Content area
Aim
This study aims to evaluate the impact of virtual gaming simulation on perceived learning outcomes and self-directed learning ability among psychiatric nursing students, offering an innovative approach to address the limitations of traditional clinical training in psychiatric nursing education.
Background
Psychiatric nursing poses unique challenges due to complex patient interactions and limited student experience, often leading to anxiety. Virtual gaming simulations (VGS) replicate clinical experiences, enhancing students' knowledge, confidence, and competence, particularly in mental health care, further boosting engagement, motivation, and self-directed learning.
Design
A quasi-experimental design with a pre-and post-test non-equivalent control group at Menoufia University, Egypt.
Methods
We implemented the "Therapeutic Communication and Mental Health Assessment" VGS to simulate real-world patient interactions. Data collection involved the Gameful Experience Scale (GAMEX), Perceived Learning Scale (CAP), and Self-Directed Learning Instrument (SDLI) for 247 fourth-year students, divided into control and study groups. Data were analysed using chi-square, t-tests, and generalized estimating equations (GEE).
Results
VGS significantly enhanced learning outcomes and had a profound impact on students, with significant improvements observed across all GAMEX dimensions and a highly significant increase in perceived learning (p < 0.001).
Students demonstrated enhanced self-directed learning skills post-VGS.
Conclusion
VGS is a transformative teaching strategy in psychiatric nursing education. It augments traditional simulations with gamification and fosters the development of clinical skills while paving the way for future research and incorporating virtual reality technology.
1 Introduction
As one of the most crucial professions in health services, nursing plays an essential role in the continuity of health care. Based on the quality requirements of higher education in Egypt, nursing students are required to achieve higher skills and competencies in critical thinking, problem-solving, and analysis. These skills are vital for their role in complex and dynamic clinical environments, and they prepare them for the complexities of evolving healthcare trends. They also focus on fostering their higher-order thinking skills ( Tawfik et al., 2020). To achieve this, the incorporation of innovative teaching methods has become increasingly evident ( O’Flaherty and Costabile, 2020).
Nursing students practice in a complex and dynamic environment that can sometimes be very stressful, challenging, and even hostile; they are often faced with rapidly evolving situations that require quick and clear-minded decision-making. Psychiatric students may experience difficulties as they must interact with patients with mental disorders and behavioral problems for the first time, which may make the clinical area an unsafe environment for students. Therefore, it can cause anxiety and fear in students and can be challenging ( Titus and Ng’ambi, 2014; Gelu and Muza, 2011). Consequently, practical experience in psychiatric nursing is low compared with other types of clinical practice as it is not always practical ( Happell and Gaskin, 2013).
2 Background
Technological developments in the twenty-first century significantly impact people, the healthcare industry and society, affecting many facets of life ( Sarıkoç, 2016; Adib-Hajbaghery and Sharifi, 2017; Gedik, 2020; Goktas et al., 2023). Notably, a change from conventional teaching methods to more innovative methods has become necessary due to the rapid advancement of information and communication technologies, especially in the fields of methods due to the rapid advancement of information and communication technologies, especially in healthcare and education. As a result, educators, instructors, and students must adjust to these developments by adopting new approaches to instruction and learning ( Ulupınar and Toygar, 2020). Nursing education has significantly benefited from incorporating modern technologies such as artificial intelligence, virtual reality, simulation and the Internet of Things, which represents a networked environment where people, devices and services ( Siegel et al., 2017) are linked together, enabling them to sense, exchange data, derive insights and operate across broad contexts ( Tedeschi et al., 2017; Office USGA, 2017).
Using simulation, nurse educators can provide high-quality clinical experiences that prepare students to care for mental health patients. Simulation improves students’ knowledge acquisition, confidence, clinical competence, and communication skills when providing mental healthcare ( Oh et al., 2015; Alexander et al., 2018) and helps to address any limitations related to the clinical setting (including the availability of patients, patients’ safety, security issues, etc.) ( Verkuyl et al., 2021). Recent reviews and evidence-based practice highly recommend virtual simulation, which has also been used as a learning approach since COVID-19 started ( Cant et al., 2022). Virtual Simulations offer a safe, engaging, and accessible learning environment that replicates clinical practice and can be as effective or superior to traditional in-person simulations in enhancing safety, clinical reasoning, engagement, and procedural and team skills ( Foronda et al., 2020; Kononowicz et al., 2019). In virtual environments, users can solve problems, learn from mistakes, and receive constant feedback ( Verkuyl et al., 2018), and the ability to repeat exercises as many times as a learner wishes in a safe environment ( Padilha et al., 2019). Therefore, the virtual simulation approach may help address the learning gap by providing ongoing clinical training to students when face-to-face is impossible.
