Skip to main content
Download PDF

Crossing horizons: unraveling perspectives on enhancing medical students’ success through at-risk factor exploration

BMC Medical Education volume 24, Article number: 835 (2024) Cite this article

Abstract

Background

Medical universities often face the ongoing challenge of identifying and supporting at-risk students to enhance retention rates and academic success. This study explores a comprehensive analysis of perceived at-risk factors impeding academic and career aspirations and compares the perspectives of students and faculty in a medical school.

Methods

We focused on first and second-year medical (MBBS) students and teaching faculty in an international medical college offering a twinning program in India and Malaysia. Our investigation involved a comprehensive assessment of 25 at-risk factors through Likert-type questionnaires distributed to 250 MBBS students and 50 teaching faculty.

Results

Our findings revealed distinct disparities in perceptions between faculty and students regarding mean scores of classroom engagement (p = 0.017), procrastination (p = 0.001), unrealistic goals (p = 0.026), emotional/behavioral problems (p = 0.008), limited key social skills (p = 0.023), and a non-supportive home environment (p = 0.001). These differences underscore the need for increased communication and understanding between faculty and students to address these risk factors effectively. In contrast, no significant disparities were observed among faculty and students’ perceptions concerning mean scores of various potential at-risk factors, including academic unpreparedness, cultural/language barriers, individual guidance/mentoring, limited communication skills, racism/sexism, self-confidence, self-respect, self-concept, motivation, underprepared for current academic challenges, self-discipline, negative social network, negative peer culture, transportation time, college financial cost, college evaluation culture bias, broken college relationships, teaching methodology, and learning disabilities. However, varying degrees of influence were perceived by faculty and students, suggesting the importance of individualized support.

Conclusion

This study contributes to the academic community by shedding light on the multifaceted nature of at-risk factors influencing student success. It underscores the need for proactive measures and tailored interventions to enhance student retention in higher education and academic achievement, fostering a sustainable foundation for lifelong learning and growth.

Peer Review reports

Introduction

Many universities and colleges invest significant time and resources into addressing one of their most pressing challenges, which is the identification of at-risk students. Unfortunately, they often find that when at-risk students are detected through conventional methods, it’s often too late to provide effective assistance, and these students are on the brink of dropping out. While many institutions acknowledge the need to enhance retention rates by reaching out to at-risk students, they frequently continue with their traditional approaches. It’s crucial to proactively identify risk factors that may hinder academic and career aspirations, making early detection of these factors a priority. The use of risk assessment, prioritization, and targeted interventions across various academic disciplines in higher education can significantly enhance students’ academic success. As a result, this remains a top priority for educators and administrators.

The term “at-risk” is commonly used to describe individual students or student groups with a higher likelihood of academic failure or dropout. At-risk students can include those who have made poor decisions affecting their academic performance, adult students returning to higher education after a long absence, or students with previously unidentified academic, social, physical, or mental limitations. At-risk students have been classified into four categories [1]:

  • Academically underprepared students due to inadequate educational backgrounds.

  • Students with individual risk factors such as cognitive, health, neurological, or psychological issues contributing to academic difficulties.

  • Students with familial risk factors like household problems, caregiving responsibilities, educational values, and financial constraints.

  • Students with social risk factors, including conflicts related to ethnicity, culture, or traumatic social interactions.

One of the studies suggest another group to the at-risk category: millennial generation students who graduate high school in the 21st century and often enter postsecondary institutions without essential educational planning skills [2].

Research findings indicate that when skills, knowledge, motivation, and/or academic abilities significantly lag behind those of the “typical” college student, the probability of being classified as academically at-risk becomes notably elevated [3]. Furthermore, studies also highlight that students falling into the at-risk category often exhibit a range of additional traits, including a reliance on rote memorization, holding unrealistic grade expectations, nurturing idealistic career aspirations, experiencing low self-efficacy, and demonstrating insufficient study skills necessary for academic success [3, 4].

A nation’s social and economic development is directly linked to universities and students’ academic performance [5]. The factors contributing to students’ academic improvement or underperformance in academic institutions have been extensively studied, as they are a focus for educators and researchers alike [6,7,8,9]. These studies have identified a combination of social, student-oriented, teacher-oriented, institution-oriented, environmental, personal, psychological, and financial factors.

One of the studies summarized students’ perceptions of the at-risk factors influencing academic success [10]. The first theme, student agency, included two subthemes: setting goals and self-regulation. The second theme was aptitude, with two subthemes: self-evaluation and motivation. The final theme was the type of support, which included the subthemes institutional support and external support. The challenge for higher education institutions is to integrate these student-defined attributes into the curriculum to enhance student success, particularly focusing on malleable elements such as study and social skills, and providing financial and other tailored provisions, especially for non-traditional students like first-generation students.

Academic stress significantly challenges students, often leading to mental health issues. May et al. discussed findings from the American College Health Association-National test, indicating that maladaptive effective functioning- depression, anxiety, and stress negatively impacts students’ academic performance and success [11]. Additionally, Seibert et al. examined the impact of emotion regulation on academic success, finding it crucial for performance improvement and maintenance in students [12].

Another study suggested that poor physical and psychological health can lead to social marginalization and institution-oriented discrimination, resulting in increased instability and lower academic performance [13]. Such students often suffer from low self-esteem, anxiety, mood disorders, and other issues that can reduce academic performance. Chronic diseases are also a contributing factor to low academic performance. McNelis et al. reported that students with chronic diseases experience more academic problems than their healthy peers [14]. Furthermore, psychological stressors like social media negatively impact students’ health and academic performance. Increased internet use among undergraduates can lead to cyberbullying, resulting in a lack of concentration and underperformance [15]. Learning disabilities also significantly affect academic outcomes [16, 17].

Undergraduate students’ poor academic performance is often linked to the attitude and quality of instructors. Instructor is one of the most critical factors influencing students’ attitudes and experiences in a course [18]. A classroom’s climate significantly plays an essential role in influencing students’ behavior and learning outcomes [19]. Good relationships between instructors and students are crucial for creating a positive classroom environment and improving student outcomes [20]. Teachers also influence achievement or underachievement through their expectations of students [21]. Students studying in a language other than their native tongue may have difficulty understanding their faculty, impacting their academic achievement and leading to attrition. Factors such as language, tone of voice, accent, or the speed of content delivery can create a conflict between the student and faculty, making it difficult for the student to stay focused and perform well in class [22].

