Sage Journals: Discover world-class research

Abstract

Background

Although e-learning has become routine in nursing education, many courses still lack explicit, structured guidance targeting students’ motivation in online environments. Evidence remains limited regarding whether such motivational guidelines improve objective learning achievement, particularly in maternity nursing.

Objective

To evaluate the effect of e-learning motivational guidelines on motivation and learning achievement among maternity nursing students.

Methods

A quasi-experimental design with intervention and control groups was conducted at the Faculty of Nursing during the 2021–2022 academic year. A systematic random sample of 261 students was assessed using three tools: (1) a computer skills assessment sheet, (2) a motivation for e-learning assessment sheet, and (3) an auditing checklist to evaluate learning achievement. The intervention group received an e-learning course integrated with structured motivational guidelines, whereas the control group received the usual e-learning course.

Results

Postintervention, 69.7% of the intervention group exhibited high motivation across all domains of total motivation for e-learning, compared with approximately 62% of the control group who demonstrated positive motivation across related items. A strong, statistically significant positive correlation was found between total learning achievement and total motivation in the intervention group (r = .983, p < .001).

Conclusion

Embedding structured motivational guidelines within e-learning was associated with higher motivation and improved learning achievement among maternity nursing students, addressing a key gap by linking explicit motivational support to measurable academic outcomes in this specialty context.

Keywords

e-learning learning achievement motivational guidelines maternity nursing students

Introduction

Motivation is a multifaceted construct in educational psychology that shapes multiple dimensions of learner engagement (e.g., beliefs, values, goals, and regulation). This includes influences on attention, strategy use, and engagement patterns (Eccles & Wigfield, 2002; Schunk, 2020). It affects individuals’ choices about where to invest time, the intensity of effort they apply, their cognitive and emotional responses during tasks, and their persistence in continuing those tasks (Eccles & Wigfield, 2002; Ryan & Deci, 2000). In the present study, motivation was operationalized as students’ selection of learning tasks, the time and effort devoted to them, determination to engage with learning activities, and capacity to overcome challenges—dimensions consistent with established educational motivation frameworks (Schunk et al., 2014). Ultimately, motivation is a key determinant of academic success in the teaching–learning process, with motivated learners showing stronger engagement and achievement (Pintrich & De Groot, 1990; Schunk, 2020).

Motivating students involves intentionally inspiring engagement in learning activities while cultivating adaptive study habits (self-regulated learning) and a sense of purpose that supports goal pursuit (Pintrich, 2004; Yeager et al., 2014). Motivation functions as a driving force in the teaching–learning process that sustains effort and directs behavior toward learning objectives (Eccles & Wigfield, 2002; Schunk, 2020). Accordingly, educators should actively cultivate student motivation by creating need-supportive, well-structured environments, and encouraging active participation in academic activities (Freeman et al., 2014; Jang et al., 2010).

e-Learning refers to the use of information and communication technologies to support web-based, computer-assisted, digital, or fully online learning (Moore et al., 2011; OECD, 2005). In knowledge-based economies, the expansion of network infrastructure and digital platforms has reshaped how learners access and participate in instruction (OECD, 2005). These developments have also changed how learners communicate and interact through both asynchronous and synchronous modalities, thereby influencing engagement and the experience of learning (Hrastinski, 2008). In the present study, students used the Faculty of Nursing's official e-learning platform to access lessons and complete learning activities.

Student learning achievement serves as a key indicator of both the quality of educational outcomes and the overall effectiveness of the educational process (UNESCO IIEP Learning Portal, 2023a). Strong learning achievement is often demonstrated through students’ academic performance and grades (Brookhart, 2017). An effective teaching–and–learning process plays a vital role in enhancing student success, as shown by consistent gains from active-learning approaches (Freeman et al., 2014). Learning achievement encompasses the evaluation of the knowledge, skills, and competencies students acquire during their educational experience (UNESCO IIEP Learning Portal, 2023b).

A guideline is a structured statement that provides evidence-based recommendations to assist decision making, typically grounded in systematic reviews and explicit judgments about benefits and harms (Graham et al., 2011). Its primary purpose is to give rationale-based direction for recurring practices using transparent, standardized development processes (World Health Organization, 2014). Guidelines may be produced by government bodies and professional societies, and they are used to standardize practice and improve quality (Arnett et al., 2019; Substance Abuse and Mental Health Services Administration, 2020). Although they inform best practice, guidelines generally are not legally binding and typically state that they are not a substitute for professional judgment (American College of Surgeons, 2020). Accordingly, guidelines function as frameworks to promote consistent, informed decisions across settings (Graham et al., 2011).

Egypt had invested in building its information and communication technology (ICT) infrastructure since 1985, laying foundations that later underpinned e-learning initiatives (Kamel & Hussein, 2002). These sustained ICT efforts supported growth in digital services and educational applications (Kamel et al., 2009). Egypt continued to prioritize e-learning, especially in higher education, to enhance instructor motivation and student learning, as documented in analyses of Egyptian universities (El-Sayad et al., 2021). Internet use also rose markedly from 2013 to 2019, reaching approximately 51 million users by 2019, enabling broader online learning adoption (El-Sayad et al., 2021).

The evolving competencies across nursing's three pillars—education, research (scholarship), and clinical practice—underscore the relevance of examining digital approaches in contemporary curricula (AACN, 2021; Institute of Medicine, 2011). Prior evidence suggested that integrating e-learning strategies might enhance student performance, particularly in theoretical components of nursing courses (Du et al., 2022; Liu et al., 2016). As healthcare systems increasingly relied on technology, the rationale for studying blended models that combined face-to-face instruction with digital platforms was strengthened (AACN, 2021; Du et al., 2022). Online learning was described as offering flexible, interactive, and supportive environments that could facilitate mastery of complex nursing concepts (Phillips et al., 2023; Regmi & Jones, 2020). In parallel, attention had turned to faculty professional development to build the competencies needed to design and deliver effective, motivational e-learning (Godsk & Nielsen, 2024; Kebritchi et al., 2017).

The present study added to evidence that motivational e-learning strategies such as gamification and serious games could enhance nursing students’ academic achievement and performance (Lee et al., 2024; Nylén-Eriksen et al., 2025). Future research should examine the long-term effects of these approaches on behavior and clinical skill development beyond immediate post-tests (Lee et al., 2024). In particular, further studies could evaluate how e-learning supports the acquisition of practical nursing skills, including medication administration and physical assessment techniques (Ma & Zhou, 2024; Mahou et al., 2024). Additionally, researchers should address adoption barriers such as technological limitations and variable digital health literacy to inform inclusive and accessible strategies for nursing students (Abou Hashish & Alnajjar, 2024; Khatatbeh et al., 2024).

