THE IMPACT OF ONLINE GAMES TOWARDS STUDENTS IN CARAGA
STATE UNIVERSITY
Al Franz Franco, Christian Roi Maluya, John Lawrence Cambalon
I.
Introduction
Online gaming is one of the favorites pastimes of everybody, and that includes the student from Caraga State
University. Online games can provide students with a variety of benefits, such as stress relief, socialization, problemsolving skills, and teamwork skills. However, online games can also have some negative impacts on students, such as
addiction, violence, and cyberbullying (Rajab et al., 2020; Batmaz et al., 2020; Purwaningsih & Nurmala, 2021; Erevik
et al., 2022). Online gaming can either have negative or positive effects on students at Caraga State University. When
a student plays a specific online game, this will have an effect for him, it might be positive or negative. Example of that
is when a stressed student from the school plays VALORANT. After playing, he’s relieved. In other words, the student
is relieved of their stress by playing VALORANT, and that’s a positive effect. The role of data analytics in this study is
to know the impact of online gaming on students on their performance in school, social relationships, mental and
physical health, and any other terms that are considered positive or negative effects.
In recent years, there has been a notable surge in the popularity of online gaming among students. While
acknowledging the positive aspects, such as stress relief and socialization, it is essential to recognize the potential
negative impacts on various facets of students' lives, including academic performance, social relationships, and mental
and physical health (Rajab et al., 2020; Batmaz et al., 2020; Purwaningsih & Nurmala, 2021; Erevik et al., 2022). A
study conducted in 2023 by Manandhar and Timalsena revealed that students engaging in online games for over two
hours daily tended to exhibit lower GPAs and higher rates of absenteeism, signaling a correlation between extensive
online gaming and academic hindrance. Furthermore, McInroy and Mishna's 2023 study identified that students
involved in violent online games were more prone to reporting aggressive thoughts and behaviors, indicating a potential
link between such games and aggressive tendencies among students. It's important to note, however, that not all
effects of online gaming on students are detrimental. In a 2022 study, Ting-Ting and Yueh-Min discovered that
incorporating online games into classroom instruction positively correlated with higher academic achievement and
increased engagement in learning, highlighting the potential for online games to enhance the educational experience.
While there’s a lot of research on the impact of online gaming on students in general, there are still some gaps
in the research specifically on the impact of online gaming on students at Caraga State University (CSU). One gap is
the lack of longitudinal studies (Przybylski, A., & Weinstein, N., 2014). Most of the research on online gaming has been
cross-sectional, meaning that it has only looked at the relationship between online gaming and student outcomes at
one point in time. Longitudinal studies are needed to track students over time to see how their online gaming habits
and student outcomes change (Przybylski, A., & Weinstein, N., 2014). For example, a longitudinal study could track
CSU students from their first year to their fourth year to see how their online gaming habits and academic performance
change over time. Another gap in the research is the lack of research on the specific types of online games (Przybylski,
A., & Weinstein, N., 2014). Most of the research on online gaming has focused on online games in general, with less
attention paid to specific types of online games, such as first-person shooters, role-playing games, or educational
games. More research is needed to understand how different types of online games have different impacts on students
(Griffiths, M. D., & Sargent, J. W., 2012). For example, a study could compare the impact of educational online games
and violent online games on the academic performance and social relationships of CSU students.
Online gaming can have both positive and negative effects on students at Caraga State University. While it
can provide stress relief, socialization, problem-solving skills, and teamwork skills, it can also lead to addiction,
violence, and cyberbullying (Rajab et al., 2020; Batmaz et al., 2020; Purwaningsih & Nurmala, 2021; Erevik et al.,
2022). Data analytics can help us understand the impact of online gaming on students' performance in school, social
relationships, mental and physical health, and other areas. By tracking students over time, we can develop interventions
to help students maximize the positive effects of online gaming and minimize the negative effects. This research could
help to inform policy and practice at Caraga State University to support students in their use of online gaming in a
healthy and productive way.
II.
Review of Related Literature
A. Overview of the study
As Damayunita A. et al. (2022) observe, data mining unearths hidden patterns within information, transforming
it into valuable decision-making tools. Among these tools, classification techniques predict the future category or label
of data points. This study similarly employs data analytics, through tools like Rapid Miner, to examine the multifaceted
relationship between online gaming and student life at Caraga State University (CSU). However, to gain a
comprehensive understanding beyond surface-level statistics, the study utilizes insightful student surveys. These
surveys explore personal perspectives on gaming's impact on academic focus, social connectedness, mental wellbeing, and time management, providing a nuanced picture of its influence on CSU students.
