workload, prior digital exposure, and stage of training can significantly shape learners’ motivation and attitudes toward digital education [36, 37]. These factors provide a more comprehensive understanding of the group differences observed in our study. The COVID-19 pandemic placed significant strain on healthcare workers, who played a crucial role in surveillance, health promotion, and patient support [38]. These findings underscore the importance of tailoring digital learning strategies to varying levels of motivation and workload across training stages, ensuring that both medical interns and PGY trainees receive appropriate educational support.

The “learning environment incentives” construct demonstrated the strongest association with learning attitudes in our data. This construct reflects contextual features such as intuitive user interface, relevance of digital content to clinical tasks, accessibility of learning materials, and interactive functions. In Taiwan, these elements were most commonly experienced through institutional digital learning platforms during the pandemic and likely enhanced learners’ perceptions of digital courses, explaining the robust correlation observed in our study. These findings may also be interpreted in light of established theories. For example, Self-Determination Theory emphasizes how autonomy, competence, and relatedness foster positive learning attitudes, while the Technology Acceptance Model highlights the importance of perceived ease of use and usefulness. In addition, a media-ecology perspective suggests that digital platforms are not neutral tools but environments that shape interaction, visibility, and community building [39].

Multiple studies have demonstrated promising outcomes of digital learning in various educational contexts. Across diverse digital interventions—such as simulation problem-based learning (PBL), flipped or team-based learning (TBL), interactive simulators aligned with Objective Structured Clinical Examination (OSCE)-based training, and augmented-reality modules in Advanced Cardiac Life Support (ACLS)—shared design features such as timely feedback, authentic task practice, and collaborative interaction have been associated with improved engagement and competence [40–44]. These approaches exemplify how interactive, feedback-rich environments can reinforce the motivational mechanisms identified in our data. Drawing from these insights, digital medical courses can be conceptually categorized into three complementary types: (1) knowledge-oriented courses suited for asynchronous e-learning; (2) skill-based courses requiring hybrid designs integrating online preparation with in-person OSCE practice; and (3) problem-based courses benefiting from synchronous, interactive discussion platforms. For interns, who demonstrated higher motivation and stronger self-directed learning tendencies, integrating problem-based learning (PBL) and OSCE-oriented digital modules may enhance critical thinking, problem-solving, and clinical competence. For PGY trainees, whose learning preferences emphasize structured guidance and clinical relevance, ACLS-based digital simulations and case-driven synchronous discussions may better align with their training needs. In planning digital curricula, educators should align each course with the most suitable delivery format.

Digital learning in medical education presents a dual reality—enhancing flexibility, accessibility, and engagement, yet still facing challenges in practical skill acquisition and equitable access [35, 45–47]. Barriers related to institutional support and limited instructor–learner interaction can reduce motivation [48, 49]. Our results highlight that learning environment incentives and trainee stage together influence attitudes, underscoring the importance of context-specific, blended learning models that combine digital and face-to-face elements [34–36, 41, 42, 45–50]. To sustain engagement and comprehensive competence, future curricula should integrate effective digital innovations with the experiential benefits of in-person training. This balanced approach will support a resilient, flexible, and learner-centered model of medical education.

Limitation

This study has several limitations that should be acknowledged. Data were collected through purposive sampling from participants who volunteered to take part in the study, which may limit generalizability to all medical training institutions in Taiwan. Respondents with greater motivation toward digital learning might also have been more inclined to complete the survey, introducing potential self-selection bias. Additionally, because the study was conducted during the COVID-19 pandemic within Taiwan’s educational and cultural context, its implications may differ in other countries or in post-pandemic settings where hybrid learning models have since stabilized. The cross-sectional design further limits causal interpretation of relationships among motivation, learning strategies, and perceived effectiveness, while the use of self-reported measures may not fully capture actual learning performance. Future research incorporating longitudinal designs, objective indicators such as learning analytics or assessment outcomes, and cross-cultural comparisons would strengthen causal inference and extend the generalizability of these findings. Nevertheless, the large sample, validated instrument, and rigorous analytical approach enhance confidence in the robustness and applicability of the results for advancing medical education practice.

Conclusion

The COVID-19 pandemic has catalyzed a profound transformation in medical education, accelerating the adoption of digital learning. Our study demonstrates that differences in digital learning motivation and strategies between medical interns and PGY trainees are closely linked to their perceptions of the learning environment. By identifying the strong role of learning environment incentives, our findings provide evidence for designing more targeted and responsive digital curricula. We propose that medical education programs categorize courses according to their learning objectives—knowledge-, skill-, and problem-based—and select delivery modes accordingly to optimize engagement and competence. For long-term educational reform, integrating digital learning with face-to-face experiences through context-specific blended models will be essential for sustaining both flexibility and clinical relevance. Continued research across institutions and cultural contexts will further refine these strategies and support the development of a resilient, learner-centered medical education system.

Authors’ contributions

C.H. Yu was responsible for writing the main manuscript text.C.T. Hsiao and C.M. Hsu critically reviewed and revised the manuscript.All authors have read and approved the final version of the manuscript.

Funding

This research was supported by a grant from Chang Gung Memorial Hospital (Grant No. CDRPG6L0011).

Human Ethics and Consent to Participate declarations: 

Data availability

The datasets are not publicly available due to participant confidentiality but are available from the corresponding author on reasonable request and with institutional approval.

Declarations

Ethics approval and consent to participate

This study involved human participants. Ethical approval was obtained from the Chang Gung Medical Foundation IRB (IRB No. 202100780B0A3), and all participants provided informed consent prior to participation.

All participants provided their informed consent to participate in this questionnaire-based study.

Competing interests

The authors declare no competing interests.