UNIVERSITY OF ENERGY AND NATURAL RESOURCES

DEPARTMENT OF MATHEMATICS AND STATISTICS

TOPIC
ESTIMATING CAMPUS INTERNET USAGE PATTERNS WITH
SYSTEMATIC SAMPLING
BY
NAMES INDEX NUMBERS
KISSIWAA KATE UEB3100222
APPIAH DAVID ATTA UEB3100722
YEBOAH RICHARD KWADWO UEB3101224
 ABSTRACT

This study examines internet usage patterns among university students using
systematic sampling (every 5th student). The analysis is based on a sample of 105
students and explores usage duration, purpose, device preference, satisfaction
levels, and connectivity issues. The study highlights both academic and personal
usage patterns, identifies areas of dissatisfaction, and reveals significant variations
across faculties and genders. These findings contribute to bandwidth management
strategies, student support improvements, and future research design
 CHAPTER ONE:
GENERAL INTRODUCTION

BACKGROUND OF THE STUDY

In today’s digital age, the internet is an essential part of student life, influencing
both academic activities and leisure pursuits. University campuses are expected to
provide reliable internet infrastructure that aligns with the diverse and evolving
needs of students. Understanding how students use the internet—when, where,
how long, and for what purposes—can guide better network planning and policy
development. This study investigates these usage patterns using systematic
sampling to provide a representative and actionable overview of internet behavior
on campus.

STATEMENT OF THE PROBLEM

Despite the central role of internet connectivity in higher education, there is limited
campus-specific, methodically sampled data on how students actually use this
resource. Existing studies often generalize student behavior or overlook specific
usage environments (e.g., dormitories, libraries). Additionally, they rarely apply
systematic sampling techniques, which are essential for drawing unbiased and
actionable conclusions. Without this data, institutions struggle to align bandwidth
allocation, infrastructure upgrades, and content management policies with real
student needs.
OBJECTIVE OF THE STUDY

i. To estimate the average daily internet usage duration among university

students.

ii. To distinguish between academic and personal (leisure) internet usage.

iii. To examine variations in internet usage across faculties and gender.

RESEARCH QUESTIONS

i. What is the average time students spend online daily on campus?

ii. What proportion of students primarily use the internet for academic
versus personal purposes?

iii. How does internet usage differ by faculty and gender?

SIGNIFICANCE OF THE STUDY

i. . For University Administration and IT Departments
The findings provide data-driven insights into how students use campus internet
services, distinguishing between academic and personal use. This helps in:

Optimizing bandwidth allocation based on peak usage times and dominant

purposes.
Planning infrastructure improvements tailored to actual student needs.

Identifying areas where service quality (speed, coverage, reliability) can be

enhanced.

ii. For Academic Policy Makers

Understanding that a considerable portion of students rely on internet services for
academic tasks (assignments, research, online classes) helps justify:

Continued or increased investment in digital learning platforms.

Implementation of content access policies that prioritize educational tools.

iii. For Researchers and Future Studies

The project contributes to the limited body of work that applies systematic
sampling to campus-based behavioral studies. It establishes a replicable framework
for similar research, especially those that seek to integrate spatial and demographic
data (e.g., faculty, gender).
LIMITATIONS OF THE STUDY

i. Sample Scope: While systematic sampling improves representativeness,

only 105 students were surveyed, which may not capture all usage
patterns across smaller faculties.

ii. Temporal Limitation: The data was collected during a specific academic
period; usage patterns may vary during exams, breaks, or project seasons.

iii. Self-Reported Data: Time spent online and purpose of usage were self-
reported, which introduces the potential for recall bias or subjective
interpretation.

iv. Lack of Statistical Testing: While a conceptual hypothesis was

introduced, detailed inferential statistics (e.g., Z-tests or ANOVA) were
not conducted in full.
 CHAPTER TWO:

LITERATURE REVIEW

Understanding student internet behavior has become increasingly important in the

context of higher education, where digital connectivity plays a central role in both
academic and personal life. Numerous studies have examined how students interact
with online platforms, revealing varied patterns of usage that span academic
research, social networking, entertainment, and communication (Junco, 2012;
Pasek et al., 2006). However, while these studies offer valuable insights, they often
lack precision in sampling methods and fail to account for location-based behavior
within the campus environment.