To meticulously model the intricate relationships within the collected data and unlock its predictive power, this
research harnesses a trifecta of robust techniques: Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and
Naive Bayes. Each algorithm offers a unique approach to classification, yet their comparative performance remains a
compelling question for this study. Our investigation delves into the accuracy of each method in classifying, analyzing,
and generating confusion matrix values, ultimately isolating the algorithm that reigns supreme in accurately predicting
priorities. This rigorous comparison not only identifies the optimal tool for this particular dataset but also sheds light on
the strengths and weaknesses of these algorithms in similar contexts.
SVMs, versatile supervised learning algorithms, excel at both classification and regression. They artfully craft
hyperplanes in high-dimensional spaces, maximizing separation between data classes. These boundaries hinge on
crucial support vectors, and kernel functions unlock the power of non-linear relationships. But in realms of highdimensional data, like document classification, linear SVMs shine. Their lightning-fast training speed, comparable to
non-linear SVMs, makes them an alluring choice, especially when adding features offers little performance boost.
Chauhan et al. (2019)
Zhang (2020) characterizes the K-Nearest Neighbors (KNN) classifier as a lazy learner that doesn't construct
explicit models. In contrast to model-based classification algorithms that build models using training datasets for
subsequent predictions, the KNN classifier retains all training examples in memory. This retention of training examples
is essential for the algorithm to efficiently search for the K nearest neighbors when classifying a test sample. The
distinctive feature of KNN lies in its instance-based nature, where predictions are made based on the majority class of
the nearest neighbors, emphasizing memory-intensive computations during the classification process.
According to Saritaş and Yaşar (2019), the Naive Bayes algorithm is characterized as a straightforward
probability classifier that computes a set of probabilities by tallying the frequency and combinations of values within a
given dataset. The algorithm is grounded in Bayes's theorem and operates under the assumption that all variables are
independent when considering the value of the class variable. This "naive" assumption simplifies the computation of
probabilities and makes the algorithm computationally efficient. By leveraging Bayesian principles, Naive Bayes
calculates the probability of a particular class given the observed features and selects the class with the highest
probability as the final prediction. This simplicity and efficiency make Naive Bayes particularly suitable for various
classification tasks, especially in scenarios where the independence assumption holds reasonably well, such as in text
classification or spam filtering applications.
This study's rigorous comparison of algorithms shines a light on their efficacy in predicting complex
relationships within student data. By identifying the optimal model for accurately classifying and analyzing the
multifaceted influence of online gaming on CSU students, this research contributes to the development of proactive
intervention strategies. As Liz-Doinguez et al. (2019) observe, predictive algorithms can form the basis of early warning
systems for educational settings, allowing timely interventions to support students struggling with academic focus,
social connections, or time management. By unraveling the intricate web of gaming's impact and equipping educators
with powerful predictive tools, this research paves the way for a more proactive and holistic approach to ensuring
student success at CSU and beyond.
B. Existing Literatures
Online gaming is a popular pastime for students at Caraga State University (CSU). It can provide students
with stress relief, socialization, problem-solving skills, and teamwork skills. However, online gaming can also have
negative impacts on students, such as addiction, violence, and cyberbullying.
A 2023 study by Manandhar and Timalsena found that students who played online games for more than two
hours per day were more likely to have lower GPAs and higher absenteeism rates. This suggests that excessive online
gaming can interfere with students' academic studies. Another 2023 study by McInroy and Mishna found that students
who played violent online games were more likely to report aggressive thoughts and behaviors. This suggests that
violent online games may contribute to aggressive behavior in students. However, not all of the effects of online gaming
on students are negative. A 2022 study by Ting-Ting and Yueh-Min found that students who used online games in their
classroom instruction had higher academic achievement and were more engaged in their learning. This suggests that
online games can be used to enhance student learning in a positive way. There is still a lot to learn about the impact
of online gaming on students at CSU.
The impact of online gaming on students at CSU is complex. Online gaming can have both positive and
negative effects on students' academic performance, social relationships, mental and physical health, and other areas.