Virtual simulation could eliminate the disadvantages of inadequate time, resources, and opportunities to practice in clinical fields. Adding gaming features can enrich the virtual simulation of a specific clinical experience. Thus, Virtual gaming simulations are ‘serious games’ applications that use computer game-derived technologies and design strategies to achieve educational goals ( Lynch-Sauer et al., 2011). Generating a simulated experience by incorporating game elements could effectively enhance the clinical learning experience for nursing students ( Verkuyl et al., 2019), This approach helps students become more actively engaged in the learning environment, increases their motivation, sparks interest in new learning concepts and positively influences their behavior in class ( Lima-Serrano et al., 2021; Park, 2019; Koo and Lee, 2017; Pozo Sánchez et al., 2020).
Gamification which is defined as the application of game design element to non-game context ( Sebastian et al., 2011) promotes interaction and collaboration, supporting the development of students’ communication skills and personal competencies ( Kang et al., 2018), with the added value of enhancing the learning experience by increasing motivation, enjoyment, feedback, teamwork and active learning ( Ferriz-Valero et al., 2020). A positive user experience with VGS was reported among nursing students, with player feedback indicating that the current VGS design aligns with the ten gaming experience consequences identified in the Player Experience Inventory ( Díaz-Ramírez, 2020). Nursing educators must actively support students in developing self-directed learning, a critical competency that enables nurses to seek, analyse, and apply information effectively in today’s dynamic healthcare environment ( Verkuyl et al., 2022). Incorporating web-based simulation in education allows students to transition from passive information receivers to active knowledge builders through interactive learning experiences to foster self-directed learning ( O'Shea, 2003).
2.1 Significance of virtual gaming simulation in psychiatric nursing
Integrating technology, such as VGS, can transform nursing education and better prepare a new generation of students to meet the challenges of future healthcare systems. Thus, the use of virtual reality in nursing education has the potential to shape and improve the self-directed learning abilities of nursing students.
VGS in nursing education has the potential to significantly enhance self-directed learning by providing immersive, hands-on experiences that promote critical thinking and problem-solving. We must prepare nursing students for the complexities of evolving healthcare trends, ensuring they have the necessary skills and knowledge of emerging healthcare technologies. Evidence proposed that gamification in HE can increase knowledge acquisition, develop skills, improve attitudes, and stimulate critical thinking, consequently expanding student experience and satisfaction ( Díaz-Ramírez, 2020; Kang et al., 2020a).
In the current context of nursing education in Egypt, limited access to psychiatric clinical environments and restricted patient interaction hinder the adequate development of essential competencies in students. Therefore, exploring innovative solutions, such as virtual gaming simulation is critical. These simulations can provide safe and practical learning experiences, enabling students to develop vital skills for their future professional practice. This study aims to evaluate the impact of virtual gaming simulation on perceived learning outcomes and self-directed learning ability among psychiatric nursing students, using a quasi-experimental design with 247 participants.
3 Material and methods
3.1 Research design and setting
A quasi-experimental research design with a pre-post-test non-equivalent control group was used to achieve the study's goal. The study was conducted at the Faculty of Nursing, Menoufia University, Egypt. The study's target population represented fourth-year nursing students in both semesters of the academic year 2022–2023. To avoid the spread of the study effect, the researchers assigned the first-term students (September 2022) as the control group and the second-term students (February 2023) as the study group. We employed the convenient sampling technique to recruit the required sample size of 247 nursing students (based on a significance level α = 0.05, two groups, an effect size of 0.50 and a power of 0.80 for an independent sample t-test using G*Power 3.1.4. This study used the effect size interpretation thresholds proposed by Cohen ( Gómez-Urquiza, 2019) for the independent sample t-test. Sample size: 124 students in the study group (52 Male, 72 Female) from second term 4th year and 123 control group (57 Male and 66 Female) from second-term 4th-year psychiatric nursing students).
