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6Copyright © 2024 by Cherkas Global University
Published in the USA
International Journal of Media and Information Literacy Issued since 2016. E-ISSN: 2500-106X 2024. 9(1): 52-59
DOI: 10.13187/ijmil.2024.1.52 https://ijmil.cherkasgu.press
Addiction to Social Networks and Its Influence on the Academic Performance of the Students: An Analysis from the Bayesian Approach
Milka E. Escalera-Chávez a, Felipe Pozos-Texon b , *, Violetta S. Molchanova c > d
a Universidad Autónoma de San Luis Potosí, Mexico b UCC Business School, Mexico c Cherkas Global University, Washington, DC, USA d Volgograd State University, Volgograd, Russian Federation
Abstract
Social networks are websites and applications that favor the teaching-learning process because you can communicate, share information and establish relationships between students and teachers. In this research, an estimation of the relationship between the addition to social networks and the academic performance of university students is carried out. For this, the Bayesian factor is used, in whose analysis technique it characterizes the posterior distribution and the Bayes factor is estimated, which measures the linear relationship between two variables of the scale, following a bivariate normal distribution. For this purpose, a sample of students from the Middle Zone Multidisciplinary Academic Unit of the UASLP is analyzed. The sample includes 200 students. The Social Networks The instrument is made up of three dimensions: obsession with social networks, lack of personal control in the use of social networks and excessive use of the social network. The results indicate that there is not enough evidence to say that there is a relationship between obsession with social networks, lack of personal control in the use of social networks and excessive use of social networks with academic performance, because the Bayesian Factor value is between 10 and 30.
Keywords: social networks, academic performance, factor Bayes, college students.
1. Introduction
Information technologies have achieved that some activities that were carried out in a conventional way, are now carried out in a different way. At the beginning of the year 2023, Digital (2023) reports 100.6 million Internet users in Mexico, of which 94.00 million are users of social networks, equivalent to 73.4 percent of the total population. The most widely used means of access is the mobile phone, with 123.5 million active connections. The social network with most access is Facebook with 94 million users who access it.
In this sense, the Internet Association (2023) reports in the 19th Study on the Habits of Internet Users in Mexico 2023 that 42.7 % of those surveyed in the study spend an internet connection time of 7 to more than 9 hours. Generation Z (11 and 26 years old) are the users who connect to the Internet the most (27 %), in second place, Generation X between 43 and 58 years old (25 %) Internet Association (2023). In third place are users of the Millennial Generation between 27 to 42 (22 %). The most common use is to social networks WhatsApp, Facebook and Instagram continue to be the most used social networks by those surveyed.
* Corresponding author
E-mail addresses: fpozost@gmail.com (F. Pozos-Texon)
Other applications where technology is immersed, as Smit (2012) refers, is the use of different instant messaging platforms in education, which have the potential to increase learning. In this idea, Bouhnik and Deshen (2014); have expressed the advantage of using social networks such as WhatsApp in the teaching-learning process because it can be used at any time and place, and it is useful in collaborative learning. Limas and Vargas (2021) mention that universities have adapted to social networks, and have managed to change the teaching process and improve the quality of education.
In the study carried out by Cetinkaya (2017), refers that the students had a positive opinion about the application of social networks in their courses, especially WhatsApp, also points out that in this modality, learning occurs more spontaneously and, if they are used images learning is more effective.On the other hand, Abraham and Fanny (2019) point out that different investigations report that Facebook, YouTube, Twitter and other social platforms are preferred by young people and conclude that students are highly dependent on social networks, affecting significant in the teaching-learning process.
Some authors (Matikiti et al., 2017; Hajarian et al., 2017 cited by Van Rhyne et al., 2019) explain that social networks are "websites and applications designed to allow people to share content in a fast, efficient and in real time". For his part, Dyer (2020) expands the explanation, pointing out that: social networks are a challenging task, since; it is an area that continuously changes. Social networks are websites and applications that favor the teaching-learning process because it is possible to communicate, share information and establish relationships between students and teachers.Torres (2011) shows that social networks in universities have facilitated communication and collaborative learning and have introduced new ways of working among the actors in the training processes. Meso (2010, cited by Torres, Alcantar, 2011) published the results of a study in Spain, 83 percent of Spanish youth use at least one network. Its use makes various processes efficient and helps to solve problems; at the same time, they are widely used to improve classroom performance.