Personal factors can influence students’ achievement and performance. Lack of motivation, ineffective study skills, misaligned learning styles with teaching methods and assessments, and the amount and types of use of academic campus services can all contribute to academic underachievement [22,23,24]. Emotional issues and learning disabilities impact students’ self-efficacy [25]. Individuals with learning disabilities often suffer from low self-esteem and self-efficacy, set low expectations for themselves, struggle with underachievement, and have few friends [26]. Emotional challenges also play a significant role in academic performance and achievement [27]. These influences can lead to academic procrastination, the voluntary delay of an intended course of study-related action. Academic procrastination is often thought of as avoiding difficult tasks or those that cause anxiety [28]. There is a strong association between academic procrastination and poor academic outcomes such as low grades, poor quality of work, lack of knowledge, and increased time pressure [29,30,31].

Achieving success in the 21st century demands a combination of robust academic education, state-of-the-art technical skills, and a solid groundwork that encourages ongoing learning and personal development, both in the context of college and throughout one’s career and life [32]. For many students, higher education represents a pivotal and life-altering period, marked by substantial investments not only in terms of finances but also emotionally, as well as in terms of time and effort. It is crucial that students are equipped with the skills to become effective learners and that they address critical risk factors that could potentially obstruct their aspirations for success in both college and their future careers. Those students who lack a solid foundation in learning methods may find themselves excluded from significant economic, academic, and social opportunities. In today’s world, sustainability necessitates not only the application of academic knowledge but also a foundation that supports continuous learning and growth. Lifelong learning is a national and global priority, and improving student retention and success is crucial, as evidenced by the Australian government’s inclusion of retention as a funding indicator for higher education [33, 34]. The importance of reducing dropout rates and improving students’ academic success- specifically by identifying the factors contributing to lagging academic performance among undergraduate students in advance has become increasingly critical for policymakers, higher education institutions, and students alike [35].

It is of paramount importance to conduct research into the elements that could hinder the academic and career ambitions of medical students, underscoring the need to prioritize the early detection of such risk factors. The topic of potential factors that might jeopardize college persistence and achievement among medical students remains relatively underexamined. In light of the growing prevalence of high student attrition and dropout rates on a global scale, there is a pressing requirement for a study aimed at identifying the factors that both medical students and faculty consider to be significantly impactful in detrimentally affecting academic success.

Methods

Study design, study participants and ethical considerations

This cross-sectional study involved first and second-year medical (MBBS) students and teaching faculty with over two years of experience in an international medical college offering a twinning medical program in India and Malaysia. The study received ethical approval from the Institutional Ethics Committee (IEC/KH/617/2018). A total of 300 participants (250 MBBS students and 50 teaching faculty) were recruited with their consent, ensuring anonymity and confidentiality of demographic information. Participants had the right to withdraw from the study at any point. It was communicated to participants that study results might be published in journals, presented at conferences, and used for further research. The study posed minimal risk to participants, with precautions taken to maintain anonymity and data confidentiality. We developed a questionnaire for our study featuring 25 at-risk factors presented in a Likert-type format (Supplementary file). This was designed to assess participants’ perceptions of the factors influencing academic success, with responses ranging from “not influential” (1) to “very influential” (4).

Content validity of questionnaire items

For the content validity of questionnaire items, we included a panel consisted of experienced faculty members and experts in the field of medical education and student counseling, ensuring a thorough and comprehensive validation. The calculation of Scale-Level Content Validity Index (S-CVI) was performed to showcase content validity. Following informed consent, participants rated each questionnaire item on a 4-point Likert scale, ranging from 1 (highly irrelevant) to 4 (highly relevant). Scores 1 and 2 were grouped as irrelevant (assigned a value of 0), while scores 3 and 4 were considered relevant (assigned a value of 1). S-CVI/Ave was then computed to determine the mean I-CVI for each item. Assessing content validity measures the extent to which an item in an assessment instrument is pertinent to its intended purpose. A higher content validity test signifies greater accuracy in measuring the target construct. An I-CVI > 0.79 indicates an item’s relevance without the need for further revision, and an S-CVI/Ave > 0.9 signifies excellent content validity [36,37,38].

Construct validity of questionnaire items

Construct validity measurement involved calculating corrected item-total correlation, indicating the correlation between a specific item and all other items. A correlation between 0.30 and 0.49 is considered medium, while a correlation exceeding 0.50 is deemed strong [39].

Reliability of questionnaire items

Cronbach’s alpha measurement was employed to assess the instrument’s reliability to establish internal consistency. An item is considered reliable if the Cronbach’s alpha score is greater than 0.6, deemed acceptable within the range of 0.6 to 0.8 [39].

Administration of validated revised questionnaire

The validated revised questionnaire, containing 25 at-risk factors in a Likert-type format, was distributed to gauge the participants’ perceptions of factors influencing academic success, ranging from “not influential” (1) to “very influential” (4). Students received questionnaires during lectures, and faculty members returned them at their convenience.

Statistical analysis

Data were analyzed using SPSS (version 29), incorporating mean calculations, standard error of mean, percentages, one-way ANOVA, independent T-tests, and significance testing with p ≤ 0.05 as the threshold for statistical significance. Due to practical constraints, a convenience sampling method was employed, resulting in a non-random sample of participants. Although the sample size was not large, efforts were made to ensure that the sample was diverse and representative of the student population. To strengthen the validity of the results, various assumptions underlying linear models were examined. Normality for the distribution of each variable was assessed using Shapiro-Wilk test. Scatter plots and Pearson correlation coefficients were used to examine the linearity of relationships between variables. The constant variance (homoscedasticity) assumption was tested by plotting residuals versus predicted values. The Bonferroni correction was applied to address the issue of inflated Type I error due to multiple comparisons. The significance level was adjusted to p = 0.05/25 = 0.002 for the 25 at-risk factors listed in the self-developed questionnaire (Supplementary file).