Research Tools: Three tools were used to gather information for this investigation: The Gameful Experience Scale (GAMEX). The GAMEX scale, a validated and widely used tool that was used to assess the students' experiences with gamification from various dimension. The scale was initially developed by Cohe, (1988). and translated into Arabic by the researchers. It consists of 27 items classified into six dimensions: Enjoyment (items 1–6), Absorption (items 7–12), Creative thinking (items 13–16), Activation (items 17–20), Absence of negative effects (items 21–23) and Dominance (items 24–27). Each item is measured on a Likert-type scale ranging from 1 (never) to 5 (always)
Perceived Learning Scale (CAP): It is a self-report instrument developed by Epp Mann et al. (2018). to assess learning across three different domains: cognitive, affective, and psychomotor. It was translated into Arabic by the researchers. It consists of 9 items representing perceived learning across all three of Bloom's ( Rovai et al., 2009) domains: cognitive learning includes items (1,2, and 5), affective learning consists of items (4,6, and 9), and psychomotor learning includes items (3,7, and 8) within online and face-to-face educational environments. Each item is measured on a 7-point Likert scale ranging from 0 (not at all) to 6 (very much). Total scores range from 0 to a maximum of 54. Interpret higher CAP scores as higher perceptions of total learning.
The Self–Directed Learning Instrument (SDLI): This instrument was developed and validated by Cheng and her colleagues ( Bloom, 1956) and translated into Arabic by the researchers. It was developed to assess the self-directed learning (SDL) abilities of nursing students. It consists of 20 items categorized into four dimensions of SDL learning: motivation (6 items), plan and execution (6 items), self-monitoring (4 items), and interpersonal relationships (4 items). The items are based on a five-point Likert-type scale ranging (from 1 strongly disagree to 5 strongly agree) and represent a score range from 20 to 100, with higher scores suggesting higher levels of SDL abilities.
As for the validity of all the used instruments , the face and content validity of the translated instruments were tested by a peer review committee of five bilingual (English/ Arabic) academic professors who are experts in psychiatric health nursing, family and community health nursing, and psychiatric medicine to investigate their clarity, relevance, and comprehensiveness. Based on the committee's feedback, minor modifications were incorporated (changing very few Arabic words in the final version to be more familiar to the students). The reliability of the instruments was investigated using test-retest reliability with a two-week interval and internal consistency. The Cronbach Alpha reliability for GAMEX was 0.93, and the composite reliability coefficient was 0.95. Meanwhile, for CAP, it was reliable at 0.86, and for SDLI, it was 0.90.
Additionally, a pilot study was carried out to assess the feasibility and clarity of the instruments and to determine the average time required for their completion. The obtained favourable feedback underscored the appropriateness and effectiveness of the study tools.
3.2 Data collection and implementation of VGS
Permission to conduct the study was obtained from the Research and Ethics Committee of the Faculty of Nursing, Menoufia University, Egypt. After obtaining written informed consent from all participating students and assuring them about the confidentiality of the collected data. The study was conducted from the beginning of the academic year (2022–2023), October 2022, to the end of May 2023.
For the control group, printed copies of the CAP and SDLI questionnaires were administered at the beginning and at the end of the first semester to students who agreed to participate in the study. Written informed consent was obtained from all participants. The control group did not receive any alternative intervention; rather, they were exposed to the traditional learning environment, which included lectures, classroom discussions and case study–based teaching. Traditional teaching methods were chosen as the control condition because they represent the standard instructional approach in the curriculum, allowing for a valid comparison of the effectiveness of the game-based virtual simulation intervention against routine educational practice.
The researchers adopted the online learning resource titled “Therapeutic Communication and Mental Health Assessment: Knowledge and Practice” (
3.3 Preparation and orientation
Before the intervention, permission to use the college’s computer labs was obtained. The researchers conducted 30-minute orientation sessions to all second-term psychiatric and mental health nursing students (N = 268) to explain the aim of the study and to encourage participation. Of these, 247 students agreed to participate and signed a written informed consent. Students were then allocated into groups in a non-random manner, based on computer lab capacity and availability, as well as students’ academic schedules.
Each group was supervised by one of the researchers, who provided a detailed structured orientation session to ensure consistency.
The orientation session included an overview of the aim of the study and its significance. Then participated students were asked to complete the printed pre test questionnaires before starting the module under researcher supervision to ensure standardized conditions. Students were allowed to complete it at their own pace, typically within 20–30 min. Responses were linked to coded identifiers to match pre- and post-test data while maintaining confidentiality. After that a detailed description of the “Therapeutic Communication and Mental Health Assessment” resource. This part included a structured overview of the learning resource, covering its purpose, intended learning outcomes, the number and content of modules and the procedures for navigating the modules.
