Abstract
Background
Vital signs measurement is a fundamental nursing responsibility that requires cognitive and psychomotor competence. In alignment with the ethical principle of nonmaleficence, it is important to provide learning environments where students can practice and make mistakes without causing harm. This study aims to examine the effect of a 3D game-based simulation application developed for vital signs on students’ academic achievement, learning satisfaction and self-confidence levels.
Methods
A randomized controlled experimental design was employed with 73 nursing students, assigned to either the intervention group (n = 37) or the control group (n = 36). The intervention group used a game-based simulation developed with 3D animation technology, while the control group received traditional instruction. Data were collected using the Student Introductory Characteristics Form, Academic Achievement Test of Vital Signs Measurement Skills, and the Student Satisfaction and Self-Confidence in Learning Scale. Statistical analyses were conducted using SPSS version 25, with significance set at p < 0.05.
Results
The results indicated that students’ current learning satisfaction subscale scores were high in the traditional teaching group and that there was a statistically significant difference between the groups (p = 0.047). There was no statistically significant difference between the groups in terms of academic achievement (p = 0.932) and self-confidence levels in learning (p = 0.375). However, both groups had high mean scores in academic achievement, satisfaction with current learning, and self-confidence in learning.
Conclusions
The game simulation method supported by 3D animation may be insufficient for significantly improving nursing students’ academic performance, satisfaction with the learning process, and self-confidence in learning. Therefore, it is recommended that such technology-supported methods be used in blended learning environments alongside traditional teaching methods in nursing education.
Trial registration
ClinicalTrials.gov: NCT07009275; retrospectively registered.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12909-025-07980-4.
Keywords: Nursing education, Vital signs, Animation, Academic achievement, Satisfaction
Background
The nursing profession is a discipline that requires competence in both cognitive and psychomotor skills in order to protect and improve the health of individuals and provide appropriate care in cases of illness [1]. In this context, the effective acquisition of psychomotor skills is considered to be one of the fundamental objectives of nursing education [2]. During the education process, students are expected to acquire many skills of varying degrees of difficulty and complexity and become proficient in their application [3]. Among these skills, the accurate assessment, measurement and monitoring of vital signs is one of the important responsibilities of nurses [4].
Importance of vital signs
Vital signs are key physiological indicators used to determine the general health status of a patient [5]. Objective data obtained from vital signs enable healthcare professionals to make clinical assessments of the patient’s general condition and initiate necessary interventions in a timely manner [6, 7]. Therefore, the accurate measurement, recording and interpretation of vital signs are essential for monitoring a patient’s response to care and treatment [8].
During clinical rotations, the responsibility for monitoring vital signs is often assigned to nursing students with basic knowledge of the procedure [9]. Owing to the non-invasive nature of these tasks, students usually perform these applications with minimal or no supervision from clinical instructors or nurses. However, due to the non-invasive nature of vital signs applications, students often perform these applications with minimal or no supervision from a clinical nurse educators or nurses [6, 10]. The literature indicates notable deficiencies in students’ knowledge [9], practical application skills [11], and attitudes [10] regarding vital signs. However, it is essential that students apply the clinical skills they acquired correctly and consistently to enhance patient safety and quality of care [12, 13]. Therefore, more effective and contemporary teaching strategies are needed to improve instruction in vital signs measurement.
Challenges encountered in teaching
Vital signs are an important topic in nursing education, requiring the integration of both cognitive and psychomotor skills [1]. The demonstration method is traditionally used in teaching psychomotor skills. However, contemporary skills training faces challenges such as increased student enrolment, shortages of teaching staff, limited laboratory resources, insufficient opportunities for students to safely repeat practical exercises, and difficulties in applying theoretical knowledge in clinical settings [14]. At the same time, the fact that students belong to the millennial and Z generations has caused their learning needs to change [15]. For this reason, it has become essential to align nursing education with current needs by incorporating technology into training programmes [16].
An innovative approach to nursing education: 3D game-based simulation
Virtual game simulation is a form of simulation that transfers real-life situations into a computer environment and incorporates game elements [17]. Recently, the use of virtual simulation games has increased in nursing education as a means of addressing challenges associated with face-to-face simulation, such as instructor competence, equipment costs, and accessibility [18]. By design, these applications enable students to actively participate in their own learning in an environment without time and space constraints [19–21]. They also provide immediate feedback, performance monitoring, objective assessment [22] and the opportunity to repeat the game until they are satisfied with their performance [20]. Therefore, virtual game simulations can improve academic achievement [22] and increase permanence of information [23]. At the same time, virtual gaming simulation as a teaching strategy enables students to participate in a realistic clinical scenario, reducing their anxiety and supporting their self-confidence [21, 24]. Gentry et al. (2019) provide a strong theoretical basis for this approach through their systematic review of 171 studies on serious gaming in health professions education, which found that serious games improve knowledge retention and skill competence [25]. These findings support the investigation of 3D animation as a serious game strategy in nursing education. Similarly, Toqan et al. (2023) reported that high-fidelity simulation applications significantly increased nursing students’ self-confidence and satisfaction levels, serving as a precedent for the outcome variables evaluated in the present study [26]. Previous research on virtual game simulations has typically focused on tracheostomy care [21], urinary catheterisation [27], decontamination [28], advanced cardiac life support [29], and cardiopulmonary resuscitation [30]. However, no studies were identified that compared the effectiveness of virtual game simulations with traditional methods in teaching vital signs. This may be because vital signs monitoring is often regarded as one of the most basic and easily learned skills in nursing practice. Educators may also perceive it as a routine, straightforward skill due to its non-invasive nature and the relative independence with which students can practice it [10]. However, in a study examining nursing students’ knowledge levels and attitudes towards measuring vital signs, it was reported that students had negative attitudes towards basic parameters, especially critical indicators such as respiratory rate. Some students view such measurements as boring and time-consuming, which can reduce motivation to learn [31]. Furthermore, existing literature indicates that nursing students have poor knowledge and attitudes regarding monitoring and interpreting vital signs in patient care [10, 32]. This suggests that, contrary to common assumptions, teaching vital signs requires not only the acquisition of basic technical skills but also more comprehensive and interactive instructional approaches aimed at fostering accurate interpretation, patient safety awareness, and the development of appropriate attitudes. In line with this, this study aims to examine the effect of a 3D game-based simulation application developed for vital signs on students’ academic achievement, learning satisfaction and self-confidence levels. The hypotheses of the study are as follows:
H1: The academic achievements of students who use a 3D game-based simulation application are higher than those who use traditional learning methods.