Results

Content, construct validity and reliability of questionnaire items

The internal validity test for the rational scale revealed individual item Content Validity Index (I-CVI) scores within the range of 0.94–1.0, accompanied by a Scale-Level Content Validity Index (S-CVI/Ave) of 0.99. This outcome suggests that participants considered 99% of the items in the rational scale to be both relevant and clear. In contrast, the I-CVI for these items varied between 0.84 and 1. The S-CVI/Ave of the questionnaire reached 0.93, indicating that 93% of the items were clear, unambiguous, and relevant to the study participants.

Corrected item-total correlation assesses the relationship between a specific item and the total score of other items. A score exceeding 0.5 indicates a strong, positive correlation, while a score ranging from 0.3 to 0.5 is considered acceptable. In this research, 22 out of 25 items demonstrated a corrected item-total correlation surpassing 0.5. However, three items (Item 1, 3, and 5) had correlations of 0.44, 0.11, and 0.42, respectively. The item with a 0.11 correlation was revised for improved clarity. Despite the lower correlation, both authors and the expert panel opted to retain the item due to its essential role in identifying at-risk students.

Cronbach’s alpha (α) gauges the internal consistency of an assessment instrument, with a value between 0.6 and 0.8 considered acceptable. In this study, the Cronbach’s alpha for all items exceeded 0.6, ranging from 0.78 to 0.81.

Linear model assumption testing

Shapiro-Wilk test results indicated non-significant p-values for the majority of variables (p > 0.05), supporting the assumption of normality. Some variables showed slight deviations, but these were not deemed substantial enough to invalidate the analysis. Analysis of scatter plots and Pearson correlation coefficients confirmed linear relationships between most variables. Plots of residuals versus predicted values showed no apparent pattern or funnel shape, suggesting that the homoscedasticity assumption was satisfied.

Perceptions of faculty and students on at-risk factors

Classroom engagement

Significant differences in perceptions were noted between faculty (Mean Score: 2.60 ± 0.17) and students (Mean Score: 2.13 ± 0.06) regarding classroom engagement (p = 0.017, Bonferroni-corrected p > 0.002 but notable). A substantial number of faculty members believe that students’ lack of engagement in the classroom significantly hinders their academic success (Figs. 1a and 2d).

Fig. 1
figure 1

Disparities in perceptions between students and faculty regarding (a) Classroom Engagement, (b) Procrastination, (c) Unrealistic Goals, (d) Emotional/Behavioral Problems, (e) Limited Social Competence, (f) Supportiveness of Home Environment

Full size image
Fig. 2
figure 2

Comparative insights of students and faculty regarding potential at-risk factors: (a) Deficiency in Self-Discipline, (b) Unsupportive Home Environment, (c) Restricted Communication Competence, (d) Classroom Disengagement, (e) Cultural or Linguistic Hurdles, and (f) Absence of Personalized Guidance or Mentorship

Full size image

Procrastination

There were significant disparities in perceptions between faculty (Mean Score: 3.31 ± 0.13) and students (Mean Score: 2.68 ± 0.06) regarding procrastination (p = 0.001, Bonferroni-corrected p < 0.002). Many faculty members firmly believe that procrastination among students plays a substantial role in impeding their academic success (Figs. 1b and 3a).

Fig. 3
figure 3

Comparative views of students and faculty regarding potential at-risk factors: (a) Procrastination, (b) Teaching Approaches, (c) Incidence of Learning or Physical Disabilities (Diagnosed or Undiagnosed), (d) Levels of Academic Motivation, (e) Emotional, Psychological, or Behavioral Challenges, and (f) Preparedness for Current Academic Demands

Full size image

Unrealistic goals

There were noteworthy differences in perceptions between faculty (Mean Score: 3.00 ± 0.14) and students (Mean Score: 2.62 ± 0.05) concerning the lack of goal clarity, which were statistically significant (p = 0.026, Bonferroni-corrected p > 0.002 but notable). A considerable number of faculty members believe that the lack of goal clarity among students hampers their academic success (Figs. 1c and 4a).

Fig. 4
figure 4

Comparative perspectives of students and faculty concerning potential at-risk factors: (a) Aspirations Out of Alignment with Reality, (b) Fragile Self-Perception (Judgmental or Fearful of Failure), (c) Adverse Social Circles or Cultural Norms, (d) Diminished Self-Confidence, (e) Detrimental Peer Influences (Social Exclusion or Group Dynamics), and (f) Subdued Self-Respect or Self-Esteem

Full size image

Emotional/behavioral problems

Clear disparities in perceptions were apparent between faculty (Mean Score: 2.97 ± 0.12) and students (Mean Score: 2.51 ± 0.06) regarding emotional/behavioral problems (p = 0.008, Bonferroni-corrected p < 0.002 but notable). Many faculty members believe that emotional/behavioral issues among students significantly deter their academic success (Figs. 1d and 3e).

Limited social competence

Perceptions varied distinctly between faculty (Mean Score: 3.31 ± 0.12) and students (Mean Score: 3.60 ± 0.04) regarding limited key social skills, revealing statistically significant differences (p = 0.023, Bonferroni-corrected p > 0.002 but notable). Many students perceive limited social skills among their peers as a hindrance to academic success (Figs. 1e and 5b).

Fig. 5
figure 5

Comparative perspectives of students and faculty regarding potential at-risk factors: (a) Discrimination (Racism or Sexism), (b) Social Competence with Limited Key Social Skills, (c) Escalating College Expenses, (d) Academic Unreadiness, (e) Strained College Relationships, (f) Mounting Transportation Expenditures and Time Commitments, and (g) Bias in College Evaluation Culture or Academic Fit Mismatch

Full size image

Non-supportive home environment

Significant disparities were evident between faculty (Mean Score: 3.14 ± 0.13) and students (Mean Score: 2.64 ± 0.05) regarding the non-supportive home environment (p = 0.001, Bonferroni-corrected p < 0.002). Many faculty members believe that a non-supportive home environment of students significantly hampers their academic success (Figs. 1f and 2b).

