Journal of Organizational Culture, Communications and Conflict (Print ISSN: 1544-0508; Online ISSN: 1939-4691 )

Research Article: 2023 Vol: 27 Issue: 5

Unlocking Success: Factors Shaping Time Management in Medical Education

Ahmad Alsulimani, Jazan University

Citation Information: Alsulimani, A. (2023). Unlocking success: Factors shaping time management in medical education. Journal of Organizational Culture Communications and Conflict, 27(S5), 1-20


Introduction: This study employs Structural Equation Modeling (SEM) to investigate key aspects that impact time management in medical education. The importance of faculty support becomes evident as a crucial element, exhibiting a robust positive association with student motivation. This study emphasizes the need to cultivate faculty-student relationships to boost students' intrinsic motivation for efficient time management. Furthermore, the study uncovers a significant correlation between peer influence and inspiration, underscoring the significance of a nurturing peer milieu in fostering drive. To cultivate such settings, institutions are advised to promote the establishment of peer mentorship programs and facilitate collaborative activities. Method: The research employs 2 step sampling technique. Quota sampling is used to identify medical colleges, and convenience sampling is used to collect participant data. These applied medical science colleges are among highly regarded colleges in Saudi Arabia, known for its extensive medical curricula, which align with the research objectives. The study involved selecting a representative sample of 500 medical students The determination of the sample size was guided by statistical considerations, which aimed to ensure the integrity and statistical significance of the obtained data. The decision to choose five universities from north, west and south of Saudi Arabia as the research sites was thoroughly justified, considering its pertinence, ease of access, ethical implications, and feasibility. This research uses a mixed-methods research methodology to investigate the multifaceted problem of time allocation in medical education in as much depth as possible. We will employ stratified random sampling to guarantee a sample truly representative of the community. Some criteria that will be considered include the years spent in medical school, the type of medical school attended, and other demographic information. If we apply this methodology, our results will be more credible to people in other parts of the world. During the quantitative phase, we will use a standardized questionnaire derived from instruments that have already been verified. This survey will include questions about various topics, including workload, motivation, and course content. Conclusion: The research highlights the necessity of incorporating contemporary technology into education, establishing a strong connection between support systems and technological resources. The allocation of resources towards technology advancements and digital tools is paramount for schools seeking to enhance time management in medical education. Although some moderate correlations exist between students' self-reported academic success and other factors, the research emphasizes the importance of employing transparent evaluation methodologies to have a thorough knowledge of scholarly advancement. In summary, this research provides significant findings for institutions aiming to improve faculty-student relationships, foster positive peer influences, adopt technology, and increase assessment processes, ultimately leading to an enhanced educational experience in medical education.


Management, Medical Education, Education System, Curriculum Structure, Workload.


Training for the noble profession of treating illness and caring for the sick requires a long and challenging educational process known as medical school. Students and medicine practitioners encounter various hurdles to becoming competent and caring healthcare providers (Biesta & van Braak, 2020). Time management is one of these obstacles that significantly affects students' productivity in the classroom, happiness in their personal lives, and their ability to make positive contributions to the healthcare field in the future (Martínez-Sánchez et al., 2022).

The article "Factors Influencing Time Allocation in Medical Education" addresses an issue of critical relevance in the modern medical education system: how time is allocated. Time management is dividing a person's time and energy between competing priorities, including schoolwork, clinical rotations, extracurriculars, and some semblance of a social life (Murphy et al., 2022). Optimizing the learning process, maintaining student well-being, and, eventually, improving the quality of healthcare delivery all depend on an accurate understanding of the complex web of factors determining how individuals allocate their time during the medical education journey (Jordan et al., 2020).

Through this study, researchers hope to understand the complex factors that influence the time-management choices of future doctors and healthcare workers. It digs into the complex interaction of elements such as course design, workload, motivation, learning environment, peer impact, instructor support, individual traits, and the incorporation of technological aids (Sum et al., 2022). These factors individually and collectively shape medical students' lives and routines, shaping their academic success and development as people.

This study's importance goes far beyond its immediate application to medical instruction. Patients trust doctors and nurses; therefore, it stands to reason that their treatment should be of the highest possible standard (Rolfe et al., 2014). Better academic success, less stress, improved clinical skills, and greater happiness with the medical education experience can all result from more efficiently allocated time (Holden et al., 2012). Conversely, effective time management has been linked to burnout, academic difficulties, and better job satisfaction among healthcare professionals (Zafarullah & Pertti, 2017).

Changes in patient demographics, medical technology, and healthcare delivery models contribute to a dynamic and ever-evolving global healthcare system (Braithwaite et al., 2018). The setting makes it more important to learn how medical students and practitioners adjust their time budgets considering these shifts. For instance, the introduction of novel healthcare technologies like telemedicine, AI, and personalized treatment requires a rethinking of how time is spent and how knowledge is acquired (Ko et al., 2015).

With this study, we hope to understand these interrelated factors better. Medical facilities, and schools can benefit from interventions and suggestions grounded in solid research if we can determine what factors affect how people spend their time. These findings can guide the development of medical programs, the implementation of support infrastructures, and the training of doctors and other healthcare workers to be flexible and adaptable in the face of new problems.

The research shall begin a deep dive into the intricate medical education environment in the following sections. We hope that our research and analysis will shed light on medical students' decisions as they prioritize how they spend their time training to become competent and caring doctors.

Review Literature

How time is spent in medical school is a significant factor in students' academic, personal, and professional success (Ferreira et al., 2019). Medical education programs benefit significantly from a deeper understanding of the many elements influencing students' time allocation decisions Alweis et al., 2021). Curriculum design Scherzer & Redmond (2022), workload Byrne (2019), motivation Byrne (2019), learning environment Naeem et al. (2023), peer influence Abercrombie et al. (2015), instructor support Neufeld & Malin (2020), personal characteristics Triemstra et al. (2021), and technology aids Wei et al. (2022) are only a few factors examined in this research review Figure 1.

Figure 1 Framework of Study

Curriculum Structure

The curriculum heavily influences how students use their time throughout medical school. A well-thought-out curriculum with specific learning goals and a logical progression might help students manage their time better. Proponents point out that a well-organized course load can help students save time and effort by reducing unnecessary material and helping them memorize and retain more of what they learn (Howell et al., 2019). Critics, however, argue that such constraints on learning could lead to increased employee stress and burnout (Buja, 2019).


The cumulative academic and clinical burden heavily influences time allocation in medical school.(Hirani et al., 2022) Students are better equipped to balance school and extracurricular activities when their workload is reasonable. Reasonable workloads, proponents say, enhance students' ability to learn and lessen their stress levels. Some say that too much work is bad for your health and well-being since it can cause burnout, limit your ability to learn broadly and stress you out (Sun et al., 2019).