H2: Students who use a 3D game-based simulation application have higher levels of satisfaction and self-confidence in learning than those who use traditional learning methods.
Methods
Research design
A randomized controlled experimental design was used with two groups of students. The intervention group consisted of students using the game-based simulation application developed using 3D animation technology, while the control group consisted of students using traditional learning methods.
Study setting and sample
The study was conducted at the Faculty of Health Sciences in the province of Sakarya, Türkiye. The population of this study consisted of first year nursing students in the academic year 2023–2024 (N = 121). Inclusion criteria were as follows: enrolment in the Faculty of Nursing; willingness to participate in the study; no prior theoretical or practical training in vital signs; non-international student status; and no transfer from other health sciences programs with differing examination practices.
The sample size for this study was calculated using G.Power 3.1.9.4 with a coefficient of reliability of α = 0.05 and a confidence level of 95%. When calculating the sample size, the effect size was determined to be 1.03 using the study by Dincer et al. (2019) entitled ‘The Effect of 3D Animation Supported Education on Nursing Students’ Knowledge Levels for Respiratory Assessment’ as an example [33]. The sample size of the study was calculated with a theoretical power of 0.95, and the minimum sample size was 52, with 26 in the control group and 26 in the intervention group.
A total of 121 first-year nursing students were enrolled in the Faculty of Health Sciences where the study was conducted. Students were excluded from the study because 28 of them held international student status, 9 of them were enrolled in the Department of Nursing from other areas of health sciences with different exams, and 6 of them had previously received training in vital signs. This left 78 students who met the inclusion criteria. A stratified random sampling method (lottery) was applied to these students. They were stratified into five equal performance-based groups according to their cumulative grade point averages (CGPAs). Within each stratum, simple randomization was performed using a computer-generated random number list to assign students to either the intervention or control group. The random sequence was generated by an independent researcher not involved in the implementation or evaluation phases of the study and was used to ensure unbiased allocation.(Fig.1)
Fig. 1.
CONSORT flow diagram
Data collection tools
This study collected data using the ‘Student Introductory Characteristics Form’, the ‘Academic Achievement Test of Vital Signs Measurement Skills’, and the ‘Student Satisfaction and Self-Confidence in Learning Scale’.
Student introductory characteristics form
This was prepared by the researchers to determine some socio-demographic characteristics of the students participating in the study. In this form, there were a total of six questions including gender, age, academic grade average, willingness to choose the profession, information about the simulation, and participation in the simulation-related practice.
Academic achievement test of vital sings measurement skills
The researchers developed the Academic Achievement Test of Vital Signs Measurement Skills to measure vital signs. The researchers first identified the gains that students should make in measuring vital signs. In line with the literature [6, 10, 34], five-choice multiple-choice questions were prepared covering the goals and behaviours determined for each outcome. A total of 58 questions, including 23 questions on blood pressure measurement skills, 12 questions on apical pressure measurement skills, 10 questions on body temperature measurement skills, 7 questions on pulse taking skills, and 6 questions on respiration counting skills, were submitted for expert opinion by email. The questions were rearranged according to the experts’ feedback. The revised questions were administered to 303 nursing students as a pretest. Internal consistency was assessed using the Kuder-Richardson Formula 20 (KR-20), appropriate for dichotomously scored items [35]. The final 26-item version of the test demonstrated acceptable internal consistency with a KR-20 reliability coefficient of 0.77. The average item difficulty index was 0.62, and the average discrimination index was 0.39, indicating that the test comprised moderately difficult and sufficiently discriminative items. Test scores had a mean of 16.19, standard deviation of 4.6, skewness of −0.528, and kurtosis of 0.045, supporting normal distribution. These results indicate that the test has satisfactory content validity and reliability, and that it is composed of items that effectively distinguish among students with varying levels of achievement. Each correct response was scored as 1 point and each incorrect response as 0 points, yielding a possible total score range of 0 to 26.
Student satisfaction and Self-Confidence in learning scale
The original scale was developed by Jeffries and Rizzolo (2006) with 13 items, and the total number of items was reduced to 12 during its adaptation to Turkish [36, 37]. It is a 5-point Likert-type scale consisting of two subscales: ‘Satisfaction with Current Learning’ and ‘Confidence in Learning’. Satisfaction with current learning sub-item consists of 5 items, confidence in learning sub-item consists of 7 items, and there are no negative items. Cronbach’s alpha coefficients were reported as 0.85 for ‘Satisfaction with Current Learning’, 0.77 for ‘Confidence in Learning’, and 0.89 for the total scale. Higher total scores indicate greater student satisfaction and confidence in learning. The scale has also been validated in different nursing education contexts. For example, Jawabreh et al. (2025) reported high internal consistency (α = 0.89 for the confidence subscale) in a mental health nursing simulation study, supporting the scale’s cross-context applicability and reliability.
Implementation of the study
The implementation of this research was carried out in three stages: ‘preparation’, ‘practice’ and ‘evaluation’.