Integrating e-learning strategies into prelicensure nursing education policy is a critical consideration for preparing a digitally capable workforce (Pearson & Shumway, 2025; World Health Organization, 2020). Policymakers should develop evidence-based guidelines that promote e-learning tools embedded with motivational components (e.g., serious games and gamification) to improve learning outcomes (Lee et al., 2024; Nylén-Eriksen et al., 2025). As healthcare systems increasingly rely on digital solutions, aligning educational methods with the demand for technologically competent professionals is essential (International Council of Nurses, 2023; World Health Organization, 2020).

Policymakers should also allocate funding and resources for robust e-learning infrastructure such as virtual simulation platforms, especially in underserved or rural regions where access to traditional training is limited (Hayden et al., 2014; World Health Organization, 2020). Furthermore, nursing regulatory bodies should explore integrating digital and e-learning competencies into licensure or registration requirements to ensure graduates can leverage technology in practice (Nursing and Midwifery Board of Ireland, 2023; Nursing and Midwifery Council, 2018/2024). Evidence from higher education also shows that well-designed online learning can support student achievement when platforms and pedagogy are thoughtfully implemented (Coman et al., 2020).

In the current study, the researchers used several key principles and variables. e-Learning referred to students’ use of the Faculty of Nursing's official e-learning platform to access their curriculum, participate in learning activities, and utilize instructional resources related to maternal and neonatal health nursing. However, despite the widespread adoption of e-learning in nursing education, there was limited specialty-specific evidence in maternity nursing on whether embedding explicit, structured motivational guidelines within a course improved objectively assessed learning outcomes. Most prior work emphasized readiness, satisfaction, or self-reported motivation rather than audited academic performance, leaving uncertainty about effects on achievement. In the institutional context, no studies were identified that evaluated such guidelines within maternal and neonatal health nursing using a quasi-experimental design with a concurrent control group.

Learning achievement in this study was also assessed by analyzing students’ official academic records, specifically their scores on the midterm examination in the maternal and neonatal health nursing course. This examination was conducted by the course department as part of the formal evaluation process during the study period. Using official midterm scores as the primary outcome directly addressed the common limitation of relying on self-report measures and provided an objective test of educational impact.

Another major variable was motivational guidelines, which referred to a set of structured strategies developed and applied by the researchers to enhance nursing students’ motivation toward e-learning. These strategies incorporated elements from the ARCS model of motivation (Attention, Relevance, Confidence, and Satisfaction) and self-determination theory, focusing on intrinsic and extrinsic motivators. The guidelines also included steps for improving students’ technological competencies and addressing common barriers to e-learning, along with practical methods for overcoming them. A supportive printed booklet, created by the researchers and grounded in relevant literature and scientific references, was distributed. This booklet presented the content in clear, accessible language and was supplemented with illustrative visuals to ensure comprehension and engagement across varying student literacy levels. By integrating motivational design (ARCS/SDT) with technology skills scaffolding and barrier mitigation steps in a single, structured package, the intervention targeted both psychological drivers of engagement and practical obstacles—an approach that had been rarely tested in maternity nursing e-learning.

Research gap and aim: Taken together, the absence of specialty-specific, quasi-experimental evidence linking explicit motivational guidelines in e-learning to objective academic achievement delineated a clear gap. Therefore, this study aimed (1) to evaluate the effect of structured e-learning motivational guidelines on nursing students’ motivation and (2) to determine their impact on objective learning achievement (official midterm scores) compared with usual e-learning.

Literature Review

Prior work showed that e-learning could match or outperform traditional instruction in knowledge and skills (Pei & Wu, 2019) and that adding motivational strategies could raise motivation, performance, and course interest (Akpen et al., 2024; Nylén-Eriksen et al., 2025; Ucar & Kumtepe, 2020). At a systems level, policy reports framed e-learning as an opportunity to reimagine education under evolving societal and economic pressures and urged frameworks that kept student success and wellbeing central (OECD, 2019; UNESCO, 2021). Meta-analytic and large-scale syntheses also showed that motivational factors predicted academic performance and interplayed with self-regulated learning processes (Jansen et al., 2019; Richardson et al., 2012).

However, three practice-critical gaps remained. First, much of the literature stopped at what motivated learners and rarely translated theory into actionable, replicable guideline packages that could be embedded into course design and daily teaching decisions; practical “how-to” guidance remained underdeveloped in e-learning contexts (Regmi & Jones, 2020). Second, effects were often demonstrated through self-reported outcomes (e.g., motivation and satisfaction) rather than objectively assessed achievement, which limited inferences about academic impact and prompted calls for stronger designs and clearer mechanisms (Janson & Janke, 2024; Regmi & Jones, 2020). Third, even when motivation was targeted, studies seldom integrated motivational design with supports that removed common e-learning barriers (e.g., variable technology skills, platform navigation, and digital study routines), which left a practical gap between intention and sustained engagement.

The present study was conducted to address these gaps. It evaluated a structured, course-embedded package of motivational guidelines that (a) explicitly mapped to ARCS and self-determination theory, (b) paired motivational strategies with technology skills scaffolding and barrier mitigation steps, and (c) was delivered in a concise, implementation-ready format (a researcher-developed booklet with clear language and visuals). Crucially, the study examined objective learning achievement using official midterm examination scores rather than only self-reports within a clearly defined context (maternal and neonatal health nursing). By testing a replicable, theory-informed, and practicality-oriented guideline package against usual e-learning, this study directly responded to the literature's calls for applied guidance and stronger outcome evidence and offered educators concrete procedures to enhance motivation and measurable academic outcomes (Akpen et al., 2024; Jansen et al., 2019; Nylén-Eriksen et al., 2025; OECD, 2019; Pei & Wu, 2019; Regmi et al., 2020; Richardson et al., 2012; UNESCO, 2021).

Methods and Subjects

This study was reported in accordance with the TREND (Transparent Reporting of Evaluations with Nonrandomized Designs) statement for nonrandomized evaluations and used the TIDieR (Template for Intervention Description and Replication) checklist to fully describe the intervention (Des Jarlais et al., 2004; Hoffmann et al., 2014).

Research Design

This study employed a quasi-experimental, nonequivalent groups design with allocation by academic term to minimize cross-contamination: third-year students in term 1 formed the control cohort (usual e-learning), and those in term 2 formed the intervention cohort (usual e-learning plus motivational guidelines).

Research Setting

The study was conducted at the Faculty of Nursing, which was established in the early 1980s and received accreditation in 2013. The faculty offered Bachelor of Science (BSc), diploma, master's, and PhD programs and served a large undergraduate and postgraduate student population, functioning as a leading institution for nursing education in the region.

Study population and sampling technique. The study population comprised all third-year students who were registered in the maternal and neonatal health nursing course during the 2021–2022 academic year at the Faculty of Nursing (total N = 261). Given that the total population matched the target sample, all 261 eligible students were included (census sampling). Group assignment followed a quasi-experimental, nonequivalent groups design by academic term (term 1, control; term 2, intervention); no individual-level randomization was performed.

