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IMPACT OF INTERNET ADDICTION ON LEARNING STYLES OF COLLEGE STUDENTS

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Internet Addiction refers to excessive Internet use that interferes with daily life. College students use Internet more than other people do in order to meet their educational needs. For this reason, they are more prone to the Internet Addiction. Students learn through multi-sensory approach in Internet. Students receive and process the information in different ways and adopt different Learning Styles. Student
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IMPACT OF INTERNET ADDICTION ON LEARNING STYLES OF COLLEGE STUDENTS.
M.Vijayalakshmi
M.Sc., M.Phil. (Life Sciences), M.Ed., M.Phil. (Education), NET(Education), PGDBI
M.Sc., Applied Psychology (Student), School of Distance Education, Bharathiar
University - 641 046, Tamil Nadu, India.
Abstract
Internet Addiction refers to excessive Internet use that interferes with daily life. College students use Internet
more than other people do in order to meet their educational needs. For this reason, they are more prone to the
Internet Addiction. Students learn through multi-sensory approach in Internet. Students receive and process the
information in different ways and adopt different Learning Styles. Student Learning Styles fall into three
categories: Visual Learners, Auditory Learners and Kinesthetic Learners. The objective of the present study was
(i) to find whether the Internet Addiction has impact on Learning Styles of College students. (ii) To find out
whether there is a significant difference between the Internet Addiction and Learning Styles of College students
with respect to the demographic variables such as Gender, Age, Degree pursuing and Type of device used. (iii) To
explore the significant relationship between Internet Addiction and Learning Styles of College students. In the
present study Normative Survey Method was used. By using Purposive Stratified Sampling Technique 330
samples were collected through online. Investigator used Dr. Young's Internet Addiction Test (IAT) questionnaire
and modified Learning Style Inventory Survey for the present study. Mean, Standard Deviation, t test, F ratio
and Correlation were used for analyzing the collected data. Results showed that the College students Internet
Addiction level is low and they prefer Visual Learning Style. Internet Addiction of College students with respect
to Gender, Age and Degree Pursuing were found to be significant at 0.05 level. Learning Style preferences of
College students with respect to Age and Type of device used were found to be significant at 0.05 level. College
students show negligible and negative relationship between Internet Addiction and Learning Style Preferences.
The findings prove that the Internet Addiction has no impact on Learning Styles of College students.
Keywords: Internet Addiction, Learning Style Preferences
Introduction
In the digital era, Internet usage is a common action and it becomes inevitable in the human life. It provides
information, knowledge, learning, connectivity, communication, sharing, mapping, banking, shopping, selling,
making money, collaboration, work from home, entertainment, access to global, etc. These are done with the help
of search engines, web page, YouTube, e-mail, chat, forums, GPS, Net Banking, etc. Now-a-days virtual learning
is very popular and become a part of educational system. College students are more familiar with these Internet
tools and used widely for their learning. For this purpose they use mobile, laptop, PC, tablet, etc.
In college students life, Internet is slowly replacing the traditional mode of teaching-learning process. It enhances
the students learning in all areas such as lectures, assignments, demonstrations, class discussions, interaction with
teachers, sharing the resources etc (Shao, Y., Zheng, T., Wang, Y. et al, 2018).Using of Internet within the limit
and for learning or educational purpose is acceptable one. Excessive usage of Internet leads to drastic changes in
students study habits, learning styles, health, social relationship, work and daily activities.
Internet Addiction refers to excessive internet use that interferes with daily life. Due to its negative impact on
college students’ study and life, discovering students’ Internet Addiction tendencies and making correct guidance
for them timely is necessary (Wei Peng, Xinlei Zhang, and Xin Li, 2019).
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A Learning Style is the method a person uses to learn. Students receive and process the information in different
ways and adopt different Learning Styles. They can be determined by looking at how a student's personality
influences the way they receive and process information, how they interact with classmates and the type of
learning environment and methods they prefer.
Student Learning Styles fall into three categories: Visual Learners, Auditory Learners and Kinesthetic Learners. It
is important for educators to understand the differences in their students’ Learning Styles, so that they can
implement best practice strategies into their daily activities, curriculum and assessments.
This study tries to discover whether the Internet Addiction of College students have an impact on their Learning
Styles.
