Effectiveness of Task-Trainer Simulation Versus Peer Education on Nursing Students’ Learning Outcomes in Mechanical Ventilation: A Randomized Controlled Trial

Abstract

Introduction

Learning mechanical ventilation principles can reduce patient complications. This study examined the effects of task-trainer simulation and peer education on nursing students’ knowledge, clinical performance, and action speed related to basic principles of mechanical ventilation (BPMV).

Methods

This three-arm randomized controlled trial (2023-2024) included 60 students in Tehran, Iran. Students who were purposively selected and randomly assigned to task-trainer simulator training (n=20), peer education (n=20), and control (n=20) groups. BPMV was taught to the first group using a task-trainer simulator by a researcher, and to the second by two trained peer students. Data were collected using validated knowledge and skill assessment tools for BPMV.

Results

The students’ knowledge (P=.122) and performance (P=.149) mean scores in the experimental groups did not differ significantly immediately after the education, but one month after the intervention, the educational groups were higher than the control group (P<.0001). Also, immediately after the education, the speed of action of students in the simulation group was significantly higher than that of the peer education group (P<.0001). One month after the intervention, the speed of action of the simulation and peer education groups was higher than that of the control group (P<.0001). After the intervention, the average scores of knowledge, performance, and speed of action of students in the simulation group were better than those of the peer group, and the peer education group was better than the control group.

Conclusion

Both task-trainer simulation and peer-based, student-centered training improve nursing students’ knowledge, performance, and response speed in managing BPMV. However, simulation leads to faster, immediate action and greater overall gains than peer education in this setting. It is recommended to use these useful methods to increase learning outcomes.

Keywords

peer group education training simulation training learning student nursing mechanical ventilation clinical competence randomized controlled trial

1. Introduction

One of the specialized tasks of nurses is the proper care of patients undergoing mechanical ventilation (MV), which requires acquiring knowledge and performing the correct procedure. Because patients requiring MV have critical conditions, care must be provided as quickly as possible; therefore, healthcare workers, especially nurses, must be able to act quickly to deliver care. During the COVID-19 pandemic, the number of patients requiring MV increased, and the critical role of nurses in caring for these patients became more apparent than ever.¹ Although MV can be life-saving, its improper use can cause serious complications and injuries to the patient,² prolonged hospital stays, and potentially increased mortality.³ Ventilator-related injuries are usually caused by improper settings. Therefore, nurses must have the necessary technical skills to minimize complications of MV.⁴ Nursing students, by acquiring sufficient knowledge and skills, will be able to provide patient care as professional nurses after graduation. Therefore, clinical instructors and preceptors should educate students using active and effective teaching-learning methods^5,6 to train competent nurses who have the necessary knowledge, attitude, and skills to maintain and promote the health of all members of society.⁷ Clinical instructors often face challenges in teaching these methods due to the multiplicity and diversity of clinical skills in nursing.^5,6 For example, Sajjadi et al showed that some instructors did not use appropriate methods for teaching the principles of MV to learners and that new, useful, and efficient practical methods should be used to teach these topics more effectively.⁷ Despite studies recommending the use of new teaching-learning methods, many teachers still use traditional methods such as lectures, which lead to student fatigue, superficial learning, low retention of learned content, student inactivity, one-way teacher-student communication, insufficient opportunity for questions and answers (Q&A), and student demotivation. This can result in students not having sufficient knowledge and appropriate performance to care for patients.^7,8 Clinical settings are a good place to develop skills and prepare students to enter the nursing profession. However, factors such as the increasing number of students, limited length of internship, small number of clinical instructors, different talents and abilities of trainees in acquiring skills, and unintentional injuries to patients result in fewer opportunities for students to learn practical procedures in real clinical settings. Also, students’ insufficient knowledge and clinical experience cause anxiety and fear of being unable to perform practical procedures, which can lead to an increase in errors.^6,9 Evidence suggests that nursing students have inadequate knowledge^10,11 in the basic principles of mechanical ventilation (BPMV), and there is limited published evidence specifically assessing their practical skills in BPMV. The principles of MV should be taught theoretically and practically to reduce the gap between knowledge and practical experience and to facilitate the implementation of clinical skills in the real world.¹² In recent years, healthcare institutions and universities have emphasized the need to review traditional teaching methods and adopt active, innovative, and student-centered learning styles, which play an important role in developing students’ thinking, learning more easily, and in-depth knowledge and skills.^5,6,8,13 In active teaching-learning methods, learners directly participate in the learning process and reflect on what they are doing.^6,14 Simulation-based training and peer education are collaborative and learner-centered teaching methods.^14-16 Simulation-based learning (SBL) elicits immersive experiences in learners, and when combined with debriefing and reflection, these experiences contribute to deeper learning, which is consistent with the principles of Experiential Learning Theory (ELT) (i.e., engaging in realistic tasks, reflecting, conceptualizing, and applying learning).¹⁷ It is one of the most effective teaching methods for consolidating learning in the learner’s mind, and its use has increased due to changes in healthcare systems. Simulation mimics essential aspects of a real situation in cases where real-world training is time-consuming, expensive, or dangerous.^18,19 This teaching method includes simulation using tapes and videos, demonstrations, standardized patients (SPs), mannequins, cadavers, and high-fidelity simulators (HFS), such as task-oriented simulators (or Simulation-Task-Trainers).^5,20 The significant and widespread use of simulators in medical sciences is mainly driven by the high costs of providing cadavers and laboratory animals, the inability to practice and repeat various practical procedures in a real environment, and the possibility of harming the real patient.²¹ Task-trainer simulators are specialized, realistic models of the human anatomy designed to teach competency-based skills and practical procedures, such as airway management, nasogastric tube placement, lumbar puncture, and intravenous access. These simulators break down the execution of a specific procedure into simpler steps and help the learner acquire the correct skills and practice repeatedly. In this case, the learner can practice invasive procedures without the risk of harm to the patient and without the fear, worry, and anxiety of performing a procedure incorrectly.^5,20,22 Other benefits of simulation include facilitating learning, easy assessment, increasing self-confidence, satisfaction, and creating interest and attractiveness in education.⁵ The closer the training environment is to the real work environment, the better the learning, knowledge, and performance of learners.¹⁹ Task-trainer simulators designed for airway management and ventilation, with their inspiratory and expiratory capabilities, largely meet the educational needs of learners in the areas of anesthesia training, airway management, and connection to MV. These advantages reveal the advantages of these devices over other simulators, real patients, manikins, cadavers, and SPs.^4,5,22 Another active learning method is peer-assisted learning,⁹ which develops learners’ knowledge and skills. This method draws on Albert Bandura’s Social Learning Theory, in which learners acquire knowledge and skills through observation, imitation, and modeling.^5,23 In this method, peers educate students under the supervision of an instructor. Peers are students at the same or higher level (one year or more) in terms of academic or clinical experience than the learners.^9,24 Students who experience peer-assisted learning show greater gains in academic performance and skills than those who do not.²⁵ Peer education increases students’ self-confidence, presentation skills, sense of responsibility, critical thinking skills, teamwork, exam scores, and opportunities for Q&A.⁵ According to Edgar Dale’s Cone of Experience, active learning methods—such as teaching others through peer education—allow learners to engage directly with content, which can enhance understanding and retention of knowledge and skills.²⁶ Although some studies have reported the effectiveness of simulation^27-29 and peer education^23,25,30 in improving learners’ knowledge and skills, to the best of researchers’ knowledge, no study has examined the differences between these two educational methods in conducting BPMV. Therefore, this study aimed to investigate the effects of task-trainer simulation and peer education on nursing students’ knowledge, performance, and speed of action in conducting BPMV.