Sample Size

The sample size was calculated using G*Power software, based on the study by Elfaki et al. (2019), which compared attitudes toward e-learning between an experimental and a control group. The reported mean and standard deviation (SD) for perceived e-learning usage were 8.74 ± 2.71 in the experimental group and 6.26 ± 2.86 in the control group, with a statistically significant difference (p = .015) and an effect size (R² = .725), α = .05, power = .80. The minimum required sample was 48 students (24 per group). Allowing 10% to 15% attrition, the target was increased to 54 to 56 students.

Regarding actual enrollment (census), in the study year all third-year students registered in the maternal and neonatal health nursing course were available and eligible; therefore, a census was conducted, and N = 261 students were included (term 1 control = 129; term 2 intervention = 132). This approach improved precision, supported subgroup and sensitivity analyses, and ensured adequate power for the primary, objective outcome (official midterm examination score). With this N, the minimum detectable effect at 80% power was approximately d = .35 (two-tailed, α = .05), enabling detection of more modest but educationally meaningful differences.

Inclusion Criteria

All third-year students who were registered in the Department of Maternity and Gynecological Nursing (maternal and neonatal health nursing course) during the first and second semesters of the 2021–2022 academic year.

Exclusion Criteria

Students who were not registered in the Department of Maternity and Gynecological Nursing during 2021–2022. Operationally, any student who lacked the primary outcome (official midterm score) or who declined participation, if applicable, was also excluded.

Data Collection Tools

In this study, the independent variable was the e-learning motivational guideline, while learning achievement served as the dependent variable. To collect data, three tools were utilized. These tools were selected and modified to align with the study objectives and to ensure relevance, validity, and clarity.

Computer Skills Assessment Sheet

This tool was adopted from Ismail and El Wahab (2017) and was deemed appropriate for the purpose of the study. Modifications were made to improve clarity and relevance, including revisions to the tool's design and the removal of nonessential items such as academic semester, gender, and previous e-course experience.

The tool consisted of two parts, each comprising multiple-choice and closed-ended questions, and was administered as a preassessment for both the control and study groups. Part one (Q1–Q5) assessed general characteristics of the participating maternity nursing students, including name, age, and academic year; part two (Q6–Q11) evaluated students’ computer skills and previous experience with e-learning.

Motivation for e-Learning Assessment Sheet

This tool was adopted from Fowler (2018) and was modified to better suit the objectives of the study. Modifications included changes in layout and wording for improved clarity (e.g., simplifying complex item statements) and the removal of nonessential items such as the task value component.

The tool comprised 32 items grouped into six subscales, each designed to assess a specific dimension of motivation in an e-learning context: intrinsic goal orientation (Q1–Q4), extrinsic goal orientation (Q5–Q8), control of learning beliefs (Q9–Q12), self-efficacy (Q13–Q20), social engagement (Q21–Q25), and instructor support (Q26–Q32). This assessment was administered as a pretest for both the control and study groups to evaluate baseline motivational levels.

Scoring System—Motivation with e-learning Assessment Sheet:

This tool comprised a total of 32 items. Each item was scored on a three-point Likert scale as follows:

2	1	0
Always	Sometimes	Never

The total score ranged from 0 to 64. A total score between 0 and 32 was interpreted as indicating low motivation, while a score between 33 and 64 indicated high motivation.

Auditing Checklist: This checklist was used to assess student learning achievement. It was adapted from Ismail & El Wahab (2017) and modified by the researcher. The checklist was administered once to both groups after the midterm examination.

It categorized student performance into levels based on the scoring system of the faculty curriculum, as shown below:

Equivalent degree	Percentage	Scores
Excellent	85–100%	5
Very good	75– < 85%	4
Good	65– < 75%	3
Pass	60– < 65%	2
Fail	< 60%	1

Validity and Reliability of the Tools

First and Third Tools

Content Validity

Content validity referred to the extent to which a tool accurately measured the intended construct. For both the first and third tools, content validity was established through expert review. A jury committee comprising three professors from the maternity and neonatal nursing department evaluated the tools for relevance, clarity, and comprehensiveness. While no modifications were necessary for the tools themselves, the committee provided suggestions regarding the accompanying supportive materials, which were subsequently taken into consideration.

Reliability

The internal consistency of the first tool (computer skills assessment) was assessed using Cronbach's alpha coefficient. The analysis yielded a Cronbach's alpha value of .83, indicating good internal consistency and acceptable reliability.

Second Tool

Validity and Reliability

The second tool, the Motivated Strategies for Learning Questionnaire (MSLQ), had previously undergone validation and reliability testing by other researchers who adapted it for various educational contexts. The construct validity and internal consistency of the MSLQ were established through comprehensive psychometric evaluation.

The instrument included six subscales, each comprising four to eight items. Internal consistency for these subscales was determined by calculating Cronbach's alpha coefficients, which ranged from .62 to .93, indicating acceptable to excellent reliability and homogeneity among the items within each scale.

Language and Cultural Adaptation of Instruments

Original and Administration Languages

All three instruments (computer skills assessment sheet, motivation for e-learning assessment sheet [MSLQ-based], and the auditing checklist) were originally developed in English. For this study, they were administered to students in Arabic (Modern Standard Arabic) to maximize clarity and accessibility.

Translation and Back-Translation Process

The research team followed established guidance for cross-cultural adaptation: forward translation → reconciliation → back-translation → expert committee review → cognitive debriefing/pilot testing → finalization. Specifically:

Forward translation: Two independent bilingual nursing educators translated the English versions into Arabic, emphasizing conceptual (rather than literal) equivalence.

Reconciliation: A third senior educator reconciled discrepancies and produced a single Arabic draft.

Back-translation: An independent professional translator, blinded to the originals, back-translated the reconciled Arabic version into English.

Expert committee review: A bilingual panel (nursing education and measurement experts) compared the back-translation with the source, resolved semantic and idiomatic issues, and ensured content equivalence.

Cognitive debriefing/pilot: The pre-final Arabic instruments were piloted with third-year students to confirm clarity, relevance, and cultural appropriateness; minor wording refinements were made.

Finalization and documentation: The finalized Arabic versions were approved by the research team; all adaptations (e.g., simplified wording, removal of non-essential items, 3-point Likert-type scaling) were documented in the instrument appendix.

Fieldwork

Prior to the implementation of the guidelines, and to strengthen their abilities in e-learning, the researchers underwent a training session on e-learning activities conducted by the e-learning unit coordinator of the department. Additionally, the researchers attended an orientation session on the use of the e-learning platform at the beginning of the academic semester, organized by the e-learning unit. They also reviewed all official tutorial videos related to the platform's use to ensure full familiarity with its functions and features.

Data collection was carried out in two phases: from the control group during the first semester of the 2021–2022 academic year, and from the study group during the second semester. Before participation, written informed consent was obtained from all students after the study's aim had been clearly explained. Participants were assured of the confidentiality of their responses and were informed that the data would be used solely for research purposes without any impact on their academic grades. Detailed instructions on how to complete the assessment tools were also provided.

The participating students were divided into two groups: control group, students enrolled in the first semester who did not receive any intervention; and study group, students enrolled in the second semester who received a motivational strategies intervention developed and implemented by the researchers.