3.4 Description of the learning resource
The learning resource is composed of four modules: The first three modules compromise of a well-designed and prepared material that focus on: ( Tawfik et al., 2020) therapeutic relationships and communication techniques; ( O’Flaherty and Costabile, 2020) assessment of depression; and ( Titus and Ng’ambi, 2014) domestic violence. The content was developed by four mental health experts in collaboration with an e-learning strategist, ensuring accuracy and consistency. The content was aligned with the learning outcomes of the psychiatric and mental health nursing course. To maintain quality across groups, all modules were delivered in their standardized form, and researchers did not alter the content. The modules integrate short video lectures, case-based examples, self-assessment quizzes and demonstration videos of therapeutic communication skills.
Virtual Gaming Simulation (VGS): After completing the first three modules, students progressed to the VGS. In this simulation, they interact with a standardized patient through a series of decision-making scenarios. Students watch short video clips and then choose one of several possible responses. Each choice immediately leads to a new video showing the consequences of that decision. Correct choices allow progression, while incorrect responses trigger rapid feedback and a chance to try again. The simulation continues until the student completes a virtual home visit. At the end, the system generates a brief report summarizing the student’s decisions and linking them back to the relevant module content.
3.5 The implementation of the "therapeutic communication and mental health assessment”
To ensure equitable access, all students used the resource in the computer labs under the supervision of the researchers. This arrangement prevented technical barriers and guaranteed that each student had the opportunity to complete the modules and the VGS. Researchers were present throughout the sessions to answer questions and provide technical support if needed.
Students were instructed to complete the modules thoroughly before attempting the game. On average, completing the modules required 45–60 min, while the game took an additional 30–45 min. No strict time limits were imposed; however, all activities were completed within the scheduled lab sessions. Students’ interactions with the simulation were not formally recorded for analysis; instead, the evaluation relied on their pre- and post-test questionnaire scores.
3.6 Evaluation and debriefing
Immediately after finishing the VGS, a short debriefing session was conducted where students shared their experiences and clarified any difficulties with the assigned researcher. Following this, each student completed the printed post-test questionnaire, again under supervision, to ensure consistency and minimize external influence.
3.7 Data analysis
Categorical variables were presented as frequencies and percentages. Continuous variables were presented as means and standard deviations. The chi-squared test was used to compare categorical variables and the independent t-test was used to compare numerical variables between the study and control groups at baseline. Generalized estimating equation (GEE) analyses were conducted to estimate the mean differences between the pre-and post-VGS and the mean differences between the study and control groups concerning Perceived Learning and Self-directed Learning dimensions. The mean differences in GAMEX scale pre- and post-VGS measurements in the study group were also estimated by GEE. All GEE-based analyses were conducted with and without adjustment for covariates. All analyses were performed in R 4.2.2. Data wrangling and summaries used “tidyverse” and base R. Generalized Estimating Equations (GEE) were fit with the “geeglm” function from the “geepack” package using a Gaussian family with identity link and exchangeable working correlation, clustering on participant ID. Figures were produced with ggplot2.
4 Results
4.1 Sociodemographic characteristics and baseline perceived learning scale (CAP) and the self–directed learning instrument (SDLI)
A total of 247 participants divided into the study group (n = 124) and the control group (n = 123). Sociodemographic characteristics and baseline Perceived Learning Scale (CAP) and The Self–Directed Learning Instrument (SDLI) are presented in
4.2 Gameful experience scale (GAMEX)
A detailed analysis of the differences between post and pre-measurements of various dimensions on the GAMEX scale in the study group, are presented in
4.3 Effect on perceived learning
4.4 Effect on self-directed learning domains
5 Discussion
Our study revealed a significant increase across all dimensions of the GAMEX scale (enjoyment, absorption, creative thinking, activation, absence of negative effects and dominance) from pre- to post-VGS. These findings demonstrate that VGS positively influenced students’ learning experiences, consistent with Cheng et al. (2010). and Antón-Solanas et al. (2022)., who found that digital gamified learning promoted high levels of motivation and engagement. Remarkably, three GAMEX dimensions (enjoyment, creative thinking and absence of negative effects) achieved mean scores of three or above, further underscoring the positive learning experience.