First stage: Preparation
Designing the website of the game-based simulation application with 3D animation technology
Under the guidance of a software company, a game-based simulation application with 3D animation technology was designed for the skills of measuring vital signs (measuring blood pressure, counting radial artery pulse, taking apical route pulse, measuring body temperature and counting respiratory rate), which is the first stage of the research.
The website for the game-based simulation application was built under the name https://www.yasambulgularioyun.com/. The development of the simulation was a collaborative endeavour that spanned a period of approximately three months, undertaken in conjunction with a professional software company. The 3D animation components were created using a combination of Blender, MetaHuman, and pre-model 3D scanned objects. Facial expressions and body movements were captured and digitised using motion capture technology to enhance realism. The integration of these assets, scene creation, character animations, and rendering processes were executed in Unreal Engine 5, which also enabled the use of ray tracing to simulate realistic lighting effects. The web side of the project was built on a WordPress infrastructure, incorporating HTML, PHP, and JavaScript. The data were stored in a MySQL database, thus enabling expert access and user-based tracking of responses. This application was also compatible with mobile devices such as smartphones and tablets. The website had an educational module to watch videos created with 3D animation technology and a game module to reinforce what they had learned by playing games. To access the game, students registered using their corporate email accounts (Fig. 2).
Fig. 2.
User interface of the interactive game-based learning platform
Development of training videos using 3D animation technology
In the study, instructional videos were prepared on blood pressure measurement, radial artery pulse assessment, apical artery pulse assessment, body temperature measurement, and respiratory rate measurement. The content of these videos was developed by the researchers in accordance with the literature [1, 8, 10, 38]. Scenarios for each vital sign skill were designed under the guidance of a researcher with expertise in simulation-based education. Each scenario involved two settings: a medication preparation room and a patient room, and two characters: a patient and a nurse.
The scenarios were sent to the animation designer, who produced demo videos for all vital sign skills. These demos were evaluated by a team of three professionals with more than five years of academic and clinical experience. After receiving approval, the final versions of all videos were produced.
The blood pressure measurement video is 5 min 30 s in length, the pulse assessment and respiratory rate measurement video is 2 min 18 s, the body temperature measurement video is 3 min 16 s, and the apical artery pulse assessment video is 3 min. The animation designer integrated these videos into the website, where they can be accessed via mobile phones, tablets, or computers by logging in with a username and password.
Development of game-based simulation with 3D animation technology
At this stage, game videos were developed according to the applications and explanations in the training videos. Each game video was adapted from the corresponding instructional video. For each application, three or four questions were prepared. At designated points in the video, playback was paused, and a multiple-choice question was displayed. Correct answers triggered an applause sound, while incorrect answers generated an error sound and displayed a text-based explanation. Students could continue selecting answers until the correct option was chosen. The interactive features incorporated embedded multiple-choice questions that were dynamically triggered at specific time points within the instructional videos. The questions under consideration were drawn from a predefined question bank and integrated directly into the JavaScript codebase. Upon reaching a designated segment, the video automatically paused and presented a question interface to the student. Correct answers were met with auditory feedback (e.g., applause), while incorrect answers prompted text-based explanations and allowed for repeat attempts until the correct option was selected. Student responses were stored in a MySQL database under each user’s profile, enabling subsequent review and expert evaluation. This configuration supported real-time formative assessment and self-paced learning. Each question had four answer options, and when a student selected an incorrect response, a corresponding explanation was displayed to clarify the mistake (Fig. 2).
Second stage: practice
All students who participated in the study first received a six-hour theoretical lesson on vital signs measurement, delivered by the same educator. Following this session, they were assigned to either the intervention or control group.
The students in the control group received practical training on vital signs measurement using the demonstration method in the skills laboratory, delivered in a face-to-face format by an instructor. This four-hour session included hands-on demonstrations and supervised practice on blood pressure measurement, apical pulse measurement, radial pulse assessment, body temperature measurement, and respiratory rate counting. Students observed procedures performed on mannequins and then practiced the skills under supervision.
The students in the intervention group (n = 37) learned vital signs through a game-based simulation application conducted in the computer laboratory. Under the supervision of a researcher, each student logged into the platform using their individual username and password, watched all instructional videos in the training section, and then proceeded to complete all game-based exercises. The simulation design aligns with Kolb’s Experiential Learning Theory, as students actively participated in simulated clinical scenarios, reflected on their performance through feedback, conceptualized knowledge via instructional videos, and re-applied it in varied game-based tasks. The simulation intervention lasted six hours, consistent with the findings of Gentry et al. (2019), who reported that simulation-based interventions exceeding four hours significantly improve learning outcomes. To minimize contamination between the intervention and control groups, training sessions were conducted simultaneously but in separate settings, each supervised by a different researcher. Data were collected via structured questionnaire forms administered in person between April and May 2024.
Third stage: evaluation
In the final stage of the study, the Academic Achievement Test of Vital Signs Measurement Skills and the Student Satisfaction and Self-Confidence in Learning Scale were administered to the students in both groups. All outcome measures were administered immediately after the completion of training sessions for both groups. To minimize bias, the study was conducted as a single-blind design. The educators who delivered both the demonstration and the game-based simulation training were blinded to the scoring process of the academic achievement test to ensure objective assessment. Additionally, the data analysis was performed by a separate researcher who was blinded to group assignments, preventing detection bias during statistical evaluation. The study was reported in accordance with the CONSORT guidelines for randomized controlled trials.
Data analysis
The data obtained from the study were analysed using SPSS version 25. Numbers, percentages and arithmetic means were used to analyse the descriptive characteristics of the students. Independent sample t test and Chi-square test were used to evaluate the homogeneity of the groups in terms of demographic data after randomisation. The Shapiro–Wilk test was applied to examine normality. Results indicated that the academic achievement test scores were normally distributed (p = 0.544), whereas the Student Satisfaction with Learning and Self-Confidence scale scores did not follow a normal distribution (p < 0.05). Accordingly, parametric tests (independent samples t-test) were applied to the academic achievement data, and non-parametric tests (Mann-Whitney U test) were applied to the satisfaction and confidence scores that did not meet the normality assumption.
