Stress Factors in Higher Education: A Data Analysis Case

Rodolfo Bojorque

^{1,2,*,†,‡}

Fernando Moscoso

^1,‡

Fernando Pesántez

^1,‡

and

Ángela Flores

^1,‡

Campus El Vecino, Universidad Politécnica Salesiana, Cuenca 010102, Ecuador

Math Innovation Group, Universidad Politécnica Salesiana, Cuenca 010102, Ecuador

Author to whom correspondence should be addressed.

^†

Current address: Sede Matriz Cuenca, Universidad Politécnica Salesiana, Calle Vieja 12-30, Cuenca 010102, Ecuador.

^‡

These authors contributed equally to this work.

Data2025,10(2), 22;https://doi.org/10.3390/data10020022

Submission received: 14 January 2025 /Revised: 31 January 2025 /Accepted: 5 February 2025 /Published: 7 February 2025

(This article belongs to the Special IssueData Mining and Computational Intelligence for E-Learning and Education—3rd Edition)

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Abstract

This study investigates stressors in higher education, focusing on their impact on students and faculty at Universidad Politécnica Salesiana (UPS) and using eight years of comprehensive data. Employing data mining techniques, the research analyzed enrollment, retention, graduation, employability, socioeconomic status, academic performance, and faculty workload to uncover patterns affecting academic outcomes. The study found that UPS exhibits a stable educational system, maintaining consistent metrics across student success indicators. However, the COVID-19 pandemic presented unique stressors, evidenced by a paradoxical increase in student grades during heightened faculty stress levels. This anomaly suggests a potential link between academic rigor and faculty well-being during systemic disruptions. Stressors affecting students directly correlated with reduced academic performance, highlighting the importance of early detection and intervention. Conversely, faculty stress was reflected in adjustments to grading practices, raising questions about institutional pressures and faculty motivation. These findings emphasize the value of proactive data analytics in identifying stress-induced anomalies to support student success and faculty well-being. The study advocates for further research on faculty burnout, motivation, and institutional strategies to mitigate stressors, underscoring the potential of data-driven approaches to enhance the quality and sustainability of higher education ecosystems.

Keywords:

educational data analysis;higher education stressor variables;educational data mining

1. Introduction

Several studies confirm the existence of stressors in higher education [1,2,3,4,5,6]. Stress and anxiety are adaptive responses of the body to situations that are perceived as threatening or challenging [7]. However, when these responses become chronic or excessive, they can have detrimental effects on various aspects of life, including academic performance. Several studies in medical education support this observation [2,8]. In the university context, where academic demands are high and expectations are significant, it is crucial to understand how stress and anxiety can affect students’ ability to effectively cope with the challenges of finishing their career.

Exploratory Data Analysis (EDA) is a process that is used to understand, interpret, and analyze data through statistical and graphical tools. It involves examining datasets to summarize their main characteristics, often with visual methods [9,10]. EDA is an approach rather than a set of techniques, focusing on discovering patterns, spotting anomalies, testing hypotheses, and checking assumptions with the help of summary statistics and graphical representations.

Studies examining stress factors within the educational field typically focus on exploratory and descriptive research [8,11], making significant contributions to the body of knowledge. This study is based on extensive data mining of information collected over eight years at Universidad Politécnica Salesiana (UPS). The research encompasses data on students across all phases of the educational cycle, including enrollment, retention, graduation, and employability. Furthermore, it includes data on faculty related to professional development, age, teaching experience, and scientific production. It also covers socioeconomic status, academic records, academic counseling, and employment information. Additionally, the study inter-relates faculty information, including workload, academic history, and research outcomes, as previous studies based on the same data have suggested an influence of faculty decisions on students’ stress levels [12,13]. This work can be considered the final consolidation of years of research in a diverse environment, since UPS has campuses in Ecuador’s three main cities. Being a private institution, it offers a differentiated tuition system, which has enabled the collection of socioeconomic data that are often lacking in other studies, as all students seek to benefit from the scholarship system. Data collection began in 2015, with an average annual student population of 22,000 to 24,000 for undergraduate studies and a faculty of 950 to 1100 professors.

2. Related Works

Data mining enables the effective detection of patterns within higher education information systems. Indeed, several studies have addressed the prediction of various aspects, particularly student dropout, through machine learning techniques [14,15]. Pattern detection has a wide range of applications, allowing for the early identification of issues that are inherent in the educational process, thereby facilitating proactive and appropriate management by institutions [16]. According to the literature, there are three approaches to addressing stressors in higher education.