In addition, students reported a statistically significant increase in perceived learning, both in the study group and in comparison, with the control group at post-test. These results align with previous studies that gamification enhances student engagement, motivation and achievement across cognitive, affective and psychomotor domains ( Anguas-Gracia et al., 2021; An, 2021; Sanz-Martos et al., 2024; Ordu and Çalışkan, 2021). VGS offers experiential learning opportunities that allow students to apply knowledge in safe, realistic environments while receiving immediate feedback, thereby promoting confidence, reflection and decision-making skills ( Verkuyl et al., 2018; An, 2021; Sanz-Martos et al., 2024; Taghinejad et al., 2024).
Furthermore, a key outcome of this study was the enhancement of self-directed learning (SDL) skills. Students in the VGS group demonstrated higher SDL scores post-intervention, even after adjusting for demographic factors. This finding is consistent with prior research reporting significant improvements in SDL through gamification by encouraging autonomy, reflection and active knowledge acquisition ( Nylén-Eriksen et al., 2025; Palaniappan and Noor, 2022; Yeo and Jang, 2023). Thus, Palaniappan and Noor ( Nylén-Eriksen et al., 2025) observed increased SDL scores in undergraduate students, while online gamification in ECG training significantly enhanced both knowledge and SDL among nursing students as an effective teaching method for critical hospital-based learning ( Palaniappan and Noor, 2022). Similarly, web-based simulations in Korea activated cognitive and metacognitive processes, enabling students to reflect on errors and develop their competencies ( Taghinejad et al., 2024). Beyond nursing, serious games in logistics education have also been shown to enhance SDL, critical thinking and learning enjoyment ( Yeo and Jang, 2023). The significant enhancement of SDL skills through VGS should instill confidence in its effectiveness in promoting independent learning.
Although some studies have reported mixed outcomes—for example, Pacheco-Velazquez et al. (2024). found no overall SDL improvement except in the “gathering resources for learning” subscale—these results still emphasize the importance of gamification in fostering continuous learning and resource-seeking behaviours, both essential for nursing practice. While Kang et al. (2020b). found that nursing students significantly improved their clinical competence, self-efficacy and the application of communication and critical thinking skills in traditional clinical settings. However, a notable limitation of their study is that it did not have a control group. Our study, with inclusion of the control group, revealed that VGS fosters self-directed learning skills. Students in our study group consistently outperformed the control group across all dimensions, even after adjusting for demographic factors.
The benefit of gamification and hospital-based learning is well established in the literature, yet our study adds another layer with the consideration of virtual and simulation. Collectively, the evidence highlights VGS as a promising and innovative educational strategy that enhances SDL, equips students with the necessary skills to thrive in complex, diverse environments and ultimately contributes to the greater good of improving patient outcomes. The findings of this study have several important implications for nursing education. First, the reinforcement and relevance of Self-Determination Theory are supported by gamification, which fosters autonomy, competence and relatedness ( Ryan and Deci, 2000). Second, the alignment with Experiential Learning Theory ( Kolb, 1984) is evident, as VGS authentically provides opportunities for students to engage in experience, with reflection and conceptualization through application. Our study builds on previous literature and links gamification with simulation; consequently, it introduces an innovative pedagogical approach that not only enhances engagement and learning outcomes but also transforms traditional teaching practices. Thus, integrating gamification strategies into nursing curricula will eventually foster continuous learning habits, strengthen professional preparation, and equip students with essential competencies to meet the demands of complex healthcare systems. This highlights the transformative potential of VGS in bridging theoretical knowledge with clinical application, especially in contexts where clinical placements are limited.
5.1 Strengths and limitations
This study has several limitations. First, it was conducted within a single course, which may limit generalizability to other nursing courses. The sample size and design also pose constraints; although a control group was used, the quasi-experimental, non-randomized nature of the study introduces the possibility of allocation bias as randomization was not feasible due to course requirements and lab availability. Furthermore, voluntary participation may have led to self-selection bias, as students more motivated or interested in gamification may have been overrepresented, reducing external validity. Additionally, data collection relied on self-reported measures of perceived and self-directed learning, which may be subject to social desirability or overestimation; objective performance assessments could strengthen future studies. The study measured only immediate pre–post effects without long-term follow-up, limiting conclusions about the durability of learning outcomes. The study focused on quantitative data and lacks the qualitative observation which might enrich the research findings. Finally, other uncontrolled factors—such as prior experience with gamification or personal motivation—may have influenced results.