As part of the intervention, a printed guideline was distributed to all students in the study group. This guideline was designed to support maternity and neonatal health nursing students in enhancing their motivation for e-learning. Its objective was to improve academic achievement by helping students understand key concepts related to e-learning, the impact of e-learning on learning outcomes, motivational strategies and models applicable to e-learning environments, barriers to effective learning in e-learning settings, and practical methods to overcome these barriers.

The intervention in this study was conducted in three phases: assessment, implementation, and evaluation, as detailed below.

Assessment Phase

All participants from both the control and study groups were assessed for personal data, computer skills, and prior experience with technological tools using the computer skills assessment sheet (tool 1). Following this, students’ motivation levels toward e-learning were assessed using the motivation for e-learning assessment sheet (tool 2).

The administration of these assessment tools required approximately 20 minutes per participant in both groups.

Implementation Phase (Study Group Only—Second Semester)

The study group received a motivational guideline in the form of a self-learning booklet, developed by the researcher. This booklet covered the following topics: definition and types of e-learning, objectives of the guideline, concept and types of motivation, and strategies to enhance student motivation for e-learning.

Additionally, a training session was conducted by the researchers in collaboration with the e-learning unit coordinator from the Faculty of Nursing. This session provided students with practical guidance on how to access and interact with online lectures and course materials effectively.

To further support the study group, a WhatsApp group was created to offer continuous communication, clarification of booklet content, and assistance with challenges related to the e-learning process.

Evaluation Phase

After the midterm examination in the nursing subject, both the control group (first semester) and the study group (second semester) were evaluated as follows: motivation toward e-learning was assessed again using the motivation for e-learning assessment sheet (tool 2), and learning achievement was evaluated using the auditing checklist (tool 3), based on students’ midterm grades. This phase aimed to measure the impact of the intervention on students’ motivation and academic performance.

Strategies to Minimize Bias

To minimize potential sources of bias, the following strategies were implemented: random assignment, whereby participants were assigned to either the intervention (study) group or the control group to reduce selection bias; double-blind procedure, in which neither participants nor assessors were aware of group allocation during the evaluation phase; diverse sampling, with students drawn from varied socioeconomic backgrounds to enhance generalizability; and tool validation and reliability testing, whereby all research tools were reviewed and validated by subject-matter experts. Test–retest reliability was conducted to ensure the consistency and stability of outcome measurements over time.

Ethical Considerations

Ethics Approval and Consent to Participate

The study protocol was reviewed and approved by the research ethics committee (REC), Faculty of Nursing. Departmental endorsement was obtained for course logistics; this did not replace institutional ethical review. The study adhered to the Declaration of Helsinki and internationally recognized ethical guidelines. Written informed consent was obtained from all students before any data collection. Participation was voluntary and had no effect on grades or course standing. Identifiable information was not included in the analytic dataset; records were stored on password-protected servers accessible only to the research team.

Statistical Analysis

Data were analyzed using the Statistical Package for the Social Sciences (SPSS), version 27 (IBM Corp., Armonk, NY). Descriptive statistics were employed to summarize participants’ demographic and baseline characteristics. Frequencies and percentages were used to describe categorical variables, while means and standard deviations (SDs) were used for continuous variables.

To examine associations between categorical variables, the chi-square (χ²) test was utilized. Correlation coefficients were calculated to assess the strength and direction of linear relationships between continuous variables. A p < .05 was considered statistically significant, while p < .01 was considered highly statistically significant.

Results

Table 1 showed that the vast majority of students in both the study and control groups were aged 21 to 23 years, accounting for 100% of the study group and 96.8% of the control group. The mean age was 20.6 ± 0.71 years for the study group and 20.4 ± 1.23 years for the control group.

Table 1.

Number and Percentage Distribution of Students in the Study and Control Groups According to General Characteristics (n = 261).

Items	Study Group N = 132		Control Group N = 129
Items	N	%	N	%
Age
<21–23	132	100	125	96.8
<24–26	0	0	2	1.6
<27–29	0	0	2	1.6
Mean ± SD	20.6 ± 0.71	20.4 ± 1.23
Educational background
Technical nursing institute	13	9.8	12	9.3
General secondary degree	119	90.2	117	90.7

Regarding educational background, most students in both groups had a general secondary education, representing 90.2% of the study group and 90.7% of the control group. A smaller proportion of students came from technical nursing institutes (9.8% in the study group and 9.3% in the control group).

Figure 1 illustrated the levels of total motivation toward e-learning among students in both the study and control groups. A greater proportion of students in the study group (69.7%) demonstrated positive motivation compared to 62.0% in the control group.

Figure 1.

Distribution of total motivation toward e-learning among students in the study and control groups (n = 261).

Figure 2 illustrated the levels of learning achievement in both the study and control groups. Among the study group, 30.3% of students achieved an excellent level of performance, while only 3.8% experienced failure. In comparison, 30.2% of the control group attained a very good level of achievement, whereas 8.5% demonstrated poor learning outcomes.

Figure 2.

Distribution of learning achievement among students in the study and control (n = 261).

Figure 3 showed a strong, statistically significant positive correlation between total motivation toward e-learning and total learning achievement among students in the study group. The Pearson correlation coefficient was r = .983, with p < .001, indicating a highly statistically significant relationship. This finding suggested that higher levels of motivation toward e-learning were closely associated with improved academic performance.

Figure 3.

Correlation between students’ motivation toward e-learning and their learning achievement in the study group (n = 132).

Figure 4 presented the correlation between total motivation toward e-learning and total learning achievement among students in the control group. A statistically significant positive correlation was observed, with a Pearson correlation coefficient of r = .693 and p = .001. This indicated a moderate-to-strong positive relationship, suggesting that, even in the absence of an intervention, higher motivation levels were associated with better academic performance.

Figure 4.

Correlation between students’ motivation toward e-learning and their learning achievement in the control group (n = 129).

Discussion

The higher education system was undergoing continuous transformation as universities responded to rapidly evolving learner needs and societal demands (OECD, 2023; UNESCO, 2021). Motivating students to engage with information technologies and e-learning systems was a critical determinant of success in digital education ecosystems (Regmi & Jones, 2020; Richardson et al., 2012). Universities were increasingly investing in digital platforms and educational technologies, with studies documenting widespread adoption and student perceptions of online learning in higher education (Coman et al., 2020; OECD, 2023).

This study aimed to evaluate the effectiveness of motivational guidelines for e-learning on the academic achievement of maternity nursing students. This objective was addressed through the following research hypothesis: implementing motivational guidelines for e-learning would enhance the learning achievement of maternity nursing students.

The present study revealed that the majority of students in both the study and control groups held a general secondary education certificate. This finding was consistent with the results of a study conducted by Elfaki et al. (2019). Similarly, Diab and Elgahsh (2020) reported that most nursing students in their sample had completed general secondary education. In contrast, a study by Koirala et al. (2020) reported a higher mean age of nursing students, recorded as 22.3 ± 2.9 years. This variation may be attributed to differences in academic levels or years of study among student populations across the respective studies.