2.1. Exploratory Data Analysis in the Education Realm

In the educational field, there are various applications that highlight the current importance of EDA.

Academic Performance Prediction: EDA is used to analyze various factors affecting student performance, including demographic, social, and academic attributes. This helps in identifying key predictors of academic success and areas needing intervention [17].
Improving Teaching Processes: By understanding the data on students’ performance and behavior, educators can adjust teaching techniques and educational tools to better meet student needs [18].
Dropout Rate Analysis: EDA can help identify factors contributing to high dropout rates, enabling the development of strategies to retain students [18].
Personalized Learning: Insights from EDA can be used to tailor educational content and methods to individual student needs, enhancing learning outcomes [19].
MOOC Analysis: In the context of Massive Open Online Courses (MOOCs), EDA helps analyze learner behavior and predict course completion rates, which can inform course design and delivery [20].

2.2. Common Stressors Faced by Students in Higher Education

Academic Stressors: Studies have highlighted academic stressors such as family-related pressures, scholarship requirements, financial burdens, happiness, and competition in class [21,22,23,24].
Transition to University: The transition from secondary education to university life has been identified as a pivotal juncture, accompanied by stressors related to the adaptation to new educational and social environments [21,25].
Financial Obligations and Academic Expectations: Financial obligations, academic expectations, and social and emotional difficulties have been recognized as multidimensional stressors that are faced by higher education students [26].

2.3. Impact of Stressors on Students’ Mental Health

Negative Impact: The prevalence of stress has been found to negatively impact students’ mental and physical health, academic performance, and overall quality of life [26].
Physical and Psychological Issues: Stressors trigger physical and psychological issues such as a lack of energy, loss of appetite, headaches, sleep problems, and gastrointestinal problems [21].
Health Symptoms: Academic stress has been found to be moderately correlated with health symptoms such as physical exhaustion, sleeping disorders, irascibility, negative thoughts, and feeling nervous [27].

2.4. Strategies to Mitigate Stressors in Higher Education

Support Systems and Initiatives: It is essential for institutions to comprehend stressors to put in place efficient support systems and initiatives that foster student resilience and well-being [26].
Stress Management Programs: Higher education institutions focus on students’ involvement in co-curricular activities and provide counseling facilities and stress management programs that are tailored to students’ needs and levels of stress [22].
Counseling and Stress Management: Stress management, assertiveness skills, time management, and counseling sessions have been identified as effective in reducing the stress experienced by students [28].

2.5. Long-Term Effects on Academic Performance and Career Outcomes

Academic Achievement: Academic-related stress has been found to reduce academic achievement, decrease motivation, and increase the risk of school dropout [29].
Impact on Learning Capacity: Stress has been shown to impact students’ learning capacity, academic performance, mental health problems, sleep disturbances, and substance use [29].
Health and Academic Performance: Long-term and frequent exposure to stress can lead to health complications and may cause students to make behavioral changes and sacrifices that impact their health and academic performance [30].

It is important to note that stress and its effects can also involve other stakeholders within the educational system, such as faculty members. Most studies typically focus on analyzing and describing stressors for students, but there is a lack of research that correlates stress factors affecting faculty with those impacting students and how these may overlap. The objective of this work is to determine, using statistical methods, the correlations between stressors affecting both students and faculty.

2.6. Common Stressors Faced by Faculty in Higher Education

The most common stressors that are faced by faculty in higher education include work overload, role conflict, faculty interaction, academic advancement, and age considerations [31,32].
University faculty members experience occupational stress due to teaching and scientific research pressures, professional ranking, age, and the length of teaching [32].
Non-academic activities such as maintaining records, institutional branding, irregular pay, and unfavorable employment conditions also contribute to stress among teaching faculty [33].
Factors such as pressure from seniors, working conditions, and the nature of work contribute to stress among faculty members [33,34].

2.7. Impact of Stressors on Faculty Performance and Well-Being

Stress affects the job performance of faculty members, particularly in teaching and research activities [33].
Excessive non-academic responsibilities lead to a decline in overall performance in teaching and research among faculty members [33].
Stress among faculty members can lead to physical and psychological issues, affecting their health and well-being [35].
The prevalence of stress among faculty members can have negative socioeconomic impacts, including the cost of medical treatments, leaves of absence, and loss of individual productivity [36].