Other potential at-risk factors

No significant disparities (p > 0.05) were observed in faculty and students’ perceptions regarding mean score of certain at-risk factors that could hinder students’ academic success. Nevertheless, there were varying degrees of influence perceived by students and faculty regarding these potential risk factors that could impede academic success (Figs. 2, 3, 4 and 5).

Both faculty and students perceived some of the at-risk factors as highly influential in hindering academic success. These factors include academic unpreparedness (54.28% of faculty and 50.76% of students), procrastination (48.57% of faculty and 71.53% of students), lack of motivation for performing well (42.85% of faculty and 50.76% of students), teaching methodologies (40% of faculty and 42% of students), emotional/behavioral problems (54.28% of faculty and 47.69% of students), low levels of self-confidence (48.57% of faculty and 40% of students), lack of self-discipline (45.71% of faculty and 60% of students), and low levels of self-respect (42.85% of faculty and 39.23% of students).

Furthermore, both groups considered other at-risk factors as fairly influential. This included limited key social skills (42.85% of faculty and 41.53% of students), cultural/language barriers (51.42% of faculty and 32.30% of students), diagnosed/undiagnosed learning/physical disabilities (37.14% of faculty and 38.07% of students), lack of classroom engagement (51.42% of faculty and 41.53% of students), negative peer culture (42.85% of faculty and 34.23% of students), lack of goal clarity (54.28% of faculty and 36.53% of students), bias in college evaluation culture (62.85% of faculty and 30.38% of students), being underprepared for current academic challenges (54.28% of faculty and 43.46% of students), negative social networks/cultural norms (31.42% of faculty and 29.61% of students), limited communication skills (51.42% of faculty and 39.23% of students), lack of individual guidance/mentoring (54.28% of faculty and 34.23% of students), weak self-concept (48.57% of faculty and 35.38% of students), and strained college relationships (48.57% of faculty and 31.53% of students), all of which can impede students’ academic success.

Discussion

The results of our study have provided valuable insights into the perceptions of both students and faculty on several critical at-risk factors that could affect students’ academic success. The assessment of the questionnaire’s content, construct validity, and reliability reveals promising findings, with the internal validity test indicating high Content Validity Index (CVI) scores. The individual item CVI (I-CVI) values ranging from 0.94 to 1.0 and a Scale-Level CVI (S-CVI/Ave) of 0.99 suggest that nearly all items were deemed relevant and clear by participants. This level of content validity is essential for ensuring that the instrument accurately captures the intended constructs, which is critical for the subsequent data analysis and interpretation [40]. The acceptable corrected item-total correlation for most items further underscores the reliability of the questionnaire, suggesting that it effectively measures the underlying constructs of interest [41]. In addition to content validity, the reliability assessment indicated a Cronbach’s alpha ranging from 0.78 to 0.81. This level of internal consistency is deemed satisfactory, as a Cronbach’s alpha above 0.7 is generally accepted in the social sciences [42]. These findings support the use of this questionnaire in further studies, as instruments with established reliability are more likely to yield reproducible results [43]. The results of the linear model assumption tests indicate that the data reasonably meet the assumptions underlying linear models’ analysis, including normality, linearity, and constant variance. These findings provide a solid foundation for the subsequent analysis and interpretation of the relationships between the variables of interest.

Students pursuing higher education in universities, spanning a wide range of academic disciplines, often encounter a multitude of organizational, instructional, and interpersonal obstacles in their pursuit of academic achievement. We found significant disparity in perceptions between faculty and students regarding classroom engagement. In our study, a substantial portion of faculty believes that students’ lack of classroom engagement significantly hampers their academic success. This finding suggests a need for improved communication and collaboration between students and faculty to address this issue. Faculty members may benefit from exploring more engaging teaching methods, while students could consider being more proactive in their approach to learning. One of the studies highlighted the significance of student engagement, stating that academic involvement promotes a positive attitude, which in turn is reflected in academic performance [44]. Academically engaged students typically comply with school demands, such as attendance and discipline, which enables them to achieve overall academic success and navigate academic challenges. In contrast, disengaged students are more likely to skip classes, harbor negative attitudes toward their institutions, and underperform academically. Therefore, low attendance can significantly contribute to underperformance in higher education. Additionally, financial hardships can lead to student disengagement, as the extra effort required to make ends meet, including part-time jobs and dealing with depression and anxiety, can result in low engagement [45].

We identified a significant difference in the mean scores reflecting faculty and student perceptions about the impact of non-supportive home environments on academic success. Faculty members perceive non-supportive home environments as potentially hindering students’ academic performance, whereas students’ opinions on this matter diverge significantly from faculty perceptions. The disparities in perceptions regarding a non-supportive home environment raise important questions about the impact of external factors on students’ success. This difference may be influenced by the inclusion of international students in our study, who live in campus hostels or rented accommodations far from their home countries. Family circumstances such as parents’ educational background, occupation, and domestic environment can impact students’ academic performance. One of the studies correlated students’ academic achievement with their parents’ level of education, highlighting the role of a literate home environment and parental support [46]. Gregg-Jolly et al. emphasized the importance of adequate social support for students facing academic pressures [47]. They noted that support from family, friends, peers, and other social relationships can help students manage academic challenges and feel more engaged in their educational journey. This underscores the need for additional support systems and resources for students facing challenging home situations.

Social skills refer to the ability to effectively communicate, interact, and build relationships with others. These skills encompass verbal and nonverbal communication, empathy, active listening, problem-solving, and conflict resolution, and are expressed through both spoken language and body language. They are crucial for navigating social situations and forming meaningful connections. In the present study, we found that students perceived limited social competence as a more significant barrier than faculty, which indicates a possible disconnect in understanding the social dynamics affecting student experiences in academic settings. Social skills contribute to academic achievement, personal growth, enhanced understanding, productivity, employability, and career success [48]. Jeffrey et al. reported that social skills influence various academic competencies, such as study habits, classroom behavior, and peer interactions, which collectively impact academic performance [49]. When students can effectively communicate their ideas, listen to others, and collaborate, they are more likely to succeed both academically and in life. A lack of social skills can have widespread effects, affecting relationships, academic and professional outcomes, as well as mental and emotional well-being. Recognizing the importance of addressing social skill deficits is the first step toward seeking suitable interventions and support. Solutions may include Social Emotional Learning programs, individual therapy, or parental involvement, all aimed at helping individuals develop and enhance their social skills.

