literature review on internet usage

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Health Promot Int

Internet use and health in higher education students: a scoping review

Hanna rouvinen.

1 Department of Nursing Science, Faculty of Health Sciences, University of Eastern Finland, Yliopistonranta 1C, PO Box 1627, Kuopio FI-70211, Finland

Krista Jokiniemi

Marjorita sormunen.

2 Institute of Public Health and Clinical Nutrition, Faculty of Health Sciences, University of Eastern Finland, Yliopistonranta 1C, PO Box 1627, Kuopio FI-70211, Finland

Hannele Turunen

Associated data.

The amount of time spent online has increased over the last decade among higher education students. Students engage in online activities related to studies, work, leisure, entertainment and electronic services (e-services) use. The Internet is also used for health-related matters. The increase in the use of the Internet has influenced students’ health, especially mental and physical health and well-being. This scoping review scrutinizes the literature between 2015 and 2020 ( N = 55) on the association between Internet use and health in higher education students. A methodological framework, outlined by Arksey and O'Malley, was applied to conduct this review. Systematic searches were carried out in the CINAHL, PubMed and Scopus databases and in the available grey literature. For the data, a thematic analysis by Braun and Clarke was utilized. Two major themes of ‘Health-promoting Internet use’ and ‘Health-threatening Internet use’ emerged and are described in this review.

LAY SUMMARY

Internet use for higher education students is a way of life, and for some, it is even a problem. Previous research has identified Internet use effects on health, especially on mental and physical health. Our research indicated that Internet use has positive effects (promoting) or negative effects (threatening) on health among students. We believe that the results of this review can be utilized in promoting higher education students’ health and well-being.

INTRODUCTION

The Internet, the global system of networks, is characterized as one of the most significant information-finding and sharing forums that higher education (HE) students use daily ( Geyer et al. , 2017 ). Students exhibit a high level of competency in Internet use with digital technologies, such as smartphones or tablets ( Essel et al. , 2018 ; Lepp et al. , 2019 ). According to previous studies, HE students’ daily Internet use varies from fewer than four hours to over eight hours, with the average being four to five hours ( Al-Gamal et al. , 2015 ; Qader et al. , 2015 ; Sumaiyah Jamaludin et al ., 2018 ). Students engage in online activities related to studies and work, leisure and entertainment and the use of electronic services (e-services) ( Geyer et al ., 2017 ; Mou et al. , 2017 ; Chern and Huang, 2018 ). Additionally, health-related Internet use is common. Students use online health information to address or solve a health problem and communicate about health issues online ( Mou et al. , 2017 ; Yang et al., 2017 ). The use of health services provided online—as well as web-based health interventions and treatments—is increasing ( Merchant et al. , 2017 ; Mou et al. , 2017 ).

Against the positive sides of HE students’ online activities, Internet use has become a problem for growing number of students, ascending to pathological or addictive Internet use ( Young and de Abreu, 2011 ; Li et al. , 2015 ; Kumar and Mondal, 2018 ). This problematic Internet use is described by numerous terms, for instance, ‘excessive Internet use’, ‘psychopathological Internet use’, ‘problematic Internet use’, ‘Internet dependence’, ‘iDisorder’ and ‘compulsive computer use’ ( Nath et al. , 2016 ; Li et al. , 2018 ), meaning a negative influence on various interpersonal, social, psychological and physical health domains of students’ life ( Maurya et al. , 2018 ). Students with problematic Internet use exhibit obesity and sleep disorders ( Li et al. , 2016 ), comorbid mood and anxiety disorders ( Kuss and Lopez-Fernandez, 2016 ) and behavioral problems, such as sedentary lifestyles and lower levels of physical activity ( Penglee et al. , 2019 ). However, effective professional treatments exist to address these issues, for example, new clinical centers have been established to treat Internet-use-related problems ( Kuss and Lopez-Fernandez, 2016 ).

During the HE years, students undergo a transition to adulthood. They are in a developmental stage when autonomy from their parents is increased (moving away from the family home) and changes in financial status are experienced. Students are known to experience demanding studies, pressure to graduate and make career choices ( Aceijas et al. , 2017 ; Auerbach et al. , 2018 ). Additionally, HE students’ abilities, self-regulation and overall control are developing, and therefore, physical and mental developments are still evolving ( Shao et al. , 2018 ). Hence, the HE era is associated with taking part in risky health behaviors, such as substance use, risky sexual behavior, not getting enough sleep, not eating healthily and being sedentary more than recommended ( Evans-Polce et al. , 2016 ; Mou et al. , 2017 ; Mnich et al. , 2019 ; Vainshelboim et al. , 2019 ). Above all, contemporary HE students consider themselves healthy, even though they suffer from different health symptoms, illnesses or injuries. Among students, the prevalence of various diseases has continued to exist at a somewhat unchanged level, whereas diagnoses of depression and anxiety syndrome have almost tripled since the year 2000 ( Kunttu et al. , 2017 ). Anxiety, together with stress, continues to be the leading health concern among the HE student population ( Calamidas and Crowell, 2018 ). A considerable amount of research to date has studied HE students’ health and influential factors. However, the literature is not as co-directional regarding the implications of Internet use effects on health, as the evidence is still emerging.

This scoping review aims to present a wide-ranging view of the current literature between 2015 and 2020 on the association of Internet use and health in HE students. The phenomenon is approached with a holistic perspective, meaning that Internet use is viewed without categorizing the use to problematic use or to specific online activities. Health is approached from a comprehensive viewpoint, considering physical, mental, social, spiritual and emotional dimensions ( Eberst, 1984 ). The consistent conceptualisations vary in the literature on how Internet use is described and how health is approached, despite which the research is growing. In addition, the assessment and classification of the association between Internet use and health is multidimensional. It is expected that the results of this review may help identify gaps and indications for future research on the topic. In addition, this review’s intention is to summarize findings in an accessible way to inform evidence-informed policy and practice at HE levels. As far as we know, no other scoping review with this topic, on this population has yet been published. However, reviews on Internet addiction and problematic Internet use effects on health exist ( Kuss and Lopez-Fernandez, 2016 ; Hinojo-Lucena, et al. , 2019 ).

METHODS AND ANALYSIS

A scoping review was performed to identify and explore literature on the association of Internet use and health among HE students. The review was carried out using a framework defined by Arksey and O’Malley (Arksey and O'Malley, 2005 ) for scoping reviews. Consistent with the methodology, the review was executed in five stages as follows: (i and ii) research question and the relevant articles identification; (iii) article selection; (iv) data charting; and (v) results collating and summarizing, as well as reporting. The sixth stage, which encompasses an optional consultation, was left out of the process.

Research question identification

The objectives of this review were to map the accessible literature on the association of Internet use and health among HE students and to describe the key findings and identify emerging themes. The broad question addressed for the review was: ‘what is known from the existing literature about the associations between HE students’ Internet use and health?’ The certain inclusion and exclusion criteria were set according to the Population-Concept-Context (PCC) framework to define the research question ( Joanna Briggs Institute, 2019 ) ( Supplementary File S1 ).

Identification of relevant articles

Key concepts underpinning the research area were identified and clarified to align with the research question. In doing this, the key search terms were developed. An academic librarian confirmed the search strategy. Search terms were ‘HE student (university, college, tertiary, polytechnic)’; ‘Internet (net, web, online activities, social media, smart/mobile device) use’; and ‘health’. An extensive search was conducted in the electronic databases of CINAHL, PubMed and Scopus. Additionally, a search of the relevant grey literature was carried out to include the World Health Organization (WHO) Library database (WHOLIS), Google and Google Scholar search engines and dissertation databases. Additionally, targeted websites of relevant national organizations, such as The Finnish Student Health Service, The Research Foundation for Studies and Education, The Finnish Society of Media Education and The Family Federation of Finland, were searched. Experts from these national organizations were consulted. Furthermore, manual searches of the reference lists of all selected articles were conducted. When articles were unavailable, authors were contacted. The time limit for the searches was 6 years, 2015–2020. The language was limited to English, Finnish or Swedish, with articles addressing evidence globally. Search results were exported to ProQuest RefWorks to be further reviewed ( ProQuest L. L. C, 2020 ). The selection process is reported as recommended by the PRISMA statement ( Moher et al. , 2009 ), which is also recommended for scoping reviews in the PRISMA Extension for Scoping Reviews (PRISMA-ScR) ( Tricco et al. , 2018 ) ( Figure 1 ).

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PRISMA flow diagram of the search and evidence selection process (Source: Moher et al. , 2009).

Selection of articles according to the established criteria

Two reviewers (H.R.) and (K.J.) screened all articles independently for eligibility and to establish interrater reliability. This was performed with a developed screening matrix and used with Microsoft Excel ( Microsoft Corporation, 2020 ). The Cohen’s kappa coefficient with a 95% confidence interval was counted to determine interrater agreement for the consistency of screening ( Stemler, 2004 ). It was calculated using the number of includes and excludes during the three-round review process. Kappa results indicated substantial level of agreement (0.79, 0.64, 0.62) ( McHugh, 2012 ). Disagreements on the eligibility of the article for inclusion were discussed and resolved through consensus. One reviewer (H.R.) conducted the grey literature search using the same criteria and phases of article selection. Furthermore, the selection of studies and literature was executed in consultation with the review team.