2. Materials and Methods

2.1. Design

This is part of a three-arm randomized controlled trial that was conducted from 2023 to 2024, and it was registered in the TCTR clinical trials system (No. TCTR20231012001, Date: 12 October 2023. It was registered before participant enrollment, which started at 13 December 2023).

2.2. Participants and Sampling

The sample size was estimated to be 51 students using G*Power version 3.0.10, based on the effect size (ES) from previous studies,^29,31 with a 95% confidence interval and 90% power. To account for a 20% dropout rate, 60 sixth-semester undergraduate nursing students from a nursing school in Tehran, Iran, were purposively selected and randomly assigned to three groups: simulation (n=20), peer education (n=20), and control (n=20). The second author generated the random sequence using the online Random Generator software, and a researcher assistant who was blind to the study design assigned students into groups. Inclusion criteria were willingness to participate in the study and completion of the Intensive Care Unit (ICU) theoretical course. Exclusion criteria were unwillingness to continue participating in the study, incomplete completion of questionnaires, and absence from more than one training session. There was no dropout in this study (Flow diagram 1).

Flow Diagram 1.

The study process

2.3. Intervention

First, educational content on the BPMV was prepared based on the nursing student curriculum and approved by two clinical instructors specializing in this field. The educational package included a lesson plan, a timetable, a teaching content booklet, slides prepared in PowerPoint software, and educational scenarios that were the same in the two experimental groups. To facilitate the implementation of the interventions and make learning easier and more effective, students in the two experimental groups were divided into groups of five.

The BPMV was taught to the first group using a task-trainer simulator by one of the researchers and to the second group by two trained peer undergraduate nursing students. Based on the curriculum communicated by the Ministry of Health, Treatment, and Medical Education³² at Iranian medical universities, the BPMV was taught to nursing students in two experimental groups in two 2-hour sessions. The training consisted of two parts: theoretical topics (60 minutes) and practical work (180 minutes). After introducing themselves and the lesson topic, the instructors (the first researcher and peers) explained the importance of the topic and the educational objectives to the students. They taught theoretical topics through interactive lectures and Q&A sessions, using educational aids such as slides prepared in PowerPoint software, to students in the conference hall of the research setting hospital (simulation group) and in the ICU classroom (peer group). For practical training, the necessary tools and equipment were prepared, and their correct functioning was ensured.

The first researcher, a critical care master’s student, taught 20 undergraduate students (five students each day). The researcher taught the students the BPMV in a hands-on manner using a task-trainer simulator and a ventilator. The task-trainer simulator was a silicone adult torso training model with a head, neck, and chest capable of being intubated and connected to mechanical ventilators and performing inhalation and exhalation. After the training, the students individually engaged in practical exercises and worked with the ventilator connected to the task-trainer simulator (Figure 1).

Figure 1.

Teaching students in simulation and peer-assisted learning groups: 1) Researcher teaching the simulation group’s students; 2) Task-trainer simulator connected to the ventilator; 3) Practical training of the simulation group’s students; 4) Peer student teaching other students; and 5) Practical exercise of the peer group’s students

Two eighth-semester nursing students were selected as peers. The criteria for their selection were having the highest overall Grade Point Average (GPA) and the highest score in the critical care course, being interested and willing to collaborate in the study, and having appropriate communication skills. Then, the researchers provided theoretical and practical training to the peers in a two-hour workshop on BPMV, objectives, stages, and research methodology. In the following phase, the researchers evaluated the peers’ knowledge and performance related to BPMV using oral, written, and hands-on assessments. Researchers provided feedback to them during Q&A sessions. Before the intervention, two peer students practiced using the ventilator and MV equipment under the supervision of a third researcher, who corrected any errors. Afterwards, the researchers provided the training package to them. Each peer student instructed ten study participants, five per day, teaching them practical ventilator skills in the ICU with a real patient, under the supervision of the first and third researchers. During the teaching, students’ questions were answered. Each learner was individually trained, with the help of a peer student and under the supervision of researchers (Figure 1).

2.4. Data Gathering

An individual characteristics questionnaire, a knowledge questionnaire, and a skill assessment checklist regarding BPMV were used to collect data.

The individual characteristics questionnaire included age, gender, marital status, previous semester GPA, overall GPA, nursing student work experience, history of participation in webinars, workshops, or virtual groups on the MV.

A knowledge questionnaire was designed by the researchers with 20 four-choice questions in three dimensions. The first dimension was about the physiology and anatomy of respiration (items 1 to 3), the second dimension was about the settings, parameters, and types of modes of the MV device (items 4 to 12), and the third dimension was about the care of the patient under MV (items 13 to 20). A score of one and zero was assigned for each correct and incorrect answer to each question, respectively. The range of the questionnaire score was between zero and 20, with a higher score indicating more comprehensive knowledge (Supplementary 1).

The checklist was designed by the researchers and included 25 items in four dimensions. The first dimension was about the initial preparation of the MV device (items 1 to 3), the second dimension was about making initial adjustments to the MV device based on the clinical scenario or patient conditions (items 4 to 14), and the third dimension was about monitoring and interpreting the information received from the MV device and making the required adjustments (items 15 to 20 and 23 to 25), and the fourth dimension was about management and settings related to device alarms and warnings (items 21 and 22). The checklist was scored on a Likert scale (completely correct performance: 4, somewhat correct: 3, incomplete performance: 2, incorrect performance: 1, and no action taken: 0). The checklist score range is 0 to 100, with a higher score indicating a more comprehensive skill (Supplementary 2).

