Page 1018
www.rsisinternational.org
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue V, May 2026
Academic Task Overload and its Influence on the Productivity and
Mental Health of Information Technology Students: A Basis for
Curriculum Review
Mary Fatima S. Collado
1
, Harold R. Lucero
2
, Danel M. Tungpalan
3
, Cyril Yvette D. Espiritu
4
, Alyssa S.
Gal
5
, Justine Keith D. Meneses
6
College of Computer Studies, Quezon City University
DOI: https://doi.org/10.51583/IJLTEMAS.2026.150500087
Received: 13 May 2026; Accepted: 18 May 2026; Published: 02 June 2026
ABSTRACT
Academic task overload has become a growing concern among university students, particularly in demanding
programs such as Information Technology, due to its potential effects on students’ productivity and mental
health. This study aimed to determine the influence of academic task overload on the productivity and mental
health of Information Technology students at Quezon City University and to provide a basis for curriculum
review. Specifically, the study examined the respondents’ demographic profile, the level of academic task
overload, productivity, and mental health, as well as the significant relationships and differences among the
identified variables. The study employed a quantitative correlational research design utilizing a structured survey
questionnaire distributed to 400 Information Technology students from first year to fourth year levels. The
gathered data were analyzed using descriptive statistics, Pearson Product-Moment Correlation Coefficient,
Independent Samples t-test, and One-Way Analysis of Variance (ANOVA). The findings revealed that the
respondents experienced a high level of academic task overload, maintained a high level of academic
productivity, and commonly experienced mental health concerns associated with academic demands. The study
further revealed significant relationships between academic task overload and both productivity and mental
health. In addition, significant differences were identified in selected variables when respondents were grouped
according to gender, year level, and primary study habits. Overall, the study highlights the importance of
managing academic workload and promoting strategies that support students’ productivity and mental well-
being within Information Technology education programs.
Keywords: academic productivity, academic task overload, mental health, quantitative correlational research,
study habits
INTRODUCTION
As higher education prepares graduates for complex digital environments, unmanaged academic rigor frequently
leads to task overload—a state defined by overwhelming projects, excessive workloads, and overlapping
deadlines (Barbayannis et al., 2022; Cheng et al., 2026; Gusy et al., 2021; Majerová et al., 2025). This overload
severely hampers student productivity, causing divided attention, reduced motivation, and lower output quality
(Hysenbegasi et al., 2005; Olson et al., 2025; Qin et al., 2025). Furthermore, prolonged academic pressure
triggers emotional exhaustion and psychological distress, significantly deteriorating overall life satisfaction
(Chong et al., 2025; Pereira et al., 2025; World Health Organization, 2022). Even though healthier coping
patterns exist, persistent, unmanaged pressure remains strongly linked to heightened anxiety and diminished
well-being (Dresen et al., 2025; Hadi et al., 2025; Pérez-Jorge et al., 2025).
This strain is exceptionally demanding in Information Technology (IT) programs, where continuous logical
reasoning and intensive technical tasks—such as coding and systems analysis—create a workload drastically
different from other disciplines (Cheng et al., 2026; Hadi et al., 2025; Majerová et al., 2025; Olson et al., 2025).
Despite broad research on general university stress (Ribeiro et al., 2018), a critical gap remains regarding how
task overload simultaneously degrades both productivity and mental wellness specifically among IT students.
Page 1019
www.rsisinternational.org
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue V, May 2026
Most existing literature addresses isolated burnout or relies on foreign educational frameworks, necessitating
localized evidence within Philippine higher education to prevent poorly sequenced tasks from ruining student
engagement (Majerová et al., 2025; Olson et al., 2025; Qin et al., 2025).
To address this gap, this study investigates Academic Task Overload and its Influence on the Productivity and
Mental Health of Information Technology Students: A Basis for Curriculum Review. Understanding these
specific risk factors is vital for designing manageable academic systems without sacrificing rigorous standards
(Pérez-Jorge et al., 2025; Sukamto, 2026). Ultimately, this research will foster balanced environments that
improve student outcomes (Barbayannis et al., 2022), guide faculty in crafting meaningful requirements (Cheng
et al., 2026), assist administrators in workload and curriculum planning (Olson et al., 2025), and expand the
foundational literature on academic wellness for future researchers (Ribeiro et al., 2018).
Statement of the Problem
Academic task overload has emerged as a critical concern in higher education, particularly within rigorous
programs such as Information Technology. Excessive academic demands have the potential to compromise
students' productivity and deteriorate their mental health, which ultimately affects their overall well-being and
academic performance. To foster a healthier learning environment and improve current educational structures,
it is essential to investigate how this academic burden influences these key variables. Therefore, this study aims
to examine the influence of academic task overload on the productivity and mental health of enrolled Information
Technology students at Quezon City University AY:2025-2026 1st Semester, with the findings serving as an
empirical basis for curriculum review.