Charting the data

A ‘descriptive analytical method’, as described by the review methodology, was used to extract information on the included articles. This technique included sifting, charting and sorting material for synthesis and for data interpretation ( Arksey and O'Malley, 2005 ). Articles were categorized by author information, study/article objective, study/article design and sample, outcome measures and main findings ( Supplementary File S2 ).

Collating and summarizing the results

As typical with scoping reviews, a descriptive summary and a thematic analysis of the included articles were conducted ( Arksey and O'Malley, 2005 ). The analysis was performed in stages, thusly: the article data familiarization; generating and searching for codes and themes; reviewing and defining the themes, and writing the final report ( Braun and Clarke, 2006 ). An example of the analysis process is presented in Table 1 .

An example of thematic analysis process with their associated codes

Data	Code	Sub-themes	Theme	Main theme
The same technologies also offer several opportunities for the enhancement of mental health and the treatment of mental illness ( , 2019).	Internet technologies offer opportunities for the enhancement of mental health	Promoting factors for mental health and well-being	Promoting and threatening factors for mental health and well-being	Internet use and health among higher education students: health promoting and health-threatening factors
Internet technologies offer opportunities to treat mental illness
Excessive Internet usage leads to anxiety, depression and adverse mental health ( , 2020)	Excessive Internet use leads to anxiety Excessive Internet use leads to depression Excessive Internet use leads to adverse mental health	Threatening factors for mental health and well-being

Characteristics of included articles

The included articles ( N = 55) had a year range from 2015 to 2020. All articles were written in English and conducted in 28 countries from five continents: Asia ( n = 33); North America ( n = 12); South America ( n = 1); Europe ( n = 8); and Africa ( n = 2). The articles included a variety of HE study populations and settings. The most common concept of the Internet use described was addictive or problematic Internet use. In the areas of health addressed, mental health issues were the most investigated ( Supplementary File S2 ).

Thematic findings

The review identified two themes amongst the included articles. ‘Health-promoting Internet use’, included factors promoting mental, physical, social and intellectual health and well-being, and ‘Health-threatening Internet use’, contained factors threatening mental, physical and social health and well-being. The evidence was larger in the latter theme ( Figure 2 ).

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Health-promoting and Health-threatening Internet use. I, Internet use; IT, Internet-enhanced technology use; OHI, online health information-seeking behaviour; PIA, problematic or addictive Internet use; SMA, social media addiction; SM, social media use

Health-promoting Internet use

Factors promoting mental health and well-being included Internet-enhanced technology use and social media use. This category combined evidence on the enhancement of mental health, possibilities of treating mental illness ( Lattie et al. , 2019 ) and satisfaction with daily routines ( Austin-McCain, 2017 ). Also, information on better stress management related to relationships and work ( Saini et al. , 2020 ).

Physical health and well-being category approached Internet use as social media use or Internet-enhanced technology use. Articles included information on the protective effect to sleep ( Orzech et al. , 2016 ) and better sleep quality ( Xu et al., 2016 ). Additionally, with an increase in physical activity levels ( Wong, 2017 ) and with relaxation and leisure ( Austin-McCain, 2017 ).

Health-promoting Internet use within social health and well-being approached Internet use mainly as social media use. Evidence about social participation activities ( Austin-McCain, 2017 ) and social support were addressed ( Mahapatra and Schatz, 2015 ). Also, active engagement with peers and expansion of social networks ( Lattie et al. , 2019 ) were expressed.

Intellectual health and well-being contained Internet use or online health information-seeking behavior. This category included information on interacting with health professionals online ( Asibey et al. , 2017 ; Lattie et al. , 2019 ), using the Internet for health purposes or to seek health information ( Asibey et al. , 2017 ; Bati et al. , 2018 ; Levin et al. , 2020 ; Tariq et al. , 2020 ; Schwartz and Richardson, 2015 ). Evidence on how electronic health (e-health) literacy is promoting general student health was included ( Britt et al. , 2017 ).

Health-threatening internet use

Factors threatening mental health and well-being approached Internet use mainly from the problematic/addictive perspective including evidence with broad concept of poor mental health and well-being ( Tangmunkongvorakul et al. , 2019 ; Tenzin et al. , 2018 ; Zhou et al., 2020 ; Hou et al. , 2019 ; Lattie et al. , 2019 ). Some of the articles also specified the factors in more detail, for example: distress ( Al-Gamal et al. , 2015 ; Mamun et al. , 2020 ; Gedam et al. , 2017 ); depression ( Khalil et al. , 2016 , Othman and Lee, 2017 , Peterka-Bonetta et al. , 2019 ; Younes et al., 2016 ; Iwamoto and Chun, 2020 ; Gedam et al. , 2017 ; Tao et al. , 2017 ; Asibong et al. , 2020 ; Chupradit et al. , 2020 ; Haand and Shuwang, 2020 ; Pang, 2020 ; Visnjic et al. , 2018 ); anxiety ( Younes et al. , 2016 ; Campisi et al. , 2017 ; Iwamoto and Chun, 2020 ; Asibong et al. , 2020 ; Panova et al. , 2020 ; Gedam et al. , 2017 ); stress ( Younes et al. , 2016 ; Campisi et al. , 2017 ; Liu et al. , 2017 ; Iwamoto and Chun, 2020 ; Unsar et al. , 2020 ); social anxiety ( Weinstein et al., 2015 ); fear of missing out or FOMO ( Lattie et al. , 2019 ; Pang, 2020 ); low happiness ( Kitazawa et al. , 2019 ) and increase in suicide risk ( Alpaslan et al. , 2015 ; Kurt, 2015 ; Poorolajal et al. , 2019 ).

Almost all articles addressing factors threatening physical health and well-being viewed Internet use as problematic/addictive or as social media use. This category included findings about lower health status ( Jairoun and Shahwan, 2020 ; Kawyannejad et al. , 2019 ; Mohammadbeigi et al. , 2016a ); a high prevalence of upper extremity and neck symptoms ( Kalirathinam et al. , 2017 ; Rahman et al. , 2020 ). Behavioral aspects concerning Internet use while sedentary ( Kalirathinam et al. , 2017 ) were also identified. Having fewer hours of sleep at night ( Orzech et al. , 2016 ; Nasirudeem et al. , 2017 ; Mohammadbeigi et al. , 2016b ; Thakur et al. , 2017 ; Whipps et al., 2018 ; Wang et al., 2020 ) was distinguishable. Increased odds of illegal drug use ( Fogel and Shlivko, 2016 ) and smoking and alcohol use were also found ( Tao et al. , 2017 ).

Health-threatening Internet use within the context of social health and well-being approached Internet use mainly from the Internet-enhanced technology perspective. This category included information on lower health-related quality of life in the social domain ( Chern and Huang, 2018 ), fewer numbers of close friends ( Lee et al. , 2016 ), hyper-connectivity with peers and peer comparison ( Lattie et al. , 2019 ).

A summary of the thematic findings

In summary, the findings indicated that Internet use among the HE student population is both health-promoting and health-threatening. Health-promoting Internet use provided beneficial health factors for the main aspects of personal health and wellbeing. On the contrary, health-threatening Internet use demonstrated that certain factors were risks, and threatened the health and wellbeing elements. The concepts used within these two findings are summarized in Supplementary File S3 .

Our study found that Internet use is associated with health from health-promoting and health-threatening dimensions. Factors promoting or threatening mental, physical, social and intellectual health and well-being were expressed. Furthermore, some of the health and well-being factors were bidirectional, belonging to both dimensions with different manners of approaches. For example, within the category of physical health and well-being, Internet use was associated with a protective effect for sleep (health-promoting) and with poor sleep quality (health-threatening). In general, the evidence of health-threatening Internet use was more prominent than evidence of health-promoting Internet use. A reason for this could be that the research on potential problems of excessive Internet use and addiction has increased considerably in recent years; the presence of Internet addiction and its associated behaviors, have been highlighted since the early 1990s ( Shek et al. , 2013 ).