The face validity of the questionnaire was confirmed by 10 eighth-semester nursing students. The level of difficulty, the appropriateness and ambiguity of the options, and the importance of each item were examined, and the students’ opinions were applied to the questionnaire, and ambiguous items were revised.

The face validity of the checklist was also reviewed by 10 nurses working in the ICU with more than 10 years of experience, and their opinions regarding the importance of the items in the checklist were applied. Then, the formula for calculating the Item Impact Score (IIS) of the item was used to determine the face validity and the importance of each item. Then, the importance score and its impact coefficient were calculated. The optimal IIS was considered to be greater than 1.5.³³ All questions and items that achieved the optimal score were retained in the questionnaire and checklist for further review.

Importance Score = Ʃ (f * Importance) / N

Item Impact Score = F (%) * Importance

To determine the content validity of the knowledge questionnaire and skill checklist, the Content Validity Ratio (CVR) and Content Validity Index (CVI) were used.

To calculate the CVR, ten expert faculty members (eight faculty members with a PhD in nursing and two master’s degree instructors in critical care with over 10 years of teaching experience) were asked to comment on the content of the tools. The experts rated each item in terms of its necessity (necessary, useful but not essential, and not essential). Based on the following formula and using the Lawshe table and the number of expert professors, the minimum acceptable CVR was considered to be .8. In this formula, ne is the number of experts who agree that an item is essential, and N is the total number of experts.³⁴

CVR = [ne - N / 2] / [N / 2]

The CVR of the knowledge questionnaire and skill checklist were .90 and .88, respectively.

To determine the CVI, experts were asked to rate the items for relevance using a four-point Likert scale (fully relevant: 4, relevant but needs revision: 3, needs revision: 2, and irrelevant: 1). Then, the CVI of the questionnaire and checklist was calculated using the Lawshe table with the following formula. Items with a score greater than .79 were retained.³³

CVI Total number of experts/Number of experts who gave the item a score of 3 and 4.

The CVI of the knowledge questionnaire and skill checklist were 0.95 and 0.98, respectively.

The reliability of the questionnaire was assessed by stability over time and internal consistency. The questionnaire was completed by 30 nursing students at two-week intervals, with a Pearson correlation coefficient of .93 and a Cronbach’s alpha coefficient of .7. The reliability of the checklist was calculated using inter-rater agreement and internal consistency. Two raters (two critical care masters with experience teaching critical care courses and over five years of work experience in critical care units) simultaneously and independently assessed the performance of 40 undergraduate nursing students on the BPMV using a designed checklist. The inter-rater agreement coefficient and Cronbach’s alpha coefficient were 0.95 and 0.88, respectively.³⁵

Data were collected before, immediately, and one month after the intervention. To assess the students’ skills, two scenarios were prepared. The students’ speed during the assessment of the desired skill from the beginning to the end of using the MV device was recorded by an identical stopwatch by the researcher. Before the intervention, the knowledge questionnaire was completed by the students in the three groups. Also, the researcher evaluated the skills of the students in the three groups using the skill checklist. For this purpose, a simulated environment was used in the hospital skill lab using a task-trainer simulator and an EDP-TS (Ehya Darman Pishrafteh-Touch Screen) ventilator.

Data were collected first from the control group and then from the experimental groups. During the study, the researchers did not perform any intervention in the control group. After the study, the researchers taught the BPMV to the control group students through the task-trainer simulator.

2.5. Data Analysis

Version 24 of the Statistical Package for the Social Sciences (SPSS, Inc., Chicago, IL, USA) was used to analyze data. The analyst verified data entry and checked the data ranges to improve data quality. Data analysis was performed using descriptive statistical tests (mean, standard deviation (SD), frequency, and percentage) and inferential tests, including Fisher’s Exact Test, Chi-Squared Test, Independent Sample t-Test, Paired t-Test, One-way ANOVA, repeated measures-ANOVA (RM-ANOVA), and ANCOVA. The Kolmogorov-Smirnov test was used to check the normality of the data. The significance level was considered to be P < .05. After Bonferroni correction for post hoc tests (LSD), the significance level was considered to be P < .017. The statistical analyst was unaware of the allocation of students to the experimental and control groups. Specifically, group assignments were coded using neutral identifiers (Group A, B, and C) by one of the researchers who was not involved in data analysis. The data set did not include any identifying information about the interventions, and group identities were only revealed after the completion of the analysis.

3. Results

3.1. Participant Characteristics

The mean age of the students was 22.15 ± 1.41 (range 20-30) years. 58.3% were male, and 85% were single. Their mean overall GPA and the previous semester GPA were 16.93 ± .94 and 17.36 ± .91, respectively. Seventy percent of the participants reported having no prior work experience as nursing students. Some students had experience participating in webinars (15%) and workshops (6.7%), and membership in virtual groups on MV principles (1.7%). Most students (91.3%) had no experience working with a ventilator. Also, 93.3% had no experience working with the devices and equipment of MV. The experimental and control groups had no statistically significant difference in terms of personal characteristics (P > .05) (Table 1).

Table 1.

Comparison of Individual Characteristics of Students in Task-Trainer Simulation, Peer Education, and Control Groups

Groups	Simulation (n=20)	Peer education (n=20)	Control (n=20)	Total	Statistic	df	P-value
Groups	Mean (SD)	Mean (SD)	Mean (SD)	Mean (SD)	Statistic	df	P-value
Age, year	22.25±.97	22.40±2.04	21.08±.95	22.15±1.41	F=.977^a	2	.383
Overall GPA	16.94±.97	16.91±.84	16.95±1.06	16.94±.95	F=.009^a	2	.991
Last semester’s GPA	17.40±.62	17.55±1.13	17.46±.95	17.40±.92	F=.344^a	2	.710