The independent samples t-test was used to evaluate the difference between the academic achievement test scores of the intervention and control groups. Student satisfaction and confidence in learning scale and its sub-dimensions were evaluated by Mann-Whitney U test in intervention and control groups. A p-value of less than 0.05 was considered statistically significant.
Ethical approvals
Ethical approval for the study was obtained from the Ethics Committee of Sakarya University of Applied Sciences (Approval No: E-26428519-044-65435). Participation was entirely voluntary, and all participants were informed that they could withdraw at any time without any academic consequences, and that their participation would not influence their course grades. Personal data were handled in strict confidentiality and were not disclosed to third parties under any circumstances. The study was conducted in accordance with the principles outlined in the Declaration of Helsinki. To prevent interaction between groups, each student was assigned a unique username and password. Students in the control group were informed that they would be granted access to the virtual game simulation after the study concluded.
Results
The mean age of students was 19.00 ± 0.66 years in the intervention group and 19.19 ± 0.88 years in the control group. The mean academic grade of the students was 2.77 ± 0.53 in the intervention group and 2.75 ± 0.64 in the control group. In the intervention group, 78.4% of students were female, compared with 72.2% in the control group. Additionally, 59.5% of the intervention group and 50.0% of the control group reported willingly choosing the nursing department. When the demographic characteristics of the students in the intervention and control groups were evaluated, it was found that there was no significant difference between the groups and the distribution was similar in both groups (p > 0.05) (Table 1).
Table 1.
Demographic characteristics of students in the intervention and control groups
| Descriptive characteristics | Intervention group | Control group | Statistical analysis |
|---|---|---|---|
| Mean ± SD | Mean ± SD | ||
| Age | 19.00 ± 0.66 | 19.19 ± 0.88 |
t=−1.055 p = 0.295 |
| Academic Grade Point Average | 2.77 ± 0.53 | 2.75 ± 0.64 |
t = 0.149 p = 0.295 |
| n (%) | n (%) | ||
| Gender | |||
| Female | 29 (78.4) | 26 (72.2) |
x2 = 0.085 p = 0.882 |
| Male | 8 (21.6) | 10 (27.8) | |
| Nursing Preference | |||
| Willingly | 22 (59.5) | 18 (50.0) |
x2 = 1.205 p = 0.548 |
| Not willingly | 4 (10.8) | 7 (19.4) | |
| Partly willing | 11 (29.7) | 11 (30.6) | |
| Total | 37 (100.0) | 36 (100.0) | |
SD Standard deviation, t Independent sample t-test; χ² Chi-square test
In Table 2, the mean academic achievement test scores of the intervention and control groups were found to be 17.86 and 17.80, respectively. As a result of the independent sample t-test, there was no statistically significant difference between these two groups (p > 0.05).
Table 2.
Comparison of academic achievement test scores between intervention and control groups
| Groups | n | Mean ± SD | t | p | d |
|---|---|---|---|---|---|
| Intervention | 37 | 17.86 ± 3.08 | 0.085 | 0.932 | 0.02 |
| Control | 36 | 17.80 ± 2.84 |
SD Standard deviation, t Independent sample t test, d Cohen’s d (effect size)
Student satisfaction results are presented in Table 3. Two sub-dimensions, satisfaction with current learning and confidence in learning, were used to assess student satisfaction. In the study, the intervention group had a total score of 3.73 ± 0.68 for student satisfaction and self-confidence in learning and the control group had a total scale score of 3.97 ± 0.43. Higher scores on this scale indicate greater satisfaction and self-confidence in learning. In both groups, the descriptive mean scores for satisfaction with current learning and self-confidence were relatively high.
Table 3.
Descriptive statistics for student satisfaction and Self-Confidence in learning scale in each group
| Groups | Satisfaction with current learning | Self-confidence in learning | Total mean score | |
|---|---|---|---|---|
|
Intervention (n = 37) |
Mean | 3.76 | 3.71 | 3.73 |
| SD | 0.80 | 0.68 | 0.68 | |
| Min | 1.40 | 1.14 | 1.25 | |
| Max | 5.00 | 4.71 | 4.67 | |
|
Control (n = 36) |
Mean | 4.08 | 3.89 | 3.97 |
| SD | 0.52 | 0.44 | 0.43 | |
| Min | 2.80 | 3.00 | 3.00 | |
| Max | 5.00 | 5.00 | 5.00 | |
SD Standard deviation, Min Minimum, Max Maximum
The mean ranks of the Student Satisfaction in Learning and Self-Confidence Scale were compared between the intervention and control groups, as presented in Table 4. A statistically significant difference was identified in the sub-dimension of satisfaction with current learning, with the control group demonstrating higher levels of satisfaction (p < 0.047). The effect size for this difference was small (r = 0.23), indicating a weak effect. No statistically significant differences were observed between the groups in the sub-dimension of self-confidence in learning (p = 0.375), or in the total scale scores (p = 0.151). Even though both groups reported relatively high levels of satisfaction and self-confidence in the descriptive results (Table 3), the difference in satisfaction with current learning was statistically significant, whereas other dimensions showed no meaningful difference.
Table 4.