Students' time management strategies are heavily influenced by their motivation levels (Wolters & Brady, 2020). Reason from within, in the form of genuine curiosity and enthusiasm about medicine, is more likely to result in productive time use (Lisiswanti, 2022). Proponents maintain that inspired kids are better able to focus on their studies. However, some argue that grade-based or extrinsic motivations lead to shallow memory rather than genuine understanding (Walker et al., 2006).

Learning Environment

How students spend their time in class is affected by various elements related to the classroom setting (Kirschner et al., 2021). Time management skills may flourish in a welcoming classroom where questions are welcomed and teachers are accessible (Alrashdan et al., 2022). Supporters maintain that a positive atmosphere encourages participation and growth in the school. Self-directed learning and efficient time management are criticized because they can be hampered by a lack of tools and guidance.

Peer Influence

Students' peer relationships may influence time management in medical school (Karakose, 2015). Collaborative learning and peer assistance can foster successful study habits and time management. Peer pressure, proponents say, can get kids to study more complex subjects. Opponents counter that lousy study habits, including last-minute cramming, might result from negative peer pressure or competition (Khan et al., 2019).

Faculty Support

The help and direction of teachers are essential for efficient time management (Valente et al., 2020). Academic advisors who are approachable and sympathetic can guide students in making intelligent choices regarding their time, study methods, and future endeavours (Miraj et al., 2022). Those who advocate for a growth-oriented classroom atmosphere emphasize the importance of teachers' backing. Many worry that pupils will feel lost and overwhelmed if they don't receive enough teacher guidance (Bilquise et al., 2022).

Personal Factors

Individual differences in learning style, time management abilities, and other factors majorly shape how people spend their time. Advocates stress the significance of flexibility and self-awareness in maximizing time (Tibus & Ledesma, 2021). However, detractors argue that unpredictable human aspects make it difficult to execute standardized approaches (Sawaragi, 2020).

Technological Tools

E-learning platforms and other digital resources impact students' time on their studies. Technology may expand students' access to learning resources when used effectively and make studying independently easier (García-Peñalvo & Seoane-Pardo, 2015). Proponent’s stress that technological advancements can boost productivity and flexibility. However, detractors warn that excessive use of technology might cause distraction and cut down on actual human contact (Katznelson & Gerke, 2021).

Allocating time in medical school is a complex endeavour with several arguments and criticisms. The ability of medical educators and institutions to maximize learning outcomes and enhance student well-being depends on their understanding of these aspects. It is essential to strike a balance while establishing medical education programs between the influence of factors such as curriculum structure, workload, motivation, learning environment, peer influence, faculty support, personal considerations, and technological aids (Woodruff, 2019).

Mediation Effect

Students' ability to effectively manage their time is essential to academic and personal development (Winkler et al., 2021). Several circumstances can affect how students divide their time between school, work, and personal lives. This literature review investigates the role of stress, perceptions of academic success, study habits, and social support systems in shaping students' time management practices.

Stress Level as a Mediator

Overall stress levels significantly mediate time spent studying. High stress levels have been linked to ineffective time management and poor academic performance (Alotaibi et al., 2020). The demands of extracurricular activities and school have been linked to increased stress levels. Students' ability to focus, set priorities, and strike a work-life balance are all hit when stress levels are high (Alrashed et al., 2022).

High stress levels have been linked to increased procrastination, poor study habits, and subpar academic achievement (Jowkar et al., 2022). Some suggest mild stress can incentivize efficient time management, even though anxiety can hurt time allocation.

Perceived Academic Performance as a Mediator

An additional critical factor of time allocation is the perception of academic accomplishment (Ogundipe & Falade, 2014). Students' study habits are affected by their confidence in their academic ability. According to the literature, students who think highly of themselves are likelier to devote extra time to learning. Both prior academic success and instructor evaluation might impact this impression (Wilson et al., 2021; Artz & Welsch, 2013).

Students who have a positive self-perception about their academic performance are likelier to make good use of their study time (Ogunyemi et al., 2022). Reviewers point out that inflated senses of academic competence can lead to sloppy time management and neglect of more challenging coursework(Alyami et al., 2021).

Study Habits as a Mediator

How much time students devote to studying directly relates to their study habits. Time management, setting goals, and using active learning tactics are all examples of efficient study habits that can favourably influence how much time is spent on schoolwork. According to the literature, students with strong study habits are more likely to prioritize their education. Improved academic performance can be achieved by cultivating and maintaining efficient study habits(Bansal et al., 2021).

Poor or inconsistent study habits can sabotage best time management attempts, leading to wasted study time.

Support Systems as a Mediator

Various factors, such as the availability of resources and encouragement from friends and family, moderate time spent on schoolwork. Having a group of people with your back may do wonders for your mental health, drive, and ability to get things done on time. According to research, Students with stable social networks are more likely to manage their time wisely(Wambua et al., 2021). Better academic outcomes are argued to result from more access to appropriate support systems that help alleviate pressure, instil a sense of purpose, and facilitate more efficient use of time (Deniz & Ersan, 2002). Relying too heavily on others might stunt the growth of independence and competence in managing one's time.

When deciding how to spend one's time in the classroom, several variables come into play; among the most important are one's stress levels, one's opinion of one's academic achievement, one's study habits, and one's availability of outside resources. Learning about the mediators between time management practices, academic outcomes, and student well-being is crucial for students, teachers, and educational institutions. A student's academic achievement and personal growth depend on their ability to balance the various demands on their time.

Research Methodology

A rigorous methodology was applied in selecting the right Saudi medical institutions as the research sites for this study titled "Unlocking Success: Factors Shaping Time Management in Medical Education." The main objective of this study is to have a thorough understanding of the various elements that impact time management behaviours among medical students at applied medical sciences colleges in Saudi Arabia. The selected medical institutions must be located within Saudi Arabia to maintain unity with the research's emphasis on the Saudi educational system. The medical college chosen for this study was mandated to provide undergraduate medical education programs, as these programs serve as our research's primary area of interest. The medical college's readiness to engage and actively participate in the study was a crucial factor in the selection process. The teamwork and cooperation among individuals played a critical role in facilitating the process of data gathering, conducting interviews, and obtaining access to students.

The research employs 2 step sampling technique. Quota sampling is used to identify medical colleges, and convenience sampling is used to collect participant data. These applied medical science colleges are among highly regarded college in Saudi Arabia, known for its extensive medical curricula, which align with the research objectives. The institution's considerable size, vast diversity, and outstanding reputation position it as an exemplary entity for appreciating the intricate elements that influence time management methods in medical education. The college administration demonstrated a high commitment to collaboration, which considerably helped the data-gathering process, conducting student interviews, and obtaining access to institutional information.