2.8. Strategies to Mitigate Stressors for Faculty in Higher Education

Faculty members should recognize the positive impact of occupational stress while striving to eliminate stressors [32].
Implementing efficient support systems and initiatives that foster faculty resilience and well-being is essential [37].
Strategies such as exercise, meditation, taking short breaks, flexible work schedules, and open communication can help faculty cope with stress [38].
Formulating public policies to organize their teaching career and considering the incidence of stress is recommended to mitigate stress among faculty members [36].

2.9. Long-Term Effects of Stressors on Faculty in Higher Education

The long-term effects of stressors on faculty in higher education can lead to burnout and negative repercussions, impacting the quality and productivity of academic life [39].
Stress among faculty members can result in poor health, low work satisfaction, decreased work performance, and intentions of leaving the profession [40].
Addressing stress among faculty members is crucial for their all-round development and the overall quality of higher education [38].

The teaching–learning system is closely interconnected; however, most studies have separated the issue of stressors. On the one hand, theis research delves into the stress factors affecting students, while on the other, it examines the stressors impacting faculty members, often treating these as distinct and unrelated issues.

3. Materials and Methods

Our methodology consists of three stages: (1) data collection, (2) performing an Exploratory Data Analysis (EDA) globally on UPS data, including both student and faculty data, and (3) identifying relationships between the study’s relevant variables. The research is based on data collected over an eight-year period (2015–2022) from UPS, a private higher education institution with campuses in Ecuador’s three main cities. The dataset comprises academic and socioeconomic information from both students and faculty, allowing for a comprehensive assessment of stress factors in higher education.

3.1. Data Collection

To ensure consistency and replicability, this study follows standardized procedures for data collection and operational definitions:

Data Sources: The primary dataset was obtained from the university’s official academic information system, which records key variables related to student performance, retention, graduation, employability, and faculty workload. These data sources are institutional records that adhere to national higher education regulations and internal university policies.
Student Variables: The dataset includes information on student enrollment, retention rates, graduation rates, employability, academic performance (GPA), and socioeconomic status. The socioeconomic status is determined based on a mandatory self-reported survey that is used to assess scholarship eligibility, classifying students into five quintiles.
Faculty Variables: Data on faculty members include academic qualifications (PhD rate), years of teaching experience, research output (Scimago Research Rank), teaching load (number of students per faculty member), and faculty evaluations by students. These indicators align with internationally recognized academic performance metrics.
Data Cleaning and Preprocessing: The raw data underwent a preprocessing stage, including data validation, outlier detection, and handling of missing values to ensure consistency. Standard statistical methods and machine learning preprocessing techniques were applied to minimize bias in the analysis.

3.2. Exploratory Data Analysis

This study employs widely recognized definitions for key variables, including the following:

For Students: To understand student behavior regarding enrollment and retention, the retention rate was used. According to the equation, this was summarized as the proportion of students continuing their studies into the fifth semester of their program:

R e t e n t i o n = \frac{# s t u d e n t s_{y 3}}{# e n r o l l m e n t s_{y 1}}

(1)

where

$# e n r o l l m e n t s_{y 1}$ represents the number of new enrollments in the first years;
$# s t u d e n t s_{y 3}$ is the number of students who are continuing their undergraduate education.

Successful graduation is measured using the graduation rate. As per the equation, this represents the percentage of students who graduate within an additional year beyond their program’s expected duration:

G r a d u a t i o n = \frac{# g r a d u a t e d_{+ y 1}}{# e n r o l l m e n t s_{y 1}}

(2)

where

$# g r a d u a t e d_{+ y 1}$ represents the number of students who graduated until a year after their curriculum duration.

Finally, from a social responsibility perspective, the university evaluates professional success using the employability rate, defined as the percentage of graduates who are employed, self-employed, or have started a business within one year after graduation.

E m p l o y a b i l i t y = \frac{# e m p l o y m e n t_{+ y 1}}{# g r a d u a t e d_{+ y 1}}

(3)

where

$# e m p l o y m e n t_{+ y 1}$ represents the number of graduates who have secured employment or started their own businesses in their professional field within one year after graduation.

For Faculty: The Exploratory Data Analysis highlights the following variables:

The Rate of Professors with a PhD: The ratio of faculty members with a PhD to the total number of faculty.
Average Age: A statistic indicating the average age of faculty members.
Years of Experience: This measures faculty tenure and seniority.
Students per Faculty Member: The ratio of students to faculty. This is a significant stressor, often directly proportional to the number of students—more students result in increased stress levels.
Faculty Members per Student: This indicator reflects the average number of different instructors that a student has each year.
Scimago Research Rank: Research, as a core academic responsibility, is perceived by faculty as another stress factor.