		f(%)	f(%)	f(%)	f(%)
Gender	Male	12(60)	11(55)	12(60)	35(58.3)	X²=.137^b	2	.934
Gender	Female	8(40)	9(45)	8(40)	25(41.7)	X²=.137^b	2	.934
Marital status	Single	17(85)	16(80)	18(90)	51(85)	X²=.841^c	-	.900
Marital status	Married	3(15)	4(20)	2(10)	9(15)	X²=.841^c	-	.900
Student work history	Yes	6(30)	4(20)	8(40)	18(30)	X²=1.950^b	2	.386
Student work history	No	14(70)	16(80)	12(60)	42(70)	X²=1.950^b	2	.386
Participate in the webinar^d	Yes	2(10)	4(20)	3(15)	9(15)	X²=.841^c	-	.900
Participate in the webinar^d	No	18(90)	16(80)	17(85)	51(85)	X²=.841^c	-	.900
Participate in a workshop^d	Yes	1(5)	0(0)	3(15)	4(6.7)	X²=3.111^c	-	.310
Participate in a workshop^d	No	19(95)	20(100)	17(85)	56(93.3)	X²=3.111^c	-	.310
Membership in virtual groups^d	Yes	0(0)	1(5)	0(0)	1(1.7)	X²=1.851^c	-	1.000
Membership in virtual groups^d	No	20(100)	19(95)	20(100)	59(98.3)	X²=1.851^c	-	1.000
Experience working with a ventilator	Yes	2(10)	2(10)	1(5)	5(8.3)	X²=.622^c	-	1.000
Experience working with a ventilator	No	18(90)	18(90)	19(95)	55(91.7)	X²=.622^c	-	1.000
Experience working with mechanical ventilation equipment	Yes	5(25)	1(5)	4(20)	10(16.7)	X²=3.120^b	2	.210
Experience working with mechanical ventilation equipment	No	15(75)	19(95)	16(80)	50(83.3)	X²=3.120^b	2	.210

SD: Standard Deviation; f: frequency; df: degrees of freedom.

^aOne way ANOVA.

^bChi-squared.

^cFisher’s exact test.

^dAbout the principles of mechanical ventilation.

3.2. Students’ Knowledge Scores

The one-way ANOVA showed that the average knowledge score of students in the three groups before the intervention was significantly different (P = .030). So, it was considered a confounder, and the ANCOVA test showed that the average knowledge score of students in the two experimental groups immediately after the intervention was not significantly different (P = .122), but one month after the intervention, the simulation and peer groups were higher than the control group (P < .0001) (Table 2).

Table 2.

Comparison of the Mean Score of Knowledge and Performance Related to the Principles of Basic Mechanical Ventilation and the Nursing Students’ Speed of Action in Three Groups before, Immediately, and One Month After the Intervention

Variable	Groups Stage	Task-trainer simulation Mean (SD) (n=20)	Peer education Mean (SD) (n=20)	Control Mean (SD) (n=20)	Test, df, P-value
Knowledge	Pre-intervention	11.35±2.56	9.00±2.05	9.75±3.52	F=3.731, df=2, P=.030^a
	Post-intervention	17.00±1.48	16.55±1.82	-	F=2.226, df=2, P=.122^b
	Follow-up	17.60±1.60	17.05±2.90	12.80±2.78	F=41.494, df=3, P<.0001^b
	Mauchly’s Test of Sphericity, df, P-value	Mauchly’s W=.802, df=2, P=.137	Mauchly’s W=.642, df=2, P=.018	-	Mauchly’s W=.536, df=2, P<.0001
	Statistics, df, P-value	F=95.093, df=2,P<.0001c^f	F=172.48, df=1.472, P<.0001^c^g	t=-9.072, df=19,P<.0001^d	Time Groupa
	Statistics, df, P-value	F=95.093, df=2,P<.0001c^f	F=172.48, df=1.472, P<.0001^c^g	t=-9.072, df=19,P<.0001^d	F=12.237, df=1.366, P<.0001, η²p=.244^c^g
Performance	Pre-intervention	45.70±10.60	40.05±12.10	41.70±12.22	F=1.240, df=2, P=.297^a
	Post-intervention	90.11±4.67	87.75±5.39	-	t=1.471, df=38, P=.149^e
	Follow-up	87.00±4.61	80.10±5.55	56.75±8.69	F=118.118, df=2, P<.0001^a
	Mauchly’s Test of Sphericity, df, P-value	Mauchly’s W=.542, df=2, P=.004	Mauchly’s W=.310, df=2, P<.0001	-	Mauchly’s W=.441, df=2, P<.001
	Statistics, df, P-value	F=309.202, df=1.371, P<.0001^c^g	F=351.781, df=1.183, P<.0001^c^g	t=-7.319, df=19, P<.0001^d	Time Groupa
	Statistics, df, P-value	F=309.202, df=1.371, P<.0001^c^g	F=351.781, df=1.183, P<.0001^c^g	t=-7.319, df=19, P<.0001^d	F=1.434, df=1.283, P=.244, η²p=.036^c^g
Speed of action (min)	Pre-intervention	18.41±.99	18.66±2.0	18.15±1.55	F=.528, df=2, P=.593^a
	Post-intervention	12.37±1.42	15.26±1.88	-	t=-5.487, df=38, P<.0001^e
	Follow-up	12.24±1.33	14.49±1.64	16.99±1.20	F=56.929, df=2, P<.0001^a
	Mauchly’s Test of Sphericity, df, P-value	Mauchly’s W=.932, df=2, P=.529	Mauchly’s W=.702, df=2, P=.041	-	Mauchly’s W=.924, df=2, P=.230
	Statistics, df, P-value	F=163.120, df=2, P<.0001^c^f	F=57.620, df=1.541, P<.0001^c^g	t=3.775, df=19, P<.001^d	Time Groupa
	Statistics, df, P-value	F=163.120, df=2, P<.0001^c^f	F=57.620, df=1.541, P<.0001^c^g	t=3.775, df=19, P<.001^d	F=11.811, df=2, P<.0001, η²p=.237^c^f

SD: standard deviation; df: degrees of freedom; η²p= partial eta squared (η²p: .01 = small, .06 = medium, .14 = large).

^aOne-way ANOVA.

^bANCOVA (The pre-test knowledge score was considered as a confounder).

^cRM-ANOVA.

^dPaired t-test.

^eIndependent t-test.

^fSphericity Assumed.

^gGreenhouse-Geisser.

An RM-ANOVA showed that the students’ knowledge scores in the task-trainer simulator education (P < .0001) and peer education (P < .0001) groups increased immediately and one month after the intervention. Paired t-test also indicated an increase in the students’ knowledge scores in the control group (P < .0001). Additionally, after controlling for time, the RM-ANOVA revealed a significant difference in the change in average knowledge scores between the simulation and peer education groups, with the simulation group’s scores consistently higher than those of the peer education group at all stages (P < .0001), and the ES was large (η²p = .244), suggesting that a substantial proportion of the variance in knowledge change was attributable to group assignment over time (Table 2). The trend in students’ knowledge scores across the two experimental groups is shown in Figure 2.

Figure 2.

Comparison of changes in knowledge scores in the Task Trainer Simulator and Peer Education groups before, immediately, and one month after the intervention

3.3. Students’ Performance Scores

One-way ANOVA revealed no significant difference in the mean performance scores of students across the three groups before the intervention (P = .297). An independent t-test showed that the mean performance score of the students in the two experimental groups did not differ significantly immediately after the intervention (P = .149), but the one-way ANOVA showed that the performance score of the students one month after the intervention was higher in the simulation and peer education groups than in the control group (P < .0001).