The availability of information is a crucial aspect of this study, where comprehensive records and databases are maintained to document students' academic performance and time allocation. The abundance of data available is of immense value for our research endeavours. Ethical Considerations: The selected institution possesses a robust ethical framework for conducting research, which guarantees the safeguarding of participants' rights and the preservation of data confidentiality and integrity. Practicality and Resource Constraints: The geographical placement of the colleges is well-suited to our logistical capacities, facilitating the practicality and feasibility of conducting on-site research. Furthermore, our research team has previous expertise in partnering with this university. The study involved selecting a representative sample of 500 medical students The determination of the sample size was guided by statistical considerations, which aimed to ensure the integrity and statistical significance of the obtained data. The decision to choose five universities from north, west and south of Saudi Arabia as the research sites was thoroughly justified, considering its pertinence, ease of access, ethical implications, and feasibility. By incorporating this option, the study's efficacy in attaining its aims is enhanced as it yields valuable insights into the influential aspects that impact time management in medical education, specifically within Saudi Arabia.

This research uses a mixed-methods research methodology to investigate the multifaceted problem of time allocation in medical education in as much depth as possible. We will employ stratified random sampling to guarantee a sample truly representative of the community. Some criteria that will be considered include the years spent in medical school, the type of medical school attended, and other demographic information. If we apply this methodology, our results will be more credible to people in other parts of the world. During the quantitative phase, we will use a standardized questionnaire derived from instruments that have already been verified(Jung, 2019). This survey will include questions about various topics, including workload, motivation, and course content. Quantitative data will be subjected to stringent statistical analysis, including inferential statistics, to shed light on the relationships between the independent factors and the dependent variables(Gill, 2020). Parallel to this, we will conduct semi-structured interviews with the participants throughout the qualitative phase to delve deeper into how they prioritize their time(Galletta, 2013). The legitimacy and reliability of qualitative findings can be significantly improved through thematic analysis in conjunction with member checking. The use of mediation analysis will allow us to have a better understanding of the role that mediating factors play in the relationship. Whenever we interact with participants, we will do it in a manner that is guided by ethical principles, and we will request approval from the IRB. This research aims to provide stakeholders in medical education with practical suggestions and make significant contributions to the existing body of knowledge. The unique mixed-methods strategy will accomplish these goals(Shah et al., 2018).

Construct Reliability and Validity

Construct reliability and validity evaluation is essential in SmartPLS and other structural equation modelling (SEM) methodologies, as it plays a crucial role in maintaining the integrity and credibility of research outcomes. The construction of dependability and validity serves multiple essential functions(Mohamad et al., 2015). The construct of reliability pertains to the degree of consistency and stability exhibited by measurements within a particular construct. In the context of SmartPLS, scholars commonly employ statistical measures such as Cronbach's Alpha and Composite Reliability to evaluate the aspect above. High reliability indicates that the items or indicators used to assess a specific construct exhibit consistency and dependability in accurately expressing the underlying notion. The importance of this matter is that using incorrect metrics might add extraneous factors and undermine the precision of the outcomes obtained by your model(Parasuraman et al., 1988). Construct validity pertains to the extent to which the measuring items selected accurately and authentically represent the intended construct. Average Variance Extracted (AVE) is a crucial fact metric in the SmartPLS software. The assessment quantifies the extent to which a construct captures variance about the presence of measurement error. A high average variance extracted (AVE) indicates robust convergent validity, signifying that the construct captures the desired notion for measurement purposes (dos Santos & Cirillo, 2023). The evaluation of discriminant validity, which investigates the distinctiveness of constructs, is an additional aspect of construct validity. Multicollinearity is a phenomenon that occurs when there is a high correlation between constructs in a model, which can result in incorrect or misleading outcomes. In general, implementing reliability and validity tests inside SmartPLS aids researchers in verifying the robustness of their measurement models, the dependability of their selected indicators, and the appropriate representation of their constructs about the intended concepts. Consequently, this phenomenon bolsters the trustworthiness of study outcomes and the soundness of inferences derived from structural equation modeling (SEM) assessments. Construct dependability and validity are essential in SmartPLS and structural equation modeling (SEM) (Pering, 2020). The primary purpose of measurement models is to ascertain the robustness of the measurements and the accuracy of the correlations between constructs in a research project. The best values for these parameters may exhibit variability contingent upon the research setting and field. However, having a Cronbach's alpha exceeding 0.7 and an Average Variance Extracted (AVE) equal to or more than 0.5 is commonly deemed acceptable(dos Santos & Cirillo, 2023). The values suggest that the measurement instruments are highly effective in accurately assessing the intended constructs. Furthermore, these constructs demonstrate a greater degree of explanatory power about their indicators when compared to the presence of measurement error. In essence, using construct reliability and validity within the context of SmartPLS bolsters the credibility of study findings. It guarantees that the measurement tools effectively capture the fundamental concepts under investigation (Ringle et al., 2022).

Discriminants Validity

Evaluating discriminant validity in SmartPLS is essential to ensure the reliability and validity of structural equation modeling (SEM) models. This procedure guarantees that the constructs under investigation are distinct and not influenced by solid connections, hence augmenting the precision and dependability of research outcomes. The Fornell-Larcker criteria, a statistical technique employed in SmartPLS, assess discriminant validity by comparing the square root of average variance extracted (AVE) with correlations of correlations. To enhance the robustness of their model and facilitate accurate results drawn from structural equation modeling (SEM) investigations, researchers commonly utilize a blend of these methods to establish discriminant solid validity (Fornell & Larcker, 1981).

Data Analysis Results and Discussion

The findings obtained from the SmartPLS evaluation of the data analysis yield significant insights into the dependability and soundness of the examined constructs. To begin with, it is worth noting that Cronbach's Alpha values, which serve as a metric for assessing internal consistency, are above the established threshold of 0.7 for all structures. This finding suggests that the items within each construct consistently measure the underlying notion. Similarly, it is worth noting that the Composite Reliability values, specifically rh o_and rho_c, exceed the established criterion of 0.7 for all constructs. This reinforces the strength and reliability of the measuring instruments (Peterson & Kim, 2013). Moreover, the evaluation of convergent validity using the Average Variance Extracted (AVE) demonstrates that most constructs reach or exceed the suggested threshold of 0.5, indicating satisfactory convergent validity. Nevertheless, the construct of "Perceived Academic Performance" does not meet the established standard, indicating the need for enhancement in terms of its measurement quality. Hence, the findings from the SmartPLS study suggest that the constructs generally display robust internal consistency and convergent validity. However, there is a potential to improve the "Perceived Academic Performance" assessment to better match it with the other variables examined in the research (Hajjar, 2018) Figure 2.