Research on student technology habits consistently highlights the complexity of

digital engagement. For instance, Selwyn (2008) and Margaryan et al. (2011)
suggest that students’ use of technology is influenced not only by their academic
needs but also by personal preferences and contextual factors, such as time of day
and availability of resources. Yet, few studies address how internet behavior
fluctuates across different campus locations—such as libraries, dormitories, and
recreational areas—nor do they apply systematic sampling techniques to explore
these differences in a methodical manner.
In terms of methodology, systematic sampling has proven to be an effective
approach in behavioral studies, particularly those aiming to identify trends across
well-defined populations and environments (Lohr, 2010; Cochran, 1977). This
approach ensures even coverage of diverse sub-groups and settings, which is
essential for drawing reliable inferences in multifaceted environments like
university campuses. Despite its proven utility, the application of systematic
sampling in studies of student internet behavior remains limited, leaving a notable
gap in the literature.

Moreover, existing research often focuses on generalized or large-scale surveys

that do not consider spatial variables or site-specific usage patterns. This omission
is significant, as it undermines the ability of university administrators and IT
planners to make informed, data-driven decisions regarding the allocation and
management of campus network resources. Studies by Greenhow and Robelia
(2009) and Dahlstrom et al. (2015) emphasize the importance of aligning
technological infrastructure with students’ actual usage trends, yet fall short of
recommending methodologies that facilitate such alignment.

From a theoretical standpoint, the integration of behavioral statistics with data-

driven decision-making frameworks offers a promising lens through which to
examine internet usage on campus. These frameworks prioritize the collection of
empirical data to guide policy development and resource optimization.
Nonetheless, without a campus-specific and methodically sampled dataset, such
frameworks risk being under-informed or misaligned with real-world usage.
Finally, the literature reveals an ongoing debate about the role of internet access in
educational settings—particularly the balance between academic and non-
academic usage. While unrestricted access supports student autonomy and
engagement, concerns persist about potential distractions and bandwidth
consumption for non-educational purposes (Kirschner & Karpinski, 2010). This
debate underscores the need for granular, context-sensitive data that can inform
nuanced policy decisions rather than blanket restrictions.

In summary, while there is a substantial body of research on student digital

behavior and systematic sampling, there remains a significant gap in studies that
combine both elements to examine internet usage across campus-specific locations.
Addressing this gap is crucial for enhancing academic productivity, optimizing
resource management, and informing equitable technology policies in higher
education institutions.
 CHAPTER THREE:
METHODOLOGY
This study employed a systematic sampling method to ensure unbiased and
representative data collection. A total of 105 students were selected by choosing
every 5th student from the population. Data was gathered through structured
questions focusing on internet usage duration, purpose (academic, personal, or
both), device preference, satisfaction levels, and connectivity issues. The approach
allowed for a balanced representation across faculties and genders, and the
consistent sampling interval helped avoid selection bias. Quantitative analysis was
used to interpret patterns and trends from the collected data.

RESEARCH PROCEDURES:

i. Define the population.

ii. Determine the sample size.
iii. Choose the sampling size.
iv. Select a random starting point.
v. Using the chosen interval of 5, select data points from the starting point.
vi. Continue until the sample size is reached.

KIND OF DATA:

Systematic sampling is typically used for quantitative data. It involves selecting

sample from a population at a regular interval, which allows for numerical analysis
and statistical inference.

COLLECTION PROCEDURES:

i. List all the members of the population

ii. Determine the sample interval in 5th student
 iii. Randomly choose a starting point from the sampling frame
iv. Select samples
v. Gather the required data from the selected sample
vi. Document and store the collected data for analysis
vii. Check the collected data for accuracy and completeness

INTERPRETATION:

Students’ ae more likely to use campus internet academic purposes, which aligns with the intended

functions of campus network. However, nearly 30% use it more for leisure, highlighting the need for

balanced bandwidth and content control policies.

SELECTION AND ACCESS

i. We used systematic sampling — e.g. , selecting every 5th student — which ensures

random-like, unbiased selection.

ii. Access was likely gained through a specific environment (e.g., library), and you must

note whether permission was granted by campus authorities or librarians to survey

students.

ETHICS STATEMENT

All participants were informed of the purpose of the study and voluntarily agreed to participate. No

identifying personal data were collected, and all responses were kept anonymous and confidential.

The study adhered to the principles of ethical research.

THE COST AND FUNDING OF OUR PROJECT

This research received no specific grant from any funding agency in the public, commercial, or not-

for-profit sectors.