Comparison of mean ranks on student satisfaction and Self-Confidence in learning scale between groups
| Scale | Groups | Mean Rank | U | p | r | |
|---|---|---|---|---|---|---|
| Satisfaction with current learning | Intervention (n = 37) | 32.24 | 490.00 | 0.047* | 0.23 | |
| Control (n = 36) | 41.89 | |||||
| Self-confidence in learning | Intervention (n = 37) | 34.49 | 586.00 | 0.375 | - | |
| Control (n = 36) | 39.22 | |||||
| Student satisfaction and self-confidence in learning | Intervention (n = 37) | 33.49 | 536.00 | 0.151 | - | |
| Control (n = 36) | 40.61 | |||||
*p < 0.05, U = Mann Whitney U test, r = Effect size
Discussion
This study evaluated whether the use of a game-based simulation application developed with 3D animation technology is an effective method for teaching vital signs measurement skills in nursing education. For this purpose, first-year nursing students with no prior knowledge or skills in measuring vital signs were assigned to either an intervention group or a control group for comparison.
Comparison of academic achievement between groups
It was determined that academic achievement related to vital signs did not differ between the intervention group and the group receiving traditional instruction. Rather than viewing this as a failure of the intervention, this finding can be seen as evidence that the 3D game-based simulation method is non-inferior. Chao et al. (2021) reported in their study comparing traditional presentation videos with immersive 3D interactive video programmes that there was no significant difference in academic achievement between the two groups [12]. Similarly, in another study conducted at two comparable schools, the use of an animated and sound-supported computer application for teaching vital signs did not result in any change in factual knowledge between the groups [38]. In their study examining the effect of simulation-based teaching methods applied to three different groups in teaching vital signs, Eyikara and Göçmen Baykara (2018) reported that there was no statistically significant difference between the groups’ pre-test cognitive knowledge scores; however, their post-test scores increased significantly [1]. Similarly, Hanson et al. (2019) emphasised the supportive effect of this method on academic achievement, stating that there was a significant increase in students’ knowledge levels in pharmacology education using 3D visualisation technology [39]. This situation shows that the effectiveness of technology-supported learning methods may vary depending on the content, teaching time, students’ prior knowledge, and characteristics of the learning environment. In the study, the fact that both groups received vital signs training from the same instructor for the same duration (6 h) can be considered a possible factor explaining the similarity in their knowledge levels (Table 5).
Table 5.
Comparison of study findings with previous literature
| Study | Participants | Intervention Type | Academic Achievement | Satisfaction | Self-Confidence |
|---|---|---|---|---|---|
| This Study | First-year nursing students (n = 73) | 3D animation-based game simulation | No significant difference between groups | Higher in control group (traditional method) | No difference between groups |
| Eyikara & Göçmen Baykara (2018) | First-year nursing students (n = 90) | High-fidelity simulation with SimMan® | No difference between groups in the pre-test; significant increase in the post-test | Not specified | Not specified |
| Kaveevivitchai et al. (2009) | Nursing students (n = 117) | Multimedia-supported computer training integrated with anatomy and physiology | No significant difference between groups | Not specified | Not specified |
| Chao et al. (2021) | Nursing students (n = 45) | Immersive 3D interactive video programme | No significant difference between groups | Higher in the intervention group | No significant difference between groups |
| Hanson et al. (2019) | Nursing and Midwifery students (n = 187) | Pharmacology education supported by 3D visualisation | Significant increase in the intervention group | Positive attitudes reported (no quantitative data available) | Not specified |
| Pence (2022) | Nursing students (n = 28) | Animation-based vSim application | Not specified | High satisfaction reported | High self-confidence reported |
| Opsal et al. (2025) | Undergraduate nursing students (n = 54) | Blended learning-based skills training | Not specified | High satisfaction reported | High self-confidence reported |
| Widiasih et al. (2022) | Undergraduate nursing students (n = 139) | VNursLab 3D web-based nursing skills simulator | Significant improvement in nursing skill knowledge | Higher satisfaction in intervention group | Higher self-confidence in intervention group |
| Toqan et al. (2023) | Nursing students (n = 150) | High-fidelity simulation | Not specified | Higher in the intervention group | Higher in the intervention group |
| Gentry et al. (2019) | Health professions students (27 RCTs and 3 cluster RCTs with 3634 participants) | Serious games and gamification | Improved | Positive impact, limited evidence | Positive impact, limited evidence |
| Jawabreh et al. (2025) | Nursing students (n = 75) | High-fidelity simulation (Mental Health Nursing) | Not specified | Higher satisfaction in experimental group | Higher self-confidence in experimental group |
Student satisfaction and self-confidence in learning
Given that the maximum score that can be obtained from the “Student Satisfaction and Self-Confidence in Learning Scale” used in the study is 5, it was determined that the overall satisfaction and self-confidence scores of students in both groups were considered high. This finding shows that both traditional teaching methods and 3D animation-supported simulation applications can positively affect students’ satisfaction and confidence levels in learning. Pence (2022) reported that the computer-assisted and animation-based vSim application used in nursing education was found to be useful by students and increased their satisfaction levels; however, they noted that learning mainly took place in laboratory or clinical settings [40]. Similarly, Opsal et al. (2025) and Widiasih et al. (2022) emphasised that 3D animation and game-based technologies used in nursing education can be effective in increasing students’ satisfaction and self-confidence levels, but that these technologies cannot fully replace direct practical experience [41, 42]. In this regard, rather than emphasising the absolute superiority of traditional methods over technology-supported learning methods, it can be said that both approaches are effective in their own contexts and can generate high levels of satisfaction and self-confidence among students (Table 5).