Need and significance of the study
Students learn through multi-sensory approach in Internet. Students use Internet more than other people do in
order to meet their educational needs. For this reason, they are more prone to the Internet Addiction. Each student
has distinct and consistent preferred ways of perception, organization and retention. These Learning Styles are
characteristic of cognitive, affective, and physiological behaviors that serve as pretty good indicators of how
learners perceive, interact with, and respond to the learning environment. This makes the student to adopt a
particular type of Learning Style in their educational life. If educationists and teachers identify the students
diversified Learning Style, they can provide a suitable learning environment to the students. It prevents the
students Internet addictive behavior and makes their learning joyful. In order to identify the impact of Internet
Addiction on Learning Style of College students, this study was conducted.
Statement of the study
The investigator planned to do the research on the topic entitled as Impact of Internet Addiction on Learning
Styles of College students.
Objectives of the study
To measure the level of Internet Addiction of College students.
To assess the Learning Style Preferences of the College students.
To find out the significant differences, if any, between the Internet Addiction and Learning Styles of
College students with respect to the demographic variables such as Gender (Male/Female), Age (Below
20 years/20 years 25 years/Above 25 years), Degree pursuing (UG/PG/M.Phil/Ph.D) and Type of
device used (Mobile/Laptop)
To explore the significant relationship between Internet Addiction and Learning Styles of College
students.
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Hypotheses of the study
The level of Internet Addiction of College students is Moderate.
The Learning Style Preferences of College student is Visual/Auditory/Kinesthetic.
There is no significant difference between the Internet Addiction and Learning Styles of College students
with respect to the demographic variables such as Gender (Male/Female), Age (Below 20 years/20 years
25 years/Above 25 years), Degree pursuing (UG/PG/M.Phil/Ph.D) and Type of device used
(Mobile/Laptop)
There is no significant relationship between the Internet Addiction and Learning Styles of College
students.
Methodology of the study
The investigator preferred Normative Survey Method. It describes and interpretswhat exists at present.
Population & Sample
The population for the present study is confined with College students in Coimbatore District. 330 data samples
were collected through online from the College students who are pursuing UG, PG, M.Phil. and Ph.D. in
Coimbatore district. Thus the sampling technique used in the present study is Purposive Stratified Sampling
Technique.
Tools used
The investigator used Dr. Young's Internet Addiction Test (IAT) questionnaire and modified Learning Style
Inventory Survey for the present study. It was modified according to the present study by the investigator.
Description of the Tool
The Internet Addiction Test consists of 20 statements. Items are constructed based upon the 5 point Likert scale
and scored as following: ‘0’ for Not applicable, 1for Rarely, 2for Occasionally, 3for Frequently, ‘4’ for
Often and ‘5’ for Always.
Learning Style Inventory Survey consists of 24 statements. It consists of 3 types of preferences such as Visual,
Auditory and Kinesthetic. Each preference consists of 8 statements with 3 responses and scores as 3for Often,
‘2’ for Sometimes and 1for Never.
The reliability of the tool was established using the Cronbachs Alpha Reliability and the score for Internet
Addiction Test was 0.89 and for Learning Style Inventory was 0.74.
Statistical Analysis
Mean, Standard Deviation, t test, F ratio and Correlation were used for analyzing the collected data. The
statistical analysis was done by using SPSS package.
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Analysis, Interpretation and Discussion of the data
Table 1
Frequency and Percentage of College students for Internet Addiction Level
and Learning Style Preference
Variable
Range
Category
Frequency
Percentage
Internet
Addiction
Level
1 - 20
Very Low
4
1.2 %
20 - 40
Low
167
50.6 %
40 - 60
Moderate
132
40.0 %
60 - 80
High
18
5.5 %
80 - 100
Very High
9
2.7 %
Visual
25 - 48
Moderate
63
19.1 %
49 - 72
High
267
80.9 %
Auditory
25 - 48
Moderate
75
22.7 %
49 - 72
High
255
77.3 %
Kinesthetic
25 - 48
Moderate
158
47.9 %
49 - 72
High
172
52.1 %
From the above table, the Internet Addiction level of College students in the low category is found to be more
than very low, moderate, high and very high categories. Therefore, it is concluded that the Internet Addiction
level of College students is low and the hypothesis is rejected. Table 1 also states that the Learning Style
Preference of College students are high in Visual than the Auditory and Kinesthetic. So it is concluded
that the Learning Style Preference of College students is Visual.