Specifically, this study seeks to answer the following questions:
1. What is the demographic profile of respondents in terms of:
a. Age
b. Gender
c. Year Level
d. General Weighted Average (AY: 2025-2026 1st Semester)
e. Primary Study Habits
2. What is the level of academic task overload experienced by Information Technology students in terms of:
a. Number of academic tasks
b. Time constraints
c. Task difficulty
3. What is the level of productivity among Information Technology students in terms of:
a. Time management
b. Academic performance
4. What is the level of mental health of Information Technology students in terms of:
a. Stress
b. Anxiety
c. Emotional well-being
5. Is there a significant relationship between academic task overload and the students’:
a. Productivity
b. Mental health
Page 1020
www.rsisinternational.org
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue V, May 2026
6. Is there a significant difference in the level of academic task overload, productivity, and mental health when
respondents are grouped according to their:
a. Year level
b. Gender
c. Primary study habits
Related Studies
Academic task overload—driven by curriculum overcrowding, poorly coordinated deadlines, and unrealistic
expectations—consistently emerges as a primary detriment to student well-being and academic efficiency
(Acosta-Enriquez et al., 2025; Eduwem & Ezeonwumelu, 2020; Mehta, 2023; Osei & Mensah, 2025; Williams
et al., 2026). When academic demands exceed students' temporal and cognitive capacities, it leads to cognitive
depletion, reduced motivation, and poor productivity (Akanpaadgi et al., 2023; Reyes-de-Cózar et al., 2023;
Wang et al., 2025). Furthermore, prolonged exposure to this excessive workload triggers severe psychological
consequences, including anxiety, burnout, emotional exhaustion, and insomnia, which collectively diminish
overall learning effectiveness (Anne et al., 2025; Batucan et al., 2024; Kashif et al., 2024; Koch, 2025;
Noviekayati et al., 2025; Petrache, 2019; Rreddy & Bachelor, 2022; Sukamto et al., 2026; Tayoto, 2025).
These negative impacts are notably magnified among Information Technology students due to the technical
complexity of their coursework. The continuous cognitive load required for programming, debugging, and
managing multiple system development projects simultaneously induces significant techno-overload, frustration,
and psychological strain (Jiang et al., 2024; Pang et al., 2025; Takaoka & Sharma, 2024; Upadhyaya & Vrinda,
2021). While adaptive coping strategies, such as time management and social support, can partially mitigate
these stressors (Dvořáková et al., 2019; Li et al., 2021; Pascoe et al., 2019; Rice et al., 2020; Salmela-Aro et al.,
2022), the synthesized literature emphasizes that individual resilience is insufficient when institutional academic
demands consistently surpass manageable limits.
DESIGN AND METHODOLOGY
Research Design
This study employed a quantitative correlational research design to examine the naturally occurring relationships
between academic task overload, productivity, and mental health among Information Technology students at
Quezon City University (Creswell & Creswell, 2018; Fraenkel et al., 2019; Putri et al., 2024). Data were
collected through a structured, 5-point Likert scale questionnaire distributed via online and paper formats. This
self-report instrument was utilized to effectively quantify students' subjective experiences, measuring specific
constructs of task volume, productivity levels, and mental health indicators such as stress and anxiety (Boone &
Boone, 2012; Cohen et al., 2018; Dörnyei, 2007; Robinson, 2014; Wright, 2005).
To analyze the collected data, the study utilized both descriptive and inferential statistics at a standard 0.05 level
of significance (Creswell & Creswell, 2018; Gravetter & Wallnau, 2014). The Pearson Product-Moment
Correlation Coefficient (Pearson r) was applied to evaluate the strength and direction of the relationships
between the primary variables (Schober et al., 2018). Additionally, independent sample t-tests and one-way
Analysis of Variance (ANOVA) were employed to assess significant differences across demographic groups,
ensuring a systematic, evidence-based foundation for curriculum review recommendations (Field, 2018;
Sutradhar et al., 2023).
Data Gathering
Primary data were gathered from a target sample of approximately 400 Bachelor of Science in Information
Technology students at Quezon City University. Participants were selected using a convenience sampling
technique, a non-probability method chosen for its practicality in recruiting readily available subjects through
class group chats and scheduled room-to-room visits (Creswell & Creswell, 2018). To maximize response rates
Page 1021
www.rsisinternational.org
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue V, May 2026
and accommodate varying student accessibility, the researchers utilized a mixed-mode survey administration
(Wright, 2005). Online surveys were deployed through Google Forms for their efficiency and automatic data
recording capabilities (An et al., 2025; Pang et al., 2025), while printed questionnaires were distributed in person
to ensure comprehensive participation. All responses were carefully consolidated into a single digital dataset for
analysis.
The structured questionnaire utilized a 5-point Likert scale to quantify student perceptions (Boone & Boone,
2012; Robinson, 2014) and was adapted from validated measures tailored to the IT context. Academic task
overload and productivity items were based on Upadhyaya and Vrinda (2021) and supported by Pang et al.