Figure 2 Perceived Academic Performance

The Fornell-Larcker Criterion is essential as an assessment tool within the Confirmatory Factor Analysis (CFA) domain. Its primary purpose is to evaluate the discriminant validity of constructs present in a research model. Discriminant validity is a crucial concept in measurement modelling, as each construct inside the model is unique. This ensures that the measurement model effectively reflects the underlying theoretical framework. This criterion involves the comparison of the square root of the Average Variance Extracted (AVE) for each construct with the correlations between that construct and all other constructs. If the square root of the average variance extracted (AVE) for a particular construct exceeds its correlations with other constructs, it provides evidence that the concept possesses discriminant validity (Hilkenmeierr et al., 2020).

Within the dataset, we shall examine the construct known as "Curriculum Structure." The average variance extracted (AVE) of the variable is 0.868, and the square root of the AVE (√0.868) is around 0.931 Table 1. When examining the association between the value of 0.931 and the correlations between "Curriculum Structure" and other constructs, such as "Faculty Support" and "Learning Environment," it is observed that the value of 0.931 surpasses all of these relationships. The result above suggests that the "Curriculum Structure" construct exhibits discriminant validity, as it satisfies the criteria outlined in the Fornell-Larcker Criterion Table 2. The thorough evaluation guarantees the dependability of the research framework by verifying that the constructs effectively gauge distinct facets of the phenomena being studied.

Table 1 Construct Reliability and Validity
  Cronbach's alpha Composite reliability (rho_a) Composite reliability (rho_c) Average variance extracted (AVE)
Curriculum Structure 0.849 0.854 0.930 0.868
Faculty Support 0.833 0.835 0.923 0.857
Learning Environment 0.790 0.800 0.905 0.826
Motivation 0.840 0.848 0.926 0.862
Peer Influence 0.854 0.856 0.932 0.873
Perceived Academic Performance 0.855 0.880 0.901 0.696
Personal Factors 0.824 0.844 0.919 0.849
Study Habits 0.679 0.800 0.855 0.747
Support Systems 0.836 0.836 0.924 0.859
Technological Tools 0.801 0.813 0.909 0.833
Workload 0.832 0.862 0.900 0.752
Table 2 Discriminant Validity Through Fronell-Larcker Criterion
  Curriculum Structure Faculty Support Learning Environment Motivation Peer Influence Perceived Academic Performance Personal Factors Stress Level Study Habits Support Systems Technological Tools Time Allocation Workload
Curriculum Structure 0.932                        
Faculty Support 0.678 0.926                      
Learning Environment 0.607 0.657 0.909                    
Motivation 0.789 0.680 0.681 0.928                  
Peer Influence 0.572 0.685 0.755 0.636 0.934                
Perceived Academic Performance 0.651 0.622 0.591 0.688 0.556 0.791              
Personal Factors 0.738 0.836 0.730 0.787 0.743 0.685 0.922            
Stress Level 0.664 0.695 0.654 0.699 0.588 0.686 0.762 1.000          
Study Habits 0.241 0.254 0.180 0.275 0.226 0.461 0.261 0.246 0.864        
Support Systems 0.237 0.219 0.178 0.286 0.226 0.381 0.252 0.212 0.578 0.927      
Technological Tools 0.680 0.750 0.713 0.757 0.692 0.653 0.847 0.717 0.266 0.274 0.913    
Time Allocation 0.355 0.310 0.179 0.392 0.204 0.426 0.337 0.334 0.580 0.734 0.345 1.000  
Workload 0.719 0.657 0.720 0.797 0.608 0.597 0.754 0.668 0.212 0.257 0.682 0.323 0.867

Specific indirect effects Table 3 revealed in the data shed light on the intricate links between the researched variables and their impact on "Time Allocation." These outcomes shed light on how extracurricular activities can have a knock-on effect on schoolwork. Let's talk about some interesting results:

Table 3 Specific Indirect Effects
  Specific indirect effects
Peer Influence -> Support Systems -> Time Allocation 0.069
Faculty Support -> Study Habits -> Time Allocation 0.018
Curriculum Structure -> Support Systems -> Time Allocation -0.002
Workload -> Support Systems -> Time Allocation 0.071
Workload -> Stress Level -> Time Allocation 0.010
Personal Factors -> Study Habits -> Time Allocation -0.003
Curriculum Structure -> Perceived Academic Performance -> Time Allocation 0.000
Curriculum Structure -> Stress Level -> Time Allocation 0.017
Learning Environment -> Study Habits -> Time Allocation -0.025
Learning Environment -> Support Systems -> Time Allocation -0.106
Motivation -> Support Systems -> Time Allocation 0.093
Technological Tools -> Study Habits -> Time Allocation 0.023
Faculty Support -> Perceived Academic Performance -> Time Allocation 0.000
Technological Tools -> Perceived Academic Performance -> Time Allocation 0.000
Motivation -> Study Habits -> Time Allocation 0.037
Personal Factors -> Perceived Academic Performance -> Time Allocation 0.000
Faculty Support -> Support Systems -> Time Allocation -0.009
Peer Influence -> Study Habits -> Time Allocation 0.017
Personal Factors -> Stress Level -> Time Allocation 0.046
Technological Tools -> Support Systems -> Time Allocation 0.106
Learning Environment -> Perceived Academic Performance -> Time Allocation 0.000
Personal Factors -> Support Systems -> Time Allocation -0.034
Learning Environment -> Stress Level -> Time Allocation 0.023
Workload -> Study Habits -> Time Allocation -0.007
Technological Tools -> Stress Level -> Time Allocation 0.022
Motivation -> Perceived Academic Performance -> Time Allocation 0.000
Peer Influence -> Stress Level -> Time Allocation -0.013
Curriculum Structure -> Study Habits -> Time Allocation 0.005
Workload -> Perceived Academic Performance -> Time Allocation 0.000
Peer Influence -> Perceived Academic Performance -> Time Allocation 0.000
Faculty Support -> Stress Level -> Time Allocation 0.021
Motivation -> Stress Level -> Time Allocation 0.016

Students whom their peers inspire are more inclined to seek out support networks (such as academic assistance or mentoring), which has a beneficial effect on the amount of time students devote to their studies (r=0.069). This negative indirect link between learning environment and support systems and time allocation (-0.106) suggests that students who believe their learning environment to be favourable may not need support systems for their studies. High motivation prompts students to look for aid, which benefits their time management and productivity in the classroom (0.093). This demonstrates the importance of intrinsic motivation in promoting independent learning. Interestingly, students' time allocation for institutional work improves as their stress levels rise, suggesting that students' circumstances play a role in both. This may indicate that pressure is a form of intrinsic motivation to study. The favorable influence of technological tools on students' use of support networks suggests that students who use these resources are more likely to devote more time to their academics. This exemplifies the interplay between technological advancements and pedagogical aid. An indirect positive effect shows that a good learning environment might reduce stress, which can influence how much time is set aside for academics (0.023). There is a negative correlation between increased workload and time devoted to studying (-0.007), which is an exciting finding. This may suggest that too much work prevents one from forming good study habits. Motivated students are more likely to establish beneficial study habits, which leads to more time devoted to academic pursuits (r=0.037). Positive faculty support affects students' study habits, which leads to more time dedicated to college work (R2=0.018). These indirect impacts show the complex web of relationships among the many elements affecting students' time allocation for academic assignments. When developing interventions or support systems meant to improve students' academic performance and time management abilities, it is crucial to consider these indirect impacts.