The findings of the present study demonstrated a notable improvement in total motivation among students in the intervention group compared to the control group. While more than half of the students in the control group exhibited positive motivation across items related to e-learning, approximately two-thirds of the intervention group demonstrated positive motivation. From an interpretive perspective, this difference may be attributed to the influence of the motivational guidelines, which appeared to enhance students’ engagement and motivation toward e-learning.

According to the study findings, the effectiveness of e-learning motivation lay in its ability to offer equitable, convenient, and frequent access to online educational resources while accommodating diverse student learning styles. Additionally, the flexible nature of the e-learning environment further contributed to increased student interest. As highlighted in the literature review, the researcher posited that the integration of various instructional techniques—such as task value, critical thinking, and peer learning—may have played a key role in elevating students’ motivation (Elshareif & Mohamed, 2021).

These results were in line with previous studies. For instance, Ucar and Kumtepe (2020) reported a high level of student motivation following the implementation of motivational strategies. Similarly, Naciri et al. (2022) confirmed that motivation scores improved in the intervention group when compared to the control group. Park and Yun (2018) also observed that over half of the students in the control group demonstrated positive motivation. Furthermore, Kew et al. (2018) found that the majority of students in the control group exhibited an upper-medium level of motivation in e-learning. Collectively, these findings from multiple studies underscore the positive influence of motivational strategies in fostering student motivation within various educational contexts.

Consistent with the results of the present study, research conducted by Valencia-Vallejo et al. (2018) indicated that a majority of students in their study group exhibited positive motivation. Similarly, a study performed by Ucar and Kumtepe (2020) found that the group utilizing motivational strategies demonstrated significantly higher gains in motivation. This similarity in findings may be attributed to the e-learning motivational guideline employed, which may have raised students’ motivation, course interest, volition, and general performance levels.

Regarding maternity nursing students’ learning achievement following the implementation of motivational guidelines, the present study demonstrated that the majority of students in the study group achieved excellent and very good scores. In contrast, less than one-fifth and less than one-third of students in the control group attained excellent and very good scores, respectively. Furthermore, only a small proportion of students in the study group experienced failure, while nearly one-quarter of the control group received failing scores, indicating a highly statistically significant difference between the two groups. These disparities may be attributed to intrinsic factors such as the curiosity stimulated by engagement in innovative teaching methods among the study group. Additionally, the motivational strategies employed likely contributed to the observed improvements in learning achievement.

These findings were consistent with those of Naciri et al. (2022), who examined student motivation and performance in a flipped classroom setting among nursing students and reported significant improvement in post-test scores compared to pretest scores. Similarly, Alamri (2023) found a significant enhancement in learning achievement following the implementation of a motivational model. Fernandez et al. (2022) also emphasized the positive impact of the e-learning environment, digital readiness, and academic engagement, highlighting how academic engagement significantly influenced learning achievement. Their results supported the notion that institutions fostering a collaborative and motivating virtual learning environment—by aligning the goals of educators and students—were better positioned to promote academic success.

The present study revealed a highly statistically significant positive correlation between total learning achievement and total motivation toward e-learning among students in the study group. This finding suggested that e-learning motivation could substantially enhance academic performance among nursing students. Flexible, interactive, and personalized learning environments fostered greater engagement, content retention, and interest in nursing education, which could ultimately lead to better learning outcomes and more competent healthcare professionals.

This result aligned with previous research by Na et al. (2020), who emphasized that students’ motivation levels were a key factor in determining their intention to engage in online learning. These insights highlighted the importance for educators to design appropriate and engaging e-learning resources tailored to students’ motivational needs, thereby increasing the likelihood of academic success. Similarly, Yavuzalp and Bahcivan (2021) found a significant positive correlation between self-regulation skills and academic achievement, supporting the idea that students with higher motivation and self-directed learning skills tended to perform better in online education settings.

A highly statistically significant positive correlation was observed between the control group's total learning achievement and their total motivation toward e-learning. This aligned with earlier research by Alkış and Temizel (2018), which demonstrated a significant relationship between students’ motivation for e-learning and their academic achievement. Similarly, Fernandez et al. (2022) found that motivation had a positive effect on academic success in online learning environments. In agreement with these findings, Hoerunnisa et al. (2019) also reported that motivation, as a key element of self-regulated learning, significantly influenced students’ learning experiences, including achievement, satisfaction, and course completion outcomes.

Strengths and Limitations

A key strength of this quasi-experimental study was the inclusion of a full census of all eligible third-year maternity nursing students (N = 261), which increased statistical power and reduced the risk of selection bias. The intervention was theory informed, explicitly mapping to the ARCS model of motivation and self-determination theory and being described in detail using the TREND and TIDieR frameworks, thereby enhancing transparency and reproducibility. Additional strengths included the use of validated, culturally adapted instruments with acceptable to excellent internal consistency and reliance on an objective outcome measure based on official midterm examination scores rather than self-reported performance.

This study used a nonrandomized, quasi-experimental design, which limited causal inference and left the possibility of residual confounding despite efforts to standardize teaching across groups (Des Jarlais et al., 2004). The research was conducted in a single faculty and course context (maternal and neonatal health nursing), which may have constrained generalizability to other nursing specialties, institutions, or educational systems (Pei & Wu, 2019). The primary outcome was a midterm examination score, emphasizing cognitive achievement in theoretical components; clinical competencies (e.g., OSCEs) were not assessed, and therefore effects on practical skill performance could not be determined (Hayden et al., 2014). Intervention fidelity was not prospectively tracked using detailed learning management system (LMS) usage analytics (e.g., time on task and module completion milestones), which limited certainty about dose delivered versus dose received (Hoffmann et al., 2014). Finally, implementation occurred during ongoing digital transformation efforts, so technology access and digital literacy may have varied among students, potentially influencing engagement and outcomes (Regmi & Jones, 2020).

Implications for Practice

For prelicensure nursing education, the results suggested that embedding motivational design principles into e-learning (e.g., ARCS-based strategies, serious games, and structured engagement prompts) could be a pragmatic approach to improve achievement in theoretical courses (Nylén-Eriksen et al., 2025; Ucar & Kumtepe, 2020). Programs should pair these designs with faculty development that targets online pedagogy, assessment alignment, and feedback practices to sustain quality at scale (Kebritchi et al., 2017). Institutions were encouraged to invest in LMS analytics and virtual simulation capacity to support monitoring of engagement and to extend learning into practice-proximal tasks, particularly where clinical exposure was constrained (Hayden et al., 2014). To promote equity and access, curricula should incorporate digital literacy support and inclusive design so that motivational e-learning benefits are realized across diverse student populations (Regmi & Jones, 2020; World Health Organization, 2020). Future course cycles could integrate routine intervention fidelity tracking and skill-based assessments (e.g., OSCEs) to link motivational e-learning more directly with competency attainment (Hayden et al., 2014; Hoffmann et al., 2014).