(2025), while mental health indicators evaluating stress and emotional well-being were adapted from Takaoka
and Sharma (2024). Additional programming-specific cognitive demands and technical stressors were informed
by Jiang et al. (2024). Throughout the data collection process, strict ethical standards were maintained; all
participants provided informed consent, participation remained entirely voluntary with the right to withdraw,
and responses were kept strictly anonymous and confidential (An et al., 2025).
Statistical Treatment of Data
The data gathered in this study were analyzed using both descriptive and inferential statistical tools to address
the research questions regarding academic task overload and its influence on the productivity and mental health
of Information Technology students at Quezon City University.
Frequency and percentage distribution were used to describe the demographic profile of the respondents in terms
of gender, year level, and primary study habits.
Formula:
Percentage = (f / N) × 100
Where:
f = frequency
N = total number of respondents
The mean and standard deviation were used to determine the average and variability of the respondents’ age,
General Weighted Average (GWA), academic task overload, academic productivity, and mental health.
Formula:
x
= Σ(f · x) / N
Where:
x
= weighted mean
f = frequency
x = scale value
N = total number of respondents
The following scale was used in interpreting the mean scores obtained from the five-point Likert scale:
Table 1. 5-Point Likert Scale
Range
Interpretation
4.21 – 5.00
Very High / Strongly Agree
3.41 – 4.20
High / Agree
Page 1022
www.rsisinternational.org
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue V, May 2026
2.61 – 3.40
Moderate / Neutral
1.81 – 2.60
Low / Disagree
1.00 – 1.80
Very Low / Strongly Disagree
The Pearson Product-Moment Correlation Coefficient (Pearson r) was used to determine whether there is a
significant relationship between academic task overload and the students’ academic productivity and mental
health.
Formula:
r = Σ[(x − x
)(y − ȳ)] / √[Σ(x − x
)² Σ(y − ȳ)²]
Where:
r = Pearson correlation coefficient
x and y = variables being correlated
x
and ȳ = mean values of the variables
The Independent Samples t-test was used to determine significant differences when respondents were grouped
according to gender, while the One-Way Analysis of Variance (ANOVA) was used to determine significant
differences when respondents were grouped according to year level and primary study habits.
All statistical tests in this study were evaluated using a 0.05 level of significance. A p-value less than 0.05
indicates a statistically significant result, leading to the rejection of the null hypothesis, while a p-value greater
than 0.05 indicates that there is insufficient evidence to reject the null hypothesis.
● If p < 0.05 → Reject the null hypothesis
● If p > 0.05 → Fail to reject the null hypothesis
RESULT AND DISCUSSION
This section presents, analyzes, and interprets the findings gathered from the respondents regarding academic
task overload and its influence on the productivity and mental health of Information Technology students at
Quezon City University.
The presentation of results follows the sequence of the study’s specific objectives, including the demographic
profile of the respondents, the level of academic task overload, productivity, and mental health, as well as the
significant relationships and differences among the identified variables. The findings are further discussed and
supported using relevant theories and related studies to provide a clearer understanding of the results of the study.
A. Demographic Profile of Respondents
Table 2. Demographic Profile of Respondents in Terms of Age
Variable
Mean
Minimum
Maximum
Age
20.9
18
37
Table 2 presents the demographic profile of the respondents in terms of age. The respondents obtained a mean
age of 20.9 years, with the youngest respondent being 18 years old and the oldest being 37 years old. The findings
indicate that the respondents generally belong to the typical college-age range of Bachelor of Science in
Information Technology students.
Page 1023
www.rsisinternational.org
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue V, May 2026
Table 3. Demographic Profile of Respondents in Terms of Gender
Gender
Frequency
Percentage
Male
256
64%
Female
144
36%
Total
400
100%
Table 3 presents the demographic profile of the respondents in terms of gender. Out of 400 respondents, 256 or
64% were male, while 144 or 36% were female. The findings indicate that the majority of the respondents were
male, which may reflect the higher representation of male students in the Information Technology program.
Table 4. Demographic Profile of Respondents in Terms of Year Level
Year Level
Frequency
Percentage
1st Year
41
10.25%
2nd Year
58
14.5%
3rd Year
284
71%
4th Year
17
4.25%
Total
400
100%
Table 4 presents the demographic profile of the respondents in terms of year level. Among the 400 respondents,
the majority were third-year students, comprising 284 or 71% of the total respondents. This was followed by
second-year students with 58 respondents or 14.5%, first-year students with 41 respondents or 10.25%, and
fourth-year students with 17 respondents or 4.25%. The findings indicate that most of the respondents came from
the third-year level.