From the above table, it is inferred that the College students are not addicted to the Internet and they prefer more
Visual Learning Style. For Visual Learning Style Preference, the College students use internet at low level.
The findings of the present study fall in line with the findings of Safrul Muluk, et al., (2020) were the result of the
study indicated that students preferred visual Learning Styles. The findings of the present study also fall in
contradictory with the findings of Natalia Rosa Keliat (2016) were the results showed that the largest percentage
of Learning Styles that were used by the students of biology education were Auditory Learning Styles by 32%
and the VAK (Visual Auditory Kinesthetic) Learning Style was the least used by education students by 2%.
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Table 2
Mean, Standard Deviation, and t-values for the scores of Internet Addiction
and Learning Style Preference with respect to Gender
Variable
Gender
N
Mean
Std.
Deviation
t - value
Result at
0.05% Level
Internet Addiction Test
Internet
Addiction
Male
102
48.29
12.095
6.558
Significant
Female
228
38.56
13.253
Learning Style Preference
Visual
Male
102
18.03
3.078
3.905
Significant
Female
228
19.40
2.656
Auditory
Male
102
18.32
2.422
1.472
Not
Significant
Female
228
18.74
2.204
Kinesthetic
Male
102
17.21
2.271
1.369
Not
Significant
Female
228
16.82
2.483
Total
Learning Style
Male
102
53.56
6.300
1.935
Not
Significant
Female
228
54.96
5.626
From the above table, it is revealed that the mean score of Internet Addiction level with respect to Gender was
higher for male College students (48.29) than the female College students (38.56).It also inferred that the total
mean score of Learning Style Preference with respect to Gender was higher for female College students (54.96)
than the male College students (53.56).
The male and female College students differed significantly in the Internet Addiction at 0.05 level. The male and
female College students differ significantly in the preferences of Learning Style namely Visual and did not differ
significantly in the preferences of Learning Style namely Auditory, Kinesthetic and total Learning Style at 0.05
level.
Therefore, the Null Hypothesis,“There is no significant difference between the Internet Addiction of College
students with respect to demographic variable genderis not accepted. And There is no significant difference
between the Learning Styles of College students with respect to demographic variable genderis accepted.
The findings of the present study fall in line with the findings of Shao, Zheng, Wang, et al., (2018) were the
Internet Addiction detection rate was higher in male students (16%) than female students (8%). And also
coincides with the findings of Ekenze, Okafor, Ekenze, et al., (2017) were the overall response rate of application
of Internet tools in surgical education was 78% (227/291) comprising 151 (66.5%) males and 76 (33.5%) females.
The findings of the present study accord with the findings of Kumar, et al., (2019) that the prevalence of Internet
Addiction between gender was 58.22% in males and 41.78% in females. The findings of the present study
associate with the findings of Suprihadi Suprihadi, Atik Rokhayani, (2017) were there is no significant
relationship between the probability of the students of having certain Learning Styles dimensions and gender.
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Table 3
Mean, Standard Deviation, and F-ratio for the scores of Internet Addiction
and Learning Style Preference with respect to Age
Variable
Sum of
Squares
df
Mean
Square
F - ratio
Result at
0.05%
Level
Internet Addiction Test
Internet
Addiction
Between Groups
3050.369
2
1525.185
8.558
Significant
Within Groups
58276.664
327
178.216
Total
61327.033
329
Learning Style Preference
Visual
Between Groups
77.796
2
38.898
4.868
Significant
Within Groups
2613.056
327
7.991
Total
2690.852
329
Auditory
Between Groups
56.448
2
28.224
5.593
Significant
Within Groups
1650.125
327
5.046
Total
1706.573
329
Kinesthetic
Between Groups
27.116
2
13.558
2.330
Not
Significant
Within Groups
1902.790
327
5.819
Total
1929.906
329
Total
Learning Style
Between Groups
211.554
2
105.777
3.110
Significant
Table 2 indicated that the College students belonging to different age groups (below 20 years, 20 years 25 years
and above 25 years) differed significantly in the Internet Addiction at 0.05 level.