We observed that perceptions of faculty surrounding procrastination, lack of goal clarity and emotional/behavioral problems demonstrated marked differences, with students attributing its detrimental effects on academic outcomes. One of the studies reported that academic procrastination is prevalent among university students and substantially correlated with conscientiousness and academic achievement [50]. This underscores the importance of time management and study skills training for students. Faculty can also play a role in encouraging students to manage their time more effectively. Frumos et al. reported that both approach and avoidance goal orientations positively correlated with academic achievement. This indicates that higher education instructors should focus on the goals, emotions, and learning strategies utilized by students, as well as the interplay between these variables, to enhance academic performance [51]. Emotional regulation and control are crucial skills that play a significant role in motivating students and enhancing their performance. Emotional responses reflect an individual’s tendencies to react adaptively to primary stressors [52]. Self-regulation involves a set of metacognitive and behavioral strategies that learners employ to enhance their academic achievements and outcomes. A lack of help-seeking behavior and deficiencies in self-regulation are key factors contributing to poor academic performance [53]. Unfortunately, help-seeking behavior is sometimes viewed negatively by students, leading them to neglect their own needs and avoid seeking advice or assistance.

We did not observe significant disparities in faculty and students’ perceptions regarding mean score of certain at-risk factors that could hinder students’ academic success. However, there were varying degrees of influence perceived by students and faculty regarding these at-risk factors. This suggests that while these factors may not be perceived as significant barriers universally, they still warrant attention, as they could affect certain individuals or subgroups of students differently. Faculty bear the responsibility of understanding their students’ learning styles to effectively align their teaching methods with them [54]. A mismatch between learning styles, course material, and teaching approaches can diminish student motivation. One of the studies reported that mismatches between learning and teaching styles can significantly hinder students’ progress [55]. Chetty et al. elaborated on the importance of aligning teaching styles with students’ learning preferences, noting that this alignment correlates with enhanced academic performance [56]. They recommended the use of visual teaching-learning strategies to achieve this goal. Low aspirations and lack of student motivation result in decreased academic performance [57]. Implementing strategies like outdoor learning can boost student motivation and engagement, promoting emotional, social, and personal growth while also improving cognitive skills [58]. One of the studies reported that a high level of coherence is a significant predictor of better grades and overall academic success among first-year undergraduate nursing students [59]. Another study highlighted the significance of self-efficacy, which is defined as an individual’s belief in their ability to succeed, and pointed out its link to academic performance [60]. Academic motivation impacts various aspects of classroom engagement [61]. Motivated students tend to be more engaged and achieve higher academic success. These students demonstrate skills such as self-efficacy and intrinsic value, along with metacognitive abilities like critical thinking and self-regulation, all of which positively influence their academic performance.

Institutional support enhances learning, alleviates anxiety, and cultivates a sense of belonging [62]. It is crucial for universities to establish this foundation early to prevent attrition and help students acclimate to the university environment. Non-traditional students, including first-generation and ethnic minority individuals, often face sociocultural challenges [63]. Universities should have a responsibility to foster a strong sense of belonging for these students [64]. This can be achieved not by asking students to change, but by adapting the university culture to ensure equal opportunities for all students to fulfill their personal aspirations. It’s important for students to be aware of the support available to them and how to access it.

Higher education institutions should cultivate resilient lifelong learners who can confront challenges in both their professional and personal lives. Educators carry a social and professional responsibility to support students in reaching their performance objectives. This is especially crucial when dealing with learners who encounter difficulties within their learning environments, prompting educators to adapt their approaches to identify potential high-risk factors at the early stage of students’ career [65, 66]. Our study’s strengths lie in its comprehensive assessment of 25 at-risk factors among medical students, incorporating perspectives from both students and faculty, a substantial sample size of 250 medical students and 50 teaching faculty, and its international context with a twinning program in India and Malaysia. These insights can inform proactive measures for student support. However, limitations include potential bias in Likert-type questionnaires, reliance on self-reported data, limited generalizability, and single institution data cautioning researchers to interpret findings accordingly.

Conclusion

Our study has revealed the importance of addressing the disparities in perceptions between medical students and faculty regarding factors that could hinder academic success. We have demonstrated the potential areas for intervention, such as improving engagement, addressing procrastination, promoting better social competence, enhancing mentorship opportunities, and utilizing student support services. Students must be encouraged to engage socially within their institution and connect with peers in their courses to benefit from peer support. Moreover, our study underscores the need for fostering inclusive and supportive learning environments and open dialogues on issues of diversity and inclusion. Addressing these factors collectively can contribute to improving overall student success and the educational experience. Further research and efforts in these areas are essential for the holistic development and success of students in higher education.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

S:

CVI-Scale Level Content Validity Index

AVE:

Average

I:

CVI-Item Level Content Validity Index

IEC:

Institutional Ethics Committee

SPSS:

Statistical Package for Social Sciences

ANOVA:

Analysis of Variance

RAKMHSU:

RAK Medical and Health Sciences University

MBBS:

Bachelor of Medicine and Bachelor of Surgery

RAK:

Ras Al Khaimah

UAE:

United Arab Emirates

References

  1. King R. Mentoring at risk students: a School-based Action Research Project. School Support Services Division, Department of Education; 1999.

  2. Keeling S. Advising the millennial generation. NACADA J. 2003;23(1–2):30–6. https://doi.org/10.12930/0271-9517-23.1-2.30

    Article  Google Scholar 

  3. Ender SC, Wilkie CJ. (2000). Advising students with special needs. Academic advising: A comprehensive handbook, 1, 118–143.

  4. Gordon VN, Habley WR, Grites TJ. Academic advising. Wiley; 2008.

  5. Singh S, Malik S, Singh P. Research paper factors affecting academic performance of students. Indian J Res. 2016;5(4):176–8.