Evidence on HE students’ health-promoting Internet use accumulated mostly to categories of social and intellectual health and well-being. The promoting factors in social health and well-being identified issues, such as social participation activities and social support through social media. Hence, according to Bekalu et al. (2019) , a routine social media use, meaning using social media within daily routines and responding to shared content, is in positive terms associated with social well-being. As students spend time social networking, they also develop relationships that can result in meaningful socio-psychological resources, supporting positive health behaviors ( Paige et al. , 2017 ). Currently, social media is considered popular among HE students, especially networking sites such as Instagram, Facebook and Twitter, as well as multimedia messaging apps like Snapchat and the online video-sharing platform YouTube, which are used alongside different gaming sites, blogs and podcasts ( Bragdon and Dowler, 2016 ; Lien et al. , 2018 ; Sutherland et al. , 2018 ; The Knight Foundation, 2020 ). Results in intellectual health and well-being suggest that HE students use the Internet for health purposes and to interact with health professionals online. Thus, the Internet enables easy accessibility to online health service by providing communicating channels with care providers and possibilities to receive care at home ( Young and Nesbitt, 2017 ). Evidence on e-health literacy’s health-promoting aspect was also included. It comprises using electronic sources to address or resolve health problems with the proficiency to search, obtain, understand and evaluate health information ( Yang et al. , 2017 ). However, health information-seekers are worried about obtaining deceptive material and exploring risk-promoting messages online ( Mou et al. , 2017 ) – thus indicating the need for the activity of providing accurate health information in the online platforms where HE students operate, for example, in social media.

Results on HE students’ health-threatening Internet use indicated that factors threatening mental and physical health and well-being were the most comprehensive. Some factors were expressed broadly, and some in more detail. Most of the evidence supporting the factors threatening mental and physical health and well-being were from Asian countries and focusing on problematic or addictive Internet use. As reviewed by Li et al. (Li et al. , 2018 ), the prevalence of Internet addiction disorders (IAD) is greater in Asia than in Europe. For instance, in China, Internet addiction is acknowledged as an official disorder ( Kuss and Lopez-Fernandez, 2016 ). HE students, together with high school students, are known to be more vulnerable to these addictions compared with other student groups ( Turnbull et al. , 2018 ), although children and adolescents are also becoming increasingly addicted to playing Internet games ( Bener et al. , 2016 ). Overall, currently, the addictive or problematic form of Internet use is viewed as a notable growing health problem among HE students, affecting their mental and physical health ( Kuss and Lopez-Fernandez, 2016 ; Shao et al. , 2018 ; Fernandes et al. , 2019 ). Conclusively, health-threatening Internet use demonstrates the necessity of preventive actions, such as focused health-promoting social marketing actions, to avoid risky behaviors from occurring among students.

This comprehensive scoping review captured the majority of the relevant literature on the association of Internet use and health. A systematic, rigorous and transparent methodology was used based on a methodological framework. The results provided a broad overview of the topic in accordance with the research question. The results have less depth because the literature is vast and complex ( Arksey and O'Malley, 2005 ; Peterson et al. , 2017 ). The majority of the articles in this review were from Asian countries. This might be because in the Asia-Pacific regions, the Internet use related addiction is viewed as a current concern in public health amongst young adults ( Tang et al. , 2017 ). Limitations of this research include the use of articles written only in English, Finnish or Swedish. Also, as typical for scoping reviews, the quality of included articles was not examined ( Arksey and O'Malley, 2005 ). The judgment of the trustworthiness within the value and relevance of the articles included needs to be taken into account, in accordance with the aim of this review. Further, the results of this scoping review can be utilized in planning a future systematic review that exploits a quality appraisal ( Munn et al ., 2018 ).

This scoping review characterizes and describes the evidence on the association between Internet use and health among HE students. Internet use is health-promoting mostly for social and intellectual health and well-being, and health-threatening primarily for mental and physical health and well-being. This bifurcation should be taken into account in promoting HE students’ health. We hope that the findings of our review can assist ongoing research to further clarify and enhance the association between Internet use and health.

SUPPLEMENTARY MATERIAL

Supplementary material is available at Health Promotion International online.

AUTHORS’ CONTRIBUTIONS

H.R. was responsible for the literature searches and the data analysis via the thematic analysis method. K.J. and H.R. conducted the dual-review process. K.J., M.S. and H.T. made critical revisions to the paper. M.S. and H.T. verified all the processes in conducting this scoping review and supervised the study.

This research was funded by the University of Eastern Finland’s Doctoral School, the Doctoral Programme in Health Sciences and the Department of Nursing Science.

Conflict of Interest: The authors declare that they have no conflict of interest.

Supplementary Material

Daab007_supplementary_data.

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Internet addiction and problematic Internet use: A systematic review of clinical research

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1 Daria J Kuss, International Gaming Research Unit, Nottingham Trent University, Nottingham NG1 4BU, United Kingdom.
PMID: 27014605
PMCID: PMC4804263
DOI: 10.5498/wjp.v6.i1.143

Aim: To provide a comprehensive overview of clinical studies on the clinical picture of Internet-use related addictions from a holistic perspective. A literature search was conducted using the database Web of Science.

Methods: Over the last 15 years, the number of Internet users has increased by 1000%, and at the same time, research on addictive Internet use has proliferated. Internet addiction has not yet been understood very well, and research on its etiology and natural history is still in its infancy. In 2013, the American Psychiatric Association included Internet Gaming Disorder in the appendix of the updated version of the Diagnostic and Statistical Manual for Mental Disorders (DSM-5) as condition that requires further research prior to official inclusion in the main manual, with important repercussions for research and treatment. To date, reviews have focused on clinical and treatment studies of Internet addiction and Internet Gaming Disorder. This arguably limits the analysis to a specific diagnosis of a potential disorder that has not yet been officially recognised in the Western world, rather than a comprehensive and inclusive investigation of Internet-use related addictions (including problematic Internet use) more generally.

Results: The systematic literature review identified a total of 46 relevant studies. The included studies used clinical samples, and focused on characteristics of treatment seekers and online addiction treatment. Four main types of clinical research studies were identified, namely research involving (1) treatment seeker characteristics; (2) psychopharmacotherapy; (3) psychological therapy; and (4) combined treatment.

Conclusion: A consensus regarding diagnostic criteria and measures is needed to improve reliability across studies and to develop effective and efficient treatment approaches for treatment seekers.

Keywords: Clinical studies; Gaming addiction; Internet Gaming Disorder; Internet addiction; Problematic Internet use; Therapy; Treatment; Treatment seekers.

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Broadband Internet Access, Economic Growth, and Wellbeing

Between 2000 and 2008, access to high-speed, broadband internet grew significantly in the United States, but there is debate on whether access to high-speed internet improves or harms wellbeing. We find that a ten percent increase in the proportion of county residents with access to broadband internet leads to a 1.01 percent reduction in the number of suicides in a county, as well as improvements in self-reported mental and physical health. We further find that this reduction in suicide deaths is likely due to economic improvements in counties that have access to broadband internet. Counties with increased access to broadband internet see reductions in poverty rate and unemployment rate. In addition, zip codes that gain access to broadband internet see increases in the numbers of employees and establishments. In addition, heterogeneity analysis indicates that the positive effects are concentrated in the working age population, those between 25 and 64 years old. This pattern is precisely what is predicted by the literature linking economic conditions to suicide risk.

We are grateful to participants at the Association of Public Policy and Management and the Washington Area Labor Symposium conferences for their helpful comments. Any errors or conclusions are our own. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.

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Internet use and academic performance: An interval approach

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Published: 21 May 2022
Volume 27 , pages 11831–11873, ( 2022 )

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María Ladrón de Guevara Rodríguez ORCID: orcid.org/0000-0002-5087-422X 1 , 2 ,
Luis Alejandro Lopez-Agudo ORCID: orcid.org/0000-0002-0906-3206 2 ,
Claudia Prieto-Latorre ORCID: orcid.org/0000-0002-6510-3057 2 &
Oscar David Marcenaro-Gutierrez ORCID: orcid.org/0000-0003-0939-5064 2

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As children spend more and more time on electronic devices and social networks, there is a growing concern about the influence that these activities may have on their development and social well-being. In this context, the present research is aimed at analysing the influence that Internet use may have on 6 th grade primary school students’ academic performance in Spain. In order to do so, we have employed a methodological approach that combines econometric and interval multiobjective programming techniques, which has let us identify the traits and Internet use patterns that allow students to maximise their academic performance in terms of scores in four competences. Our results show that, while daily use of the Internet to listen to music or search for information about other topics of interest can favor the maximization of educational outcomes, the use of social networks should be limited as much as possible to avoid hindering the educational process.

Effect of internet use and electronic game-play on academic performance of Australian children

The impact of different uses of the Internet on students’ performance progression throughout primary education

Internet use at and outside of school in relation to low- and high-stakes mathematics test scores across 3 years

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1 Introduction

The Internet has become an indispensable tool, particularly for young people. For those who have been surrounded by digital technology since birth, it is not only an information tool, but a major innovation that has conditioned how they spend their leisure time and engage in non-leisure activities. The Internet has been fundamental in society’s development; it created a new dimension for digital natives (Prensky, 2001 ) and has also made it possible to digitise social and paperwork structures that were traditionally “physical” by promoting “virtual mobility” and allowing certain daily activities to be carried out through telework, telehealth and e-learning (Mouratidis & Papagiannakis, 2021 ).