Vasquez et al. (2022), carried out a bibliographic review in 30 articles in which they analyze the role that social networks play in the teaching-learning process of university students during the period 2016-2021. In their findings, they conclude that it has a real effect on the learning, since it moves students from passive receivers of information to active participants; it also allows updating knowledge and providing feedback. Therefore, the incorporation of these technological platforms in Higher Education is very convenient and beneficial, even with the inconveniences that they can cause, such as distraction and lack of concentration in the process.
In this time, technology has become a tool to obtain greater knowledge. Recent media such as social media, Twitter, Instagram and Facebook have made a difference in the dissemination of information and knowledge due to their effect and influence on society. Currently, people believe that these tools are useful to achieve goals, as well as to understand what is happening in society (Adegbola, Gearhart, 2019 cited by Mohamed et al., 2020).
Owusu-Boakye (2020) talks about the impact of the technological tools of the 21st century and mentions that they have come to transform the teaching-learning process. There are two causes: 1) the student has a different way of learning and 2) the teacher is forced to modify his teaching profession due to the presence of digital technology. A similar conceptualization has been referred to by Mynbayeva and Sadvakassova (Mynbayeva, Sadvakassova, 2018, cited by Owusu-Boakye, 2020) when considering this technological tool as a transformed process. In this idea, Owusu-Boakye (2020) refers that WhatsApp is a useful tool in the field of learning at any time and in any place and increases attributed collaborative learning since it increases learning. In this sense, Adamson (Adamson, 2012, cited by Wiid, 2014) indicates that social media network systems do not prevent them from be used in teaching, since information can be shared between teachers and students at any time and from any location.
However, excessive use can generate addiction, which would generate concern in the health sector, which has focused more on the issue due to the addictive characteristics and psychological consequences that social networks cause in people. There is growing evidence that social media addiction is a persistent problem among students. Dominguez-Vergara and Ibanez-Carranza (2016) found that adolescent students from 12 to 16 years old show a moderately significant level of addiction to social networks. It also indicates that, the greater the addiction to social networks, the lower level of social skills in adolescents is demonstrated.
Kog and Turan (2020) clearly show in their results that most of the participants use Instagram as their main app. However, we did not find any significant relationship between academic performance and addiction to social networks. Nevertheless, they report a gender difference, with women spending more time on SNS than men do, while men have more online friends than women.
Amador-Ortiz (2021) reports that there is a significant association between university students failing school and social networks addiction. The associations are presented in two factors: the obsession with social networks and their excessive use. Chavez and Coaquira (2022) report three levels of addiction to social networks in university students; the most relevant is the medium level with 48 %, 27.4 % of the students have a low level and only 24.6 % have a high level. The high-level reports that 25.6 % are obsessed with social networks, 29 % lack personal control and 30.8 % use social networks excessively.
Alarcon-Allain and Salas-Blas (2022) studied the relationship between social networks addiction and emotional intelligence in 279 students of different technical degrees from a state institute in Callao-Peru. Regarding social networks addiction, it reports that students who spend more time online (more than 6 hours) score higher in the ARS dimensions (obsession, lack of control, and excessive use). Of the three dimensions, the excessive use of social networks presents the biggest difference. The authors conclude that students who are online for longer periods obtain higher scores on the ARS.
As a follow-up to this topic, Reynoso (2022) applied Escurraand Salas (2014) social media addiction scale to students, showing that students have low levels of addiction. However, the addiction level is affected by age since the level increases with age and it differs whether or not the student has a work activity. In addition, he reports that the greater the use of social networks, the greater the increase in academic stress in higher-level students.
Weihongand Fethi (2023) studied the influence of social networks (SNS) addiction on academic performance in 251 college students from the southwestern United States. Their findings indicate that social media addiction does not have an impact on college students' grade point average (GPA). However, they report that they have a positive effect on their anxiety and stress.
On the other hand, Valencia et al. (2023) report that for students there is no risk derived from the use of social networks, nor does it create problems in any context: social, work or academic. They do not feel the "obsession" or discomfort to know what is happening in SN. Likewise, men and women do not differ in the ARS, those who spend more hours connected are more prone to show higher scores in addiction to networks.
This research is based on the Media Dependency Theory (MDT). It is used to interpret the data and propose explanations for social networks addiction causes or influences. According to the MDT, a mean would become an integral part of an individual, if he/she is too dependent on it to satisfy his/her needs. Regarding this theory, it can be said that social networks are the mean that students use in the teaching-learning process to achieve academic performance (Nul et al., 2020).