    Google Scholar 

  6. Amuzu S, Ankalibazuk E, Abdulai S. Low performance of pupils in BECE: a case study of Sagnarigu District in Northern Region, Ghana. Int J Adv Res Sci Eng Technol. 2017;4(7):4176–84.

    Google Scholar 

  7. Inuusah M, Regine K, Ansu AM. Head teachers’ perception of the influence of their role behaviours on students’ academic performance in the Berekum Municipality. Br J Educ. 2018;6(7):10–30.

    Google Scholar 

  8. Edo H, Jones A, Hashimu M. Investigating pupils’ strategies on mathematics achievement among private and public basic schools in the Chorkor Circuit of Accra Metropolis. Integr J Educ Train. 2022;6(5):91–100. https://doi.org/10.31248/IJET2022.145. Article Number: 14D211E81.

    Article  Google Scholar 

  9. Duorinaah E, Alhassan E. Influence of leadership styles of headteachers on pupils learning outcomes in junior high schools in Northern Ghana. Int J Humanit Social Stud. 2021;9(4):123–33. https://doi.org/10.24940/theijhss/2021/v9/i4/HS2104-024

    Article  Google Scholar 

  10. Lynam S, Cachia M, Stock R. An evaluation of the factors that influence academic success as defined by engaged students. Educational Rev. 2024;76(3):586–604. https://doi.org/10.1080/00131911.2022.2052808

    Article  Google Scholar 

  11. May RW, Bauer KN, Seibert GS, Jaurequi ME, Fincham FD. School burnout is related to sleep quality and perseverative cognition regulation at bedtime in young adults. Learn Individual Differences. 2020;78:101821. https://doi.org/10.1016/j.lindif.2020.101821

    Article  Google Scholar 

  12. Seibert GS, Bauer KN, May RW, Fincham FD. Emotion regulation and academic underperformance: the role of school burnout. Learn Individual Differences. 2017;60:1–9. https://doi.org/10.1016/j.lindif.2017.10.001

    Article  Google Scholar 

  13. Kranjac AW. The moderating effect of self-efficacy on normal-weight, overweight, and obese children’s math achievement: a longitudinal analysis. Soc Sci Med. 2015;128:168–77. https://doi.org/10.1016/j.socscimed.2015.01.007

    Article  Google Scholar 

  14. McNelis AM, Dunn DW, Johnson CS, Austin JK, Perkins SM. Academic performance in children with new-onset seizures and asthma: a prospective study. Epilepsy Behav. 2007;10(2):311–8. https://doi.org/10.1016/j.yebeh.2006.12.012

    Article  Google Scholar 

  15. Peled Y. Cyberbullying and its influence on academic, social, and emotional development of undergraduate students. Heliyon. 2019;5(3):e01393. https://doi.org/10.1016/j.heliyon.2019.e01393

    Article  Google Scholar 

  16. Imeraj L, Antrop I, Sonuga-Barke E, Deboutte D, Deschepper E, Bal S, Roeyers H. The impact of instructional context on classroom on-task behavior: a matched comparison of children with ADHD and non-ADHD classmates. J Sch Psychol. 2013;51(4):487–98. https://doi.org/10.1016/j.jsp.2013.05.004

    Article  Google Scholar 

  17. Prevatt F, Young JL. Recognizing and treating attention-deficit/hyperactivity disorder in college students. J Coll Student Psychother. 2014;28(3):182–200. https://doi.org/10.1080/87568225.2014.914825

    Article  Google Scholar 

  18. Ekperi P, Onwuka U, Nyejirime W. Teachers’ attitude as a correlate of students’ academic performance. Int J Res Innov Social Sci. 2019;3(1):205–9.

    Google Scholar 

  19. Ma Y, Wei C. The relationship between perceived classroom climate and academic performance among English-major teacher education students in Guangxi, China: the mediating role of student engagement. Front Psychol. 2022;13:939661. https://doi.org/10.3389/fpsyg.2022.939661.

    Article  Google Scholar 

  20. Stronge JH, Ward TJ, Grant LW. What makes good teachers good? A cross-case analysis of the connection between teacher effectiveness and student achievement. J Teacher Educ. 2011;62(4):339–55. https://doi.org/10.1177/0022487111404241

    Article  Google Scholar 

  21. Al-Tameemi RAN, Johnson C, Gitay R, Abdel-Salam ASG, Hazaa A, BenSaid K, A., Romanowski MH. Determinants of poor academic performance among undergraduate students- A systematic literature review. Int J Educational Res Open. 2023;4:100232. https://doi.org/10.1016/j.ijedro.2023.100232

    Article  Google Scholar 

  22. Johnson C, Gitay R, Al Hazaa K, Abdel-Salam A-SG, Mohamed RI, BenSaid A, Al-Tameemi RAN, Romanowski MH. Causes of undergraduate students’ underachievement in a Gulf Cooperation Council Country (GCC) University. SAGE Open. 2022;12(3):21582440221079850. https://doi.org/10.1177/21582440221079847

    Article  Google Scholar 

  23. Yilmaz E, Sahin M, Turgut M. Variables affecting student motivation based on academic publications. J Educ Pract. 2017;8(12):112–20.

    Google Scholar 

  24. Rogowsky BA, Calhoun BM, Tallal P. Matching learning style to instructional method: effects on comprehension. J Educ Psychol. 2015;107:64–78. https://doi.org/10.1037/a0037478

    Article  Google Scholar 

  25. Andreassen CS, Pallesen S, Griffiths MD. The relationship between addictive use of social media, narcissism, and self-esteem: findings from a large national survey. Addict Behav. 2017;64:287–93. https://doi.org/10.1016/j.addbeh.2016.03.006

    Article  Google Scholar 

  26. Goroshit M, Hen M. Academic procrastination and academic performance: do learning disabilities matter? Curr Psychol. 2021;40:2490–8. https://doi.org/10.1007/s12144-019-00183-3

    Article  Google Scholar 

  27. Eisenberg D, Golberstein E, Hunt JB. Mental health and academic success in College. B E J Economic Anal Policy. 2009;9(1). https://doi.org/10.2202/1935-1682.2191