In this context, the Internet has not only enabled young people around the world to stay in touch with each other, but has also provided them with new learning opportunities, as it is an endless source of information that can speed up the educational process. It allows the creation of “educational environments” that complement the traditional system and fill educational gaps that may be left by face-to-face education. The Internet can even promote an active and independent type of learning adapted to students’ characteristics and their own learning pace (O’Flaherty & Phillips, 2015 ).

In addition to learning, the Internet is a means of entertainment and communication (Zhang et al., 2018 ) that has enabled young people around the world to develop and nurture relationships while strengthening their sense of community (Pendry & Salvatore, 2015 ) and social well-being (Castellacci & Tveito, 2018 ; Alivernini et al., 2019 ). Furthermore, using different mobile devices from an early age enables the acquisition of so-called digital skills, such as the ability to search for and evaluate information (Van Deursen & Van Dijk, 2008 ), which can be extremely useful when writing reports or completing assignments (Pagani et al., 2016 ).

The Internet has become indispensable in teenagers’ lives, but its excessive or inappropriate use also has undesirable consequences for young people, especially if we consider that they are more likely to develop a certain degree of Internet addiction than adults (Fineberg et al., 2018 ; Ko et al., 2012 ). Excessive use can lead to withdrawal and weaker social skills as well as mental health and family problems (O’Day & Heimberg, 2021 ; Song et al., 2019 ; Twenge, 2017 ). Besides, such dependence can negatively interfere with the educational process and consequently reduce academic performance (Azizi et al., 2019 ; Kates et al., 2018 ; Koca & Berk, 2019 ; Sengupta et al., 2018 ; Wammes et al., 2019 ).

Given the relevance of the Internet in our lives, it seems reasonable to question whether its use from an early age can negatively affect a person’s psycho-emotional educational and professional development and, specially, if we take into account that late childhood and adolescence are critical stages in human life, as teenagers are supposed not only to develop educational and career goals, but also to ask themselves who they are and who they want to be (Verhoeven et al., 2019 ).

Therefore, the aim of our study is to identify the profile of students who are able to maximise their academic performance in reading, mathematics, science and English given the different ways in which they can use the Internet. That said, we focus on Spain, where 91.4% of households had a fixed or mobile broadband Internet connection and 92.9% (INE, 2019 ) of children aged 10–15 use it in 2019, with the average time spent on the Internet being 3 or more hours per day in 2019 (Qustodio, 2019 ). Particularly, within Spain, we will use a recent database from the Spanish region of the Canary Islands which collected the census of primary school students in 6 th grade in 2018–2019.

We have to bear in mind that, according to the latest Programme for International Student Assessment (PISA) report (2018), the Canary Islands are at the bottom of the Spanish educational ranking. For instance, Canarian students scored 19 points below the OECD average in science and 13 points below Spain (MEPF, 2018 ). In this sense, given the increase in both poor academic results and Internet use by the younger generations, in our study we seek to analyse the influence that Internet use can have on academic performance. To do so, we will make use of interval multiobjective programming, specifically the algorithm proposed in Henriques et al. ( 2019 ). This methodology has been used in applications to analyse workers’ well-being (Henriques et al., 2020 , 2021 ) and in the educational context (Prieto-Latorre et al., 2021 ).

In short, this study aims to enrich the existing literature in, at least, three aspects. Firstly, it assesses how social networks (and additionally WhatsApp) influence on students’ academic performance in different cognitive domains in late childhood, while studies usually focus on secondary school and university students. Secondly, we provide up-to-date evidence for Spain, to the extent that previous literature is limited and the databases used are outdated. Thirdly, by using interval multiobjective programming, we offer a potentially useful tool in the design of educational policies and parental guidance.

The article is structured as follows. First, we provide a brief review of the relevant literature on the influence of Internet use on academic performance. Then, we present the main characteristics of the dataset. Sections 4 and 5 describe the methodology employed and the results obtained. Finally, we discuss and present the main findings, including implications for socio-economic policies.

2 Literature review

The digital dependency and the effect it may have on young people’s academic performance and personal development has sparked an increased interest among researchers. Specifically, some studies have shown how using the Internet can improve academic performance (Çebi & Güyer 2020 ; Chen et al., 2014 ; Hou et al., 2021 ; Gil, 2012 ; Naqshbandi et al., 2017 ; Zhu et al., 2011 ). On the one hand, Chen et al. ( 2014 ) analysed the relationship between Internet information seeking, academic performance and academic self-efficacy, with the latter being the mediator between the first two. The authors distinguished between educational and leisure-oriented Internet use, concluding that both types had a positive impact on twelfth-grade students’ academic self-efficacy, indirectly improving their academic performance. On the other hand, Hou et al. ( 2021 ) examined the impact that the Chinese social network, WeChat, may have on university students’ academic performance. The authors concluded that the impact of using WeChat was largely due to students’ self-control, with the effect of sharing information through the application being positive when students had high self-control.

Likewise, researchers have found a positive relationship between ICT use and academic performance (Mo et al., 2014 ; Cabras & Tena Horrillo, 2016 ; Gubbels et al., 2020 ; Lei et al., 2021 ). In particular, Cabras and Tena Horrillo ( 2016 ), using data provided by PISA 2012, found a causal effect of ICT use on Spanish students’ mathematics performance, with the effect being stronger for lower-income students. Recently, Gubbels et al. ( 2020 ) showed that moderate ICT use was positively related to the reading achievement of 15-year-old Dutch students, with a negative impact when ICT was overused. Similarly, Machin et al. ( 2007 ) highlight that the increase of ICT investment at schools in England caused a positive impact on reading competence and science, but it had no significant influence in mathematics, while Villafuerte and Romero ( 2017 ) found that watching videos and using social networks help to improve English skills, as motivation and engagement facilitate English learning, both in writing and listening skills. This result is opposed to most empirical evidence, which usually finds a negative effect of social networks on educational attainment (see meta-analysis by Liu et al., 2017 ).

However, far from reaching the same conclusion, other studies have found a negative relationship between Internet/ICT use and academic performance (Azizi et al., 2019 ; Chang et al., 2019 ; Hsiao et al., 2017 ; Junco, 2015 ; Karpinski et al., 2013 ; Kim et al., 2017 ; Koca & Berk, 2019 ; Michikyan et al., 2015 ; Sengupta et al., 2018 ; Vigdor et al., 2014 ). Particularly, Kim et al. ( 2017 ) considered confounding factors such as gender, drug use or parental education level, and found that using the Internet for general purposes was negatively correlated with higher school performance, in contrast to when the Internet was used for study.

Finally, some studies point to the lack of significant effect of Internet and ICT on educational outcomes (Cristia et al., 2017 ; Fairlie & Robinson, 2013 ; Leuven et al., 2007 ; Mbaeze et al., 2010 ; Raines, 2012 ; Spiezia, 2011 ; Woessmann & Fuchs, 2004 ).

The influence that the Internet has on the teaching–learning process may depend on the type of analysis conducted, the potential existence of selection bias (Bulman & Fairlie, 2016 ), how it is used and whether it is more or less academically oriented (Chang et al., 2019 ; Gil, 2012 ; Kim et al., 2017 ; Lau, 2017 ; Torres-Díaz et al., 2016 ). In particular, while online information seeking tools and word processing are associated with higher academic performance in 15-year-old students (Gil, 2012 ), video games or streaming entertainment hinder the educational process (Lopez-Agudo & Marcenaro-Gutierrez, 2020 ; Rideout et al., 2010 ).

In this regard, it seems that the problem lies in excessive or inappropriate use of the Internet (Zhou et al., 2020 ) resulting in situations where users are unable to control the time they spend on online activities and neglect their daily activities (Wąsiński & Tomczyk, 2015 ). This “addiction”, which some studies refer to as “Internet use disorder” (Peterka-Bonetta et al., 2019 ; Sha et al., 2019 ), can lead to socioemotional problems among young people (Pontes et al., 2015 ), as well as negatively affect their academic performance (Berte et al., 2021 ; Flisher, 2010 ; Siciliano et al., 2015 ) by reducing academic engagement and increasing disaffection with learning activities (Feng et al., 2019 ; Karpinski et al., 2013 ; Zhang et al., 2018 ).

Focusing on Spain, the studies that have analysed this issue are limited (Fernández-Gutiérrez et al., 2020 ; García-Martín & Cantón-Mayo, 2019 ; Gómez-Fernández & Mediavilla, 2021 ). For instance, García-Martín and Cantón-Mayo ( 2019 ) assessed how different types of Internet use might affect academic skills, concluding that each type of Internet use was associated with different cognitive domains. Fernández-Gutiérrez et al. ( 2020 ) employ PISA data from three waves (2009, 2012 and 2015) to evaluate the use of ICT at secondary school, finding significant effects in students’ outcomes in science, but not in reading and mathematics. Particularly relevant is the study carried out by Prieto-Latorre et al. ( 2021 ), in which they analysed the effect that Internet use may have on school grades (content-based knowledge) and test scores (competences) of a cohort of 8 th grade students in 2011–2012. Footnote 1 The authors concluded that using the internet academically and for hobbies should be prioritised over continued use of social networks.