The RM-ANOVA showed that the performance scores of students in the simulation (P < .0001) and peer education (P < .0001) groups increased significantly immediately after the education and remained higher than before the intervention, with a slight decrease one month after the intervention. Paired t-test also indicated an increase in students’ performance scores in the control group (P < .0001). After controlling for the effect of time, RM-ANOVA showed that the change in the mean performance score of students in the simulation and peer education groups was not significantly different (P = .244), and the ES was small (η²p = .036) (Table 2). The trend of changes in students’ performance scores in the two experimental groups is shown in Figure 3.

Figure 3.

Comparison of changes in performance scores in the Task Trainer Simulator and Peer Education groups before, immediately, and one month after the intervention

3.4. The Speed of Students’ Actions

One-way ANOVA showed that the average speed of students in the three groups before the intervention was not significantly different (P = .593). The independent samples t-test indicated that, immediately following the education, students in the task-trainer simulation group demonstrated a higher speed than those in the peer education group (P < .0001). Additionally, one-way ANOVA showed a significant difference in the mean students’ speed of action one month after the education, with lower values observed in both the experimental groups compared to the control group (P < .0001).

RM-ANOVA showed that the average speed of students in the simulation group (P < .0001) and peer education group (P < .0001) was significantly different immediately and one month after the intervention, so that immediately after the intervention, the reduction in the students’ speed of action was significant, and one month after the intervention, with a slight decrease, it was still lower than before the education. Paired t-test also indicated a decrease in the students’ speed of action in the control group (P < .001). After controlling for the effect of time, the RM-ANOVA showed that the change in the average speed of students’ actions in the two educational groups was significantly different (P<.0001), and the ES was large (η²p = .237) (Table 2). The trend in the average time spent on ventilators for students in the two educational groups is shown in Figure 4.

Figure 4.

Comparison of changes in the students’ action of speed scores in the Task Trainer Simulator and Peer Education groups before, immediately, and one month after the intervention

4. Discussion

This study showed that the average scores of knowledge, performance, and speed of action of students in the three groups regarding the BPMV had a significant improvement one month after the intervention, such that it was better in the simulation and peer education groups than in the control group.

In this study, the knowledge, performance, and speed scores of students in the task-trainer simulator group were better than those in the control group. Similarly, Bakhshi et al reported that integrated simulated-practical training with HamiltonC2 software and practical training had similar effectiveness in nursing students’ knowledge of ventilator use; however, the simulation method resulted in greater skill and faster performance of the students.³¹ Hayashi et al also reported that MV management skills were better in internal medicine residents trained in a simulated environment than in residents trained in a real clinical environment. They suggested incorporating MV training using task-based simulators in the student curriculum.²⁷ Dogru et al reported that training with HFS resulted in greater increases in nursing students’ knowledge and skills regarding cardiac auscultation compared to traditional teaching methods.³⁶ Also, Salameh et al showed that training in MV through simulation using an airway management simulator with HFS increased the knowledge and clinical judgment of undergraduate nursing students in the experimental group more than in the control group. They also emphasized the inclusion of teaching topics related to MV through simulation using various models and techniques in the curriculum of undergraduate nursing students.²⁹ Sajjadi et al also reported that the performance and speed of nursing students trained in MV topics using an online simulator were higher compared to students trained using a lecture method immediately after and 10 days after the intervention.⁸ In the present study, the task-trainer simulator closely resembled the real patient in terms of imitating the breathing pattern and performing inhalation and exhalation when connected to the ventilator, and the students performed their practical task with less stress or fear of harming the real patient. This led to an increase in their knowledge, performance, and speed of action. The effectiveness of simulation is often explained through Kolb’s ELT, which holds that learners build knowledge by engaging in realistic clinical tasks and reflecting on their performance. Simulation provides learners with hands-on experiences (concrete experiences), while debriefing sessions promote reflection (reflective observation), conceptual understanding (abstract conceptualization), and the opportunity to apply and test new knowledge (active experimentation).^5,17,37

In the present study, the knowledge, performance, and speed scores of the peer group students increased immediately and one month after education. Similarly, Javaheri-Arasteh et al found that peer-based CPR training increased nursing students’ knowledge and performance more immediately and three months after the intervention than did conventional teaching methods.³⁸ Shaaban et al found that peer education had a significant effect on nursing students’ knowledge and clinical performance regarding hemodialysis, and there was no significant difference in knowledge and performance between the peer group and the group that received conventional training.³⁹ Bahar et al also showed that peer education had a similar effect on nursing students’ psychomotor skills regarding airway management compared to traditional education.²⁴ Likely, the experiences and mental accumulations of the peer students were easily transferred to the learners without causing anxiety or fear of grades. They also felt more intimate and comfortable with their peers than with their instructors in performing practical tasks, which led to an increase in their level of knowledge, performance, and speed of action. Peer education in health professions education is rooted in social learning and constructivist perspectives, where learners actively construct knowledge through social interaction, observational learning, and collaborative practice, rather than passive reception of information, and is supported by evidence that structured peer-assisted learning enhances student engagement and performance across health disciplines.^5,40,41 Social constructivism emphasizes that effective teaching and learning depend on interpersonal interaction and dialogue, with an emphasis on constructing understanding through discussion. Individuals who share common interests and a similar language background gain advantages when they learn from more experienced peers.⁴¹ Therefore, it seems that using peers to overcome the large number of students in clinical skills training to reduce the workload of instructors is useful and effective.

In this study, no intervention was performed in the control group by the researchers, but this group also learned skills related to BPMV from their instructor during the internship period, so an increase in the level of knowledge and improvement in their performance and speed of action was also expected.