SAMPLE PREVIEW TABLE

GENDER FACULTY HOURS PURPOSE
ONLINE

Female School of Agriculture & 1 - 2 hours Equal Use

Technology

Female School of Sciences 1 - 2 hours Academic

Purpose

Male School of Sciences Less than 1 Personal

hour Purpose

Female School of Sciences 1 - 2 hours Equal Use

Female School of Engineering 2 - 3 hours Personal
Purpose

Male School of Sciences 1 - 2 hours Academic

Purpose

Male School of Sciences Less than 1 Academic

hour Purpose

Male School of Engineering 2 - 3 hours Academic

Purpose

Male School of Sciences 1 - 2 hours Personal

Purpose

Female School of Sciences 1 - 2 hours Academic

Purpose

Female School of Agriculture & 2 - 3 hours Personal

Technology Purpose

Female School of Arts & Social 2 - 3 hours Equal Use

Sciences

Female School of Natural Resources 2 - 3 hours Equal Use

Male School of Natural Resources Less than 1 Personal

hour Purpose
 Male School of Sciences 1 - 2 hours Personal
Purpose

Male School of Sciences 1 - 2 hours Academic

Purpose

Male School of Engineering 1 - 2 hours Personal

Purpose

Male School of Engineering 3+ hours Personal

Purpose

Female School of Sciences 3+ hours Academic

Purpose

Male School of Sciences 1 - 2 hours Academic

Purpose

Male School of Engineering Less than 1 Academic

hour Purpose

Table: A preview of 21 students selected using systematic sampling (every 5th

student).
VISUAL DATA REPRESENTATION:

Figure 1: Distribution of daily internet use among students.

Figure 2: Primary purpose for using campus internet.
Figure 3: Student satisfaction with campus internet service.

FROM FIG 1, FIG 2 and FIG 3: BELOW ARE THE PRELIMINARY FINDINGS:

The increasing reliance on internet access in academic environments makes it

crucial to understand usage patterns on campus. The preliminary dataset reveals
that students engage with the internet for both academic and personal purposes,
with notable variation in average daily use. The data clearly demonstrates demand
for academic internet access while highlighting the coexistence of leisure-based
use, prompting a balanced institutional response.
 CHAPTER FOUR:

RESULTS AND DISCUSSIONS

INTRODUCTION

This chapter presents and interprets the findings of the study on campus internet
usage patterns among university students. The data collected through systematic
sampling of every 5th student is analyzed to reveal trends in daily usage duration,
purpose of use, satisfaction levels, and demographic differences such as faculty
and gender. The aim is not only to describe usage behavior but also to draw
meaningful insights that can inform policy and infrastructure decisions. The
discussion integrates the results with the objectives of the study and connects them
to broader implications for campus IT planning and student support services.

CYCLE OF USAGE COMPARIOSON (DAILY PATTERNS &

MOTIVATION)

METRIC/GROUP AVERAGE HRS USAGE MOTIVATION

Academic purpose ~2.52 hrs. Research, assignments

Personal use ~2.52 hrs. Social media, streaming

Equal use ~2.52 hrs. Balanced between both

OBSERVATION:

All groups spend similar average time online (~2.52 hrs.), indicating the campus
infrastructure supports diverse needs, but priorities differ.

HYPOTHETICAL TEST:

H0 : There is no significant difference between academic and personal use

proportions.

H1 : There is a significance difference.

Using two proportion Z-test (conceptually):

45
Α1 = = 0.433 (Academic use)
104

31
Α2 = = 0.298 (Personal use)
104

PURPOSE COUNT PERCENTAGE

Academic purpose 45 43.27%

Personal purpose 31 29.81%

Equal Use 28 26.92%

COMPARISON:

Greater proportion pf respondents (43.27%) primarily use campus internet for

academic purpose.

Personal use is second with 29.81%.

CHAPTER FIVE:
CONCLUSION AND RECOMMENDATION

INTRODUCTION
This chapter summarizes the key findings of the study and presents conclusions
based on the analysis of student internet usage patterns. It also provides practical
recommendations aimed at improving internet service delivery on campus and
aligning it with the actual needs of students. These insights are intended to support
evidence-based decision-making for university administrators, IT departments,
and policy makers.

CONCLUSION
This study set out to explore internet usage patterns among university students
using a systematic sampling approach. The analysis of data from 105 students
revealed that campus internet plays a central role in student life, with an average
usage of approximately 2.52 hours per day. While academic purposes remain the
most common reason for internet use (43.3%), a significant portion of students also
use it for personal and recreational activities (29.8%), or a balanced mix
of both (26.9%).

RECOMMENDATION
Based on these findings, the study recommends that:
i. Enhance Internet Infrastructure
Improve bandwidth allocation and expand coverage in high-demand areas such as
hostels and libraries to ensure consistent and reliable access.

ii. Balance Usage Policy

While prioritizing academic usage is important, policies should also accommodate
reasonable levels of personal use to support students’ overall well-being and digital
lifestyle.
 iii. Faculty-Specific Support
Consider tailoring internet resources and access strategies based on faculty-specific
demands, ensuring students in resource-intensive programs have what they need.

iv. Periodic Usage Monitoring

Conduct regular assessments using systematic sampling to stay informed about
changing usage trends and emerging issues.

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