Some potential understanding of the overall impacts derived from SmartPLS4 is depicted in Table illustrates: The total effects Table 4 represent the cumulative impact of both the independent factors, directly and indirectly, on the dependent variable, which is perceived academic success. The total effects can be understood as the standardized regression coefficients, which quantify the magnitude and direction of the association between each independent variable and the dependent variable. The link becomes stronger as the absolute value of the total effect increases. The sign of the total effect denotes whether the relationship is positive or negative.

Table 4 Total Effect
  Curriculum Structure Faculty Support Learning Environment Motivation Peer Influence Perceived Academic Performance Personal Factors Stress Level Study Habits Support Systems Technological Tools Time Allocation Workload
Curriculum Structure           0.180   0.104 0.026 -0.004   0.020  
Faculty Support           0.083   0.131 0.090 -0.016   0.030  
Learning Environment           0.102   0.146 -0.124 -0.182   -0.108  
Motivation           0.270   0.101 0.179 0.159   0.146  
Peer Influence           -0.003   -0.084 0.084 0.119   0.073  
Perceived Academic Performance                       0.000  
Personal Factors           0.159   0.288 -0.013 -0.059   0.009  
Stress Level                       0.160  
Study Habits                       0.204  
Support Systems                       0.583  
Technological Tools           0.102   0.140 0.111 0.182   0.151  
Time Allocation                          
Workload           -0.063   0.060 -0.036 0.122   0.073  

Perceived Academic Performance

The data indicate that motivation has the most significant positive correlation with perceived academic success, with a total effect of 0.270. This is followed by curriculum structure, with a total effect of 0.180, and personal factors, with a total effect of 0.159. Consequently, students with better motivation, a more positive impression of the curriculum framework, and fewer personal obstacles are inclined to perceive superior academic accomplishment in online learning. The independent variable that exhibits the least significant positive correlation with perceived academic performance is peer influence (total impact = - 0.003), which is nearly inconsequential. This suggests that peer influence does not have a substantial effect on students' perceived academic achievement.

The sole independent variable that exhibits a negative correlation with perceived academic success is workload, with a total effect of -0.063. These findings suggest that students with a greater workload experience a decrease in their perceived academic achievement in online learning. Nevertheless, the extent of this correlation is quite minor in comparison to the positive correlations.

Stress Level

Personal attributes had the most direct beneficial impact on stress levels, with a total effect of 0.288. This means that improvements in personal factors have a significant influence on stress levels. In contrast, peer influence has a negative overall impact of -0.084, indicating that good peer influence has a stress-relieving effect. Faculty support, learning environment, motivation, and the use of technological resources, while positively connected with stress levels, show more modest total impacts (0.131, 0.146, 0.101, and 0.140, respectively). These data indicate that improvements in these domains could lead to marginal elevations in stress levels. In addition, the curriculum structure and workload also have positive total impacts (0.104 and 0.060, respectively), suggesting that making changes to these components could result in small increases in stress levels. It is crucial to acknowledge that these impacts are direct, and comprehending the connection between these elements and stress levels may require examining intricate relationships and indirect effects within a broader structural paradigm.

Study Habits

Furthermore, faculty support (0.090) and positive peer impact (0.084) are crucial factors that significantly improve students' study habits. Students who receive support from instructors and are exposed to good peer influence are more inclined to adopt efficient study practices. Utilizing technological tools (0.111) effectively also demonstrates significant impact, suggesting that technology can be utilized to enhance study habits. In contrast, a detrimental learning environment (-0.124) and an increased workload (-0.036) have adverse overall impacts, indicating that these characteristics are linked to a decline in favorable study habits. Nevertheless, it is crucial to acknowledge that personal characteristics demonstrate a just eligible adverse impact (-0.013), indicating that although personal attributes may have a minor influence, they are not as significant in influencing study habits. These findings offer valuable knowledge for educators and institutions seeking to enhance students' study habits. They emphasize the crucial importance of motivation, faculty support, peer influence, and technological tools. Additionally, they underscore the necessity of addressing challenges related to the learning environment and workload.

System Support

Factors such as motivation (0.159), peer influence (0.119), and the utilization of technology tools (0.182) are prominent in their positive contribution to the effectiveness of support systems. Elevated levels of motivation, favorable influence from peers, and efficient utilization of technology all exert substantial beneficial impacts on the existing support systems.

In contrast, the learning environment has a considerable negative impact (-0.182), suggesting that a poor learning environment might greatly undermine the support mechanisms in place. In an unfavourable learning environment, the support systems may be negatively impacted.

The total effects of faculty support (-0.016), curricular structure (-0.004), and personal variables (-0.059) on support systems are all negative, but the impact of personal factors is relatively smaller. These aspects appear to diminish the effectiveness of support systems somewhat, indicating that enhancements in these areas may be necessary to strengthen support systems.

Remarkably, the analysis reveals that there is a positive correlation between workload (0.122) and the strength of support systems, suggesting that a higher workload is linked to more robust support networks. This implies that when students encounter higher job requirements, support systems are more likely to be activated and reinforced.

Time Allocation

Significantly, the elements that notably encourage more effective time allocation include positive study habits (0.204), motivation (0.146), and the presence of powerful support system (0.583). These findings emphasize the significance of developing effective study habits and sustaining high levels of motivation in order to maximize time management. Support systems are also crucial, indicating that a robust support network can significantly impact individuals' ability to organize their time more effectively.

On the other hand, the presence of stress levels (0.160) and a less favourable learning environment (-0.108) can have a negative impact on time allocation. Individuals may need help in efficiently managing their time when they are under stress or when the learning environment fails to accommodate their requirements.

Additional factors, including instructor support (0.030), positive peer influence (0.073), technology tools (0.151), and an increased workload (0.073), had different levels of influence on-time distribution. The encouragement and influence from faculty members and peers can serve as beneficial prompts, while the use of technology and the amount of work can motivate individuals to manage their time more effectively.