Future research should address these limitations by using randomization where possible, including multiple courses to help generalize results for other nursing students and specialties. Additionally, studies should explore knowledge retention, affective domains such as collaboration and empathy and the development of psychomotor skills through emerging technologies will be imperative. Other uncontrolled factors, such as prior experience with gamification or personal motivation should be considered in future studies. Lastly, future implications should focus on creating, implementing and incorporating VGS into nursing practice and nursing education curricula while investigating the variations in different nursing specialties. Moreover, employing mixed-method research approaches, particularly those incorporating qualitative designs, would enhance and deepen the understanding of VGS use across various nursing courses and educational settings.
Despite the identified limitations, the study's major strength is the quasi-experimental design with a pre-and post-test non-equivalent control group enhanced the study’s internal validity and better inferences about the intervention impact. Additionally, consistent findings with rigorous studies and building on the literature in the study area. Also, the validity and useability of the VGS is supported by a previous study ( Verkuyl et al., 2018), ensuring the reliability of the measurement approach. In terms of theoretical contributions, a major key strength of this study is that it builds on and is consistent with findings from rigorous studies in the field, thereby strengthening the broader evidence base on the use of gamification in nursing education. By addressing a significant research gap, specifically the lack of published studies on VGS in psychiatric mental health nursing education in Egypt, the study contributes to the literature in an underexplored area. The findings demonstrate how gamification supports two educational theories by enhancing self-directed learning and offering potential to transform existing pedagogical practices. First, the findings can be interpreted through the lens of Self-Determination Theory ( Fung et al., 2021; Deci and Ryan, 1985), which emphasizes the role of intrinsic motivation in effective learning. By incorporating gamification into simulation, the intervention fostered a sense of autonomy, competence, and relatedness which are the key drivers of motivation that support self-directed learning. Additionally, the results align with Kolb’s Experiential Learning Theory ( Ryan and Deci, 2000; Kolb, 1984; Kolb, 2014), as the virtual gaming simulation provided students with authentic experiential opportunities to engage in concrete scenarios, reflect on their actions, conceptualize new strategies, and actively apply these insights. Together, these theories explain how gamification within simulation not only enhances motivation but also strengthens experiential and reflective learning processes, ultimately promoting the development of essential nursing competencies.
Thus, this study provides valuable insights into the potential practical contribution of VGS as an innovative alternative for developing essential nursing competencies, especially considering restrictions on student training in psychiatric hospitals and limited clinical placements. Our findings provide compelling evidence that gamification can significantly enhance self-directed learning across several domains, including interpersonal communication and metacognitive skills, which are highly relevant to nursing practice and education.
6 Conclusions
This study suggests that the VGS is a strategy that has the potential to help nursing faculty augment in-person simulations and assist students in developing clinical outcomes. This study's main finding indicated a significant increase observed in all dimensions of the Gameful Experience Scale from pre- to post-measurements, indicating positive changes following the intervention. There was a substantial increase in perceived learning from the pre-test to the post-test, with a highly significant difference (p < 0.001) in the unadjusted and adjusted analyses. Similarly, the control group also demonstrated increased perceived learning from pre-test to post-test, albeit to a lesser extent than the study group, with a significant difference (p < 0.001) in the unadjusted and adjusted analyses. Furthermore, the unadjusted and adjusted analyses indicate a significant difference between the study and control groups in post-test perceived learning scores (p < 0.001), suggesting that gamification had a positive impact on perceived learning outcomes.
Regarding self-directed learning skills, the students exhibited a higher level of these abilities post-intervention than pre-intervention. The study group scored significantly higher across all self-directed learning dimensions, outperforming the control group. The pattern was consistent across all other domains. Additionally, after adjusting for age and sex, the differences remained statistically significant, reaffirming the robustness of the findings. Determining which sub-dimensions in the self-directed learning skills are the most and least influential can yield important insights for the future design of tailored learning approaches that will improve these essential abilities.
CRediT authorship contribution statement
Hanaa Abo Shereda: Writing – review & editing, Writing – original draft, Supervision, Software, Resources, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Seema Nasser: Writing – review & editing, Writing – original draft, Conceptualization. Eman Dawood: Supervision, Validation, Writing – review & editing. Safaa Shattla: Writing – original draft, Conceptualization.