Therefore, emerge some pertinent questions: Does social networks addiction have an effect on college students' academic performance? To answer the question, the following objective is proposed: to verify the relationship that exists between social networks addiction and academic performance in college students.
2. Materials and methods
In this research, an estimation of the relationship between social networks addiction and academic performance in university students is made through the Bayesian factor, where the posterior distribution is characterized and the Bayes factor is estimated, which measures the linear relationship between two scale variables jointly following a bivariate normal distribution. For this purpose, a group of students from the Middle Zone Multidisciplinary Academic Unit of the UASLP is analyzed.
The study is carried out in the municipality of Rio Verde, SLP, in the semester between the months of August to December 2022. The sample includes 200 students of which, 38.5 % are from the civil engineering degree, 24.5 % from the nursing degree, 11 % from the public accountant degree and 26 % from the administration degree. Of the total number of students, 42 % are men and 58 % women.
3. Discussion and results
Instrument
The Social Networks Addiction questionnaire designed by Escurraand Salas (2014) was used. The instrument consists of 24 items on a 5-point Likert scale ranging from never to always (to which scores from 0 to 4 were assigned). It has 23 direct questions and 1 reverse question. Three dimensions integrate the instrument: obsession with social networks, lack of personal control in the use of social networks and excessive use of the social network.
Social media obsession is characterized by constantly thinking and worrying about not being able to access or be connected to social media. The lack of personal control in the use of social networks refers to a concern for the lack of control in the use of networks, which results in the non-compliance or neglect of certain activities, and finally, the excessive use of networks Social refers to being connected to social networks for a long time. The instrument has a reliability of .95 and the dimensions have Cronbach's alpha values of .91, .89 and .92 respectively, so they have adequate internal consistency (Escurra, Salas, 2014).
For the Bayesian analysis, the IBM SPSS Statistics 27 software is used, which includes Bayesian statistics and Pearson's correlation. In this sense, firstly a Pearson linear correlation is performed, taking into account the definitions of the following hypotheses:
- H01: There is no relationship between the obsession with social networks and the academic performance of college students.
- H1: There is a relationship between the obsession with social networks and the academic performance of college students.
- H02: There is no relationship between the lack of personal control in the use of social networks and the academic performance of college students.
- H2: There is a relationship between the lack of personal control in the use of social networks and the academic performance of college students.
- H03: There is no relationship between the lack of personal control in the use of social networks and the academic performance of college students.
- H3: There is a relationship between excessive use of social networks and the academic performance of college students.
Contrasting by the Bayesian method
On many occasions, only two hypotheses are formulated: one of no difference or no association, noted as H0, and another as the opposite event. However, an advantage of this model is the possibility that with the Bayesian hypothesis test it is not based on the rejection of a null hypothesis, but on being able to contrast two hypotheses: the null or no-effect (H0) against the alternative or effect one (H1). The relationship between these two hypotheses is summarized in the Bayes factor or FB factor (see Figure 1).
FB01 FB10
Null Alternative
Hypothesis Value Hypothesis
Verv strong >30 Very strong
Strong 10.-30 Strong
Favor Moderate 3.1-10 Moderate Favor
Anecdotal 1.1-3 Anecdotal
No evidence 1 No evidence
Anccdotal 0.3-0.9 Anccdotal
In contrary . Moderate 0.29-0.1 Moderate In contrary
Strong 0.09-0.03 Strong
Very strong <0.03 Very strong
Fig. 1. Bayes Factor (own) Data analysis
The descriptive analysis of the sociodemographic variables is as follows: The greatest participation in the survey was women (n=116), versus men (n=84). The age range is between 17 and 28 years, and the highest percentage is between the ages of 20 and 22 (56 %), of which a
representative percentage studies and works. In addition, it is observed that the career where more students participated was civil engineering in the most advanced semesters (see Table 1).
In relation to the information on the use of networks shown in Table 1 we observed that, a very high percentage (98 %) connects to social networks through their cell phone and the frequency is 7 to 12 times per day that they consult or review their social networks.In addition, the students surveyed report knowing in person more than 70 % of their contacts. Finally, the use they give to social networks is both for socializing and for work and academic activities.