  28. Eckert M, Ebert DD, Lehr D, Sieland B, Berking M. Overcome procrastination: enhancing emotion regulation skills reduce procrastination. Learn Individual Differences. 2016;52:1018. https://doi.org/10.1016/j.lindif.2016.10.001

    Article  Google Scholar 

  29. Klassen RM, Krawchuk LL, Rajani S. Academic procrastination of undergraduates: low self-efficacy to self-regulate predicts higher levels of procrastination. Contemp Educ Psychol. 2008;33(4):915–31. https://doi.org/10.1016/j.cedpsych.2007.07.001

    Article  Google Scholar 

  30. Ferrari JR. Still procrastinating: the no regrets guide to getting it done. Turner Publishing Company; 2010.

  31. Grunschel C, Patrzek J, Fries S. Exploring reasons and consequences of academic procrastination: an interview study. Eur J Psychol Educ. 2013;28(3):841–61. https://doi.org/10.1007/s10212-012-0143-4

    Article  Google Scholar 

  32. Horton J. Identifying at-risk factors that affect college student success. Int J Process Educ. 2015;7(1):83–101. https://www.ijpe.online/2015/risk.pdf

    Google Scholar 

  33. Cornford IR. Learning-to-learn strategies as a basis for effective lifelong learning. Int J Lifelong Educ. 2002;21(4):357–68. https://doi.org/10.1080/02601370210141020

    Article  Google Scholar 

  34. Battersby D. (2009). An Indicator Framework for Higher Education Performance Funding. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/download

  35. Kocsis A, Molnar G. Factors influencing academic performance and dropout rates in higher education. Oxf Rev Educ. 2024;1–19. https://doi.org/10.1080/03054985.2024.2316616

  36. Koller I, Levenson MR, Gluck J. What do you think you are measuring? A mixed-methods procedure for assessing the content validity of test items and theory-based scaling. Front Psychol. 2017;8:126. https://doi.org/10.3389/fpsyg.2017.00126

    Article  Google Scholar 

  37. Zamanzadeh V, Ghahramanian A, Rassouli M, Abbaszadeh A, Alavi-Majd H, Nikanfar AR. Design and implementation content validity study: development of an instrument for measuring patient-centered communication. J Caring Sci. 2015;4(2):165. https://doi.org/10.15171/jcs.2015.017.

    Article  Google Scholar 

  38. Shi J, Mo X, Sun Z. Content validity index in scale development. J Cent South Univ Med Sci. 2012;37(2):152–5. https://doi.org/10.3969/j.issn.1672-7347.2012.02.007

    Article  Google Scholar 

  39. Hajjar ST. Statistical analysis: internal-consistency reliability and construct validity. Int J Quant Qualitative Res Methods. 2018;6(1):46–57.

    Google Scholar 

  40. Polit DF, Beck CT. The content validity index: are you sure you know what’s being reported? Critique and recommendations. Res Nurs Health. 2006;29(5):489–97. https://doi.org/10.1002/nur.20147

    Article  Google Scholar 

  41. DeVellis RF, Thorpe CT. Scale development: theory and applications. Sage; 2021.

  42. Tavakol M, Dennick R. Making sense of Cronbach’s alpha. Int J Med Educ. 2011;53. https://doi.org/10.5116/ijme.4dfb.8dfd. 2.

  43. Streiner DL, Norman GR, Cairney J. Health measurement scales: a practical guide to their development and use. Oxford University Press; 2024.

  44. Rosenbaum E, Rochford JA. Generational patterns in academic performance: the variable effects of attitudes and social capital. Soc Sci Res. 2008;37(1):350–72. https://doi.org/10.1016/j.ssresearch.2007.03.003

    Article  Google Scholar 

  45. Magnuson KA, Duncan GJ. The role of family socioeconomic resources in the black–white test score gap among young children. Dev Rev. 2006;26(4):365–99. https://doi.org/10.1016/j.dr.2006.06.004

    Article  Google Scholar 

  46. Flores-Crespo P. Ethnicity, identity and educational achievement in Mexico. Int J Educational Dev. 2007;27(3):331–9. https://doi.org/10.1016/j.ijedudev.2006.10.011

    Article  Google Scholar 

  47. Gregg-Jolly L, Swartz J, Iverson E, Stern J, Brown N, Lopatto D. Situating second-year success: understanding second-year STEM experiences at a liberal arts college. CBE—Life Sci Educ. 2016;15(3):ar43. https://doi.org/10.1187/cbe.16-01-0044.

    Article  Google Scholar 

  48. Sharma R, Goswami V, Gupta P. Social skills: their impact on academic achievement and other aspects of life. Int J Innovative Res Multidisciplinary Field. 2016;2(7):219–24.

    Google Scholar 

  49. DeVries JM, Rathmann K, Gebhardt M. How does social behavior relate to both grades and achievement scores? Front Psychol. 2018;9:857. https://doi.org/10.3389/fpsyg.2018.00857

    Article  Google Scholar 

  50. Sparfeldt JR, Schwabe S. Academic procrastination mediates the relation between conscientiousness and academic achievement. Pers Indiv Differ. 2024;218:112466. https://doi.org/10.1016/j.paid.2023.112466

    Article  Google Scholar 

  51. Frumos FV, Leonte R, Candel OS, Ciochină-Carasevici L, Ghiaţău R, Onu C. The relationship between university students’ goal orientation and academic achievement. The mediating role of motivational components and the moderating role of achievement emotions. Front Psychol. 2024;14:1296346. https://doi.org/10.3389/fpsyg.2023.1296346

    Article  Google Scholar 

  52. Tyra AT, Fergus TA, Ginty AT. Emotion suppression and acute physiological responses to stress in healthy populations: a quantitative review of experimental and correlational investigations. Health Psychol Rev. 2024;18(2):396–420. https://doi.org/10.1080/17437199.2023.2251559

    Article  Google Scholar 

  53. Lizzio A, Wilson K. Early intervention to support the academic recovery of first-year students at risk of non-continuation. Innovations Educ Teach Int. 2013;50(2):109–20. https://doi.org/10.1080/14703297.2012.760867

    Article  Google Scholar 

  54. McChlery S, Visser S. A comparative analysis of the learning styles of accounting students in the United Kingdom and South Africa. Res Post-Compulsory Educ. 2009;14(3):299–315. https://doi.org/10.1080/13596740903139404

    Article  Google Scholar 

  55. Wilson M, Scalise K. Assessment to improve learning in higher education: the BEAR Assessment System. High Educ. 2006;52(4):635–63. https://doi.org/10.1007/s10734-004-7263-y

    Article  Google Scholar 

  56. Chetty NDS, Handayani L, Sahabudin NA, Ali Z, Hamzah N, Rahman NSA, Kasim S. Learning styles and teaching styles determine students’ academic performances. Int J Evaluation Res Educ. 2019;8(4):610–5.