These findings are crucial for individuals and educational institutions aiming to improve time management abilities. Through comprehending the variables that impact time distribution, one can formulate tactics to cultivate more effective time administration techniques, resulting in enhanced production and overall achievement.


The correlation matrix provides valuable insights into the interrelationships between different aspects of educational experiences. A noteworthy finding is the robust positive association between faculty support and motivation. It indicates that when students are provided with sufficient assistance and direction from their instructors, it tends to enhance their motivation levels. This underscores the importance of faculty-student relationships in influencing students' motivation toward learning. Moreover, a positive association exists between peer impact and inspiration, indicating that students who experience good influence from their peers are more likely to exhibit higher motivation levels. Educational institutions can utilize this valuable information by promoting peer mentorship programs and facilitating collaborative group activities, thereby cultivating a conducive peer environment.

Moreover, the robust positive link between support systems and technical tools underscores the need to integrate contemporary technology into education. The availability of advanced technological resources has a beneficial influence on students' view of support systems. This highlights educational institutions' need to allocate resources towards developing technical infrastructure and digital learning tools.

Conversely, the modest associations observed between individuals reported academic performance and several other criteria suggest that students' subjective evaluations of their academic accomplishments may have limited impact on their broader educational encounters. This implies that educational institutions should prioritize utilizing objective and transparent evaluation approaches to give pupils a comprehensive comprehension of their scholarly advancement.

Hence, the correlation matrix facilitates comprehension of the intricate connections between different educational variables. The results of this study can provide valuable guidance for academic institutions seeking to improve faculty-student interactions, foster good peer influences, incorporate technology into the learning process, and implement successful assessment procedures. By implementing these recommendations, educational institutions can enhance the overall educational experience for their students.

Institutions can cultivate constructive peer influences among students by implementing many tactics that establish a culture characterized by collaboration, respect, and individual development. A productive strategy involves the implementation of peer mentoring and leadership initiatives, wherein seasoned students assume the role of mentors to guide and assist their peers. These programs offer prospects for fostering healthy role modelling and cultivating leadership qualities. Furthermore, educational institutions can arrange collective endeavours, workshops, or undertakings that necessitate collaboration and cooperation, promoting constructive interactions among students and facilitating mutual learning opportunities. The incorporation of empathy, tolerance, and diversity into the curriculum can contribute to the cultivation of a peer environment that is both inclusive and supportive. Establishing consistent channels for communication and feedback between students and instructors enhances the identification and reinforcement of positive peer behaviours. Institutions can potentially improve their students' learning experience and personal development by establishing a conducive climate that acknowledges and promotes positive peer influences.

The potential for enhancing student well-being and academic results through research on mitigating negative peer impacts within educational institutions is a promising avenue for further investigation. The following are a few significant domains that warrant further investigation.

Future Research

The offered paragraph sheds light on the complex interplay between several factors and how much time pupils devote to schoolwork. It not only explains these connections but also suggests future directions for study:

Intrinsic motivation is examined in this article because of its relevance to the development of autonomous learners. However, it doesn't discuss what drives people on a deeper level. More study is needed to determine what factors encourage kids' intrinsic motivation and how schools can foster it.

It is suggested in this text that stress might be an inherent motivator for students. This is an intriguing idea that needs more investigation. More research might investigate the complex dynamic between stress and motivation, specifically when stress becomes a beneficial or detrimental influence on academic success.

The line alludes to the excellent effect technology tools have had on students' use of support networks, but it doesn't detail which devices or platforms are best. In the future, researchers may be able to determine which precise technological interventions help children more effectively manage their time and perform better in school.

The positive effects of faculty assistance on students' study habits and time allocation are mentioned, but the section doesn't detail how this support is exerted. The most effective forms of faculty support and teaching strategies for fostering better study habits could be investigated.

The paragraph broadly discusses causal and correlative links between variables without considering potential moderating variables. A deeper comprehension may be attained by investigating if a particular person or contextual factors alter these interactions.

Long-Term Consequences: This section focuses mainly on the immediate consequences of scheduling. How, for example, early exposure to peer influence or motivation influences students' later academic and professional trajectories is a promising area for further study.

Analysis across Cultures: This section does not attempt to account for cultural differences in the patterns of interaction being described. These associations may be genuine in some cultural contexts, while others find different results.

Although this section stresses the significance of considering indirect effects when designing treatments, no solutions are provided. More work is needed to develop and evaluate intervention programs to mitigate these indirect effects.


Abercrombie, S., Parkes, J., & McCarty, T. (2015). Motivational influences of using peer evaluation in problem-based learning in medical education. Interdisciplinary Journal of Problem-Based Learning, 9(1), 8.

Google Scholar, Cross Ref

Alotaibi, A.D., Alosaimi, F.M., Alajlan, A.A., & Abdulrahman, K.A.B. (2020). The relationship between sleep quality, stress, and academic performance among medical students. Journal of family & community medicine, 27(1), 23.

Indexed at, Google Scholar, Cross Ref

Alrashdan, H., Al Ajmi, H. R., Alnasraween, M.E.S., & Karra, S. (2022). Efficiency Determinants of Educational Wastage Programs. Journal of Education and e-Learning Research, 9(2), 79-86.

Google Scholar

Alrashed, F.A., Alsubiheen, A.M., Alshammari, H., Mazi, S.I., Al-Saud, S.A., Alayoubi, S., & Dhiman, G. (2022). Stress, anxiety, and depression in pre-clinical medical students: Prevalence and association with sleep disorders. Sustainability, 14(18), 11320.

Indexed at, Google Scholar, Cross Ref

Alweis, R., Donato, A., Terry, R., Goodermote, C., Qadri, F., & Mayo, R. (2021). Benefits of developing graduate medical education programs in community health systems. Journal of Community Hospital Internal Medicine Perspectives, 11(5), 569-575.

Indexed at, Google Scholar, Cross Ref

Alyami, A., Abdulwahed, A., Azhar, A., Binsaddik, A., & Bafaraj, S.M. (2021). Impact of time-management on the student’s academic performance: a cross-sectional study. Creative Education, 12(3), 471-485.

Indexed at, Google Scholar, Cross Ref

Artz, B., & Welsch, D.M. (2013). The effect of student evaluations on academic success. Education Finance and Policy, 8(1), 100-119.

Indexed at, Google Scholar, Cross Ref

Bansal, S., Bansal, M., & White, S. (2021). Association between learning approaches and medical student academic progression during preclinical training. Advances in Medical Education and Practice, 1343-1351.

Indexed at, Google Scholar, Cross Ref

Biesta, G.J., & van Braak, M. (2020). Beyond the medical model: thinking differently about medical education and medical education research. Teaching and Learning in Medicine, 32(4), 449-456.