In Learning Style preferences, College students belonging to different age groups (below 20 years, 20 years 25
years and above 25 years) differed significantly in Visual, Auditory, and total Learning Style except Kinesthetic
preference at 0.05 level.
Therefore, the Null Hypothesis, There is no significant difference among the Internet Addiction of College
students with respect to demographic variable age” is not accepted. And There is no significant
difference among the Learning Styles of College students with respect to demographic variable age
is not accepted.
The findings of the present study fall in contradictory with the findings of Ghahremani, L., & Nazari, M. (2020)
were they found no significant relationship between Internet Addiction and variables such as age, using personal
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emails, residential location, marital status, number of failed grades, age of beginning Internet use (P>0.05).
Table 4
Mean, Standard Deviation, and F-ratio for the scores of Internet Addiction
and Learning Style Preference with respect to Degree Pursuing
Variable
Sum of
Squares
df
Mean
Square
F - ratio
Result at
0.05%
Level
Internet Addiction Test
Internet
Addiction
Between Groups
6373.435
3
2124.478
12.603
Significant
Within Groups
54953.598
326
168.569
Total
61327.033
329
Learning Style Preference
Visual
Between Groups
21.419
3
7.140
0.872
Not
Significant
Within Groups
2669.432
326
8.188
Total
2690.852
329
Auditory
Between Groups
21.768
3
7.256
1.404
Not
Significant
Within Groups
1684.805
326
5.168
Total
1706.573
329
Kinesthetic
Between Groups
4.575
3
1.525
0.258
Not
Significant
Within Groups
1925.331
326
5.906
Total
1929.906
329
Total
Learning Style
Between Groups
91.504
3
30.501
0.884
Not
Significant
Within Groups
11242.693
326
34.487
Total
11334.197
329
From the Table 4, it is indicated that the College students pursing different degree (UG, PG, M.phil, and Ph.D.)
differed significantly in the Internet Addiction at 0.05 level. In Learning Style preferences, College students
pursing different degree (UG, PG, M.Phil, and Ph.D.) did not differed significantly in all the Learning Style
preferences at 0.05 level.
Therefore, the Null Hypothesis, “There is no significant difference among the Internet Addiction of College
students with respect to demographic variable degree pursuing” is not accepted. And “There is no significant
difference among the Learning Styles of College students with respect to demographic variable degree
pursuingis accepted.
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Table 5
Mean, Standard Deviation, and F-ratio for the scores of Internet Addiction
and Learning Style Preference with respect to Type of device used
Variable
Type of
Device
used
N
Mean
Std.
Deviation
t - value
Result at
0.05% Level
Internet Addiction Test
Internet Addiction
Mobile
318
41.72
13.725
1.258
Not
Significant
Laptop
12
37.50
11.310
Learning Style Preference
Visual
Mobile
318
18.86
2.828
5.351
Significant
Laptop
12
22.00
1.954
Auditory
Mobile
318
18.51
2.234
4.959
Significant
Laptop
12
21.25
1.865
Kinesthetic
Mobile
318
16.86
2.404
4.674
Significant
Laptop
12
19.25
1.712
Total
Learning Style
Mobile
318
54.23
5.694
5.674
Significant
Laptop
12
62.50
4.927
From the table 5, it is revealed that the mean score of Internet Addiction level with respect to type of device used
was higher for mobile (41.72) than the laptop (37.50). It also inferred that the total mean score of Learning Style
Preference with respect to type of device used was higher forlaptop (62.50) than the mobile (54.23).
The mobile and laptop usage of College students differed significantly in the Internet Addiction at 0.05 level. The
mobile and laptop usage of College students differ significantly in all the preferences of Learning Style namely
Visual, Auditory, Kinesthetic and total Learning Styleat 0.05 level.
Therefore, the Null Hypothesis, There is no significant difference between the Internet Addiction of
College students with respect to demographic variable Type of device used” is accepted. And There is no
significant difference between the Learning Styles of College students with respect to demographic variable
Type of device used” is not accepted.