More research is needed to understand the impact of online gaming on students at CSU. Data analytics can play a
valuable role in this research by helping researchers to identify patterns and trends in students' online gaming habits
and student outcomes.
III.
Methods
A. Framework of the study
Figure 1. Framework of the Study
In Figure 1, we conducted a survey among students at Caraga State University using Google Forms to gather
data about the effects of online gaming on them. We then extracted the data and exported it to Excel. In Data
Preparation, we used RapidMiner to upload the file and analyze the data to determine if there is a significant effect.
We also ensured that the data was aggregated.
B. Dataset Description
This dataset contains the reason why the students had a significant impact on their overall well-being, as well
as how they resolved the situation and how they feel about it. We collected information from students about how much
they prioritize their time between online gaming and academic responsibilities, their emotional and psychological effects
either positive or negative, any changes about their social life or relationships and what game they usually play.
Data Source: The information we gathered through surveys conducted at Caraga State University and made available
online via link in a Google Form. Students are asked to respond to a series of questions, and the collected responses
are included in the dataset.
Figure 2. Google Form Survey.
Key Attributes:
College
psychological_Effects
perceive_Influence
priority_Time
socialRelationship_Changes
positive_Negative
emotional_Effects
student_Opinions
C. Evaluation Metrics
In the survey and data collection phase, we can ascertain the number of participants and their completion
times using the established framework. Employing a Google Form streamlines the data visualization process, as entries
are automatically stored in a Google Sheet, facilitating seamless importation into RapidMiner for model testing and
data preparation.
The subsequent step involves scrutinizing respondents' answers to ensure relevance to the questions,
conducting data-cleaning procedures. The Excel file is then imported into RapidMiner, where additional data-cleaning
is performed to verify its integrity. To grasp data patterns effectively, visual representation through graphs or charts is
deemed essential.
Upon initiating the data analysis in RapidMiner, diverse analysis techniques are applied (model preparation
time), ensuring that the data is refined and ready for analysis. Subsequently, various analysis techniques are evaluated
(model validation), taking into account factors like accuracy and interpretability. These systematic procedures
safeguard the reliability of our analysis, ensuring it is founded on dependable data and yields valuable insights.
IV.
Results and Discussion
A. Results
Figure 3. Counts of “How do you perceive the influence of online gaming on your academic performance”
Figure 2 shows that, there’s slight increase in academic performance in students at Caraga State University,
with 357 or 74.84% students showing a slight increase. No noticeable impact was observed in 86 or 18.03% students,
and some students showed a slight negative impact (19 or 3.98%) ). A significant improvement in academic
performance was observed in 15 or 3.14% students.
Figure 4. Counts of “ Can you describe any changes you've noticed in your social life or relationships since you
started playing online games”
Figure 3 shows that many students have slightly improved their social and interpersonal relationships (230 or
48.22%), but there are also students who have not shown any noticeable impact ( 224 or 46.96% ). Some students
have improved their social and interpersonal relationships ( 12 or 2.52% ), and a slight negative impact on their social
and interpersonal relationships was observed in 9 or 1.89% students. A significant negative impact on their social and
interpersonal relationships was observed in 2 or 0.42% students.
Figure 5. Counts of “Have you experienced any emotional or psychological effects, positive or negative, as a result
of your online gaming habits”
Figure 4 shows that students had slightly positive emotional and psychological effects ( 303 or 63.31% ), some
students had no noticeable emotional or psychological impact (141 or 29.56% ), and some had significantly negative
emotional and psychological effects ( 17 or 3.56% ). Some students had significantly positive emotional and
psychological effects (13 or ) and significantly negative emotional and psychological effects were observed on three (
3 or 0.63% ) students.
Figure 6. Counts of “How do you prioritize your time between gaming and your academic responsibilities”
Figure 5 shows that students prioritize academic responsibilities slightly more than gaming (262 or 54.9),
some students allocate their time equally between academic responsibilities and gaming (158 or 33.1%), and some
students prioritize academic responsibilities over gaming (47 or 9.9%). Some students prioritize gaming slightly more
than academic responsibilities (8 or 1.7%) and there are a few students who prioritize gaming over academic
responsibilities (2 or 0.4%)
Figure 7. Counts of “In your opinion, has online gaming had a significant impact on your overall academic wellbeing? If so, can you explain how?(choose one option only then click “Other”)”.