In any case, the evidence in Spain is still scarce and further up-to-date research is needed, as the use of the Internet and new technologies is increasing among young people.

3 Data and institutional background

In recent years, the Spanish education system has adopted a formative assessment model in line with European standards that aims to promote lifelong learning and to move away from an exam-centred educational culture. To implement this formative assessment scheme, in addition to a continuous assessment throughout the academic year, Spanish students take an individual assessment test at the end of each educational stage. Specifically, for primary education, Spanish students take the test at the end of 3 rd (LOMCE, art. 20.3; BOE, 2013 ) and 6 th grades (LOMCE, art. 21; BOE, 2013 ). These tests are designed to assess students’ numeracy skills, as well as their oral and written comprehension skills, in order to provide them with individualised attention according to their needs and to prevent them from failing at a later stage of their education.

In this study we have used the data collected by the Canarian Agency for University Quality and Educational Assessment. Specifically, as mentioned above, we have used data from the cohort of 6 th grade students in 2018–2019 (in the Autonomous Community of the Canary Islands). In total, our sample collects data from 13,296 students who completed tests in reading, mathematics, science and English. In addition, to avoid losing observations, we have introduced a set of missing flag variables.

In the database under scrutiny, to facilitate comparison with other research studies and between different measures of educational performance, the variables regarding test scores in the different competences have been standardised (through statistical normalisation) to have mean 0 and standard deviation 1 and, consequently, the results can be interpreted as effect sizes. Along with educational competences, by having both a student questionnaire and a parent questionnaire, we have information about parents’ educational level, income and occupation, among other variables. Moreover, to capture the students’ socio-economic level, the Canarian Agency provides us with an indicator of socio-economic and cultural status (ESCS). This index, which is a continuous variable, has also been standardised to facilitate comparisons.

Since our goal is to analyse how Internet use influences academic performance, this analysis is mainly based on the information collected by the student questionnaire. Concretely, we have based our analysis on the following question:

“How often do you use the Internet for the following activities?

Searching for information for your studies (Google, Wikipedia, etc.).

Searching for information about games or playing games.

Searching for information about sports.

Searching for information about music or movies (YouTube, Spotify, etc.).

Searching for information on other topics that interest you.

Communicating with other people (WhatsApp, Telegram, Hangout, etc.).

For using social networks (Facebook, Twitter, Instagram, Musically, etc.).

For these 7 Internet-related variables, the answer is one of the following options: “never or almost never”, “once or twice a month”, “once or twice a week”, “every day or almost every day”. Descriptive statistics regarding these variables are shown in Table 4 ( Appendix ). We can observe that 6 th grade students mainly use the Internet to communicate with others through applications such as WhatsApp, with 40% of students using it every day. This appears to be consistent with new trends, as the younger generation represents the new wave of users of these applications which can be broadly considered as social networks. Similarly, 32.9% of students use applications such as YouTube or Spotify on a daily basis, while they use the Internet for other topics of interest and to play games once or twice a week (36.1% and 24.1%, respectively). This underlines the aim of our analysis, as younger students tend to use the Internet more for non-academic purposes.

Since WhatsApp or Telegram can be considered social networks, we only include the variables related to social network use as a category that encompasses not only Facebook or Instagram, but also other communication channels. This will help us to avoid multicollinearity problems. However, as we will show below, the estimates corresponding to the model including the variables related to WhatsApp use have been run as a robustness check.

4 Methodology

The main goal of our analysis is to explore the influence that Internet use during late childhood may have on students’ academic performance. In doing so, we aim to provide a student profile that combines both academic and non-academic Internet use to maximise academic achievement. To this end, we will combine econometric analysis with multi-objective programming techniques.

The econometric analysis is based on the estimation by ordinary least squares (OLS) of different regression models in which academic performance is regressed on a set of variables. In these variables we include both a set of control variables and those relating to Internet use. Therefore, our base model would be defined as:

where \(Z\) is the standardised academic performance, \(i\) is the student, \(k=\mathrm{1,2},\mathrm{3,4}\) represents reading, mathematics, science and English, respectively; \({x}_{1}\) to \({x}_{4}\) represent student characteristics; \({x}_{5}\) is a school characteristic; \({x}_{6}\) to \({x}_{23}\) indicate Internet use (see Table 5 , Appendix ); \({\varepsilon }_{k}\) is the error term; \({\widehat{\beta }}_{j}\) represents estimated regression coefficients for the \(j=1,\dots , 23\) variables. Since this is a point estimate of the influence that each independent variable has on the dependent variable, there is a margin of error and, therefore, we have estimated the lower and upper bounds of the estimated coefficients at the 99% confidence level as follows:

where \(\widehat{\beta }\) is the estimated average coefficient; \(SE\) is the standard error; \(n\) is the number of observations; \(k\) is the number of conditioning variables; \(\alpha\) is the significance level and \(t\) are the probability values of the t-distribution.

Table 1 shows the lower and upper bounds of estimated coefficients for each of the educational outcomes, i.e. reading, mathematics, science and English. Footnote 2 Those coefficients that are not statistically significant, at least at 10%, are shown as “0”.

The results obtained show that females perform better than males in reading and English, but perform worse in mathematics. Specifically, girls obtain on average 0.27 standard deviations (SD) and 0.19 SD more in reading and English, respectively, than boys, while their mathematics performance drops by 0.08 SD. Far from being surprising, these findings are consistent with the evidence collected in the literature. On the one hand, females tend to perform better in reading; the Progress in International Reading Literacy Study (PIRLS) 2016 showed that, in 48 of the 50 participating countries, female students’ reading achievement was higher than boys’ and this female dominance has persisted since this assessment was implemented (Mullis et al., 2016 ). This gender gap in favour of females can be the result of certain gender stereotypes attributing greater mathematical ability to males (Cvencek et al., 2011 ; Plante et al., 2013 ; Spencer et al., 1999 , 2016 ). The corollary of this is that girls tend to have a higher linguistic self-concept than boys (Heyder et al., 2017 ; Jacobs et al., 2002 ; Wigfield et al., 1997 ) and to value reading highly, which favours their academic performance in this competence. On the other hand, females’ higher achievement in English is closely linked to reading skills (Grabe, 2010 ; Oxford, 2011 ), as developed skills in interpreting texts and processing information enable females to learn a foreign language more quickly (Wightman, 2020 ).

Similarly, we find that students’ socio-economic status is positively associated with their academic performance. This relationship has been strongly supported by the literature (Cedeño et al., 2016 ; Hanushek & Woessmann, 2011 ; Kim et al., 2019 ; Liu et al., 2020 ; Martins & Veiga, 2010 ; von Stumm, 2017 ) and is explained by the fact that students from disadvantaged backgrounds are constrained by their economic resources and cannot access tutoring and other educational resources that help to improve their educational outcomes (Crosnoe & Cooper, 2010 ; Lareau, 2011 ). In contrast, the proportion of poor students in school has a negative influence on academic performance. Children from poor socio-economic backgrounds are more likely to develop behavioural problems (Hendriks et al., 2020 ; Peverill et al., 2021 ; Piotrowska et al., 2015 ); this can negatively affect classroom climate and academic performance via peer effects (Busching & Krahé, 2020 ; Jerrim et al., 2021 ).

Regarding Internet use, we observe that using the Internet to study and complete school tasks positively correlates with academic performance in all four subjects. In contrast, using the Internet to play video games is negatively related to reading performance. This negative influence goes from 0.05 SD, on average, when the frequency of use is once or twice a month, to 0.10 SD, on average, when it is used every day. This pattern seems to be in line with previous literature (Lopez-Agudo & Marcenaro-Gutierrez, 2020 ).

Similarly, using the Internet to search for information on sports is negatively related to reading, mathematics, science and English performance, with the influence also being greater as the frequency of use increases. In contrast, using apps like YouTube or Spotify seems to have a positive influence on academic performance. However, the average influence in mathematics is lower than in the other disciplines. For example, daily use of these apps is positively related to mathematics performance (0.12 SD), while the association is 0.18 SD and 0.16 SD in science and English, respectively. Since using music with lyrics or certain genres can be more distracting (Avila et al., 2012 ; Perham & Currie, 2014 ) the level of concentration required for mathematics tasks may not be achieved.

Meanwhile, using social networks has a negative influence on academic performance, with the greater the frequency of use, the greater this correlation. In this sense, its overuse can cause students to adopt less efficient and more superficial study techniques, given a greater number of distractions (Alt, 2018 ).