Table 5. Demographic Profile of Respondents in Terms of General Weighted Average (GWA)
Variable
Mean
Standard Deviation
GWA
1.66
0.30
Table 5 presents the demographic profile of the respondents in terms of General Weighted Average (GWA). The
respondents obtained a mean GWA of 1.66 with a standard deviation of 0.30. The minimum recorded GWA was
1.00, while the maximum recorded GWA was 3.25. The findings indicate that the respondents generally obtained
satisfactory to above-average academic performance during the Academic Year 2025–2026 First Semester.
Table 6. Demographic Profile of Respondents in Terms of Primary Study Habits
Study Habit
Frequency
Percentage
Daily Studying
41
10.25%
Weekend Studying
109
27.25%
Cramming
178
44.50%
Output-Based Stuying
72
18.00%
Total
400
100%
Table 6 presents the demographic profile of the respondents in terms of primary study habits. Among the
identified study habits, cramming obtained the highest frequency with 178 respondents or 44.5%, followed by
weekend studying with 109 respondents or 27.25%, consistent daily studying with 41 respondents or 10.25%,
and output-based studying with 72 respondents or 18%. The findings suggest that a large portion of the
respondents tend to complete academic tasks near deadlines rather than following a regular study routine.
Page 1024
www.rsisinternational.org
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue V, May 2026
B. Level of Academic Task Overload
Table 7. Level of Academic Task Overload Among Information Technology Students
Indicators
Mean
SD
Interpretation
Number of Academic Tasks
3.88
0.88
High
Time Constraints
3.87
0.96
High
Task Difficulty
3.92
0.88
High
Overall Mean
3.89
0.90
High
Table 7 presents the level of academic task overload among Information Technology students in terms of number
of academic tasks, time constraints, and task difficulty. The respondents obtained an overall mean of 3.89 with
a standard deviation of 0.90, interpreted as High. Among the indicators, task difficulty obtained the highest mean
of 3.92, followed by number of academic tasks with a mean of 3.88, and time constraints with a mean of 3.87.
The findings indicate that the respondents generally experience high levels of academic workload brought about
by demanding academic requirements, limited time, and difficult technical tasks.
The findings support the study of Pang et al. (2025), which emphasized that excessive cognitive and academic
demands contribute to fatigue, frustration, and academic strain among university students. The results also align
with Cognitive Load Theory proposed by Sweller (2024), which explains that excessive academic demands may
overwhelm students’ cognitive capacity and negatively affect learning efficiency.
C. Level of Academic Productivity
Table 8. Level of Academic Productivity Among Information Technology Students
Indicators
Mean
SD
Interpretation
Time Management
3.74
0.97
High
Academic Performance
3.80
0.89
High
Overall Mean
3.77
0.93
High
Table 8 presents the level of academic productivity among Information Technology students in terms of time
management and academic performance. The respondents obtained an overall mean of 3.77 with a standard
deviation of 0.93, interpreted as High.
Among the indicators, academic performance obtained a higher mean of 3.80 compared to time management
with a mean of 3.74. The findings indicate that the respondents generally maintain a relatively productive
academic performance despite experiencing academic workload and time-related challenges.
The findings support the study of Upadhyaya and Vrinda (2021), which stated that academic demands and
technological stress influence the productivity and performance of university students. The results suggest that
students continue to accomplish academic responsibilities despite experiencing considerable academic pressures.
D. Level of Mental Health
Table 9. Level of Mental Health Among Information Technology Students
Indicators
Mean
SD
Interpretation
Stress
3.89
0.95
High
Anxiety
3.95
0.94
High
Page 1025
www.rsisinternational.org
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue V, May 2026
Emotional Well-being
3.83
0.97
High
Overall Mean
3.89
0.95
High
Table 9 presents the level of mental health among Information Technology students in terms of stress, anxiety,
and emotional well-being. The respondents obtained an overall mean of 3.89 with a standard deviation of 0.95,
interpreted as High. Among the indicators, anxiety obtained the highest mean of 3.95, followed by stress with a
mean of 3.89, and emotional well-being with a mean of 3.83. The findings indicate that the respondents
commonly experience mental health concerns associated with academic demands and workload.
The findings support the study of Takaoka and Sharma (2024), which identified stress, anxiety, and emotional
exhaustion as common mental health concerns among students and professionals in computing-related fields.
The results further suggest that academic task overload may contribute to increased psychological strain among
Information Technology students.
E. Relationship Between Academic Task Overload, Productivity, and Mental Health
Table 10. Significant Relationship Between Academic Task Overload, Productivity, and Mental Health
Variables
r-value
Interpretation
p-value
Remarks
Academic Task Overload and
Productivity
0.414
Moderate Positive
Relationship
< 0.001
Significant
Academic Task Overload and
Mental Health
0.647
Strong Positive
Relationship
< 0.001
Significant
Table 10 presents the significant relationship between academic task overload, productivity, and mental health
among Information Technology students. The findings revealed a significant moderate positive relationship
between academic task overload and productivity (r = 0.414, p < 0.001). This indicates that changes in academic
task overload are associated with changes in students’ productivity. The results suggest that despite experiencing
academic workload, students continue to adapt and maintain their academic performance and time management
practices.