The findings of the present study fall in line with the findings of Shreya Kothaneth, Ashley Robinson, Catherine
Amelink, (2012) were the tablet and PC can be used across different Learning Styles to enrich the educational
experience. The findings of the present study also accord with the findings of Tangmunkongvorakulet al., (2019)
were students with excessive use of smartphones had lower scores the psychological well-being than those who
did not use smartphone excessively (B = -1.60; P < 0.001).
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Table 6
Correlation Analysis for Internet Addiction and
Learning Style Preferences of College students
Variable
Internet
Addiction
Visual
Auditory
Kinesthetic
Total
Learning Style
Internet
Addiction
1
-0.258**
-0.087
0.146**
-0.099
Visual
1
0.470**
0.373**
0.823**
Auditory
1
0.357**
0.764**
Kinesthetic
1
0.733**
Total
Learning Style
1
**Correlation is significant at the 0.01 level (2-tailed)
From the Table 6, it is revealed that, there existed high positive significant relationship between the Visual and
Total Learning Style Preference (0.823), Auditory and Total Learning Style Preference (0.764) and Kinesthetic
and Total Learning Style Preference (0.733) at 0.01 level. It is also inferred that it have low positive significant
relationship between Visual and Auditory (0.470), Visual and Kniesthetic (0.373), Auditory and Kniesthetic
(0.357) and Internet Addiction and Kniesthetic (0.146) at 0.01 level. It further states that it have low negative
significant relationship between Internet Addiction and Visual (-0.258) at 0.01 level. Correlation between Internet
Addiction and Total Learning Style Preference (-0.099), Internet Addiction and Auditory (-0.087) are negligible
and have negative correlation at 0.01 level.
Therefore, the Null Hypothesis, There is no significant relationship existed between the Internet Addiction
and Learning Styles of College students” is accepted.
The findings of the present study fall in line with the findings of Kim, et al., (2017), were higher school
performance significantly positively correlated with Internet use for study but negatively correlated with Internet
use for general purpose. This study also coincides with the findings of Reza Ghaffari, et al., (2013) were there was
no significant relationship between Students’ academic achievement and their Learning Styles.
Educational Implications
The findings inferred that the College students prefer Visual Learning Style preferences. Therefore the
educators should plan to provide teaching-learning process more visually.
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The results show that, Male College students are more addicts to Internet usage than the Female College
students. Effective measures should be taken to prevent further Internet Addiction and improve the
current situation.
Educators should arrange psychological counseling programs for the College students in order to prevent
them from risk behaviors.
Teachers should inculcate the College students about the deleterious effects of Internet Addiction and the
health problems such as eye strain, change in eating habits, neglecting sleep, anxiety, depression, stress,
etc.
Instructors can create interest to the College students to spend more time in Internet for educational and
social awareness related purpose than the gaming and entertainment activities.
To promote healthy smart phone use, effective strategies should be designed.
Educational policy makers and Curriculum constructors should provide the learning materials for the
College students with strong preferences to Visual Learning Styles.
Conclusion
This study tried to investigate whether the Internet Addiction has an impact on Learning Styles of College
students. The results of the study stated that the there is only low Internet Addiction among the College students
and they prefer Visual Learning Style. Male College students show high Internet Addiction than the Female
College students. There is a significant difference in the Internet Addiction among the various degree pursuing
College students. Different age groups of College students differ in their Learning Style preferences. College
students differ in their usage of devices for adopting their Learning Style. Finally, there is no significant
relationship between Internet Addiction and Learning Styles of College students. This proves that the Internet
Addiction has no impact on Learning Styles of College students. It is mandatory for the educationists, policy
makers, institutions and teachers to offer a good quality educational environment to the students. It should create
interest and also a search for knowledge and skills and make the students to surf the Internet for educational
purpose. At the same time it should fulfill the needs of the students and suit to their Learning Styles.
