INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)

ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue X October 2025

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The Cause and Effect of Sleep Deprivation in Computer Engineering

Students at Bulacan State University

Dr. Ma. Magdalena V. Gatdula

Bulacan State University

DOI: https://dx.doi.org/10.47772/IJRISS.2025.910000542

Received: 02 November 2025; Accepted: 10 November 2025; Published: 18 November 2025

ABSTRACT

This study investigates the causes and effects of sleep deprivation among Computer Engineering students at

Bulacan State University. It examines lifestyle factors, academic workload, and technological habits that

influence students’ sleep quality, academic performance, and mental well-being. Using a descriptive-

correlational design, 120 participants were selected through simple random sampling. Quantitative data were

gathered through an online survey and analyzed using descriptive and inferential statistics, including

frequency, percentage, mean, and Pearson correlation. Findings revealed that sleep deprivation is highly

prevalent and strongly associated with poor sleep hygiene, irregular class schedules, and excessive technology

use. Results underscore the need for institutional interventions that promote healthy sleep habits to improve

academic performance and student wellness.

Keywords: sleep deprivation, college students, academic performance, health belief model, Bulacan State

University, computer engineering, sleep hygiene

INTRODUCTION

College is a critical developmental stage where students build foundational knowledge, skills, and

independence. However, the transition into higher education presents demanding academic workloads, tight

deadlines, and multiple institutional requirements, which often overwhelm students and cause them to sacrifice

sleep in favor of productivity.

Sleep deprivation refers to insufficient or poor-quality sleep, whether acute or chronic (Hershner & Chervin,

2014). Among college students, primary contributors include irregular sleep schedules, excessive technology

use, consumption of stimulants, and stress from academic and personal responsibilities (Brown, Buboltz, &

Soper, 2006; Hershner & Chervin, 2014).

Poor sleep hygiene—characterized by screen exposure before bed, inconsistent sleep routines, or inadequate

rest environments—has been consistently linked to diminished academic functioning and neurobehavioral

performance (Owens, Belon, & Moss, 2010). Insufficient sleep negatively affects cognitive capacity, memory

retention, learning, and emotional stability, further reducing academic efficiency (Walker, 2017).

Guided by the Health Belief Model (HBM), this study assumes that students’ perceptions of risk and perceived

benefits influence their sleep-related behaviors.

Understanding the factors associated with sleep deprivation among Computer Engineering students may help

in developing institutional initiatives that promote sleep hygiene and overall student well-being.

Methods

This section presents the methodological framework used to investigate the causes and effects of sleep

deprivation among Computer Engineering students at Bulacan State University. It outlines the procedures

INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)

ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue X October 2025

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Page 6634

undertaken to ensure that the data collected are accurate, reliable, and valid. Specifically, it describes the

research design, population and sampling method, research instrument, data gathering procedure, and

statistical treatments employed in the study. The methods were developed to align with ethical research

practices and the provisions of the Data Privacy Act of 2012.

The succeeding subsections provide detailed discussions of each methodological component. The Research

Design subsection explains the overall approach and rationale behind the chosen design. The Population and

Sample subsection describes the participants and sampling technique used. The Research Instrument

subsection details the structure, content, and validation of the survey tool. The Data Gathering Procedure

subsection explains how the data were collected ethically and systematically, while the Data Processing and

Statistical Treatment subsection presents the methods used to analyze the collected data.

Research Design

This study utilized a descriptive-correlational research design, integrating both quantitative and qualitative

approaches to examine the causes and effects of sleep deprivation among Computer Engineering students at

Bulacan State University (BulSU). The design allowed for the identification of patterns, relationships, and

potential correlations between sleep-related behaviors and academic performance.

Population and Sample

The population of the study consisted of all enrolled Computer Engineering students at BulSU during the

academic year 2024–2025. Using Andrew Fisher’s formula, the ideal sample size was determined to be 176.

From this population, a total of 120 students participated in the survey, exceeding the minimum sample size

recommended for representativeness.

The researchers employed simple random sampling to provide equal opportunity for each student to be

selected, ensuring unbiased representation across different year levels and sexes.

Research Instrument

Data were gathered using a structured online questionnaire developed through Google Forms. The instrument

was composed of 16 items distributed across the following sections:

1. Demographic Profile – year level and sex (name optional for confidentiality).

2. Sleep Behavior and Habits – assessed through 5-point Likert scale items (1 = strongly disagree, 5 =

strongly agree).

3. Lifestyle and Technological Factors – evaluated through Yes/No and multiple-choice questions.

4. Perceived Effects of Sleep Deprivation – gauged students’ self-reported experiences on academic

performance, mental health, and productivity.

The instrument underwent expert validation by two faculty members in psychology and computer engineering

to ensure content validity. A pilot test was conducted with 15 students not included in the final sample,

yielding a Cronbach’s alpha coefficient of 0.87, indicating high internal consistency of the questionnaire.

Data Gathering Procedure

The researchers sought permission from the department and coordinated with student leaders for proper

dissemination of the survey link. The online questionnaire was accessible exclusively to BulSU students using

their institutional Google accounts to uphold respondent authenticity and data integrity.