Furthermore, the findings revealed a significant strong positive relationship between academic task overload and
mental health (r = 0.647, p < 0.001). This indicates that higher levels of academic task overload are associated
with higher levels of stress, anxiety, and emotional strain among students. The findings support the study of
Pang et al. (2025), which emphasized that excessive academic and cognitive demands contribute to fatigue and
psychological strain among university students. The results also align with Cognitive Load Theory proposed by
Sweller (2024), which explains that excessive academic demands may overwhelm students’ cognitive capacity
and negatively affect their well-being and academic functioning.
F. Significant Difference According to Gender
Table 11. Significant Difference in Academic Task Overload, Productivity, and Mental Health When Grouped
According to Gender
Variables
p-value
Remarks
Academic Task Overload
0.007
Significant
Academic Productivity
0.489
Not Significant
Mental Health
< 0.001
Significant
Page 1026
www.rsisinternational.org
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue V, May 2026
Table 11 presents the significant difference in academic task overload, productivity, and mental health when
respondents are grouped according to gender. The findings revealed a significant difference in academic task
overload (p = 0.007) and mental health (p < 0.001) between male and female respondents. This indicates that
gender may influence the level of academic workload experienced by students as well as their mental health
conditions, particularly in terms of stress, anxiety, and emotional well-being.
On the other hand, no significant difference was found in academic productivity (p = 0.489) when respondents
were grouped according to gender. This suggests that male and female students demonstrate relatively similar
levels of productivity despite differences in academic workload and mental health experiences. The findings
imply that students, regardless of gender, are generally capable of maintaining their academic responsibilities
and performance under academic demands.
The findings are consistent with previous studies which suggest that academic stress and mental health
experiences may vary across gender groups due to differences in coping mechanisms, emotional responses, and
academic expectations (Akanpaadgi et al., 2023; Abdilah et al., 2025). However, the absence of significant
differences in productivity may indicate that both male and female students employ similar strategies in
managing academic requirements and maintaining academic performance.
G. Significant Difference According to Year Level
Table 12. Significant Difference in Academic Task Overload, Productivity, and Mental Health When Grouped
According to Year Level
Variables
p-value
Remarks
Academic Task Overload
0.031
Significant
Academic Productivity
< 0.001
Significant
Mental Health
0.113
Not Significant
Table 12 presents the significant difference in academic task overload, productivity, and mental health when
respondents are grouped according to year level. The findings revealed a significant difference in academic task
overload (p = 0.031) and academic productivity (p < 0.001) among the respondents when grouped according to
year level.
This indicates that students from different year levels experience varying levels of academic workload and
productivity. The findings may suggest that academic requirements, technical demands, and workload
expectations differ across year levels within the Information Technology program.
In contrast, no significant difference was found in mental health (p = 0.113) when respondents were grouped
according to year level. This suggests that students across different year levels generally experience similar
levels of stress, anxiety, and emotional well-being despite differences in academic workload and productivity.
The findings imply that mental health concerns associated with academic demands are commonly experienced
among Information Technology students regardless of year level.
The findings support the idea that academic workload and productivity demands tend to vary depending on the
complexity of courses, programming requirements, and academic responsibilities encountered at different stages
of the program (Pang et al., 2025; Jiang et al., 2024). However, the similarity in mental health experiences across
year levels may indicate that academic stress remains a common concern among students throughout their
academic journey.
Page 1027
www.rsisinternational.org
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue V, May 2026
H. Significant Difference According to Primary Study Habits
Table 13. Significant Difference in Academic Task Overload, Productivity, and Mental Health When Grouped
According to Primary Study Habits
Variables
p-value
Remarks
Academic Task Overload
0.829
Not Significant
Academic Productivity
0.022
Significant
Mental Health
0.874
Not Significant
Table 13 presents the significant difference in academic task overload, productivity, and mental health when
respondents are grouped according to primary study habits. The findings revealed no significant difference in
academic task overload (p = 0.829) and mental health (p = 0.874) when respondents were grouped according to
primary study habits. This suggests that students experience relatively similar levels of academic workload and
mental health concerns regardless of their preferred study habits.
However, a significant difference was found in academic productivity (p = 0.022) when respondents were
grouped according to primary study habits. This indicates that students’ study habits may influence their
productivity levels, particularly in terms of time management and academic performance. The findings imply
that different approaches to studying may contribute to variations in how students accomplish academic tasks
and maintain academic efficiency.
The findings support the idea that study habits play an important role in shaping students’ academic productivity,
consistent with the findings of Upadhyaya and Vrinda (2021). Students who follow more organized and
consistent study practices may demonstrate better productivity compared to those who rely on cramming or
irregular study routines. Despite these differences in productivity, the findings suggest that academic workload
and mental health concerns remain commonly experienced among students regardless of their preferred study
habits.