Following the econometric analysis, our study is carried out on the basis of interval multiobjective programming. This methodology has been applied because, as we know, there are many factors involved in the educational process that are not always controllable and that can affect students’ academic performance. Consequently, results cannot be interpreted as causal effects, but rather as conditional associations. In this context, using interval multiobjective programming models is particularly useful. Interval multiobjective programming and, specifically, the algorithm proposed in Henriques et al. ( 2019 ) and applied in Henriques et al., ( 2020 , 2021 ) will allow us to overcome the uncertainty inherent to the coefficients in multiobjective problems. This methodology, which solves Multiobjective Linear Problems (MOLP), combines the reference-point approach with the concept of interval programming (Oliveira & Antunes, 2009 ) and allows us to use objective functions that take into account the confidence intervals of the regression coefficients. Therefore, by using this methodological approach, we will be able to analyse the potential trade-offs between different students outcomes while obtaining robust results.

That said, we start from a maximisation problem of educational outcomes subject to constraints:

where each \({Z}_{k}\) stands for:

\({Z}_{k}\left(x\right)\) are the objective functions to be maximized; \({\varvec{x}}={\left({x}_{1},\dots ,{x}_{n}\right)}^{T}\) is the vector of decision variables; \({\varvec{x}}\boldsymbol{ }\in X\) is the feasible region; \({\widehat{\beta }}_{kj}^{L}\) and \({\widehat{\beta }}_{kj}^{U}\) are the lower and upper bounds of the estimated coefficients, respectively.

In this sense, the estimated coefficients will be given by the correlation coefficients in Table 1 and our objective functions will be defined as follows:

Competences in reading.

Competences in mathematics.

Competences in science.

Competences in English.

In order to obtain realistic solutions, a set of technical constraints have been defined for Internet use variables. These constraints will guarantee that the solutions are not simultaneously 1 for all binary variables in a group:

Using the Internet to study (Google, Wikipedia, etc.).

Using the Internet to play games.

Using the Internet to search for information about sports.

Using the Internet to search for information about music or cinema (YouTube, Spotify, etc.).

Using the Internet to search for information about hobbies.

Using social networks (Facebook, Twitter, Instagram, Musically, etc.)

Besides the technical constraints, we define another set of constraints reflecting the relationships between those independent variables that have significantly stronger associations. To illustrate the creation of these restrictions, an example is given below using the variables “Proportion of poor students” ( \({x}_{5}\) ) and “Immigrant status” ( \({x}_{3}\) ):

Dependence between the two variables is defined as:

To incorporate this linear regression into the model, we use 99% confidence intervals for each parameter as follows:

which implies:

This expression can be broken down into two inequalities:

By following this procedure, we can obtain the set of constraints:

Relationship between proportion of poor students (1 st quartile ESCS) and students’ ESCS.

Relationship between proportion of poor students (1 st quartile ESCS) and immigrant status.

Relationship between proportion of poor students (1 st quartile ESCS) and repeater.

Relationship between students’ ESCS and immigrant status.

Relationship between students’ ESCS and repeater.

Relationship between students’ ESCS and using the Internet to find information for study (Google, Wikipedia, etc.).

Relationship between students’ ESCS and using the Internet to search for information about other topics.

Relationship between students’ ESCS and using social networks (Facebook, Twitter, Instagram, Musically, etc.)

Therefore, our multiobjective problem has 23 decision variables (binary and continuous), 4 objective functions and 22 constraints. The type of variables and their bounds (which are considered as constraints of the multiobjective problem) are specified in Table 5 ( Appendix ).

In order to solve our interval multiobjective problem we must first solve each one of the problems for the objective functions individually. In this way, we will obtain the individual optimal values (Chinneck & Ramadan, 2000 ) and we will be able to check whether there are trade-offs between the four educational competences.

where \(k\) is the number of objective functions; \(c\) is the number of constraints; \(j\) is the number of decision variables. Consequently, the optimal solution of our multiobjective problem will be somewhere between the ideal solutions of the upper and lower bounds \({Z}_{k}^{*}=[\) \({Z}_{k}^{U*},{Z}_{k}^{L*}] ,\) where \({Z}_{k}^{U*}={{Z}_{k}^{U}(\mathrm{x}}_{k}^{U*})\) and \({Z}_{k}^{L*}={{Z}_{k}^{L}(\mathrm{x}}_{k}^{L*})\) .

Then, and to obtain the solution of the interval multiobjective problem, we use the following surrogate scalarizing problem proposed in Henriques et al. ( 2019 ):

where the term \(\rho >0\) is an augmentation coefficient that guarantees the uniqueness of the obtained solution; \(k\) is the number of objective functions; \(c\) is the number of constraints; \(j\) is the number of decision variables.

This approach considers the Tchebychev distance to the interval ideal values \({Z}_{k}^{L*}\) and \({Z}_{k}^{U*}\) , as well as the relevance of each objective function to reach these values through the weights \({\mu }_{k}^{L},{\mu }_{k}^{U}>0\) for all \(k=1,\dots ,p\) . In addition, it provides “possibly” efficient solutions, which implies that the solution will be efficient for a linear combination of the parameters \({\overline{\beta }}_{kj}\in \left[{\beta }_{kj}^{L}, {\beta }_{kj}^{U}\right]\) .

On the other hand, the algorithm proposed in Henriques et al. ( 2019 ) assumes that \({\mu }_{k}^{L}+{\mu }_{k}^{U}=1\) for all \(k=1,\dots ,p\) , which enables to assign different importance to upper or lower bounds and, therefore, the decision maker (DM) will be able to define the importance of achieving each objective function according to their preferences. However, in our analysis we have considered the same importance for reaching each corresponding ideal solution.

5.1 Main results

First, we have obtained the individual optimal values for each function. To simplify the interpretation of the results, Table 2 shows the optimal values ( \({Z}_{k}^{*}\) ) for each function instead of the ideal solutions of the upper and lower bounds ( \({Z}_{k}^{L*}\) and \({Z}_{k}^{U*}\) ).

The last row of Table 2 shows the optimal values in terms of standardised scores. In this sense, the optimal value for English is higher than for the rest of the subjects, with 0.644 SD, compared to 0.549 and 0.472 SD for science and reading, respectively. Meanwhile, the optimal value in mathematics is 0.339 SD.

Along with the individual optimal values, Table 2 shows the profile of the student that maximises their performance in each one of the competences. As we can see, there is a certain trade-off between the different objective functions. Firstly, female students are the highest achievers in reading and English. In contrast, male students are the ones who maximise their performance in science and mathematics. This is hardly surprising and is consistent with the existing literature, as these are male-dominated fields (Spencer et al., 1999 , 2016 ).

Regarding students’ socio-economic level we observe that, in order to maximise students’ academic performance, the ESCS index should not be particularly high, although higher than the sample average value. Specifically, students’ socio-economic index should be 0.149 if they want to maximise their performance in reading, mathematics and science, and 0.068 when it comes to English. This is – to some extent – unexpected, as students’ socio-economic status is positively related to their academic performance. However, given that students interact with peers from diverse socio-economic backgrounds, it seems that academic achievement is maximised when students from low socio-economic backgrounds are surrounded by a higher proportion of poor students. This proportion of poor students will be 23.6% for reading and 22.11% for English performance. In mathematics and science this proportion will be slightly lower (21.11%).

On the other hand, performance in all four subjects is maximised for those students who are not repeaters, while immigrant students are more likely to maximise their performance in English, which may be explained by their language background.

In terms of the Internet use, we can observe a more marked trade-off between the different objective functions. Specifically, students maximise their mathematics performance by continuously using the Internet to study, while it must be limited for the other academic competences. In addition, Internet use for gaming should be limited to once or twice a month to maximise students’ performance in mathematics, science and English, and reduced as much as possible to maximise their reading achievement. In the same vein, using the Internet to search for information on sports should be reduced to zero if students want to maximise their academic performance.

Alike, using the Internet for listening to music or for other hobbies daily contributes to maximise their performance in reading, science and English. In contrast, they should reduce these uses to once or twice a week to perform well in mathematics.

Finally, regarding social networks, we see that students should keep their use to the barest minimum. Given that using social networks can lead to addiction, especially in late childhood and adolescence, students should practically not use them if they want to ensure their academic performance.

Once the individual optimal values have been obtained, we have run the algorithm considering equal importance to reach each objective function and obtain “possibly” efficient solutions, presenting these results in Table 3 . This table shows that the range of variation of the achieved value in science is larger (between -0.009 and 0.922 points) than in the other subjects. In contrast, the range of variation in mathematics is the smallest (between -0.115 and 0.602 points), with the maximum value being much smaller than that achieved in the other 3 competences.

As we can see, the profile of the student who maximises her academic performance is female, non-immigrant and has not repeated before 6 th grade. Moreover, as in the mono-objective problem, it appears that academic performance is maximised when students from a low socio-economic background are surrounded by a higher proportion of poor students, with students’ socio-economic index being 0.149 and the proportion of poor students being 21.13%.