Figure 6 shows that students have slightly improved their overall well-being (50.4% or 241) with various
comments that having drawbacks like feeling depressed when having a losing streak but overall their well being is
slightly improved. There are students that have significantly improved their overall well-being (4.4% or 21) and there
are students that noticed that there is no noticeable impact on their overall well-being (43.8% or 209). There are
students that have slightly negatively affected their overall well-being (2.7% or 13) and there are students that
significantly negatively affected their overall well-being (0.4% or 2).
B. Classification Report
CLASSIFIER MODEL
ACCURACY
Support Vector Machine
59.38%
K- Nearest Neighbor
47.92%
Naive Bayes
60.42%
Table 1. Result of the Classifier Model
In Table 1, the classifier model results are presented, indicating the accuracy achieved when training and
testing the grades dataset. Notably, Naive Bayes exhibits the highest accuracy at 60.42%, surpassing both KNN
(47.92%) and SVM (59.38%).
Figure 8. Comparison of the Classifier
In Figure 7, it is illustrated that Support Vector Machine (SVM) and Naive Bayes share identical accuracy
scores of 0.6, whereas K-Nearest Neighbor achieves a slightly lower accuracy of 0.5.
C. Discussion
The findings from Caraga State University reveal a predominantly positive trend in various facets of students'
lives. A significant proportion of students exhibited improvement in academic performance, with 74.84% showcasing a
slight increase and 3.14% experiencing a significant improvement. Social and interpersonal relationships also saw
positive developments, as 48.22% reported slight improvement. Conversely, 46.96% reported no noticeable impact,
and a small percentage faced a negative impact. Emotional and psychological effects yielded a majority of 63.31%
experiencing slightly positive impacts, while 29.56% reported no noticeable change. Time allocation between academic
responsibilities and gaming demonstrated diverse preferences, with 54.9% prioritizing academics slightly more than
gaming. Notably, the discussion on overall well-being highlighted that 50.4% of students perceived a slight
improvement, 43.8% noticed no impact, and 4.4% reported a significant positive change. The analysis of classifier
models indicated Naive Bayes' superior accuracy at 60.42%, outperforming KNN (47.92%) and sharing identical
accuracy with SVM (59.38%). In essence, the comprehensive overview suggests positive trends in students' academic
and personal experiences, and the classifier model results emphasize Naive Bayes' effectiveness in predicting
academic outcomes based on the provided dataset.
V.
Conclusion
By conducting clustering and data analysis, the data from Caraga State University reveals varied effects on
students across several domains. In academic performance, a significant majority (74.84%) experienced a slight
increase, with a smaller segment showing no change (18.03%) or negative impacts. When it comes to social and
interpersonal relationships, nearly half of the students (48.22%) reported a slight improvement, while a comparable
proportion (46.96%) saw no noticeable change, and a minority faced negative effects. Emotional and psychological
aspects saw a similar trend, with 63.31% of students experiencing slight positive effects, but a notable number
remained unaffected, and a few reported negative outcomes. The balance between academic responsibilities and
gaming also varied, with over half of the students (54.9%) slightly favoring academics, a significant number equally
balancing both (33.1%), and others showing varying levels of preference. Finally, regarding overall well-being, while
half of the students (50.4%) noted a slight improvement, the rest showed diverse responses ranging from significant
improvement to notable negative impacts. These findings indicate that while the general trend is towards slight
improvement in various aspects of student life, there is a need to recognize and address the diverse experiences of
the student body, as not all are positively impacted by the changes or interventions implemented.
VI.
References
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Zhang, S. (2020). Cost-sensitive
https://doi.org/10.1016/j.neucom.2018.11.101
KNN
Questions
1. How do you perceive the influence of online gaming
on your academic performance?
2. Can you describe any changes you've noticed in
your social life or relationships since you started playing
online games?
3. Have you experienced any emotional or
psychological effects, positive or negative, as a result of
your online gaming habits?
4. How do you prioritize your time between online
gaming and your academic responsibilities?
classification.
Neurocomputing,
391,
Attributes
Impact on Academic Performance
Impact on Social Life and Relationships
Emotional and Psychological Impact
Time Management and Prioritization
234–242.
5. In your opinion, has online gaming had a significant
impact on your overall well-being? If so, can you
explain how?
Overall Well-being Impact