3.3. Relationships Between the Study Variables

Two key variables were established to identify common variables between students and faculty that may explain whether stress affects the teaching–learning process in higher education: students per faculty member and faculty members per student. Lastly, grades and student evaluations were included as two final variables in the methodology. At UPS, grades represent the culmination of the faculty member’s planning efforts and the student’s performance in response to the challenges of assessment; meanwhile, student evaluations represent the level of conformity of the students with their faculty.

4. Results

4.1. Student Level

At the student level, UPS demonstrates stable system parameters. The data inFigure 1 show consistent trends in enrollment, retention, graduation, and employability over the past eight years.

Prior studies have emphasized the role of academic stressors, such as financial burdens and institutional expectations, in shaping student performance [21,22,23,24]. The stable trends observed at UPS contrast with findings from other studies that link high academic stress levels to fluctuations in student retention [29].

The behavior of the system becomes particularly interesting when the distribution of the socioeconomic levels within the university’s student body is examined. The socioeconomic level is determined by quintiles, based on the mandatory socioeconomic form that is completed by all students, which is also used to calculate the scholarship eligibility. For UPS, quintile 1 represents the lowest-income socioeconomic group, while quintile 5 corresponds to the highest-income group.Figure 2 shows that during the last eight years, the socioeconomic system has been stable.

The distribution of socioeconomic quintiles at UPS remained stable, indicating that financial stressors did not significantly shift the student demographics. This aligns with findings from previous research, which suggests that financial aid programs and differentiated tuition structures help mitigate economic stress as a dropout factor [23]. However, as Martincová and Bílá (2023) indicate, financial obligations remain a critical stressor for students, impacting their mental health and academic engagement [26].

4.2. Faculty Level

Rate of Professors with a PhD: Obtaining a PhD has been a requirement for research activities in Ecuadorian universities since 2010, following the enactment of the Organic Law of Higher Education. Since then, universities have intensified efforts to facilitate PhD training, often requiring faculty members to balance their teaching responsibilities with pursuing advanced studies, which frequently involve short but intensive periods of work abroad. The first PhD graduates emerged precisely five years after the enactment of the law (2015).Figure 3 illustrates the growth rate of PhD holders at the university, highlighting the previously mentioned implications for research and teaching. The proportion of faculty with PhDs has steadily increased since 2015, reflecting institutional efforts to enhance the research output. However, studies have shown that increasing research demands contribute to faculty stress, particularly when combined with heavy teaching responsibilities [32,33].

Average Age: The faculty is relatively young, with an average age of 46 years

\pm 1

year over the last eight years.

Years of Experience: This indicator shows job stability at the university, as the average number of years of experience has increased by one year annually over the past eight years.Figure 4 shows that the faculty remains stable over time, as the average age remains consistent, and the faculty tenure has increased linearly.

Students per Faculty Member: While the student population has grown, the number of faculty members has increased proportionally, maintaining an average of 30 students per faculty member. The ratio remained stable at approximately 30 students per faculty member, aligning with international accreditation standards. However, the faculty’s workload is not solely determined by class sizes but also by administrative and research responsibilities, which have been identified as significant stress factors in previous studies [31].

Faculty Members per Student: On average, UPS students are taught by four different faculty members each year.Figure 5 highlights the impact of the COVID-19 pandemic, as the number of students decreased in 2020 and 2021. However, the university did not dismiss faculty members, resulting in an increase in the number of faculty members per student. It is important to note that a decrease of one student per faculty member in this indicator corresponds to approximately 1.000 to 1.200 fewer students annually. Faculty members experiencing high stress levels often report declining job satisfaction and research productivity, as has been observed in similar educational settings [36].

Scimago Research Rank: The research performance has declined globally. Although UPS remains at the forefront of the local ranking in Ecuador, its global standing has experienced a significant drop according toFigure 6. The observed increase in faculty research output until 2017, followed by a decline in 2022, suggests that research expectations may be contributing to faculty burnout, a trend that has been reported in other studies on higher education stressors [35].