CONCLUSION
Based on the findings of the study, it was established that Information Technology students at Quezon City
University experience a high level of academic task overload in terms of number of academic tasks, time
constraints, and task difficulty. Despite these academic demands, the respondents were found to maintain a
relatively high level of academic productivity, particularly in terms of time management and academic
performance. However, the findings also revealed that students commonly experience mental health concerns,
particularly stress and anxiety, associated with academic workload and academic pressures.
The study further established that academic task overload has a significant relationship with both academic
productivity and mental health among Information Technology students. This indicates that changes in academic
workload are associated with corresponding changes in students’ productivity and psychological well-being. In
addition, significant differences were identified in certain variables when respondents were grouped according
to gender, year level, and primary study habits, suggesting that students’ academic experiences may vary
depending on their demographic and academic characteristics.
Overall, the findings of the study highlight the importance of examining academic workload and its implications
on students’ productivity and mental health. The study provides empirical evidence that may serve as a basis for
curriculum review and the development of academic strategies and support mechanisms that promote balanced
workload distribution, improved academic productivity, and better mental well-being among Information
Technology students.
Page 1028
www.rsisinternational.org
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue V, May 2026
REFERENCES
1 A. Akanpaadgi, F. Binpimbu and E. N. Kuuyelleh, "The impact of stress on the academic performance
of students in the Bolgatanga Technical University," ResearchGate, 2023.
2 M. F. Abdilah, N. I. Shari and F. A. Ibrahim, "The influence of work-life balance on perceived stress
among part-time students," Sains Humanika, vol. 3, pp. 73–78, 2025.
3 B. G. Acosta-Enriquez, O. Huamaní-Jordan, J. E. Morales-Angaspilco, O. Heredia-Pérez, J. R. Ruiz-
Carrillo, L. E. Blanco-García and S. M. Veliz Palacios de Villalobos, "The mediating role of work stress
and the performance expectations in the effect of academic overload on the use of AI models among
preservice teachers: A cross-sectional study," BMC Psychology, vol. 13, p. 1026, 2025.
4 N. Aini and Sulaiman, "Gambaran academic burnout mahasiswa tingkat akhir yang bekerja," Journal of
Public Health Science (JoPHS), vol. 1, no. 2, pp. 181–186, 2024.
5 A. An, G. Qian, A. Mumtaz, K. A. Alotaibi and X. Wang, "Digital fatigue and academic resilience among
university students with grit and flexibility as mediators," Scientific Reports, vol. 15, p. 45407, 2025.
6 M. Anne, P. Sy, M. Irenea et al., "Effects of Academic Workload to the Mental Fatigue of Students
during Online Class," IEOM Society International, 2025.
7 A. D. Arumsari, M. A. Pratiwi, M. I. Ramadhan, K. Nisa’ and G. I. Syakira, "The Phenomenon of
Academic Burnout in Students: A Literature Study," Proceeding International Symposium on Global
Education, Psychology, and Cultural Synergy, vol. 2, no. 1, pp. 526–531, 2025.
8 G. Barbayannis, M. Bandari, X. Zheng, H. Baquerizo, K. W. Pecor and X. Ming, "Academic stress and
mental well-being in college students: Correlations, affected groups, and COVID-19," Frontiers in
Psychology, vol. 13, p. 886344, 2022.
9 B. Batucan, M. Dumaguing, V. Gingoyon, J. Gotico, L. Huerbana, R. Pace, Z. Puod, J. Sia, R. Uy and
B. Imogen, "The effects of heavy academic workload on the mental well-being of the Grade 11 STEM
students at Cebu Doctors' University," 2024.
10 P. Boone and L. Boone, "Analyzing Likert data," Journal of Extension, vol. 50, no. 2, pp. 1–5, 2012.
11 D. Cheng, O. Heath, D. Farooqi, E. Chou, A. Gao and J. Calver, "Systematic review of academic
procrastination interventions in computing higher education," arXiv, 2026.
12 L. Z. Chong et al., "Student burnout: A review on factors contributing to burnout across student
populations," Current Psychology, 2025.
13 L. R. Cohen, L. Manion and K. Morrison, Research Methods in Education, 8th ed., London, United
Kingdom: Routledge, 2018.
14 P. Córdova Olivera, P. Gasser Gordillo, H. Naranjo Mejía, I. La Fuente Taborga, A. Grajeda Chacón and
A. Sanjinés Unzueta, "Academic stress as a predictor of mental health in university students," Journal of
Educational Psychology, 2023.
15 J. W. Creswell and J. D. Creswell, Research Design: Qualitative, Quantitative, and Mixed Methods
Approaches, 5th ed., Thousand Oaks, CA, USA: SAGE Publications, 2018.
16 V. Dresen et al., "Stress, burnout and study-related behavior in university students: A cross-sectional
cohort analysis before, during, and after the COVID-19 pandemic," Behavioral Sciences, vol. 15, no. 6,
p. 744, 2025.