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Article
Full-text available
Internet addiction refers to excessive internet use that interferes with daily life. Due to its negative impact on college students’ study and life, discovering students’ internet addiction tendencies and making correct guidance for them timely is necessary. However, at present, the research methods used in analyzing students’ internet addiction are mainly questionnaires and statistical analysis, which relies on the domain experts heavily. Fortunately, with the development of the smart campus, students’ behavior data such as consumption and trajectory information in the campus are stored. With this information, we can analyze students’ internet addiction levels quantitatively. In this paper, we provide an approach to estimate college students’ internet addiction levels using their behavior data in the campus. In detail, we consider students’ addiction towards the internet is a hidden variable which affects students’ daily time online together with other behavior. By predicting students’ daily time online, we will find students’ internet addiction levels. Along this line, we develop a linear internet addiction (LIA) model, a neural network internet addiction (NIA) model, and a clustering-based internet addiction (CIA) model to calculate students’ internet addiction levels, respectively. These three models take the regularity of students’ behavior and the similarity among students’ behavior into consideration. Finally, extensive experiments are conducted on a real-world dataset. The experimental results show the effectiveness of our method, and it is also consistent with some psychological findings.
Thesis
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Background Globally, the number of internet users has crossed the three-billion mark, while in India users grew over 17% in the first 6 months of 2015 to 354 million. This study presented a background on internet use and the existence of excessive internet use. Aim To study the extent of internet use in 11th and 12 grade students and the psychopathology, if any, associated with excessive internet use. Methods 426 students who met the inclusion criteria were recruited from 11th and 12th grade classes from Kendriya Vidyalaya, New Delhi, India, and were assessed by Young’s Internet Addiction Test and the Strength and Difficulties Questionnaire. Results Among the 426 students, the mean internet addiction total score was 36.63 (20.78), which indicated mild level of internet addiction. 1.41% (six students) was diagnosed as excessive internet users, while 30.28% and 23.94% were classified as moderate and mild internet users, respectively. The prevalence of internet addiction between gender was 58.22% in males and 41.78% in females. While both positive (prosocial) and negative (hyperactivity, emotional, conduct and peer problem) impacts of internet use were reported by students, in the current study excessive use of internet had a negative impact on students’ lives as compared with positive impact, which was statistically significant ( p <0.0001). Conclusion Excessive internet use led to abnormal behaviours which cause negative consequences to users. Early diagnosis of risk factors related to excessive internet use, provides education about responsible use and supervision of students by family members.
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Background Despite the pervasive use of smartphones among university students, there is still a dearth of research examining the association between smartphone use and psychological well-being among this population. The current study addresses this research gap by investigating the relationship between smartphone use and psychological well-being among university students in Thailand. Methods This cross-sectional study was conducted from January to March 2018 among university students aged 18–24 years from the largest university in Chiang Mai, Thailand. The primary outcome was psychological well-being, and was assessed using the Flourishing Scale. Smartphone use, the primary independent variable, was measured by five items which had been adapted from the eight-item Young Diagnostic Questionnaire for Internet Addiction. All scores above the median value were defined as being indicative of excessive smartphone use. Results Out of the 800 respondents, 405 (50.6%) were women. In all, 366 (45.8%) students were categorized as being excessive users of smartphones. Students with excessive use of smartphones had lower scores the psychological well-being than those who did not use smartphone excessively (B = -1.60; P < 0.001). Female students had scores for psychological well-being that were, on average, 1.24 points higher than the scores of male students (P < 0.001). Conclusion This study provides some of the first insights into the negative association between excessive smartphone use and the psychological well-being of university students. Strategies designed to promote healthy smartphone use could positively impact the psychological well-being of students.
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Background With the development of economy and technology, the Internet is becoming more and more popular. Internet addiction has gradually become a serious issue in public health worldwide. The number of Internet users in China has reached 731 million, with an estimated 24 million adolescents determined as having Internet addiction. In this meta-analysis, we attempted to estimate the prevalence of Internet addiction among College Students in the People’s Republic of China in order to improve the mental health level of college students and provide evidence for the prevention of Internet addiction. Methods Eligible articles about the prevalence of Internet addiction among college students in China published between 2006 and 2017 were retrieved from online Chinese periodicals, the full-text databases of Wan Fang, VIP, and the Chinese National Knowledge Infrastructure, as well as PubMed. Stata 11.0 was used to perform the analyses. Results A total of 26 papers were included in the analyses. The overall sample size was 38,245, with 4573 diagnosed with Internet addiction. The pooled detection rate of Internet addiction was 11% (95% confidence interval [CI] 9–13%) among college students in China. The detection rate was higher in male students (16%) than female students (8%). The Internet addiction detection rate was 11% (95% CI 8–14%) in southern areas, 11% (95% CI 7–14%) in northern areas, 13% (95% CI 8–18%) in eastern areas and 9% (95% CI 8–11%) in the mid-western areas. According to different scales, the Internet addiction detection rate was 11% (95% CI 8–15%) using the Young scale and 9% (95% CI 6–11%) using the Chen scale respectively. Cumulative meta analysis showed that the detection rate had a slight upward trend and gradually stabilized in the last 3 years. Conclusion The pooled Internet addiction detection rate of Chinese college students in out study was 11%, which is higher than in some other countries and strongly demonstrates a worrisome situation. Effective measures should be taken to prevent further Internet addiction and improve the current situation.