In this study, the baseline knowledge score was significantly different among the three groups, and its confounding effect was controlled by the ANCOVA test. Findings showed that teaching the BPMV through simulation and peer education could not cause a statistically significant difference in the level of knowledge and performance of students immediately after the intervention, but one month after the intervention, the knowledge and performance of the simulation group were better than those of the peer education group. ELT, cognitive load theory, and social constructivism help explain the rapid effects of active learning strategies. Both SBL and peer education engage students in active and participatory processes that promote immediate knowledge acquisition and skill development.⁵ According to Kolb’s ELT, learning occurs through four-stage cycles,^5,37 which are facilitated in both educational methods. Also, from a cognitive perspective, both methods may impose comparable levels of intrinsic and germane cognitive load, thereby enabling efficient processing and integration of new information in the short term. Additionally, from a social constructivist lens, both methods create interactive environments in which students actively engage with others, exchange ideas, and negotiate meaning.⁵ SBL provides immersive and interactive settings that support experiential engagement⁴² and rapid acquisition of clinical competencies through collaborative problem-solving and guided reflection.⁵ Similarly, peer education promotes learning through mechanisms such as cognitive congruence, active explanation, and mutual feedback, allowing learners to process and internalize information effectively through interaction with peers.⁵ These socially mediated processes enable learners to construct understanding at a similar pace in the short term, which may explain the lack of significant differences between the two groups immediately after the intervention. However, differences between educational methods may become more apparent over time, as SBL may better support knowledge and skill retention and transfer due to its contextual fidelity, whereas peer education may differentially influence conceptual understanding and collaborative reasoning processes.

Also, immediately and one month after the intervention, students in the simulation group had better performance speed than students in the peer and control groups. In other words, the simulation group experienced a greater mean difference in performance speed than the other groups. The mentioned educational effect on the level of knowledge, performance, and speed of action of nursing students regarding the BPMV one month after the intervention indicated the durability and depth of students’ learning in these methods, which was consistent with the classification of educational methods in the Miller-Vanderlooten pyramid and the learning levels of the Edgardil pyramid. To teach skills related to MV principles using the simulation and peer method, the student first solves the problem and combines the learned theoretical knowledge with practical knowledge in their mind, and then puts the learned skill into practice, and while implementing the practical procedure, she/he can receive feedback from the trainer and correct her/his possible errors with their help.^5,26 Moradian et al mentioned that strategies such as curriculum reform, increasing students’ critical thinking skills, and using different teaching methods are effective in reducing the gap between theoretical knowledge and practice.⁴³ The high fidelity of the task-trainer simulator and the lower fear and anxiety of students in performing scenarios on the simulators may make this method more effective than the peer method.

Totally, the interactive and active nature of task-trainer simulator training and peer-to-peer methods, dividing students into smaller groups, reminding them of theoretical topics during practice, the presence of the peer student and researcher until the end of the students’ practice, conducting evaluation, receiving feedback, and resolving student mistakes, learning at their own pace were among the factors that contributed to the effectiveness of these methods. Therefore, the use of these methods by clinical instructors can lead to an increase in the quality and depth of learners’ learning regarding skills related to the BPMV. Also, the use of peers for training can be a factor in reducing the workload of clinical instructors. By selecting and using active student-centered methods, clinical instructors can reduce the gap between theory and practice and help nursing students enter the nursing profession and become motivated and competent nurses who have sufficient knowledge, skills, and appropriate speed of action when performing practical procedures.

4.1. Strengths and Limitations

One of the strengths of this study was the design of a questionnaire and a checklist to measure participants’ knowledge and performance regarding BPMV. While some studies have employed standardized tools to assess MV competencies, there is no comprehensive instrument specifically tailored to nursing students’ learning outcomes in MV across both cognitive and psychomotor domains. In this study, researcher-developed tools were designed to align closely with the study’s learning objectives, curriculum content, and the specific competencies targeted in both educational methods. This approach is consistent with recommendations in educational measurement literature, which emphasize the importance of content alignment and contextual relevance when assessing intervention outcomes.⁴⁴ These tools demonstrated acceptable CVI, CVR, and reliability, supporting their appropriateness for this context. Further psychometric evaluation would strengthen the generalizability of these tools. Future studies are recommended to adopt standardized assessment frameworks to enhance cross-study comparability.

One of the limitations of this study was the possibility of information transfer between the three groups of students. To reduce this limitation, students were asked not to share the information they obtained with others until the end of the study. Also, data were first collected from the control group, then the simulation group, and finally the peer education group. This could expose the study to the risk of time bias.

In addition, the control group was not really a pure control group, because these students also learned related content during the internship. Another limitation was the inability to blind the assessor, groups, and individuals. Additionally, this study was conducted in a nursing school that this “single-center” design limits the generalizability of findings. So, it is suggested to conduct future multi-center triple-blind randomized controlled trials. Another limitation was that some parameters, such as Pressure Support and Trigger, were not sensed by the task-trainer simulator. To overcome this limitation, these parameters were explained to the students by the instructor in a demonstration and verbally.

Although the researchers tried to control cofounders as much as possible, the difference between mentors (the researcher vs. peer student) and setting (practice with a simulator vs. practice in the ICU with a real patient) should be considered in the interpretation of findings. So, the final difference between groups may come from factors such as teacher identity, practice setting, and supervision style, not only from the educational method itself.

Totally, this study showed that both task-trainer simulation and peer education methods are useful and effective to increase learning outcomes, and maybe simulation has some advantage in this setting, but it cannot clearly confirm that the difference is caused only by the teaching method.

5. Conclusion

In summary, this study showed that teaching BPMV using task-based simulator-assisted training and peer education can increase the level of knowledge, skills, and speed of action of nursing students. Since clinical educators and instructors are always looking for effective and efficient methods to teach the principles of MV, the use of these inclusive, active, and self-directed educational methods can play an effective role in improving teaching-learning outcomes for nursing students. Nursing students need to acquire sufficient knowledge, skills, and speed of action in this regard to provide quality care services to patients requiring MV, prevent unwanted complications, and help accelerate patient health improvement and reduce hospital costs. Integrating these methods into nursing curricula may enhance students’ learning outcomes, supporting more active, student-centered educational experiences.

Future studies should include larger and more diverse samples across multiple nursing schools to enhance the generalizability of findings. Also, long-term follow-up assessments are recommended to evaluate the retention of MV knowledge and skills beyond the immediate post-intervention period. Examining learner-related outcomes, including self-efficacy and satisfaction, as well as the cost-effectiveness of these educational methods, could provide valuable insights for curriculum development.

Footnotes

Acknowledgments

The authors would like to thank all the students who participated in this study. The authors also appreciate the officials of Aja University of Medical Sciences.

ORCID iD

Zahra Farsi

Ethical Considerations

This study was approved by the Research Ethics Committee of Aja University of Medical Sciences (ID: IR.AJAUMS.REC.1402.091, Date: 07/17/2023). The researchers also adhered to the principles of the Committee of Publication Ethics.

This research was carried out with the supervision of the Vice Chancellor for Research and Technology at Aja University of Medical Sciences from the approval of the proposal until the study’s completion, and it was reviewed and validated by two peer reviewers. The results were communicated to the study setting and Aja University of Medical Sciences officials.