Comparison of student satisfaction and self-confidence in learning between groups
The study found a statistically significant difference between groups only in the sub-dimension of ‘Satisfaction with Current Learning’ (p = 0.047). This finding shows that students in the control group reported higher levels of satisfaction with traditional teaching methods. However, the fact that the p-value is very close to the significance level suggests that although this difference is statistically significant, its effect is weak. Toqan et al. (2023) stated that nursing students’ satisfaction levels with simulation-based learning methods were higher than with traditional methods [26]. Similarly, Jawabreh et al. (2025) stated that simulation applications with a high degree of realism increased students’ satisfaction and confidence levels in learning. However, the study also noted that the effect of simulation on satisfaction may vary depending on the content and may be limited, particularly in affective learning areas such as psychiatric nursing (η²= 0.12) [43]. Gentry et al. (2019) reported that serious games and gamification methods in health sciences can increase student satisfaction; however, current evidence is insufficient to fully substantiate this claim [25]. The positive impact of active, experiential learning on student satisfaction and confidence has been demonstrated by research on simulation in nursing education [1, 44, 45]. In this study, the higher learning satisfaction scores observed for traditional teaching methods may be attributed to students’ greater familiarity with this approach or to the possibility that the innovative simulation method did not fully meet their expectations. Usability-related factors such as technology fatigue, interface complexity, or limited interactivity may also have negatively affected satisfaction with the 3D simulation application. For instance, requiring students to log in with a username and password while simultaneously presenting video materials and game sections on the same interface may have contributed to a heightened cognitive load. Although traditional methods seem to provide higher satisfaction among students, 3D animation-supported simulation tools can offer significant advantages in terms of increasing the level of interaction, scalability to wider student groups, and supporting long-term knowledge retention. Therefore, integrating such technologies into nursing curricula through blended or supplementary teaching models warrants consideration (Table 5).
Limitations
This study has several limitations. First, the sample consisted exclusively of students from the nursing department of the Faculty of Health Sciences at a state university, which limits the generalisability of the findings.
Another limitation of this study is that the sample size calculation is based on a large effect size (1.03) obtained from a previous study [33]. Although the sample size is appropriate for comparison, it may be limited in determining statistically significant effects. Furthermore, no separate power analysis was performed for these secondary variables, which represents a notable methodological limitation.
The results on student satisfaction indicated higher scores in the control group compared to the intervention group. The novelty effect of 3D game-based simulation may have influenced students’ responses in a positive or negative way. There are many factors (learning style, teaching environment, instructor’s approach) that affect students’ satisfaction. Therefore, the effect of factors other than simulation implementation (e.g., students’ individual learning styles or classroom interaction) on satisfaction can be investigated. The study only evaluated cognitive outcomes and did not include behavioural or application-based performance measures.
Although qualitative student feedback was not collected in the current study, incorporating such data in future research could provide deeper insights into learners’ subjective experiences and perceptions of different instructional methods. Open-ended responses or focus group interviews would allow researchers to explore nuances behind satisfaction and confidence scores, offering a richer understanding of how students engage with both traditional and simulation-based learning environments. To strengthen future research, more comprehensive data on learning satisfaction and confidence can be obtained using mixed methods or qualitative approaches.
In addition, the study evaluated short-term learning outcomes, but did not conduct a long-term follow-up to assess knowledge retention. A more in-depth examination of the relationship between training time and interactive learning may yield healthier results. For example, the training period can be extended, or simulation games and laboratory studies can be combined.
Despite all these limitations, the study also has significant strengths. The use of a randomized controlled design increases internal validity, while the application of an innovative teaching method such as 3D game-based simulation contributes to the field of nursing education.
Conclusions
The results of this study indicate that students’ satisfaction scores for current learning were higher in the traditional teaching group, with a statistically significant difference between the groups (p = 0.047). However, given that this level of significance is limited, the findings should be interpreted with caution. Students trained using both traditional methods and 3D animation-supported game simulation showed similar results in academic achievement and self-confidence, with no statistically significant differences observed in these variables. Although the 3D animation-supported simulation did not demonstrate a clear advantage over traditional teaching methods in terms of satisfaction, self-confidence or academic achievement, it yielded comparable results in these key areas. These findings suggest that technology-enhanced approaches of this kind may serve as effective complementary tools in nursing education. Future research may focus on the long-term effects of these contemporary teaching strategies, particularly their integration into blended learning environments and their applicability to different student groups.
Supplementary Information
Acknowledgements
The authors would like to express their gratitude to the Scientific Research Projects Coordinatorship of Sakarya University of Applied Sciences for supporting this study. We also sincerely thank all the nursing students who voluntarily participated in the research.
Abbreviations
- 3D
Three-dimensional
- CONSORT
Consolidated Standards of Reporting Trials
Authors’ contributions
Fatma Tanrıkulu, Handenur Gündoğdu, and Funda Erol provided the conception and design of the study. Data collection was carried out by Fatma Tanrıkulu and Handenur Gündoğdu. Funda Erol and Yurdanur Dikmen performed the data analysis and contributed to the interpretation of the results. The first draft of the manuscript was written by Fatma Tanrıkulu, Handenur Gündoğdu, and Funda Erol. Yurdanur Dikmen critically revised the manuscript for important intellectual content. All authors read and approved the final manuscript.
Funding
This work was supported by the Scientific Research Projects Coordinatorship of Sakarya University of Applied Sciences [Grant number: 060-2021]. The funding body had no role in the design of the study, data collection, analysis, interpretation, or writing of the manuscript.
Data availability
The datasets generated and/or analyzed during the current study are not publicly available due to institutional privacy policies but are available from the corresponding author on reasonable request.