    Google Scholar 

  57. Walkey FH, McClure J, Meyer LH, Weir KF. Low expectations equal no expectations: aspirations, motivation, and achievement in secondary school. Contemp Educ Psychol. 2013;38(4):306–15. https://doi.org/10.1016/j.cedpsych.2013.06.004

    Article  Google Scholar 

  58. Mackenzie SH, Son JS, Eitel K. Using outdoor adventure to enhance intrinsic motivation and engagement in science and physical activity: an exploratory study. J Outdoor Recreation Tourism. 2018;21:76–86. https://doi.org/10.1016/j.jort.2018.01.008

    Article  Google Scholar 

  59. Salamonson Y, Glew PJ, Ramjan LM, Jefferies D, Maneze D, Crook B, Everett B. Psychometric evaluation of the 11-item English language usage scale in commencing nursing students. Nurse Educ Today. 2021;96:104599. https://doi.org/10.1016/j.nedt.2020.104599

    Article  Google Scholar 

  60. Kemp PE, Wong B, Berry MG. Female performance and participation in computer science: a national picture. ACM Trans Comput Educ (TOCE). 2019;20(1):1–28. https://doi.org/10.1145/3366016

    Article  Google Scholar 

  61. Acosta-Gonzaga E. The effects of self-esteem and academic engagement on university students’ performance. Behav Sci. 2023;13(4):348. https://doi.org/10.3390/bs13040348

    Article  Google Scholar 

  62. Masika R, Jones J. Building student belonging and engagement: insights into higher education students’ experiences of participating and learning together. Teach High Educ. 2016;21(2):138–50. https://doi.org/10.1080/13562517.2015.1122585

    Article  Google Scholar 

  63. Devlin M. Bridging socio-cultural incongruity: conceptualising the success of students from low socio-economic status backgrounds in Australian higher education. Stud High Educ. 2013;38(6):939–49. https://doi.org/10.1080/03075079.2011.613991

    Article  Google Scholar 

  64. Tinto V. Through the eyes of students. J Coll Student Retention: Res Theory Pract. 2017;19(3):254–69. https://doi.org/10.1177/1521025115621917

    Article  Google Scholar 

  65. Plak S, Cornelisz I, Meeter M, van Klaveren C. Early warning systems for more effective student counselling in higher education: evidence from a Dutch field experiment. High Educ Q. 2021;76(1):131–52. https://doi.org/10.1111/hequ.12298

    Article  Google Scholar 

  66. Baneres D, Rodriguez-Gonzalez ME, Guerrero-Roldan AE, Cortadas P. An early warning system to identify and intervene online dropout learners. Int J Educational Technol High Educ. 2023;20(1). https://doi.org/10.1186/s41239-022-00371-5

Download references

Acknowledgements

The authors express their gratitude to RAK Medical and Health Sciences University for covering the article processing charge and to Manipal Academy of Higher Education for providing the research facilities.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Author information

Authors and Affiliations

  1. Department of MBA, Institute of Engineering and Technology, Srinivas University, Mukka Campus, Mangaluru, Karnataka, India

    Rashmi Kumari

  2. Former Research Scholar, Melaka Manipal Medical College, Manipal Campus, Manipal Academy of Higher Education, Manipal, Karnataka, India

    Rashmi Kumari

  3. Department of Pharmacology, RAK College of Medical Sciences, RAK Medical and Health Sciences University, Ras Al Khaimah, UAE

    Shakta Mani Satyam & Laxminarayana Kurady Bairy

  4. Department of Pathology, RAK College of Medical Sciences, RAK Medical and Health Sciences University, Ras Al Khaimah, UAE

    Abdul Rehman

  5. Department of Anatomy, Manipal University College Malaysia, Melaka, Malaysia

    Prakashchandra Shetty

  6. Medical Education Unit, National University of Science and Technology, Muscat, Oman

    Ciraj Ali Mohammed

Authors
  1. Rashmi Kumari
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shakta Mani Satyam
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Laxminarayana Kurady Bairy
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Abdul Rehman
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Prakashchandra Shetty
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ciraj Ali Mohammed
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

S.M.S. has substantial contributions to the conception, design and supervision of this study. R.K. and S.M.S. were responsible for conducting the research. Formal analysis was carried out by R.K., S.M.S., L.K.B., A.R., P.S., and C.A.M. Data entry, statistical analysis, and interpretation were conducted by R.K. and S.M.S. The initial draft of the manuscript was written by S.M.S., with all authors reviewing and approving the final version. All the authors agree to be accountable for all aspects of the work, ensuring that any questions related to the accuracy or integrity of any part of the work are properly investigated and resolved.

Corresponding author

Correspondence to Shakta Mani Satyam.

Ethics declarations

Ethics approval and consent to participate

The study received ethics approval from the Institutional Ethics Committee, Kasturba Hospital, Manipal Academy of Higher Education, India (IEC/KH/617/2018). All the participants were recruited with their consent, ensuring anonymity and confidentiality of demographic information. Informed consent was obtained from each participant.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it.The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kumari, R., Satyam, S.M., Bairy, L.K. et al. Crossing horizons: unraveling perspectives on enhancing medical students’ success through at-risk factor exploration. BMC Med Educ 24, 835 (2024). https://doi.org/10.1186/s12909-024-05819-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12909-024-05819-y

Keywords

BMC Medical Education

ISSN: 1472-6920

Contact us