Indexed at, Google Scholar, Cross Ref

Bilquise, G., Ibrahim, S., & Shaalan, K. (2022). Bilingual AI-Driven Chatbot for Academic Advising. International Journal of Advanced Computer Science and Applications, 13(8).

Indexed at, Google Scholar, Cross Ref

Braithwaite, J., Mannion, R., Matsuyama, Y., Shekelle, P.G., Whittaker, S., Al-Adawi, S., & Hughes, C.F. (2018). The future of health systems to 2030: a roadmap for global progress and sustainability. International Journal for Quality in Health Care, 30(10), 823-831.

Indexed at, Google Scholar, Cross Ref

Buja, L.M. (2019). Medical education today: all that glitters is not gold. BMC Medical Education, 19(1), 1-11.

Google Scholar, Cross Ref

Byrne, A. (2019). The effect of education and training on mental workload in medical education. Springer International Publishing, 258-266.

Indexed at, Google Scholar, Cross Ref

Deniz, D.Z., & Ersan, I. (2002). An academic decision support system based on academic performance evaluation for student and program assessment. International Journal of Engineering Education, 18(2), 236-244.

Indexed at, Google Scholar

dos Santos, P.M., & Cirillo, M.Â. (2023). Construction of the average variance extracted index for construct validation in structural equation models with adaptive regressions. Communications in Statistics-Simulation and Computation, 52(4), 1639-1650.

Indexed at, Google Scholar, Cross Ref

Ferreira, I.S., Pinto, C.B., Velez, F.G.S., Leffa, D.T., de Toledo Piza, P.V., & Fregni, F. (2019). Recruitment challenges in stroke neurorecovery clinical trials. Contemporary Clinical Trials Communications, 15, 100404.

Indexed at, Google Scholar, Cross Ref

Fornell, C., & Larcker, D.F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50.

Indexed at, Google Scholar, Cross Ref

Galletta, A. (2013). Mastering the semi-structured interview and beyond: From research design to analysis and publication. 18.

Indexed at, Google Scholar, Cross Ref

García-Peñalvo, F.J., & Seoane-Pardo, A.M. (2015). An updated review of the concept of eLearning. Tenth anniversary. Education in the Knowledge Society, 16 (1), 119-144.

Indexed at, Google Scholar, Cross Ref

Gill, S.L. (2020). Qualitative sampling methods. Journal of Human Lactation, 36(4), 579-581.

Indexed at, Google Scholar, Cross Ref

Hajjar, S.T. (2018). Statistical analysis: Internal-consistency reliability and construct validity. International Journal of Quantitative and Qualitative Research Methods, 6(1), 27-38.

Indexed at, Google Scholar

Hilkenmeier, F., Bohndick, C., Bohndick, T., & Hilkenmeier, J. (2020). Assessing distinctiveness in multidimensional instruments without access to raw data–a manifest Fornell-Larcker criterion. Frontiers in psychology, 11, 223.

Indexed at, Google Scholar, Cross Ref

Hirani, S., Trivedi, N.A., Chauhan, J., & Chauhan, Y. (2022). A study of clinical and economic burden of surgical site infection in patients undergoing caesarian section at a tertiary care teaching hospital in India. Plos one, 17(6), e0269530.

Google Scholar

Holden, M.J., Holden, J.C., & Paterson, S. (2012). Developing preventative responses to disruptive and high-risk behaviours. Emerald Group Publishing Limited, 309-322.

Howell, B.A., Kristal, R.B., Whitmire, L.R., Gentry, M., Rabin, T.L., & Rosenbaum, J. (2019). A systematic review of advocacy curricula in graduate medical education. Journal of General Internal Medicine, 34, 2592-2601.

Google Scholar

Jordan, J., Wagner, J., Manthey, D.E., Wolff, M., Santen, S., & Cico, S. J. (2020). Optimizing lectures from a cognitive load perspective. AEM Education and Training, 4(3), 306-312.

Google Scholar, Cross Ref

Jowkar, Z., Fattah, Z., Khorshidi Asl, Z., & Hamidi, S.A. (2022). Stress, sleep quality, and academic performance among dental students in Shiraz, Iran. International Journal of Dentistry, 2022.

Indexed at, Google Scholar, Cross Ref

Jung, Y.M. (2019). Data analysis in quantitative research.  Handbook of Research Methods in Health Social Sciences.

Indexed at, Google Scholar, Cross Ref

Karakose, T. (2015). The relationship between medical students’ time management skills and academic achievement. Studies on Ethno-Medicine, 9(1), 19-24.

Google Scholar, Cross Ref

Katznelson, G., & Gerke, S. (2021). The need for health AI ethics in medical school education. Advances in Health Sciences Education, 26, 1447-1458.

Indexed at, Google Scholar, Cross Ref

Khan, I.A., Zeb, A., Ahmad, S., & Ullah, R. (2019). Relationship between university students time management skills and their academic performance. Review of Economics and Development Studies, 5(4), 853-858.

Google Scholar

Kirschner, M., Golsteijn, R. H., Sijben, S. M., Singh, A.S., Savelberg, H.H., & de Groot, R.H. (2021). A qualitative study of the feasibility and acceptability of implementing ‘sit-to-stand’desks in vocational education and training. International Journal of Environmental Research and Public Health, 18(3), 849.

Google Scholar, Cross Ref

Ko, J.J., Karagiannis, T.S., Tran, M., & Obi, E.N. (2015). Crowdsourcing healthcare technology innovation: the use of open competitions to pursue novel healthcare technology solutions. Value in Health, 18(3), A101.

Kusurkar, R.A. (2019). Autonomous motivation in medical education. Medical teacher, 41(9), 1083-1084.

Indexed at, Google Scholar, Cross Ref

Lisiswanti, R. (2022). Factors Affecting Low Academic Achievement Of Undergraduate Medical Students: Student Experience. Jurnal Pendidikan Kedokteran Indonesia-The Indonesian Journal of Medical Education, 11(1), 108-188.

Google Scholar

Martínez-Sánchez, L.M., Molina-Valencia, J.L., Rodríguez-Padilla, L.M., Ruiz-Rodríguez, J.P., & Jaramillo-Jaramillo, L.I. (2022). Relationship between research training, the strengthening of autonomous learning and time management in undergraduate medical education. Educare Electronic Magazine, 26(3), 147-167.

Indexed at, Google Scholar, Cross Ref

Miraj, M., Chuntian, L., Osei-Bonsu, R., Mohd Said, R., Ali, R., & Shereen, S. (2022). Could the academic advisor, intrinsic motivation, and time management influence students’ attitudes towards research work on campus?. Work, 1-14.