Declarations
Ethics approval and consent to participate
Ethical approval for this study was obtained from the Ethics Committee of Sakarya University of Applied Sciences (Approval No: E-26428519-044-65435). Before data collection, all participants provided informed consent. The study was conducted in accordance with the principles outlined in the Declaration of Helsinki.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
- 1.Eyikara E, Baykara ZG. Effect of simulation on the ability of first year nursing students to learn vital signs. Nurse Educ Today. 2018;60:101–6. [DOI] [PubMed] [Google Scholar]
- 2.Yılmaz D, Çınar GH. The effect of simulator use in blood pressure measurement training on nursing students’ psychomotor skills. Sağlık Ve Toplum Dergisi. 2020;20(1):104–10. [Google Scholar]
- 3.Öztürk D, Göçmen Baykara Z. The effect of peer education on the teaching of nursing skills. J Educ Res Nurs. 2019;16(4):295–300. [Google Scholar]
- 4.Kamio T, Kajiwara A, Iizuka Y, Shiotsuka J, Sanui M. Frequency of vital sign measurement among intubated patients in the general ward and nurses’ attitudes toward vital sign measurement. J Multidisciplinary Healthc. 2018:575–81. [DOI] [PMC free article] [PubMed]
- 5.Pozam M, Khorshid L, Sarı D. Determination of the nurses’ attitudes towards vital signs monitoring: a descriptive study. Turk Klin J Nurs Sci. 2022;14(2):398–406. [Google Scholar]
- 6.Kısacık ÖG, Gündoğan AÖ, Yılmaz E. Attitudes of nurses and nursing students towards vital signs: A comparatıve study. J Nurs Effect. 2024;17(4):575–90. [Google Scholar]
- 7.Dall’Ora C, Griffiths P, Hope J, Barker H, Smith GB. What is the nursing time and workload involved in taking and recording patients’ vital signs? A systematic review. J Clin Nurs. 2020;29(13–14):2053–68. [DOI] [PubMed] [Google Scholar]
- 8.Gülnar E, Yılmaz ED, Özveren H. Determination of nurses’ attitudes and applications on vital signs. J Kirikkale Univ Fac Med. 2020;22(3):377–85. [Google Scholar]
- 9.Etoom M, Alnjadat R, Al Momani E, Al-Omari A, Al-ta’ani M. Nursing and midwifery student’s attitudes toward vital signs monitoring using an Arabic version of the V-scale instrument and its influencing factors. SAGE Open Nursing. 2025;11:23779608251362354. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Alshehry AS, Cruz JP, Bashtawi MA, Almutairi KO, Tumala RB. Nursing students’ knowledge, competence and attitudes towards vital signs monitoring during clinical practice. J Clin Nurs. 2021;30(5–6):664–75. [DOI] [PubMed] [Google Scholar]
- 11.Nizamuddin A, Soomro SA, Waseem I, Khushnood A, Gill M. Analysis of clinical competencies in vital signs monitoring among BSN students at Isra university, hyderabad: focusing on knowledge, skills, and attitudes. Pakistan J Multidisciplinary Innov. 2024;3(2):20–7. [Google Scholar]
- 12.Chao Y-C, Hu SH, Chiu H-Y, Huang P-H, Tsai H-T, Chuang Y-H. The effects of an immersive 3d interactive video program on improving student nurses’ nursing skill competence: a randomized controlled trial study. Nurse Educ Today. 2021;103:104979. [DOI] [PubMed] [Google Scholar]
- 13.Özdemir FK, Güngör MCK, Cici AM. Comparison of video-assisted education and traditional classroom education in pediatric cardiopulmonary resuscitation education of nursing students. J Pediatr Nurs. 2023;73:e388–94. [DOI] [PubMed] [Google Scholar]
- 14.Yüksel A, Demirel N, Terzioğlu F. The effect of different teaching methods on the development of psychomotor skills in nursing students. J High Educ Sci. 2024;14(3):387–93. [Google Scholar]
- 15.Hrdy M, Tarver EM, Lei C, Moss HC, Wong AH, Moadel T, et al. Applying simulation learning theory to identify instructional strategies for generation Z emergency medicine residency education. AEM Educ Train. 2024;8:S56–69. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Gause G, Mokgaola IO, Rakhudu MA. Technology usage for teaching and learning in nursing education: an integrative review. Curationis. 2022;45(1):3–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Verkuyl M, Lapum JL, St-Amant O, Hughes M, Romaniuk D. Curricular uptake of virtual gaming simulation in nursing education. Nurse Educ Pract. 2021;50:102967. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Atthill S, Witmer D, Luctkar-Flude M, Tyerman J. Exploring the impact of a virtual asynchronous debriefing method after a virtual simulation game to support clinical decision-making. Clin Simul Nurs. 2021;50:10–8. [Google Scholar]
- 19.Hu H, Lai X, Li H, Nyland J. Teaching disaster evacuation management education to nursing students using virtual reality mobile game-based learning. CIN: Computers, Informatics, Nursing. 2022;40(10):705–10. [DOI] [PubMed] [Google Scholar]
- 20.Luctkar-Flude M, Tyerman J, Tregunno D, Bell C, Lalonde M, McParland T, et al. Designing a virtual simulation game as presimulation preparation for a respiratory distress simulation for senior nursing students: usability, feasibility, and perceived impact on learning. Clin Simul Nurs. 2021;52:35–42. [Google Scholar]
- 21.Bayram ŞB, Çalışkan N. Using a game-based virtual reality application in psychomotor skills acquisition: an experience sharing. J Hum Sci. 2019;16(1):155–63. [Google Scholar]
- 22.Foronda CL, Fernandez-Burgos M, Nadeau C, Kelley CN, Henry MN. Virtual simulation in nursing education: a systematic review spanning 1996 to 2018. Simul Healthc. 2020;15(1):46–54. [DOI] [PubMed] [Google Scholar]
- 23.McEnroe-Petitte D, Farris C. Using gaming as an active teaching strategy in nursing education. Teach Learn Nurs. 2020;15(1):61–5. [Google Scholar]