4.3. Relationships Between Stress Factors for Faculty and Students

The Exploratory Data Analysis revealed that the educational processes at UPS function as a stable system. Indeed, the student evaluation variable has been stable for the last eight years (Figure 7). However, there was a pivotal period during which stress levels for students and faculty reached critical limits: the COVID-19 pandemic. Specifically, the immediate effects were observed at UPS during 2020 and 2021. Over the four semesters that it took Ecuador to return to in-person classes and resume regular operations, an anomaly in student grades was identified.Figure 7 demonstrates that these grades improved by an average of 11.32% (typically, the grade rate varies between 1 and 2.5%.), an atypical occurrence at the university level. This anomaly is particularly striking when data from 2022 (a year when the organizational effects of the pandemic had largely been overcome) show that the system returned to stability. Paradoxically, when stressors affect individual students or faculty members, they are nearly imperceptible within the larger population. However, when stressors impact the entire system, the effects are immediately reflected in academic performance. This phenomenon will be examined in greater detail in theSection 5.

Interestingly, the data reveal a disconnect between faculty stress and student outcomes, as evidenced by the anomaly in student grades during the pandemic. Prior research suggests that when faculty experience heightened stress, their grading practices may shift, either becoming more lenient or more stringent [13]. The observed grade inflation during 2020–2021 supports the argument that systemic disruptions influence academic rigor, necessitating further exploration of the long-term impact on student learning.

5. Discussion

At the student level, the stability in enrollment–retention–graduation–employability rates is particularly noteworthy. UPS demonstrates a consistent and balanced system of admission, retention, graduation, and workforce integration. Over the past 30 years, the university has primarily served students from the second and third socioeconomic quintiles, focusing on young people from less advantaged sectors. In 2022, there was a notable trend of increased enrollment from the second quintile, contrasting with a decline in students from the fourth quintile.

Since 2015, research activities at UPS have intensified with the focus on faculty development through Ph.D. programs, which has positively impacted the academic quality but also introduced new challenges for faculty members. Currently, one in five faculty members holds a Ph.D. This faculty is characterized by job stability and a relatively low average age of 46, which has been consistent over the last eight years. Additionally, faculty members have maintained an average class size of 30 students, aligning with international accreditation standards and demonstrating that UPS does not employ a mass-education model.

However, the increased research responsibilities have led to signs of stress, particularly during the COVID-19 pandemic. While initial improvements in scientific production were evident in 2017, after the policy’s implementation in 2015, a decline became apparent in 2022.

Interestingly, the relationship between these variables and student grades reveals a significant phenomenon: students facing various stress factors show a decline in academic performance, whereas faculty stress factors correlate with an increase in average grades. This raises questions about faculty motivation, reduced academic rigor, and administrative pressure on faculty members. Future research should explore these dynamics further. In this case study, the stressors that were experienced by faculty during the pandemic had a direct impact on student performance, highlighting the intricate interplay between faculty well-being and academic outcomes.

While this study provides valuable insights into stress factors in higher education through an extensive data analysis, it has certain limitations. First, the study is based on data from a single institution, which, despite its diverse student population and multiple campuses, may limit the generalizability of the findings to other educational contexts. Additionally, the study relies on institutional records and self-reported socioeconomic data, which, while comprehensive, may be subject to reporting biases. Another limitation is that the analysis focuses on correlations rather than causation, meaning that while significant relationships between stressors and academic performance were identified, the precise mechanisms underlying these effects require further investigation. Finally, external factors such as policy changes, economic shifts, or cultural differences that might influence stress levels were not explicitly controlled for in this study. Future research should consider cross-institutional comparisons and integrate qualitative methodologies to complement data-driven insights.

The UPS case study can serve as a reference for similar higher education institutions, particularly those in Latin America, where socioeconomic challenges and stress factors in academia share common patterns. However, we highlight the importance of future research incorporating multi-institutional comparisons to validate the findings across different educational contexts and emphasize the necessity of integrating qualitative data in subsequent studies.

6. Conclusions

Stress factors in higher education are inherently present among students, faculty, and management. Identifying these stressors early and effectively offers opportunities to improve learning, teaching, and occupational health. Each institution represents a unique case, making generalizations difficult; however, a framework for data mining in higher education can be proposed. The first step is to evaluate whether the educational system, understood as enrollment–retention–graduation–employability, exhibits stable characteristics that allow for the prediction of future behavior.

In our case study, we found that stress factors in higher education are most prominently reflected in student grades. Paradoxically, when stressors affect the faculty, the result is often an abrupt increase in student grades. While this phenomenon invites speculation, it also highlights the need for further investigation into faculty motivation and burnout syndrome. Conversely, when stressors are experienced by students, the immediate outcome is typically a decline in academic performance. Efficient data mining should alert faculty early on to students’ past academic performance, as it is challenging for instructors to understand individual circumstances. Stressors often affect individuals rather than groups, and statistics or control metrics can mask realities that require attention.