17 Z. Dörnyei, Research Methods in Applied Linguistics. Oxford, United Kingdom: Oxford University
Press, 2007.
18 K. Dvořáková, M. Kishida, J. Li, S. Elavsky, P. C. Broderick and M. T. Greenberg, "Promoting healthy
transition to college through mindfulness training with first-year college students: A pilot randomized
controlled trial," Journal of American College Health, vol. 67, no. 4, pp. 341–351, 2019.
19 J. D. Eduwem and V. U. Ezeonwumelu, "Overloaded curriculum, excessive daily academic activities and
students' learning effectiveness," Journal of Education, Society and Behavioural Science, vol. 33, no. 8,
pp. 70–75, 2020.
20 A. Field, Discovering Statistics Using IBM SPSS Statistics, 5th ed., London, United Kingdom: SAGE
Publications, 2018.
21 J. R. Fraenkel, N. E. Wallen and H. H. Hyun, How to Design and Evaluate Research in Education, 10th
ed., New York, NY, USA: McGraw-Hill Education, 2019.
22 M. L. Gravetter and L. B. Wallnau, Statistics for the Behavioral Sciences, 10th ed., Boston, MA, USA:
Cengage Learning, 2014.
Page 1029
www.rsisinternational.org
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue V, May 2026
23 B. Gusy, T. Lesener and C. Wolter, "Time pressure and health-related loss of productivity in university
students: The mediating role of exhaustion," Frontiers in Public Health, vol. 9, p. 653440, 2021.
24 M. Hadi, I. G. A. A. Noviekayati and Suhadianto, "Systematic review on global trend in students’
academic stress: Prevalence, causal factor, and psychological implication," International Journal of
Research and Innovation in Social Science, vol. 9, no. 11, pp. 4262–4283, 2025.
25 W. D. Hapsari and K. Indriyaningrum, "Dampak beban kerja, stres kerja dan time management terhadap
work life balance mahasiswa yang bekerja," Journal of Management and Business (JOMB), vol. 6, no.
3, pp. 804–811, 2024.
26 A. Hysenbegasi, S. L. Hass and C. R. Rowland, "The impact of depression on the academic productivity
of university students," Journal of Mental Health Policy and Economics, vol. 8, no. 3, pp. 145–151, 2005.
27 Y. Jiang, H. Wu, X. Yu and T. Ji, "A study of factors influencing programming anxiety among non-
computer students," in 2024 The 9th International Conference on Information and Education Innovations
(ICIEI 2024), pp. 63–69, 2024.
28 T. Karania, "Analisis pengaruh work-life balance, tingkat stres dan burnout terhadap produktivitas
mahasiswa," Prosiding Ekonomi dan Bisnis, vol. 5, no. 1, 2025.
29 M. Kashif, R. Tabassum and S. Bibi, "Effects of academic stress on mental health issues among
university students," International Journal of Higher Education Research, 2024.
30 A. Koch, "How academic and extracurricular workload and stress impacts the mental and physical health
of college students," 2025.
31 Y. Li, J. Zhao, Z. Ma and M. McCabe, "Coping strategies and psychological adaptation among university
students during academic stress," Frontiers in Psychology, vol. 12, p. 680692, 2021.
32 N. Majerová et al., "Well-being and academic stress in higher education students: Interview and diary
study," Global Health Action, vol. 18, no. 1, 2025.
33 R. Mehta, "Evaluation of curriculum overload on students leading to academic stress: A systematic
review," Juni Khyat, vol. 13, no. 5, pp. 153–158, 2023.
34 M. A. Memon, H. Ting, J.-H. Cheah, R. Thurasamy, F. Chuah and T. H. Cham, "Sample size for survey
research: Review and recommendations," Journal of Applied Structural Equation Modeling, vol. 4, no.
2, 2020.
35 N. Olson et al., "Stress, student burnout and study engagement – A cross-sectional comparison of
university students of different academic subjects," BMC Psychology, vol. 13, p. 602, 2025.
36 S. Osei and M. A. Mensah, "Administrative strategies for managing students' academic workload in
higher education," International Journal of Science and Management Studies, vol. 8, no. 5, pp. 445–453,
2025.
37 H. Pang, X. Jin and W. Zhang, "Deciphering the underlying repercussions of cognitive overload on
university students' fatigue, frustration and academic productivity: Implementation of stimulus-
organism-response model," Acta Psychologica, vol. 260, p. 105469, 2025.
38 M. C. Pascoe, S. E. Hetrick and A. G. Parker, "The impact of stress on students in secondary school and
higher education," International Journal of Adolescence and Youth, vol. 25, no. 1, pp. 104–112, 2019.
39 D. Petrache, "Effects of students’ workload to stress and mental health BS Psychology," 2019.
40 M. G. Pereira et al., "Burnout risk profiles in psychology students: An exploratory study with machine
learning," Current Psychology, 2025.