Table 1. Socio-demographic variables
Gender % Age % Connection %
Male 42,0 17 to 19 24,5 Smartphones 98
Female 58,0 20 to22 56,0 Computers 2
23 to 25 17,0
26 to 28 2,5
Activity % Semester % Frequency %
Study 55,0 First 16,5 All the time 20,5
Study and work 44,5 Third 0,5 1 to 2 times per day 24,0
Only work 0,5 Fifth 12,5 3 to 6 times per day 26,5
Seventh 48,0 7 to 12 times per 28,0
day
Nineth 22,5 2 to 3 times per day 1,0
Carrer % Use % Frequency %
CE 38,5 Socialization 27,5 All the time 20,5
N 24,5 Academic 9,0 1 to 2 times per day 24,0
PA 11,0 Work 8,0 3 to 6 times per day 26,5
A 26,0 All the above 55,5 7 to 12 times per 29,0
_day
Note: civil engineering (CE), Nursing (N), public accounting (PA), Administration (LA) Source: own
Regarding the level of addition, Table 2 shows that the highest percentage refers to the obsession that students have about accessing social networks (94 %). Meanwhile, in the dimensions about the lack of personal control and excessive use, the level of the students corresponds to a medium level.
Table 2. Addiction to social networks level
Abuso(%) Control ( %) Uso ( %) Puntuación
1.0 3 5 Bajo
7.0 23 35.5 Medio
92 74 59.5 Alto
100 100 100
Source: own
To verify the research hypotheses (HO versus HA), the results described in Table 3 show the Bayesian factor for each of the constructs. We observe that the Bayes factor of the relationship between obsession, lack of control, and excessive use of social networks with the academic performance variable is greater than 10, indicating that there is a 10, 17 and 15 greater probabilitythat there is a large (strong) difference between the null hypothesis and the alternative hypothesis. This means that the null hypothesis is more in favor: there is no relationship between obsession, lack of control and use of social networks and the academic performance of college students; because the value of the Bayesian Factor is between 10 and 30.
Regarding the characterization of the posterior distribution, the credibility limits are observed at 95 %, which indicates that the true value of the r (Pearson) statistic is within those limits. In the case of obsession with social networks, the true value of r is between the ranges of -
,208 and ,062 in the case of lack of control it is between -,121 and ,148, finally with respect to the excessive use of social networks, the values are between -,172 and ,101 (Table 4).
Table 3. Inference of Bayes factor in pairwise correlations
Lack of
Obsession control Exccesive use Average
X1 Pearson correlations 1 ,675 ,659 -,074
Bayes factor ,000 ,000 10,431
X2 Pearson correlations 1 ,617 ,018
Bayes factor ,000 17,254
X3 Pearson correlations 1 -,037
Bayes factor 15,556
Average Pearson correlations 1
Bayes factor
Source: own
Table 4. Posterior distribution characterization
Variable Lower limit Upper limit Value r
Obsession -.208 .062 -.074
Lack of control -.121 .148 .018
Excessive use -.177 .097 -.037
Source: own
4. Conclusion
Considering that the study focused on verifying the relationship between the addition to social networks and academic performance in college students, therefore the following discussion and conclusion is obtained:
According to the instrument designed by Escurra and Salas (2014) it was possible to evaluate in each of the constructs that comprise it, that there is no relationship between the obsession with social networks, the lack of personal control in the use of social networks and excessive use of social networks with academic performance. The Bayes factor that measures the relationship between the dependent variable (academic performance) and independent variables (addition to social networks) shows a value greater than 10, indicating 10, 17, and 15 times the probability of a large difference. (strong) of the null hypothesis versus the alternate hypothesis.
These results are consistent with those proposed by Kog and Turan (2020) who report that there is no significant relationship between addition to social networks and academic performance. However, other studies Owusu-Boakye (2020) report that the use of the WhatsApp social network increases collaborative learning. These results do not coincide with those reported by Amador-Ortiz (2021) who reports that there is a relationship between school failure and the addition to social networks, mainly in the constructs with the obsession with social networks and their excessive use.
Regarding the addition to social networks, this study coincides with the results presented by Chavez and Coaquira (2022). In their study, the authors indicate that a percentage of 25,6 % are obsessed with social networks, 29 % lack personal control and 30,8 % use social networks excessively. This study reports that 94 % of students abuse social networks. However, this data partially coincides with the work of Alarcon-Allain, and Salas-Blas (2022), since they report high levels in each of the constructs; in this sense, it only coincides with the high level of abuse of social networks.
From the above, we can conclude that students feel worry too often when they cannot access social networks, however, this does not mean that it affects their academic performance. This feeling is probably because students use social networks in the learning process; therefore, it causes concern not being able to access them.
Finally, it is proposed for subsequent work to analyze the relationship between the addition to social networks in the learning process with academic performance, specifically during the semester in which the student is studying.
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