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Although overuse of the internet has been suggested to be related to poor academic performance, the effects of internet use for education on academic performance showed conflict results in previous studies. Accordingly, the associations of school performance with internet use for study and for general purpose were explored in a large population of Korean adolescents. Cross-sectional data from the 2013 Korean Youth Risk Behaviour Web-based Survey (KYRBWS) were retrieved for 59,105 12- to 18-year-old adolescents. The associations between school performance and internet use were analysed using multinomial logistic regression with complex sampling. Days of physical activity, sex, obesity, region of residence, income level, parental education level, stress, sleep time, smoking, alcohol consumption, drug use, and total study time were recorded and adjusted for as confounders. Higher school performance was positively associated with longer internet use for study (adjusted odds ratio, AOR, of 2+ h [95% confidence interval] = 2.43 [2.10–2.82], 2.02 [1.78–2.30], 1.66 [1.46–1.89], and 1.30 [1.15–1.47] for performance groups A, B, C, and D, respectively, P < 0.001) but negatively associated with longer internet use for general purpose (AOR of 3+ h [95% confidence interval] = 0.68 [0.60–0.78], 0.85 [0.76–0.94], 0.83 [0.75–0.92], and 0.98 [0.89–1.08] for performance groups A, B, C, and D, respectively, P < 0.001). Higher school performance significantly positively correlated with internet use for study but negatively correlated with internet use for general purpose. Academic use of the internet could be a means of achieving good school performance.
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Background: Considering that all activities are done using the Internet currently, the use of the Internet has turned into a necessity. Besides its various advantages, improper and excessive use has led to overt dependence on the Internet. We aimed to determine the effect of Internet addiction on educational achievement and its consequences. Methods: This cross-sectional study was done on 298 students studying at Shiraz University of Medical Sciences during 2011. Data were collected using Yang's Internet addiction questionnaire and analyzed using SPSS software. A P<0.05 was considered as significant. Results: The total mean (±SD) internet addiction score was 31.26 (±11.53). 91.6, 6.4, and 0.3% of the students had normal, mild, and severe addiction to the Internet. We found a significant relationship between addiction to the Internet and variables such as chatting, sex, having a personal laptop, hours working with the computer, and hours surfing the Internet (P<0.05). We found no significant relationship between Internet addiction and variables such as age, using personal emails, residential location, marital status, number of failed grades, age of beginning Internet use (P>0.05). Conclusion: Currently, the lives of all people including academics, is connected to the Internet causing a massive change in various aspects of people's lives. Therefore, taking necessary measures that could result in the correct use of this technology is crucial I order to improve the society.
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The main goal of the present study is to investigate the psychometric properties of a French version of the Internet Addiction Test (IAT) and to assess its relationship with both time spent on Internet and online gaming. The French version of the Young's Internet Addiction Test (IAT) was administered to a sample of 246 adults. Exploratory and confirmatory analyses were carried out. We discovered that a one-factor model of the IAT has good psychometric properties and fits the data well, which is not the case of a six-factor model as found in previous studies using exploratory methods. Correlation analysis revealed positive significant relationships between IAT scores and both the daily duration of Internet use and the fact of being an online player. In addition, younger people scored higher on the IAT. The one-factor model found in this study has to be replicated in other IAT language versions.
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  • Eskandarfathi Faribasalekranjbarzadeh
  • Susan Azar
  • Naser Hassanzadeh
  • Parisa Safaei
  • Hossein Golanbar
  • Elham Mazouchian
  • Abbasi
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  • Habiburrahimhabiburrahim Safrulmuluk
  • Siti Rechalrechal
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