Google Scholar

Mohamad, M.M., Sulaiman, N.L., Sern, L.C., & Salleh, K.M. (2015). Measuring the validity and reliability of research instruments. Procedia-Social and Behavioral Sciences, 204, 164-171.

Indexed at, Google Scholar, Cross Ref

Murphy, M., Pahwa, A., Dietrick, B., Shilkofski, N., & Blatt, C. (2022). Time Management and Task Prioritization Curriculum for Pediatric and Internal Medicine Subinternship Students. MedEdPORTAL, 18, 11221.

Indexed at, Google Scholar, Cross Ref

Naeem, N.I.K., Yusoff, M.S.B., Hadie, S.N.H., Ismail, I.M., & Iqbal, H. (2023). Understanding the Functional Components of Technology-Enhanced Learning Environment in Medical Education: A Scoping Review. Medical Science Educator, 1-15.

Indexed at, Google Scholar, Cross Ref

Neufeld, A., & Malin, G. (2020). How medical students’ perceptions of instructor autonomy-support mediate their motivation and psychological well-being. Medical Teacher, 42(6), 650-656.

Google Scholar, Cross Ref

Ogundipe, M.A., & Falade, O.A. (2014). Student-teacher perception of time allocation and academic achievement in Tai Solarin University of Education. International Journal of Learning and Development, 4(1), 65.

Indexed at, Google Scholar, Cross Ref

Ogunyemi, D., Darwish, A.G., Young, G., Cyr, E., Lee, C., Arabian, S., & Raval, N. (2022). Graduate medical education-led continuous assessment of burnout and learning environments to improve residents’ wellbeing. BMC Medical Education, 22(1), 1-11.

Indexed at, Google Scholar, Cross Ref

Parasuraman, A.B.L.L., Zeithaml, V.A., & Berry, L. (1988). SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality. 1988, 64(1), 12-40.

Indexed at, Google Scholar

Pering, I.M.A.A. (2020). Kajian Analisis Jalur Dengan Structural Equation Modeling (Sem) Smart-Pls 3.0. Jurnal Ilmiah Satyagraha, 3(2), 28-48.

Indexed at, Google Scholar, Cross Ref

Peterson, R.A., & Kim, Y. (2013). On the relationship between coefficient alpha and composite reliability. Journal of Applied Psychology, 98(1), 194.

Indexed at, Google Scholar, Cross Ref

Ringle, C.M., Wende, S., & Becker, J.M. (2022). SmartPLS 4. Oststeinbek: SmartPLS GmbH. Journal of Applied Structural Equation Modeling

Google Scholar

Rolfe, A., Cash‐Gibson, L., Car, J., Sheikh, A., & McKinstry, B. (2014). Interventions for improving patients' trust in doctors and groups of doctors. Cochrane Database of Systematic Reviews, (3).

Sawaragi, T. (2020). Design of resilient socio-technical systems by human-system co-creation. Artificial Life and Robotics, 25, 219-232.

Indexed at, Google Scholar, Cross Ref

Scherzer, R., & Redmond, M. (2022). The importance of innovative curriculum design in medical education. Annals of Allergy, Asthma & Immunology, 129(4), 403-404.

Indexed at, Google Scholar, Cross Ref

Shah, S.S., Shah, A.A., & Khaskhell, N. (2018). Pragmatism research paradigm: a philosophical framework of advocating methodological pluralism in social science research. Journal of Grassroot, 52(1).

Indexed at, Google Scholar

Sum, K., Cheshire, A., Ridge, D., & Deb, S. (2022). Supporting doctors' nutrition in the workplace: findings from a qualitative study. Proceedings of the Nutrition Society, 81(OCE5), E161.

Google Scholar, Cross Ref

Sun, H., Warner, D.O., Macario, A., Zhou, Y., Culley, D.J., & Keegan, M.T. (2019). Repeated cross-sectional surveys of burnout, distress, and depression among anesthesiology residents and first-year graduates. Anesthesiology, 131(3), 668-677.

Google Scholar, Cross Ref

Tibus, E.D., & Ledesma, S.K.G. (2021). Factors of Academic Stress: Do They Impact English Academic Performance?. International Journal of Evaluation and Research in Education, 10(4), 1446-1454.

Google Scholar, Cross Ref

Triemstra, J.D., Iyer, M.S., Hurtubise, L., Poeppelman, R.S., Turner, T.L., Dewey, C., & Fromme, H.B. (2021). Influences on and characteristics of the professional identity formation of clinician educators: a qualitative analysis. Academic Medicine, 96(4), 585-591.

Google Scholar

Valente, S., Lourenço, A.A., Alves, P., & Dominguez-Lara, S. (2020). The role of the teacher’s emotional intelligence for efficacy and classroom management. CES Psicología, 13(2), 18-31.

Google Scholar

Walker, C.O., Greene, B.A., & Mansell, R.A. (2006). Identification with academics, intrinsic/extrinsic motivation, and self-efficacy as predictors of cognitive engagement. Learning and Individual Differences, 16(1), 1-12.

Google Scholar, Cross Ref

Wambua, R.M., Gakuu, C., Kidombo, H., & Ndege, S. (2021). Learner support system and academic performance of distance learning students in selected Kenyan public universities.

Indexed at, Google Scholar, Cross Ref

Wei, L., Yu, Z., & Qinge, Z. (2022). Medical College Education Data Analysis Method Based on Improved Deep Learning Algorithm. Mobile Information Systems, 2022.

Google Scholar, Cross Ref

Wilson, R., Joiner, K., & Abbasi, A. (2021). Improving students’ performance with time management skills. Journal of University Teaching & Learning Practice, 18(4), 16.

Indexed at, Google Scholar, Cross Ref

Winkler, R., Söllner, M., & Leimeister, J.M. (2021). Enhancing problem-solving skills with smart personal assistant technology. Computers & Education, 165, 104148.

Indexed at, Google Scholar, Cross Ref

Wolters, C.A., & Brady, A.C. (2020). College students’ time management: A self-regulated learning perspective. Educational Psychology Review, 1-33.

Google Scholar

Woodruff, J.N. (2019). Accounting for complexity in medical education: a model of adaptive behaviour in medicine. Medical Education, 53(9), 861-873.

Indexed at, Google Scholar, Cross Ref

Zafarullah, S., & Pertti, V. (2017). Effect of time management on the job satisfaction and motivation of teacher educators: A narrative analysis.

Google Scholar

Received: 02-Oct-2023, Manuscript No. JOCCC-23-14095; Editor assigned: 06-Oct-2023, Pre QC No. JOCCC-23-14095(PQ); Reviewed: 20-Oct-2023, QC No. JOCCC-23-14095; Published: 28-Oct-2023

Get the App