The primary conclusion of our study is that data analytics should be employed to enhance the well-being of students and faculty. Educational ecosystems are self-regulating and tend to achieve stability, but detecting anomalies in the various stakeholders can inform strategies to address academic challenges and faculty-related issues. This proactive approach can foster a healthier and more effective educational environment.

This study highlights the potential of data mining techniques in identifying stress patterns within educational environments. Higher education institutions are encouraged to implement predictive models that facilitate the early detection of students who are at risk of poor academic performance due to stress.

To mitigate stress among faculty and students, universities should consider strategies to reduce the faculty’s workload, such as more flexible course assignments and policies that balance teaching and research responsibilities. At the student level, expanding psychological and academic support programs, particularly for low-income students who tend to experience higher levels of stress, is recommended.

We suggest that future research should incorporate faculty and student interviews to explore personal perspectives on stressors and their impact on academic performance. Qualitative methods can complement this study.

Author Contributions

Conceptualization, R.B.; methodology, F.P.; formal analysis, R.B. and F.P.; investigation, R.B. and F.M.; writing—original draft, R.B., F.M. and Á.F.; supervision, R.B. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Ethical review and approval were exempted for this study since the data used were previously anonymized in accordance with the Organic Law on Data Protection of Ecuador.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study are available on request from the corresponding author as they are the property of Universidad Politécnica Salesiana and contain specific details about its internal management. This restriction is in place to safeguard the institution’s intellectual property and maintain confidentiality regarding sensitive aspects of its operations.

Acknowledgments

The authors thank the Data Science Department for providing the anonymized data that support this work.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Enrollment, retention, graduation, and employability rates.

Figure 2. Quintile distribution of the student body. The distribution is presented for every two years: 2016, 2018, 2020, and 2022.

Figure 3. Evolution of PhD rates in UPS from 2015 to 2022.

Figure 4. Average age vs. average years of experience of faculty from 2015 to 2022.

Figure 5. Faculty members per student vs. students per faculty member from 2015 to 2022.

Figure 6. Scimago research rank from 2015 to 2022. In 2015 and 2016, UPS was not ranked.

Figure 7. Evolution of grades and student evaluations from 2015 to 2022.

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© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

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Bojorque, R.; Moscoso, F.; Pesántez, F.; Flores, Á. Stress Factors in Higher Education: A Data Analysis Case.Data2025,10, 22. https://doi.org/10.3390/data10020022

AMA Style

Bojorque R, Moscoso F, Pesántez F, Flores Á. Stress Factors in Higher Education: A Data Analysis Case.Data. 2025; 10(2):22. https://doi.org/10.3390/data10020022

Chicago/Turabian Style

Bojorque, Rodolfo, Fernando Moscoso, Fernando Pesántez, and Ángela Flores. 2025. "Stress Factors in Higher Education: A Data Analysis Case"Data 10, no. 2: 22. https://doi.org/10.3390/data10020022

APA Style

Bojorque, R., Moscoso, F., Pesántez, F., & Flores, Á. (2025). Stress Factors in Higher Education: A Data Analysis Case.Data,10(2), 22. https://doi.org/10.3390/data10020022

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Stress Factors in Higher Education: A Data Analysis Case

Abstract

1. Introduction

2. Related Works

2.1. Exploratory Data Analysis in the Education Realm

2.2. Common Stressors Faced by Students in Higher Education

2.3. Impact of Stressors on Students’ Mental Health

2.4. Strategies to Mitigate Stressors in Higher Education

2.5. Long-Term Effects on Academic Performance and Career Outcomes

2.6. Common Stressors Faced by Faculty in Higher Education

2.7. Impact of Stressors on Faculty Performance and Well-Being

2.8. Strategies to Mitigate Stressors for Faculty in Higher Education

2.9. Long-Term Effects of Stressors on Faculty in Higher Education

3. Materials and Methods

3.1. Data Collection

3.2. Exploratory Data Analysis

3.3. Relationships Between the Study Variables

4. Results

4.1. Student Level

4.2. Faculty Level

4.3. Relationships Between Stress Factors for Faculty and Students

5. Discussion

6. Conclusions

Author Contributions

Funding

Institutional Review Board Statement

Informed Consent Statement

Data Availability Statement

Acknowledgments

Conflicts of Interest

References

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