As for Internet use we can observe that, in order to achieve higher academic performance, students should reduce their use of the Internet both for studying and for searching for information about sports or playing video games. In this regard, it is worth noting that, to maximise their academic performance, students should reduce their Internet use for studying to once or twice a month, which may be the result of multitasking (Feng et al., 2019 ; Junco, 2015 ). Since younger students may lack self-control, using the Internet for schoolwork or study may lead to a non-academic use. This could hinder the educational process and encourage procrastination from an early age (Aznar-Díaz et al., 2020 ).

In contrast, our results show that students can maximise their academic performance by using the Internet daily to listen to music or to search for information on other topics of interest. In this sense, listening to music can improve not only mood, but also arousal levels and, consequently, enhance the performance of certain simple cognitive tasks (Goltz & Sadakata, 2021 ).

Finally we can observe that, if students want to maximise their academic performance, social networks use should be non-existent. Adolescents and pre-adolescents are heavy users of social networks and their desire to be constantly interacting with others can lead them to misuse their time efficiently and neglect academic work (Qiaolei, 2014 ).

5.2 Robustness check

To check the robustness of our results, we have replicated our analysis using the variables that only refer to the use of applications such as Telegram or WhatsApp (see Tables 7 , Footnote 3 9 and 10 , Appendix ).

In this sense, if we look at the results obtained for this new mono-objective problem (Table 9 ), we can observe that the optimal values for each of the subjects are pretty similar to those obtained in our main model. For example, in reading the optimal value decreases just by 0.09 points in terms of standardised scores. As for the student profile, Table 9 shows a very similar pattern to Table 2 . In order to maximise their performance in each one of the subjects, students should reduce their use of the Internet to search for information about sports, as well as to use applications such as WhatsApp. These applications should be used minimally to enhance academic performance.

On the other hand, Table 10 shows the “possibly” efficient solutions. In this sense, we can observe that the student who manages to maximise her academic performance is an immigrant female who has not repeated a grade. Her socio-economic index should be around 0.067, while the proportion of poor students (in the school) should be 22.11%.

Regarding the frequency of Internet use, we observe a pattern of use very similar to the one shown in Table 3 . Students should use the Internet as little as possible to search for information about sports, while they can listen to music and search for information about other topics of interest daily without harming their academic achievement. As for the use of applications such as WhatsApp or Telegram, this should be limited to once or twice a week.

In summary, as we have seen through the tables, the individual optimal values and the “possibly” efficient solution are pretty close to the ones obtained in our main model, which shows the consistency of our results.

6 Discussion and conclusions

Throughout this paper we have tried to analyse the influence that using the Internet may have on academic performance. In order to do so, using a database that provides us with information on a cohort of 6 th grade students in 2018–2019, we have implemented interval multiobjective programming. With this methodology, we have tried to profile those students who are able to maximise their academic performance evenly – and simultaneously – among different subjects.

In detail, we first carried out an econometric analysis to subsequently proceed with the multiobjective programming. From these estimates we observed how, e.g., using social networks had a negative influence on reading, mathematics, science and English performance, with the negative influence being greater as the frequency of use increased. The results of the interval programming model allow to assert that the profile of the student who manages to maximise her academic performance in all four subjects is a female student who has not repeated, from a medium socio-economic level and who attends a school where the proportion of students from a low socio-economic background is 21.11% (i.e. slightly below the sample average).

In addition, the results show that using the Internet from an early age has its ups and downs. To maximise academic performance, students should use the Internet to study or search for information about sports once or twice a month. Similarly, they should use the Internet to play video games 1 or 2 times a week, while they can daily use applications such as Spotify. In contrast, the use of social networks should be practically zero.

Thus, while the Internet can be helpful for the teaching–learning process, its inappropriate use can prevent students from reaching their best balanced performance. Therefore and, given the possibility that constant use of the Internet and social networks may lead to “addiction” that may affect their academic performance, it is necessary to establish some control. Hence, it would be desirable for both the family and the school to offer students some guidance to encourage them to use the Internet appropriately. In addition to promoting awareness and self-control among students, other measures should also be implemented to control both the content and frequency of use by young people. This would prevent exposure to undesirable content, as well as procrastination, that can ultimately lead young people to adopt unhealthy study techniques.

Besides, schools could find more effective ways of integrating ICT into the educational process by providing students with learning environments that are in line with the needs and trends of the twenty-first century. In this sense, while getting students to reduce their use of the Internet to play video games or to practically stop using social networks may be beyond the school’s scope of action, balancing academic performance with the ICT used in the classroom can be an objective of the school. In recent years, students’ use of computers and tablets during lessons has become increasingly common. However, in order to make it compatible with good academic performance, it is essential that schools set guidelines and control measures such as e.g. applications that limit students’ access to non-academic content during class time, in order to avoid multitasking problems.

In any case, it should be noted that our study is not free of limitations. First, as mentioned above, we are working with correlational rather than causal econometric estimates, as far as we cannot control for all variables that may affect academic performance. However, by using interval multiobjective programming, we are able to deal with this state of uncertainty. Second, Internet variables are self-reported, which may lead to measurement errors in the model. Third, our results may not be extrapolated to the rest of Spain, to the extent that we are using students’ information from only one Spanish region.

The database used dates from around 10 years ago, which limits the results obtained considerably to the extent that the Internet use in 2011 has nothing to do with internet use today.

Alternatively, Table 1 estimations have been replicated adding school fixed-effects and results are pretty similar. These results are presented in Table 6 ( Appendix ).

Table 7 ( Appendix ) estimations have been replicated adding school fixed-effects and results are pretty similar. These results are presented in Table 8 ( Appendix ).

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This work has been partly supported by FEDER funding (under Research Project PY20-00228-R); Ministerio de Ciencia e Innovación (under Research Project PID2020-119471RB-I00) and the Andalusian Regional Government (SEJ-645). We also acknowledge the scholarship FPU20/01509 of the Ministerio de Universidades and the training received from the Programa de Doctorado en Economía y Empresa of the Universidad de Malaga . The authors also acknowledge the data provided by the Agencia Canaria de Calidad Universitaria y Evaluación Educativa .

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Ladrón de Guevara Rodríguez, M., Lopez-Agudo, L.A., Prieto-Latorre, C. et al. Internet use and academic performance: An interval approach. Educ Inf Technol 27 , 11831–11873 (2022). https://doi.org/10.1007/s10639-022-11095-4

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Internet use and health in higher education students: a scoping review

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Hanna Rouvinen, Krista Jokiniemi, Marjorita Sormunen, Hannele Turunen, Internet use and health in higher education students: a scoping review, Health Promotion International , Volume 36, Issue 6, December 2021, Pages 1610–1620, https://doi.org/10.1093/heapro/daab007

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Research question identification

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PRISMA flow diagram of the search and evidence selection process (Source: Moher et al. , 2009).

Selection of articles according to the established criteria

Charting the data

Collating and summarizing the results

An example of thematic analysis process with their associated codes

Data .	Code .	Sub-themes .	Theme .	Main theme .
The same technologies also offer several opportunities for the enhancement of mental health and the treatment of mental illness ( , 2019).	Internet technologies offer opportunities for the enhancement of mental health	Promoting factors for mental health and well-being	Promoting and threatening factors for mental health and well-being	Internet use and health among higher education students: health promoting and health-threatening factors
Internet technologies offer opportunities to treat mental illness
Excessive Internet usage leads to anxiety, depression and adverse mental health ( , 2020)	Excessive Internet use leads to anxiety Excessive Internet use leads to depression Excessive Internet use leads to adverse mental health	Threatening factors for mental health and well-being

Data .	Code .	Sub-themes .	Theme .	Main theme .
The same technologies also offer several opportunities for the enhancement of mental health and the treatment of mental illness ( , 2019).	Internet technologies offer opportunities for the enhancement of mental health	Promoting factors for mental health and well-being	Promoting and threatening factors for mental health and well-being	Internet use and health among higher education students: health promoting and health-threatening factors
Internet technologies offer opportunities to treat mental illness
Excessive Internet usage leads to anxiety, depression and adverse mental health ( , 2020)	Excessive Internet use leads to anxiety Excessive Internet use leads to depression Excessive Internet use leads to adverse mental health	Threatening factors for mental health and well-being

Characteristics of included articles

Thematic findings

Health-promoting Internet use

Health-threatening internet use

A summary of the thematic findings

Supplementary material is available at Health Promotion International online.

This research was funded by the University of Eastern Finland’s Doctoral School, the Doctoral Programme in Health Sciences and the Department of Nursing Science.

Conflict of Interest: The authors declare that they have no conflict of interest.