Ethical approval and author contribution
Official approval was obtained from the Research and Ethics Committee of the Faculty of Nursing (# ERCNMA 1000 / 4/9/2024). Informed consent was obtained from all students who voluntarily participated in the study after receiving detailed information about the purpose of the study and ensuring the anonymity and confidentiality of the students’ data. Students we allowed to ask questions and have the right to withdraw at any point in the study without any constraints. The students were treated equally regardless of whether they participated in the study. Furthermore, they were guaranteed anonymity, and the researchers explained that the data would be coded and that their data would not be shared with anyone other than the researchers.
Funding
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors
Declaration of Competing Interest
This research is original and has not been published elsewhere, nor is it currently under consideration for publication elsewhere. Moreover, there is no conflict of interest to disclose.
Acknowledgment
We would like to thank Dr. Verkuyl for granting us permission to use the online learning resource “Therapeutic Communication and Mental Health Assessment: Knowledge and Practice " and the students for participating in our study.
Table 1
| Variable |
Study
(n = 124) |
Control
(n = 123) |
Total
(N = 247) |
Difference (Study-Control) (95 % CI) | Test statistic | p |
| Sex | ||||||
| Male | 52 (41.9 %) | 57 (46.3 %) | 218 (44.1 %) | 0.32 a | 0.569 | |
| Female | 72 (58.1 %) | 66 (53.7 %) | 276 (55.9 %) | |||
| Age | 20.11 ± 0.69 | 20.15 ± 0.69 | 20.13 ± 0.69 | −0.03
(−0.21, 0.14) |
−0.38 b | 0.703 |
| Perceived learning scale | 12.15 ± 1.83 | 13.76 ± 2.03 | 12.96 ± 2.09 | −1.61
(−2.10, −1.13) |
−6.5 b | < 0.001* |
| Self-directed learning dimensions | ||||||
| Learning motivation | 12.56 ± 2.83 | 12.98 ± 2.51 | 12.77 ± 2.67 | −0.42
(−1.09, 0.25) |
−1.2 b | 0.219 |
| Plan and implementing | 12.35 ± 2.38 | 12.38 ± 2.64 | 12.36 ± 2.51 | −0.04
(−0.67, 0.60) |
−0.11 b | 0.912 |
| Self-monitoring | 11.04 ± 1.95 | 11.16 ± 2.25 | 11.10 ± 2.10 | −0.12
(−0.65, 0.41) |
−0.46 b | 0.648 |
| Interpersonal communication | 9.13 ± 2.33 | 10.31 ± 1.76 | 9.72 ± 2.14 | −1.18
(−1.70, −0.66) |
−4.5 b | < 0.001* |
Table 2
| Dimension | Pre | Post | Unadjusted | Adjusted for age and sex | ||||
| Difference Post – Pre (95 % CI) | Wald | p | Difference Post – Pre (95 % CI) | Wald | p. | |||
| Enjoyment | 15.89 ± 5.18 | 18.69 ± 4.89 | 2.81
(1.56, 4.06) |
19.4 | < 0.001* | 2.81
(1.85, 3.76) |
33.4 | < 0.001* |
| Absorption | 14.02 ± 2.07 | 18.16 ± 1.95 | 4.14
(3.64, 4.63) |
265.4 | < 0.001* | 4.14
(1.65, 4.63) |
272.3 | < 0.001* |
| Creative thinking | 8.97 ± 1.66 | 12.88 ± 1.95 | 3.91
(3.46, 4.36) |
292.2 | < 0.001* | 3.91
(3.49, 4.33) |
337.3 | < 0.001* |
| Activation | 8.15 ± 1.33 | 11.75 ± 1.62 | 3.60
(3.23, 3.96) |
367.5 | < 0.001* | 3.60
(3.23, 3.96) |
378.2 | < 0.001* |
| Absence of negative affect | 7.97 ± 2.10 | 11.56 ± 2.01 | 3.59
(3.08, 4.10) |
190.3 | < 0.001* | 3.59
(3.16, 4.02) |