Conclusion

Based on the findings of the current study, it was concluded that the implementation of the e-learning motivational guideline significantly enhanced learning achievement among maternity nursing students. Furthermore, a strong positive correlation was identified between the study group's total learning achievement and their motivation toward e-learning. These results supported the stated research hypotheses and emphasized the effectiveness of motivational strategies in improving educational outcomes in online learning environments.

In light of these findings, it was recommended to implement an educational program for first-year students to enhance their online skills in alignment with faculty policies. This program should include workshops and booklets focused on improving computer skills, communication strategies (both direct and indirect), and techniques for effectively engaging with online lectures. Additionally, an awareness program aimed at enhancing students’ presentation and case study skills was recommended to be introduced, ideally through online lectures, to improve learning outcomes and boost students’ self-confidence. Furthermore, the study recommended that future research be conducted to explore additional academic support strategies that could further enhance students’ learning achievements in online learning environments.

Footnotes

Acknowledgment

Authors would like to express their great appreciation for all parturient women who agreed to participate in this study.

ORCID iD

Amira Fatouh Elsayed

Ethics Approval

Prior to initiating the study, ethical approval was obtained from the Scientific Research Ethical Committee of the Faculty of Nursing, under Approval Number 24.05.314.

Consent to Participate

The purpose of the study was clearly explained to all participants to ensure transparency and build trust. Written informed consent was obtained from each student prior to participation. Students were assured that their participation was voluntary and that they had the right to withdraw from the study at any time without any negative consequences. To maintain confidentiality, a coding system was used for data collection, and all information was handled privately and securely. Participation in the study posed no risks or harm to the students.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

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

Availability of Data and Materials

All data available in site the manuscript

References

Abou Hashish

E. A.

Alnajjar

(2024). Digital proficiency: Assessing knowledge, attitudes, and skills in digital transformation, health literacy, and artificial intelligence among university nursing students. BMC Medical Education, 24(1), 508. https://doi.org/10.1186/s12909-024-05482-3

Akpen

C. N.

Asaolu

Atobatele

Okagbue

Sampson

(2024). Impact of online learning on students’ performance and engagement: A systematic review. Discover Education, 3(1), 205. https://doi.org/10.1007/s44217-024-00253-0

Alamri

M. M.

(2023). A model of e-learning through achievement motivation and academic achievement among university students in Saudi Arabia. Sustainability, 15(3), 2264. https://doi.org/10.3390/su15032264

Alkış

Temizel

T. T.

(2018). The impact of motivation and personality on academic performance in online and blended learning environments. Journal of Educational Technology & Society, 21(3), 35–47. http://www.jstor.org/stable/26458505

American Association of Colleges of Nursing. (2021). The essentials: Core competencies for professional nursing education. American Association of Colleges of Nursing. https://www.aacnnursing.org/Portals/0/PDFs/Publications/Essentials-2021.pdf

American College of Surgeons. (2020). Best practices guidelines for acute pain management in trauma patients. https://www.facs.org/media/exob3dwk/acute_pain_guidelines.pdf

Arnett

D. K.

Blumenthal

R. S.

Albert

M. A.

Buroker

A. B.

Goldberger

Z. D.

Hahn

E. J.

Lloyd-Jones

D. M.

(2019). 2019 ACC/AHA guideline on the primary prevention of cardiovascular disease. Circulation, 140(11), e596–e646. https://doi.org/10.1161/CIR.0000000000000678

Brookhart

S. M.

(2017). How to use grading to improve learning. ASCD. https://files.ascd.org/staticfiles/ascd/pdf/siteASCD/publications/books/HowToUseGradingToImproveLearning.pdf

Coman

Țîru

L. G.

Meseșan-Schmitz

Stanciu

Bularca

M. C.

(2020). Online teaching and learning in higher education during the coronavirus pandemic: Students’ perspective. Sustainability, 12(24), 10367. https://doi.org/10.3390/su122410367

10.

Des Jarlais

D. C.

Lyles

Crepaz

; The TREND Group. (2004). Improving the reporting quality of nonrandomized evaluations of behavioral and public health interventions: The TREND statement. American Journal of Public Health, 94(3), 361–366. https://doi.org/10.2105/AJPH.94.3.361

11.

Diab

G. M. A. E.-H.

Elgahsh

N. F.

(2020). E-learning during the COVID-19 pandemic: Obstacles faced by nursing students and its effect on their attitudes while applying it. American Journal of Nursing Science, 9(4), 295–309. https://doi.org/10.11648/j.ajns.20200904.33

12.

Zhao

Wang

Huang

Nie

Wang

(2022). Blended learning vs. traditional teaching: The potential of a novel teaching strategy in nursing education—A systematic review and meta-analysis. Nurse Education in Practice, 63, 103354. https://doi.org/10.1016/j.nepr.2022.103354

13.

Eccles

J. S.

Wigfield

(2002). Motivational beliefs, values, and goals. Annual Review of Psychology, 53, 109–132. https://doi.org/10.1146/annurev.psych.53.100901.135153

14.

Elfaki

N. K.

Abdulraheem

Abdulrahim

(2019). Impact of e-learning vs. traditional learning on students’ performance and attitude. International Medical Journal, 8(10), 76–82. http://www.ijmrhs.com

15.

El-Sayad

Md Saad

N. H.

Thurasamy

(2021). How higher education students in Egypt perceived online learning engagement and satisfaction during the COVID-19 pandemic. Journal of Computers in Education, 8(4), 527–550. https://doi.org/10.1007/s40692-021-00191-y

16.

Elshareif

Mohamed

(2021). The effects of e-learning on students’ motivation to learn in higher education. Online Learning, 25(3), 128–143. https://doi.org/10.24059/olj.v25i3.2336

17.

Fernandez

A. I.

Al Radaideh

Singh Sisodia

Mathew

Jimber Del Río

J. A.

(2022). Managing university e-learning environments and academic achievement in the United Arab Emirates: An instructor and student perspective. PLoS ONE, 17(5), e0268338. https://doi.org/10.1371/journal.pone.0268338

18.

Fowler

(2018). The motivation to learn online questionnaire [Doctoral dissertation, University of Georgia]. University of Georgia Electronic Theses and Dissertations. https://getd.libs.uga.edu/pdfs/fowler_kevin_s_201805_phd.pdf

19.

Freeman

Eddy

S. L.

McDonough

Smith

M. K.

Okoroafor

Jordt

Wenderoth

M. P.

(2014). Active learning increases student performance in science, engineering, and mathematics. Proceedings of the National Academy of Sciences, 111(23), 8410–8415. https://doi.org/10.1073/pnas.1319030111

20.

Godsk

Nielsen

B. L.

(2024). Online teacher professional development (oTPD) for digital competencies in higher education: A systematic review of the literature. Nordic Journal of Systematic Reviews in Education, 2, https://doi.org/10.23865/njsre.v2.5498

21.