Consent to Participate

The provisions of the Declaration of Helsinki were observed, including explaining the objectives to the students and obtaining written informed consent to participate in the study, voluntarily entering the study and withdrawing from it at any time, and maintaining the confidentiality of the collected data. Additionally, the study observed honesty in recruiting students, collecting and analyzing data, providing study results to students upon request, and acknowledging them, as well as all those who collaborated.

Consent for Publication

Written informed consent was obtained from students for the publication of their photographs in relevant journals and books.

Author Contributions

ZF contributed to the conceptualization, planning, data analysis, interpretation, and writing the manuscript. AR contributed to the planning, data collection, and writing the first draft of the manuscript. RM was involved in the data collection and interpretation. SAS contributed to the data interpretation. All authors collaborated in the study, and they read and approved the final manuscript.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research is part of a study that was approved by Aja University of Medical Sciences (No. 97002518) and received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. This study was conducted for academic purposes and did not receive any support from organizations subject to international sanctions. Aja University of Medical Sciences was not involved in the design, execution, analysis, or reporting of the findings.

Declaration of Conflicting Interests

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

Data Availability Statement

The data that support the findings of this study are available in Persian from the corresponding author upon reasonable request.*

Clinical Trial Number

No. TCTR20231012001, Date: 04/29/2024.

AI Statement

Artificial intelligence (AI) was used to improve the manuscript’s language; the authors reviewed and verified all outputs for accuracy and integrity. The researchers had full oversight of the use of AI output throughout the study process.

References

Tsukuda

Fukuda

Shogaki

Miyawaki

. Validity and reliability of a short form of the questionnaire for the reflective practice of nursing involving invasive mechanical ventilation: A cross-sectional study. Nursing Reports. 2023;13(3):1170-1184. doi:10.3390/nursrep13030101.

Cronin

Camporota

Formenti

. Mechanical ventilation in COVID 19: A physiological perspective. Experimental physiology. 2022;107(7):683-693. doi:10.1113/ep089400.

Mariam

Hajra

Zani

Syed Muhammad Yousaf

Iqra

Maryyam

. Effect of Nurse-Led Education on Knowledge and Practice of Nurses Regarding Ventilator Modes. Journal of Health, Wellness and Community Research. %05/%30. 2025;3(5):e291. doi:10.61919/4zwpd532.

Nikravan

Tayar

Shiri

Shaarbaf

. Training Needs Assessment of Emergency Nurses Regarding the use of Mechanical Ventilation. Journal of Critical Care Nursing. 2019;12(1):24-33. https://jccnursing.com/article-1-433-en.html

Farsi

Azarmi

. Teaching-Learning Methods in Medical Sciences (Active Learning Methods). Heydari Publications; 2025. https://www.researchgate.net/publication/391646012_Teaching-Learning_Methods_in_Medical_Sciences_Active_Learning_Methods

Sadati

Hannani

Sarraf

Azadi

. Comparison of task-based learning and current method of teaching on the knowledge and practice skill of surgical technologists in Iran University of Medical Sciences. Education Strategies in Medical Sciences. 2020;13(3):213-219. https://edcbmj.ir/article-1-2161-en.html

Sajjadi

Soltani

Moghadam

Moghbel

. Comparing the effect of online simulator with lecture training on nursing students’ satisfaction and performance in mechanical ventilation issues. Sci J Nur Midwifery Paramedical Faculty. 2020;6(1):95-105. https://sjnmp.muk.ac.ir/article-1-300-fa.html

Khozaei

Zare

Moneghi

Sadeghi

Taraghdar

. Effects of quantum-learning and conventional teaching methods on learning achievement, motivation to learn, and retention among nursing students during critical care nursing education. Smart Learning Environments. 2022;9(1):18. doi:10.1186/s40561-022-00198-7.

Salehi

Parchehbafieh

Mashoof

Safavi

Nekavand

. Nursing students' satisfaction with peer group clinical teaching. Medical Science Journal of Islamic Azad University-Tehran Medical Branch. 2022;32(4):445-453. doi:10.52547/iau.32.4.445.

10.

Rani

RJRP

Muthukrishnan

AEB

Nomani

ICL

, et al. Undergraduate Nursing Students’ Knowledge and Attitude: Care of Patients of Mechanical Ventilators. Indian Journal of Science and Technology. 2023;16(11):795-802. doi:10.17485/IJST/V16I11.599.

11.

Zainib

Rattani

Asif

Maqbool

HMA

. Nurses' knowledge, attitude, and perceived barriers toward protective lung strategies of pediatrics mechanically ventilated patients in a tertiary care hospital in Pakistan. Acute Crit Care. 2025;40(1):128-135. doi:10.4266/acc.004761.

12.

Aghal

Moradian

Bahrami

Saeed

. Clinical Competence of Nurses in Mechanical Ventilation; Design and validation of Objective Structured Clinical Examination. Journal of Critical Care Nursing. 2023;16(3):23-30. doi:10.30491/JCC.16.3.23.

13.

Kiani

Yazdankhahfard

Ravanipour

Bagherzadeh

Hajinejad

. The Effectiveness of Cardiopulmonary Resuscitation (CPR) Training of patients with covid- 19 Using the Flipped Classroom and the Jigsaw Method on the Level of Nursing Students` Knowledge of Bushehr University of Medical Sciences. Research. Research in Medical Education. 2023;15(1):31-39. doi:10.32592/rmegums.15.1.31.

14.

Dorri

Ashghali Farahani

Maserat

Haghani

. Comparison of role play and conventional training methods on long-term learning of nursing students. Orginal. Development Strategies in Medical Education. 2020;7(1):61-77. doi:10.29252/dsme.7.1.61.

15.

Lei

Y-Y

Zhu

YTR

Cui

X-S

. Effects of High-Fidelity Simulation teaching on nursing students' knowledge, professional skills, and clinical ability: A Meta-analysis and systematic review. Nurse Education in Practice. 2022;60:103306. doi:10.1016/j.nepr.2022.103306.

16.

Tibingana-Ahimbisibwe

Willis

Catherall

Butler

Harrison

. A systematic review of peer-assisted learning in fully online higher education distance learning programmes. Open Learning: The Journal of Open, Distance and e-Learning. 2022;37(3):251-272. doi:10.1080/02680513.2020.1758651.

17.

Andersson

Wihlborg

Kängström

Norberg-Boysen

Sterner

. Simulation as a foundation for experiential learning among ambulance nursing students: A qualitative observation study. Nurse Educ Today. 2025;152:106759. doi:10.1016/j.nedt.2025.106759.

18.

Vitale

Barsuk

Cohen

, et al. Simulation-based mastery learning improves critical care skills of advanced practice providers. ATS Sch. 2023;4(1):48-60. doi:10.34197/ats-scholar.2022-0065oc.