272.0 | < 0.001* |
| Dominance | 11.11 ± 2.40 | 14.65 ± 2.69 | 3.54
(2.91, 4.17) |
120.6 | < 0.001* | 3.54
(2.97, 4.11) |
146.3 | < 0.001* |
Table 3
| Group | Pre-test | Post-test | Unadjusted | Adjusted for age and sex | ||||
| Difference Post - Pre | Wald | p | Difference Post - Pre | Wald | p | |||
| Study | 12.15 ± 1.83 | 17.64 ± 1.74 | 5.48
(5.04, 5.93) |
589.2 | < 0.001* | 5.48
(5.04, 5.93) |
591.1 | < 0.001* |
| Control | 13.76 ± 2.03 | 14.85 ± 1.88 | 1.09
(0.60, 1.58) |
19.2 | < 0.001* | 1.09
(0.62, 1.56) |
20.5 | < 0.001* |
| Unadjusted Post-test Difference Study – Control | 4.39 (3.74, 5.05) | |||||||
| Wald | 171.3 | |||||||
| P | < 0.001* | |||||||
| Adjusted Post-test Difference Study – Control | 4.39 (3.74, 5.05) | |||||||
| Wald | 174.5 | |||||||
| P | < 0.001* |
Table 4
| Self-directed Learning Domain | Group | Pre-test | Post-test | Unadjusted | Adjusted for age and sex | ||||
| Difference Post - Pre | Wald | P | Difference Post - Pre | Wald | p | ||||
| Learning motivation | Study | 12.56 ± 2.83 | 19.25 ± 3.43 | 6.69
(5.91, 7.46) |
282.8 | < 0.001* | 6.69
(5.99, 7.38) |
355.6 | < 0.001* |
| Control | 12.98 ± 2.51 | 13.88 ± 2.44 | 0.89
(0.28, 1.51) |
8.1 | 0.004* | 0.89
(0.29, 1.50) |
8.4 | 0.004* | |
| Unadjusted Post-test Difference Study – Control | 5.79 (4.80, 6.78) | ||||||||
| Wald | 130.6 | ||||||||
| P | < 0.001* | ||||||||
| Adjusted Post-test Difference Study – Control | 5.79 (4.85, 6.73) | ||||||||
| Wald | 145.3 | ||||||||
| P | < 0.001* | ||||||||
| Plan and implementing | Study | 12.35 ± 2.38 | 19.85 ± 3.63 | 7.50
(6.74, 8.26) |
373.4 | < 0.001* | 7.50
(6.75, 8.25) |
388.3 | < 0.001* |
| Control | 12.38 ± 2.64 | 13.30 ± 3.18 | 0.92
(0.19, 1.65) |
6.1 | 0.013* | 0.92
(0.22, 1.61) |
6.7 | 0.010* | |
| Unadjusted Post-test Difference Study – Control | 6.58 (5.53, 7.63) | ||||||||
| Wald | 150.2 | ||||||||
| P | < 0.001* | ||||||||
| Adjusted Post-test Difference Study – Control | 6.58 (5.56, 7.60) | ||||||||
| Wald | 159.8 | ||||||||
| P | < 0.001* | ||||||||
| Self-monitoring | Study | 11.04 ± 1.95 | 16.35 ± 2.08 | 5.31
(4.81, 5.81) |
433.8 | < 0.001* | 5.31
(4.82, 5.81) |
441.3 | < 0.001* |
| Control | 11.16 ± 2.25 | 12.27 ± 2.01 | 1.11
(0.57, 1.64) |
16.6 | < 0.001* | 1.11
(0.58, 1.63) |
16.8 | < 0.001* | |
| Unadjusted Post-test Difference Study – Control | 4.21 (3.48, 4.94) | ||||||||
| Wald | 127.9 | ||||||||
| P | < 0.001* | ||||||||
| Adjusted Post-test Difference Study – Control | 4.21 (3.48, 4.94) | ||||||||
| Wald | 127.9 | ||||||||
| P | < 0.001* | ||||||||
| Interpersonal communication | Study | 9.13 ± 2.33 | 14.65 ± 2.36 | 5.52
(4.94, 6.10) |
347.6 | < 0.001* | 5.52
(4.95, 6.10) |
351.8 | < 0.001* |
| Control | 10.31 ± 1.76 | 11.04 ± 1.71 | 0.73
(0.30, 1.16) |
11.0 | 0.001* | 0.73
(0.31, 1.16) |
11.4 | 0.001* | |
| Unadjusted Post-test Difference Study – Control | 4.79 (4.07, 5.52) | ||||||||
| Wald | 168.6 | ||||||||
| P | < 0.001* | ||||||||
| Adjusted Post-test Difference Study – Control | 4.79 (4.08, 5.51) | ||||||||
| Wald | 171.9 | ||||||||
| P | < 0.001* |
© 2025 Elsevier Ltd