Graham

Mancher

Miller Wolman

Greenfield

Steinberg

(eds) (2011). Clinical practice guidelines we can trust. National Academies Press. https://www.ncbi.nlm.nih.gov/books/NBK209539/

22.

Hayden

J. K.

Smiley

R. A.

Alexander

Kardong-Edgren

Jeffries

P. R.

(2014). The NCSBN national simulation study: A longitudinal, randomized, controlled study replacing clinical hours with simulation in prelicensure nursing education. Journal of Nursing Regulation, 5(2), S3–S40. https://doi.org/10.1016/S2155-8256(15)30062-4

23.

Hoerunnisa

Suryani

Efendi

(2019). The effectiveness of the use of e-learning in multimedia classes to improve vocational students’ learning achievement and motivation. Jurnal Kwangsan, 7(2), 123–137. https://doi.org/10.31800/jtp.kw.v7n2.p123–137

24.

Hoffmann

T. C.

Glasziou

P. P.

Boutron

Milne

Perera

Moher

Michie

(2014). Better reporting of interventions: Template for intervention description and replication (TIDieR) checklist and guide. BMJ, 348, g1687. https://doi.org/10.1136/bmj.g1687

25.

Hrastinski

(2008). Asynchronous and synchronous e-learning. EDUCAUSE Review, 43(6), 51–55. https://er.educause.edu/articles/2008/11/asynchronous-and-synchronous-elearning

26.

Institute of Medicine. (2011). The future of nursing: Leading change, advancing health. National Academies Press. https://nap.nationalacademies.org/read/12956/chapter/1

27.

International Council of Nurses. (2023). Digital health: Position statement. https://www.icn.ch/sites/default/files/2023-08/ICN%20Position%20Statement%20Digital%20Health%20FINAL%2030.06_EN.pdf

28.

Ismail

S. A.

El Wahab

E. A. A.

(2017). The effect of using an electronic course on satisfaction and academic achievement of undergraduate nursing students. International Journal of Research in Applied Natural and Social Sciences, 5(9), 67–86. https://oaji.net/articles/2017/491-1507286237.pdf

29.

Jang

Reeve

Deci

E. L.

(2010). Engaging students in learning activities: It is not autonomy support or structure but autonomy support and structure. Journal of Educational Psychology, 102(3), 588–600. https://doi.org/10.1037/a0019682

30.

Jansen

R. S.

van Leeuwen

Janssen

Jak

Kester

(2019). Self-regulated learning partially mediates the effect of self-regulated learning interventions on achievement in higher education: A meta-analysis. Educational Research Review, 28, 100292. https://doi.org/10.1016/j.edurev.2019.100292

31.

Janson

M. P.

Janke

(2024). The influence of e-learning on exam performance and the role of achievement goals in shaping learning patterns. International Journal of Educational Technology in Higher Education, 21, 56. https://doi.org/10.1186/s41239-024-00488-9

32.

Kamel

Hussein

(2002). The emergence of e-commerce in a developing nation: Case of Egypt. Benchmarking: An International Journal, 9(2), 146–153. https://doi.org/10.1108/14635770210421818

33.

Kamel

Rateb

El-Tawil

(2009). The impact of ICT investments on economic development in Egypt. The Electronic Journal of Information Systems in Developing Countries, 36(1), 1–21. https://doi.org/10.1002/j.1681-4835.2009.tb00248.x

34.

Kebritchi

Lipschuetz

Santiague

(2017). Issues and challenges for teaching successful online courses in higher education: A literature review. Journal of Educational Technology Systems, 46(1), 4–29. https://doi.org/10.1177/0047239516661713

35.

Kew

S. N.

Petsangsri

Ratanaolarn

Tasir

(2018). Examining the motivation level of students in e-learning in higher education institutions in Thailand: A case study. Education and Information Technologies, 23(6), 2947–2967. https://doi.org/10.1007/s10639-018-9753-z

36.

Khatatbeh

Amer

Ali

A. M.

ALBashtawy

Kurnianto

Abu-Abbas

Alshatnawi

(2024). Challenges of distance learning encountering nursing students after the COVID-19 pandemic: A study from the Middle East. BMC Nursing, 23(1), 574. https://doi.org/10.1186/s12912-024-02236-w

37.

Koirala

Silwal

Gurung

Bhattarai

V. K.

(2020). Perception toward online classes during COVID-19 among nursing students of a medical college of Kaski District, Nepal. Journal of Biomedical Research & Environmental Sciences, 1(6), 249–255. https://doi.org/10.37871/jbres1151

38.

Lee

Shin

Lee

Hong

(2024). Educational outcomes of digital serious games in nursing education: A systematic review and meta-analysis of randomized controlled trials. BMC Medical Education, 24(1), 1458. https://doi.org/10.1186/s12909-024-06464-1

39.

Liu

Peng

Zhang

Yan

(2016). The effectiveness of blended learning in health professions: A systematic review and meta-analysis. Journal of Medical Internet Research, 18(1), e2. https://doi.org/10.2196/jmir.4807

40.

Zhou

(2024). Effectiveness of blended learning in a health assessment course among undergraduate nursing students: A quasi-experimental study. Teaching and Learning in Nursing, 19(4), e715–e721. https://doi.org/10.1016/j.teln.2024.07.007

41.

Mahou

F. Z.

Decormeille

Changuiti

Mouhaoui

Khattabi

(2024). The effects of screen-based simulation on nursing students’ acquisition of medication administration and dosage calculation skills: A randomized controlled trial. BMC Nursing, 23(1), 777. https://doi.org/10.1186/s12912-024-02436-4

42.

Moore

J. L.

Dickson-Deane

Galyen

(2011). e-Learning, online learning, and distance learning environments: Are they the same? The Internet and Higher Education, 14(2), 129–135. https://doi.org/10.1016/j.iheduc.2010.10.001

43.

K. S.

Petsangsri

Tasir

(2020). The relationship between academic performance and motivation level in e-learning among Thailand university students. International Journal of Information and Education Technology, 10(3), 181–185. https://doi.org/10.18178/ijiet.2020.10.3.1360

44.

Naciri

El Hajji

Radid

Kharbach

Chemsi

(2022). Exploring student motivation and performance in the flipped classroom: A case study of nursing students. Electronic Journal of General Medicine, 19(3), em360. https://doi.org/10.29333/ejgm/11796

45.

Nursing and Midwifery Board of Ireland. (2023). Digital health competency standards and requirements (1st ed.). https://www.nmbi.ie/NMBI/media/NMBI/Digital-Health-Competency-Standards-and-Requirements-First-Edition.pdf

46.

Nursing and Midwifery Council. (2024). Standards of proficiency for registered nurses. https://www.nmc.org.uk/globalassets/sitedocuments/standards/2024/standards-of-proficiency-for-nurses.pdf

47.

Nylén-Eriksen

Stojiljkovic

Lillekroken

Lindeflaten

Hessevaagbakke

Flølo

T. N.

Aase

(2025). Game thinking: Utilizing serious games and gamification in nursing education—A systematic review and meta-analysis. BMC Medical Education, 25(1), 140. https://doi.org/10.1186/s12909-024-06531-7

48.