The survey link was distributed through official online student channels. Given the El Niño conditions that

necessitated temporary online learning modalities, all data collection was conducted virtually. The researchers

INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)

ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue X October 2025

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ensured adherence to the Data Privacy Act of 2012 (Republic Act No. 10173), guaranteeing confidentiality and

voluntary participation throughout the study.

Data Processing and Statistical Treatment

All collected responses were screened for completeness and accuracy prior to analysis. Descriptive statistics,

including frequency, percentage, and mean, were used to summarize respondent profiles and patterns of sleep

behavior.

For inferential analysis, Pearson correlation was applied to determine the relationships between sleep duration,

academic performance, and perceived mental health. Data were processed using Microsoft Excel and SPSS

version 25.

Results

This section presents the analyzed data from 120 Computer Engineering students at Bulacan State University.

Descriptive and inferential statistics were used to interpret the data, and visual representations (Figures 1–6)

support the findings.

Prevalence of Sleep Deprivation

Figure 1 shows that 88% of respondents slept fewer than seven hours per night, below the recommended 7–9

hours for young adults (Hirshkowitz et al., 2015).

Figure 1 Average Hours of Sleep Per Night

Factors Contributing to Poor Sleep Quality

As shown in Figure 2, the top contributors were:

 Academic workload and tight deadlines (92%)

 Night-time digital device use (100%)

 Consumption of food or caffeine before bed (64%)

 Irregular class schedules (78%)

Figure 2 Factors Contributing to Poor Sleep Quality

INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)

ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue X October 2025

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Effects of Sleep Deprivation

Figure 3 illustrates the major consequences reported:

 Sluggishness or fatigue – 71%

 Reduced focus – 83%

 Emotional distress – 68%

 Poor productivity – 89%

 Memory problems – 86%

Figure 3 Effects of Sleep Deprivation

Technology Use Before Sleep

All respondents (100%) reported using a smartphone or device before bedtime (Figure 4).

Figure 4 Technology Use Before Bedtime

Impact of Class Schedules

Figure 5 indicates that 78% attributed sleep disruption to early-morning or late-evening classes.

Figure 5 Impact of Class Schedule on Sleep

INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)

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Emotional and Mental Well-Being

Figure 6 shows that 68% experienced emotional or mental health decline when sleep-deprived.

Figure 6 Emotional & Mental Impact of Sleep Deprivation

Summary of Key Results

1. Majority of students (88%) experience regular sleep deprivation.

2. Poor sleep hygiene and technology use are leading causes.

3. Early and late class schedules aggravate the issue.

4. Sleep deprivation negatively affects concentration, learning, and emotional well-being.

5. Positive sleep habits correspond with higher academic performance.

DISCUSSION

The findings underscore a pressing need to address the prevalence of sleep deprivation among Computer

Engineering students at Bulacan State University. Consistent with previous studies (Owens & Weiss, 2017;

Brown et al., 2006), students’ lifestyle choices—particularly technology use and poor time management—

contribute significantly to irregular sleep patterns.

Anchored in the Health Belief Model, the results suggest that awareness of sleep-related risks influences

behavior toward healthier sleep routines. However, the data reveal limited awareness among students regarding

the long-term cognitive and psychological consequences of sleep deprivation. The identified effects—such as

fatigue, diminished focus, and mood instability—mirror the neurobehavioral impairments highlighted in prior

research (Walker, 2017).

Institutional factors, including inflexible class schedules and academic overload, exacerbate these problems.

The absence of formal programs promoting sleep health indicates a gap in BulSU’s wellness initiatives.

Implementing educational interventions on sleep hygiene, adjusting scheduling policies, and encouraging

digital detox practices could improve students’ overall well-being and academic performance.

CONCLUSIONS

Based on the results and discussion, the following conclusions were drawn:

1. Sleep deprivation is widespread among Computer Engineering students, with most sleeping less than

the recommended 7 hours per night.

2. Primary causes include demanding academic workloads, excessive use of technology before bedtime,

and irregular class schedules.

3. Sleep deprivation significantly impairs learning, memory, focus, and emotional stability.

INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)

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4. Students lack awareness of the long-term risks of sleep deprivation and the importance of good sleep

hygiene.

5. A positive relationship exists between adequate sleep and academic performance, validating the

relevance of behavioral awareness and self-regulation theories like the Health Belief Model.

RECOMMENDATIONS

In light of the findings and limitations, the following recommendations are proposed:

1. Broaden the study population to include students from other departments to achieve more

comprehensive university-wide representation.

2. Conduct further research using experimental or longitudinal designs to establish causal relationships

between sleep behavior and academic outcomes.

3. Integrate sleep health education into student orientation or wellness programs to increase awareness of

proper sleep practices.

4. Revisit academic scheduling to minimize early morning or late evening classes that disrupt students’

sleep patterns.

5. Enhance the research instrument by incorporating validated psychological scales to assess stress,

fatigue, and mental health more precisely.

6. Promote institutional interventions such as mindfulness workshops, time management seminars, and

digital detox campaigns.

7. Conduct future studies during stable academic periods with favorable conditions for face-to-face

interventions to strengthen data reliability.

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