- 24.Ordu Y, Çalışkan N. The effect of virtual game simulation on students’ perception of nursing diagnosis and clinical practice: post-test only randomized controlled trial. Nurse Educ Pract. 2023;72:103792. [DOI] [PubMed] [Google Scholar]
- 25.Gentry SV, Gauthier A, Ehrstrom BLE, Wortley D, Lilienthal A, Car LT, et al. Serious gaming and gamification education in health professions: systematic review. J Med Internet Res. 2019;21(3):e12994. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Toqan D, Ayed A, Khalaf IA, Alsadi M. Effect of high-fidelity simulation on self-satisfaction and self-confidence among nursing students. SAGE Open Nurs. 2023;9:1–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Butt AL, Kardong-Edgren S, Ellertson A. Using game-based virtual reality with haptics for skill acquisition. Clin Simul Nurs. 2018;16:25–32. [Google Scholar]
- 28.Smith SJ, Farra S, Ulrich DL, Hodgson E, Nicely S, Matcham W. Learning and retention using virtual reality in a decontamination simulation. Nurs Educ Perspect. 2016;37(4):210–4. [DOI] [PubMed] [Google Scholar]
- 29.Buttussi F, Pellis T, Vidani AC, Pausler D, Carchietti E, Chittaro L. Evaluation of a 3D serious game for advanced life support retraining. Int J Med Inform. 2013;82(9):798–809. [DOI] [PubMed] [Google Scholar]
- 30.Boada I, Rodriguez-Benitez A, Garcia-Gonzalez JM, Olivet J, Carreras V, Sbert M. Using a serious game to complement CPR instruction in a nurse faculty. Comput Methods Programs Biomed. 2015;122(2):282–91. [DOI] [PubMed] [Google Scholar]
- 31.Fidan Ö, Şanlıalp Zeyrek A. Nursing students’ knowledge and attitudes towards vital signs monitoring in clinical practices: Descriptive-cross-sectional study Turkiye klinikleri. J Nurs Sci. 2025;17(1):135–45. [Google Scholar]
- 32.Gillan PC, Delaney LJ, Tutticci N, Johnston S. Factors influencing nursing students’ ability to recognise and respond to simulated patient deterioration: a scoping review. Nurse Educ Pract. 2022;62:103350. [DOI] [PubMed] [Google Scholar]
- 33.Dincer B, Savcı C, Mutlu HH. Effect of 3D animation-assisted education on knowledge level of nursing students for the evaluation of respiration. J Acad Res Nurs (JAREN). 2019;5(3).
- 34.Mohammed QH. Knowledge and practices of nursing college student’s regarding blood pressure measurement in hospitals at Kirkuk City. Kufa J Nurs Sci. 2016;6(2):177–84. [Google Scholar]
- 35.Bademci V. A study on the Kuder-Richardson 20, cronbach’s alpha, hoyt’s analysis of variance, generalizability theory and score reliability. Dicle Univ J Ziya Gökalp Educ Fac. 2011;17:173–93. [Google Scholar]
- 36.Jeffries PR, Rizzolo MA. Designing and implementing models for the innovative use of simulation to teach nursing care of ill adults and children: A national, multi-site, multi-method study. New York, NY: National League for Nursing. 2006;5(11):500-5.
- 37.Unver V, Basak T, Watts P, Gaioso V, Moss J, Tastan S, et al. The reliability and validity of three questionnaires: the student satisfaction and self-confidence in learning scale, simulation design scale, and educational practices questionnaire. Contemp Nurse. 2017;53(1):60–74. [DOI] [PubMed] [Google Scholar]
- 38.Kaveevivitchai C, Chuengkriankrai B, Luecha Y, Thanooruk R, Panijpan B, Ruenwongsa P. Enhancing nursing students’ skills in vital signs assessment by using multimedia computer-assisted learning with integrated content of anatomy and physiology. Nurse Educ Today. 2009;29(1):65–72. [DOI] [PubMed] [Google Scholar]
- 39.Hanson J, Andersen P, Dunn PK. Effectiveness of three-dimensional visualisation on undergraduate nursing and midwifery students’ knowledge and achievement in pharmacology: a mixed methods study. Nurse Educ Today. 2019;81:19–25. [DOI] [PubMed] [Google Scholar]
- 40.Pence PL. Student satisfaction and self-confidence in learning with virtual simulations. Teach Learn Nurs. 2022;17(1):31–5. [Google Scholar]
- 41.Opsal A, Fossum M, Elf M, Undheim M, Johannessen T. Student nurses’ satisfaction and confidence: blended learning skills training on satellite campuses. Teach Learn Nurs. 2025;20(3):e705-12. [Google Scholar]
- 42.Widiasih R, Komariah M, Pramukti I, Susanti RD, Agustina HS, Arifin H, et al. Vnurslab 3D simulator: a web-based nursing skills simulation of knowledge of nursing skill, satisfaction, and self-confidence among nursing students. Sustainability. 2022;14(9):4882. [Google Scholar]
- 43.Jawabreh N, Hamdan-Mansour A, Harazne L, Ayed A. Effectiveness of high-fidelity simulation on practice, satisfaction, and self-confidence among nursing students in mental health nursing class. BMC Nurs. 2025;24(1):622. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44.Carrero-Planells A, Pol-Castaneda S, Alamillos-Guardiola MC, Prieto-Alomar A, Tomás-Sánchez M, Moreno-Mulet C. Students and teachers’ satisfaction and perspectives on high-fidelity simulation for learning fundamental nursing procedures: a mixed-method study. Nurse Educ Today. 2021;104:104981. [DOI] [PubMed] [Google Scholar]
- 45.Herron EK, Powers K, Mullen L, Burkhart B. Effect of case study versus video simulation on nursing students’ satisfaction, self-confidence, and knowledge: a quasi-experimental study. Nurse Educ Today. 2019;79:129–34. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The datasets generated and/or analyzed during the current study are not publicly available due to institutional privacy policies but are available from the corresponding author on reasonable request.