19.

Arabpur

Farsi

Butler

Habibi

. Comparative effectiveness of demonstration using hybrid simulation versus task-trainer for training nursing students in using pulse-oximeter and suction: A randomized control trial. Nurse Educ Today. 2022;110:105204. doi:10.1016/j.nedt.2021.105204.

20.

Havaldar

Krishna

Sampath

Paramasivam

. Simulation training in hemodynamic monitoring and mechanical ventilation: an assessment of physicians’ performance. Indian Journal of Critical Care Medicine: Peer-reviewed, Official Publication of Indian Society of Critical Care Medicine. 2020;24(6):423-428. doi:10.5005/jp-journals-10071-23458.

21.

Imaz

Eteraf-Oskouei

Najafi

. Evaluation of pharmacy professional ethics in drugstores and its improvement strategies from the viewpoint of students and faculty members of Tabriz School of Pharmacy. Iranian Journal of Medical Ethics and History of Medicine. 2018;11:65-79. https://ijme.tums.ac.ir/article-1-5997-en.html

22.

Singh

Restivo

. Task Trainers in Procedural Skills Acquisition in Medical Simulation. In: StatPearls [Internet]. Treasure Island (FL). StatPearls Publishing; 2025. PMID: 32644351.

23.

Bahar

Kocacal

Maras

. Impact of the peer education model on nursing students' anxiety and psychomotor skill performance: A quasi-experimental study. Nigerian Journal of Clinical Practice. 2022;25(5):677-682. doi:10.4103/njcp.njcp_1905_21.

24.

Safari

Yazdanpah

Mahmoudi

Yaghobean

. Comparing students’ rate of learning through lecturing and peer group teaching and study their viewpoints. Research in Medical Education. 2018;10(3):24-34. doi:10.29252/rme.10.3.24.

25.

Brierley

Ellis

Reid

. Peer assisted learning in medical education: A systematic review and meta analysis. Medical Education. 2022;56(4):365-373. doi:10.1111/medu.14672.

26.

Masters

. Edgar Dale's Pyramid of Learning in medical education: Further expansion of the myth. Medical education. 2020;54(1):22-32. doi:10.1111/medu.13813.

27.

Hayashi

Sousa

Garcia

Macedo

Ferreira

. Simulation-based Assessment to Measure Proficiency in Mechanical Ventilation among Residents. ATS scholar. 2022;3(2):204-219. doi:10.34197/ats-scholar.2021-0130oc.

28.

Mohammadi

Mojtahedzadeh

Najimi

Mohammadi

. Virtual Reality-Based Simulation in Medical Education: Development Strategies and Policies. Policy brief. Iranian Journal of Culture and Health Promotion. 2024;8(2):184-188. doi:10.22034/8.2.8.

29.

Salameh

Ayed

Lasater

. Effects of a complex case study and high-fidelity simulation on mechanical ventilation on knowledge and clinical judgment of undergraduate nursing students. Nurse Educator. 2021;46(4):E64-E69. doi:10.1097/nne.0000000000000938.

30.

Mehdipour-Rabori

Nematollahi

Bagherian

. Effect of classmate as peer-led education on clinical performance: A mixed-method study. Journal of Education and Health Promotion. 2021;10:58.

31.

Bakhshi

Hosseini

Nasiriani

Javadi

. Comparison of the effect of practical training and integrated simulated-practical training on the knowledge and skills of using ventilator in nursing students. Clinical Simulation In Nursing. 2025;105:101767. doi:10.1016/j.ecns.2025.101767.

32.

Iranian Supreme Council of Planning of Medical Sciences . Curriculum of continuous undergraduate nursing course of the Ministry of Health, Treatment and Medical Education approved by the 85^th meeting of the Supreme Council of Planning of Medical Sciences of Iran in 2022. https://hcmep.behdasht.gov.ir/uploads/369/doc/KP_Parastari14012.pdf.Update.

33.

Ebadi

Zarshenas

Rakhshan

Zareiyan

Sharif Nia

. Principles of Scale Development in Health Sciences. Jame-e-Negar; 2017.

34.

Ayre

Scally

. Critical values for Lawshe’s content validity ratio Ayre C, Scally A. Meas Eval Couns Dev. 2013;47:79-86. doi:10.1177/0748175613513808.

35.

Razaghi

Farsi

Sajjadi

Momen

. Design and determining the validity and reliability of skill assessment checklist related to principles of basic mechanical ventilation in Aja nursing students. Military Caring Sciences. 2025;12(2):105-118. doi:10.22034/12.2.2.

36.

Doğru

Aydın

. The effects of training with simulation on knowledge, skill and anxiety levels of the nursing students in terms of cardiac auscultation: A randomized controlled study. Nurse Education Today. 2020;84:104216. doi:10.1016/j.nedt.2019.104216.

37.

Kolb

. Experiential Learning: Experience as the Source of Learning and Development. Englewood Cliffs, NJ: Prentice Hall; 1984.

38.

Javaheri Arasteh

Najafi Ghezeljeh

Haghani

. Effects of peer-assisted education on the knowledge and performance of nursing students in basic cardiopulmonary resuscitation. Iran Journal of Nursing. 2018;31(115):6-19. doi:10.29252/ijn.31.115.6.

39.

Shaaban

Mohamed

TMS

. Effect of Peer Teaching on nursing students performance of clinical training in hemodialysis unit. Port Said Scientific Journal of Nursing. 2020;7(3):81-96. doi:10.21608/PSSJN.2020.45023.1051.

40.

Iwasiw

Goldenberg

. Peer teaching among nursing students in the clinical area: effects on student learning. J Adv Nurs. 1993;18(4):659-668. doi:10.1046/j.1365-2648.1993.18040659.x.

41.

Woods

Lotherington

Steffensen

Theophilos

. Peer-assisted learning for first-year nursing student success and retention: findings from a regional Australian study. Nurs Rep. 2025;15(7):252. doi:10.3390/nursrep15070252.

42.

Sun

Jiang

Dong

Feng

Shuai

. The evolution of simulation-based medical education research: From traditional to virtual simulations. Heliyon. 2024;10(15):e35627. doi:10.1016/j.heliyon.2024.e35627.

43.

Moradian

Nehrir

Khaghanizade

. Strategies for reducing theory-practice gap in nursing education: An integrative review. Journal of Nursing Education. 2019;8(2):58-64. https://jne.ir/article-1-930-en.html

44.

DeVellis

. Scale Development: Theory and Applications. 4th ed. Sage Publications; 2016.