41 D. Pérez-Jorge et al., "Examining the effects of academic stress on student well-being in higher
education," Humanities and Social Sciences Communications, vol. 12, p. 4698, 2025.
42 Z. Qin et al., "The impact of academic burnout on academic achievement: A moderated chain mediation
effect from the Stimulus-Organism-Response perspective," Frontiers in Psychology, vol. 16, p. 1559330,
2025.
43 A. Rahmah, R. Nastiti, Y. Bachtiar and R. Wahdah, "Productivity under pressure: The role of workload,
burnout, and work-life balance among working students," vol. 1, pp. 76–85, 2026.
44 S. Reyes-de-Cózar, A. Merino-Cajaraville and M. R. Salguero-Pazos, "Avoiding academic burnout:
Academic factors that enhance university student engagement," Behavioral Sciences, vol. 13, no. 12, p.
989, 2023.
45 Í. J. S. Ribeiro et al., "Stress and quality of life among university students: A systematic literature review,"
Health Professions Education, vol. 4, no. 2, pp. 70–77, 2018.
Page 1030
www.rsisinternational.org
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue V, May 2026
46 K. G. Rice, C. M. E. Richardson and S. Tueller, "The short-term and long-term effects of academic
procrastination," Journal of Counseling Psychology, vol. 67, no. 2, pp. 184–195, 2020.
47 D. Rochmayanti and A. S. Effasa, "Pengaruh work-life balance dan modal intelektual terhadap
produktivitas mahasiswa di era digital," Journal of Social and Economics Research, vol. 7, no. 1, pp.
963–973, 2025.
48 J. Robinson, "Likert scale," in Encyclopedia of Quality of Life and Well-Being Research, A. C. Michalos,
Ed. Dordrecht, Netherlands: Springer, 2014, pp. 3917–3918.
49 M. Rreddy and S. Bachelor, "Academic stress and the students’ mental health," 2022.
50 K. Salmela-Aro, K. Upadyaya and L. Hietajärvi, "Burnout and engagement in education: Development,
validation, and implications," Educational Psychology Review, vol. 34, pp. 1–23, 2022.
51 R. Schober, C. Boer and L. Schwarte, "Correlation coefficients: Appropriate use and interpretation,"
Anesthesia & Analgesia, vol. 126, no. 5, pp. 1763–1768, 2018.
52 Y. Septiani and M. Triariani, "Pengaruh burnout terhadap produktivitas mahasiswa PGSD Universitas
Kuningan," Jurnal Pendidikan Dasar, vol. 1, pp. 161–167, 2022.
53 I. Sukamto, "Understanding academic burnout: A systematic literature review of its risk and protective
factors," Social Sciences & Humanities Open, vol. 15, p. 102441, 2026.
54 A. Sutradhar, A. Adhikari, B. T. Bandopadhyay and S. Sen, "Use of correlation analysis in educational
research," International Research Journal of Education, vol. 5, no. 5, pp. 731–737.
55 J. Sweller, "Cognitive Load Theory and Individual Differences," Learning and Individual Differences,
vol. 110, p. 102423, 2024.
56 A. J. W. Takaoka and K. Sharma, "Mental health of computing professionals and students: A systematic
literature review," Preprint submitted to Elsevier, 2024.
57 R. A. Tayoto, "Impact of academic workload on students' mental health and well-being in higher
education: A narrative synthesis of the literature," Multidisciplinary International Journal of Research
and Development, vol. 4, no. 5, pp. 55–61, 2025.
58 P. H. Tina, P. H. Saputra and Y. T. Kusumawati, "Pengaruh work-life balance dan kesehatan mental
terhadap kinerja akademik mahasiswa part-time," Jurnal Ekonomi Bisnis dan Akuntansi, vol. 5, no. 1,
2025.
59 P. Upadhyaya and Vrinda, "Impact of technostress on academic productivity of university students,"
Education and Information Technologies, vol. 26, pp. 1647–1664, 2021.
60 X. Wang, X. Zhao and C. Yu, "The influence of information and social overload on academic
performance: The role of social media fatigue, cognitive depletion, and self-control," Revista de
Psicodidáctica (English ed.), vol. 30, no. 2, p. 500164, 2025.
61 V. L. Williams, R. Emptage, M. Haddad, M. Higginbotham and K. A. Kelley, "Assessment overload is
a hidden layer to curriculum overload, and ironically, requires more assessment," American Journal of
Pharmaceutical Education, vol. 90, p. 101971, 2026.
62 World Health Organization, World mental health report: Transforming mental health for all, 2022.
63 T. A. W. Wright, "Researching Internet-based populations: Advantages and disadvantages of online
survey research," Journal of Computer-Mediated Communication, vol. 10, no. 3, 2005.
64 J. Zhang, J. Meng and X. Wen, "The relationship between stress and academic burnout in college
students: Evidence from longitudinal data on indirect effects," Frontiers in Psychology, 2025.