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(PDF) A Literature Review on Internet of Things (IoT)
(PDF) LITERATURE REVIEW ON INTERNET BENEFITS, RISKS AND ISSUES: A CASE
Expanded Literature Part 1: Internet Literature
A Systematic Review of Literature on effect of Internet Use on Students
💋 Impact of internet usage on students academic performance. "THE
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Internet use and Problematic Internet Use: a systematic review of
The aim of this systematic literature review is to map the longitudinal research in the field of Internet Use (IU) and Problematic Internet Use (PIU) in adolescents and emergent adults. Further, this study endeavours to examine the terminology and instruments utilized in longitudinal IU and PIU research and investigate whether statistically ...
Internet addiction and problematic Internet use: A systematic review of
This systematic literature review outlines and discusses the current empirical literature base for clinical studies of Internet addiction and problematic Internet use. A total of 46 relevant studies on treatment seeker characteristics, psychopharmacotherapy, psychological therapy, and combined treatment were identified.
Problematic Internet use (PIU) in youth: a brief literature review of
Introduction. The Internet has been widely used since the 1990s [1] and can be defined as a tool for information access and exchange that aids people in their daily lives [2].Internet users can be categorized into two groups: adaptive Internet users and users with pathological behaviors [3].Adaptive users use the Internet for different and various purposes, including and beyond connection and ...
Problematic Internet Use and Resilience: A Systematic Review and Meta
1. Introduction. Internet use has grown substantially over the last few decades, with the number of users increasing by 1331.9% between 2000 and 2021 [], when a total of 4.66 billion users were counted, representing approximately 60% of the world's population [].The benefits associated with using the Internet, especially concerning information search and communication, have led people to ...
Using Theoretical Models of Problematic Internet Use to Inform
While numerous theories have been developed at this point in time, the debate about construct definition continues. Internet Addiction (IA) was originally conceptualised as having four primary diagnostic elements: (1) an increasing level of investment of resources in online activities, (2) a negative change in emotional states when offline, (3) a tolerance to the positive effects of Internet ...
Internet use and well-being: A survey and a theoretical framework
A survey of the literature on the effects of Internet use on well-being. ... White et al. (1999), in a review of Internet use for the elderly, show that use of the Internet can help avoid social isolation and improve well-being. However, when it comes to younger age groups, results are often found to be negative. ...
Internet use and health in higher education students: a scoping review
The increase in the use of the Internet has influenced students' health, especially mental and physical health and well-being. This scoping review scrutinizes the literature between 2015 and 2020 ( N = 55) on the association between Internet use and health in higher education students.
Problematic Internet Use and Loneliness: How Complex Is the ...
Purpose The Internet has become embedded into the life of billions of people worldwide. In some individuals, excessive Internet use impacts negatively on psychological and social functioning. Several studies over the last decades have focused on the relationship between Problematic Internet Use (PIU) and loneliness. The present review aims to provide an overview of the recent literature in ...
A Systematic Literature Review on Relationship Between Internet Usage
5.3 Relationship Between Internet Usage Behavior and Internet QoS. Literature review shows that most studies on Internet QoS have mentioned Internet use behavior, but few studies have clearly described their relationship. Among them, the number of people who connect Internet QoS with user analysis is more, while with user behavior model is less.
A review of Internet use among older adults
This article synthesizes the quantitative literature on Internet use among older adults, including trends in access, skills, and types of use, while exploring social inequalities in relation to each domain. We also review work on the relationship between health and Internet use, particularly relevant for older adults.
Full article: The Impact of Problematic Internet Use on Adolescent
Based on the above literature review, we formulate the following research hypotheses, And constructed a conceptual model of PIU, perceived social support, and family communication influencing adolescent loneliness, as shown in Figure 1.To explore the pathways through which problematic Internet use affects adolescents' loneliness.
Internet addiction and problematic Internet use: A systematic review of
Aim: To provide a comprehensive overview of clinical studies on the clinical picture of Internet-use related addictions from a holistic perspective. A literature search was conducted using the database Web of Science. Methods: Over the last 15 years, the number of Internet users has increased by 1000%, and at the same time, research on addictive Internet use has proliferated.
Internet use among older adults: Determinants of usage and impacts on
Literature review2.1. Internet use among Chinese older adults. The older population constitutes the main body of non-netizens (van Deursen & Helsper, 2015; Wagner et al., 2010). By the end of 2020, the proportion of internet users aged 50 and above rose to 26.3%.
Risk Factors Affecting Internet Use in Adolescents: A Literature Review
The literature review of this article was obtained from the online databases Science Direct, SpringerLink, ProQuest, EBSCO, and Pubmed in the publication year 2015-2020. ... Results: Internet use ...
PDF The Impact of Internet Usage on Students' Success in Selected Senior
LITERATURE REVIEW Internet Use The use of the internet draws users' eyes to the world's vastness around them. The internet gathers various types of data that college students and senior high school students use (Akin-Adaeamola, 2014; Yebowaah, 2018). Internet use will continue to grow if users are no longer denied accessibility (Olatokun, 2008).
Internet use and problematic internet use: A systematic review of
The aim of this systematic literature review is to map the longitudinal research in the field of Internet Use (IU) and Problematic Internet Use (PIU) in adolescents and emergent adults. Further, this study endeavours to examine the terminology and instruments utilized in longitudinal IU and PIU research and investigate whether statistically significant results have arisen from the areas of ...
Broadband Internet Access, Economic Growth, and Wellbeing
Between 2000 and 2008, access to high-speed, broadband internet grew significantly in the United States, but there is debate on whether access to high-speed internet improves or harms wellbeing. We find that a ten percent increase in the proportion of county residents with access to broadband internet leads to a 1.01 percent reduction in the ...
Internet use and academic performance: An interval approach
The article is structured as follows. First, we provide a brief review of the relevant literature on the influence of Internet use on academic performance. Then, we present the main characteristics of the dataset. Sections 4 and 5 describe the methodology employed and the results obtained. Finally, we discuss and present the main findings ...
(PDF) The Impact of Internet Use for Students
Development of Internet technology increasingly modern and sophisticated not only. benefit users but also have an effect that is not good for users, especially a mong students, from. a study of ...
(PDF) LITERATURE REVIEW ON INTERNET BENEFITS, RISKS AND ...
Therefore, a literature review has been simplified and produced a proposed scope of this field which covers the benefits, risks and issues of on internet which combine, replicate and modify 2 ...
Internet Usage, Government Trust, and Participation of Informal Workers
This study, based on data from the China Family Panel Studies (CFPS) for the years 2014, 2016, and 2018, empirically analyzes the impact of Internet usage on the participation of informal workers in the EPPS. The research finds that informal workers significantly increase the possibility of participation in the EPPS through the Internet usage.
Social media use, social anxiety, and loneliness: A systematic review
Despite the various terms related to Internet and SMU still used in the literature today, we will use the term "problematic social media use" to refer to this concept in this review. Davis's early model of pathological Internet use (2001, 2005) can also be used to conceptualize problematic SMU.
Internet use and Problematic Internet Use: a systematic review of
The aim of this systematic literature review is to map the longitudinal research in the field of Internet Use (IU) and Problematic Internet Use (PIU) in adolescents and emergent adults. Further, this study endeavours to examine the terminology and instruments utilized in longitudinal IU and
The Effect of Internet on Students Studies: a Review
Abstract. This paper is a literature review on effects of internet use on students' academic performance. Assessing to factors that affect students' use of the internet is the main objective ...
Internet use and health in higher education students: a scoping review
Conclusively, health-threatening Internet use demonstrates the necessity of preventive actions, such as focused health-promoting social marketing actions, to avoid risky behaviors from occurring among students. This comprehensive scoping review captured the majority of the relevant literature on the association of Internet use and health.
Internet & Technology
Americans' Views of Technology Companies. Most Americans are wary of social media's role in politics and its overall impact on the country, and these concerns are ticking up among Democrats. Still, Republicans stand out on several measures, with a majority believing major technology companies are biased toward liberals. short readsApr 3, 2024.
Welcome to the Purdue Online Writing Lab
Mission. The Purdue On-Campus Writing Lab and Purdue Online Writing Lab assist clients in their development as writers—no matter what their skill level—with on-campus consultations, online participation, and community engagement. The Purdue Writing Lab serves the Purdue, West Lafayette, campus and coordinates with local literacy initiatives.
The Influence of Internet Usage on Student's Academic Performance
LITERATURE REVIEW . The internet is a platform where millions of people engaged in the creation and exchange . ... H3 Internet use has also been associated with negative effects on academic .

Internet use and health in higher education students: a scoping review

Krista Jokiniemi

Hannele Turunen

LAY SUMMARY

INTRODUCTION

METHODS AND ANALYSIS

Research question identification

Identification of relevant articles

Selection of articles according to the established criteria

Charting the data

Collating and summarizing the results

Characteristics of included articles

Thematic findings

Health-promoting Internet use

Health-threatening internet use

A summary of the thematic findings

SUPPLEMENTARY MATERIAL

AUTHORS’ CONTRIBUTIONS

Supplementary Material

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Internet addiction and problematic Internet use: A systematic review of clinical research

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Article Contents

Internet use and health in higher education students: a scoping review

Research question identification

Identification of relevant articles

Selection of articles according to the established criteria

Charting the data

Collating and summarizing the results

Characteristics of included articles

Thematic findings

Health-promoting Internet use

Health-threatening internet use

A summary of the thematic findings

Supplementary data

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