Organisation for Economic Co-operation and Development. (2005). E-learning in tertiary education: Where do we stand? OECD Publishing. https://www.oecd.org/content/dam/oecd/en/publications/reports/2005/06/e-learning-in-tertiary-education_g1gh57df/9789264009219-en.pdf

49.

Organisation for Economic Co-operation and Development. (2019). OECD learning compass 2030: Concept note. https://www.oecd.org/content/dam/oecd/en/about/projects/edu/education-2040/concept-notes/OECD_Learning_Compass_2030_concept_note.pdf

50.

Organisation for Economic Co-operation and Development. (2023). OECD digital education outlook 2023: Towards an effective digital education ecosystem. https://www.oecd.org/en/publications/2023/12/oecd-digital-education-outlook-2023_c827b81a.html

51.

Park

Yun

(2018). The influence of motivational regulation strategies on online students’ behavioral, emotional, and cognitive engagement. The American Journal of Distance Education, 32(1), 43–56. https://doi.org/10.1080/08923647.2018.1412738

52.

Pearson

M. C.

Shumway

(2025). Exploring the impact on faculty of the American Association of Colleges of Nursing’s The Essentials: Core competencies for professional nursing education. Journal of Professional Nursing, 57, 139–147. https://doi.org/10.1016/j.profnurs.2025.02.003

53.

Pei

(2019). Does online learning work better than offline learning in undergraduate medical education? A systematic review and meta-analysis. Medical Education Online, 24(1), 1666538. https://doi.org/10.1080/10872981.2019.1666538

54.

Phillips

B. C.

Johnson

Khalid

Zapparrata

Albright

(2023). Benefits of an online interactive educational program over traditional textbooks. Nurse Educator, 48(5), 270–275. https://doi.org/10.1097/NNE.0000000000001398

55.

Pintrich

P. R.

(2004). A conceptual framework for assessing motivation and self-regulated learning in college students. Educational Psychology Review, 16(4), 385–407. https://doi.org/10.1007/s10648-004-0006-x

56.

Pintrich

P. R.

De Groot

E. V.

(1990). Motivational and self-regulated learning components of classroom academic performance. Journal of Educational Psychology, 82(1), 33–40. https://doi.org/10.1037/0022-0663.82.1.33

57.

Regmi

Jones

(2020). A systematic review of the factors—enablers and barriers—affecting e-learning in health sciences education. BMC Medical Education, 20(1), 91. https://doi.org/10.1186/s12909-020-02007-6

58.

Richardson

Abraham

Bond

(2012). Psychological correlates of university students’ academic performance: A systematic review and meta-analysis. Psychological Bulletin, 138(2), 353–387. https://doi.org/10.1037/a0026838

59.

Ryan

R. M.

Deci

E. L.

(2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68–78. https://doi.org/10.1037/0003-066X.55.1.68

60.

Schunk

D. H.

(2020). Motivation and social cognitive theory. Contemporary Educational Psychology, 60, 101832. https://doi.org/10.1016/j.cedpsych.2019.101832

61.

Schunk

D. H.

Meece

J. R.

Pintrich

P. R.

(2014). Motivation in education: Theory, research, and applications (4th ed.). Pearson. https://books.google.com/books/about/Motivation_in_Education.html?id=HFxtMAEACAAJ

62.

Substance Abuse and Mental Health Services Administration. (2020). National guidelines for behavioral health crisis care: Best practice toolkit. https://bja.ojp.gov/sites/g/files/xyckuh186/files/media/document/samsha-national-guidelines.pdf

63.

Ucar

Kumtepe

A. T.

(2020). Effects of the ARCS-V-based motivational strategies on online learners’ academic performance, motivation, volition, and course interest. Journal of Computer Assisted Learning, 36(3), 335–349. https://doi.org/10.1111/jcal.12404

64.

UNESCO. (2021). Reimagining our futures together: A new social contract for education. https://unevoc.unesco.org/pub/futures_of_education_report_eng.pdf

65.

UNESCO International Institute for Educational Planning. (2023a). Quality and learning indicators. UNESCO IIEP Learning Portal. https://learningportal.iiep.unesco.org/en/issue-briefs/monitor-learning/quality-and-learning-indicators

66.

UNESCO International Institute for Educational Planning. (2023b). Curriculum and expected learning outcomes. UNESCO IIEP Learning Portal. https://learningportal.iiep.unesco.org/en/issue-briefs/improvelearning/curriculum-and-expected-learning-outcomes

67.

Valencia-Vallejo

López-Vargas

Sanabria-Rodríguez

(2018). Effect of motivational scaffolding on e-learning environments: Self-efficacy, learning achievement, and cognitive style. Journal of Educators Online, 15(1), Article 5. https://doi.org/10.9743/JEO2018.15.1.5

68.

World Health Organization. (2014). WHO handbook for guideline development (2nd ed.). World Health Organization. https://www.who.int/publications/i/item/9789241548960

69.

World Health Organization. (2020). Global strategy on digital health 2020–2025. https://www.who.int/docs/default-source/documents/gs4dhdaa2a9f352b0445bafbc79ca799dce4d.pdf

70.

Yavuzalp

Bahcivan

(2021). A structural equation modeling analysis of relationships among university students’ readiness for e-learning, self-regulation skills, satisfaction, and academic achievement. Research and Practice in Technology Enhanced Learning, 16(1), 15. https://doi.org/10.1186/s41039-021-00162-y

71.

Yeager

D. S.

Henderson

M. D.

Paunesku

Walton

G. M.

D’Mello

Spitzer

B. J.

Duckworth

A. L.

(2014). Boring but important: A self-transcendent purpose for learning fosters academic self-regulation. Journal of Personality and Social Psychology, 107(4), 559–580. https://doi.org/10.1037/a0037637

E-learning Motivational Guideline for Maternity Nursing Students: Its Effect on Learning Achievement

Abstract

Background

Objective

Methods

Results

Conclusion

Keywords

Introduction

Literature Review

Methods and Subjects

Research Design

Research Setting

Sample Size

Inclusion Criteria

Exclusion Criteria

Data Collection Tools

Computer Skills Assessment Sheet

Motivation for e-Learning Assessment Sheet

Validity and Reliability of the Tools

First and Third Tools

Content Validity

Reliability

Second Tool

Validity and Reliability

Language and Cultural Adaptation of Instruments

Original and Administration Languages

Translation and Back-Translation Process

Fieldwork

Assessment Phase

Implementation Phase (Study Group Only—Second Semester)

Evaluation Phase

Strategies to Minimize Bias

Ethical Considerations

Ethics Approval and Consent to Participate

Statistical Analysis

Results

Discussion

Strengths and Limitations

Implications for Practice

Conclusion

Footnotes

Acknowledgment

ORCID iD

Ethics Approval

Consent to Participate

Funding

Declaration of Conflicting Interests

Availability of Data and Materials

References