computer literacy research study

• Open access
• Published: 08 June 2022

A systematic review on digital literacy

• Hasan Tinmaz   ORCID: orcid.org/0000-0003-4310-0848 1 ,
• Yoo-Taek Lee   ORCID: orcid.org/0000-0002-1913-9059 2 ,
• Mina Fanea-Ivanovici   ORCID: orcid.org/0000-0003-2921-2990 3 &
• Hasnan Baber   ORCID: orcid.org/0000-0002-8951-3501 4  

Smart Learning Environments volume  9 , Article number:  21 ( 2022 ) Cite this article

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The purpose of this study is to discover the main themes and categories of the research studies regarding digital literacy. To serve this purpose, the databases of WoS/Clarivate Analytics, Proquest Central, Emerald Management Journals, Jstor Business College Collections and Scopus/Elsevier were searched with four keyword-combinations and final forty-three articles were included in the dataset. The researchers applied a systematic literature review method to the dataset. The preliminary findings demonstrated that there is a growing prevalence of digital literacy articles starting from the year 2013. The dominant research methodology of the reviewed articles is qualitative. The four major themes revealed from the qualitative content analysis are: digital literacy, digital competencies, digital skills and digital thinking. Under each theme, the categories and their frequencies are analysed. Recommendations for further research and for real life implementations are generated.

Introduction

The extant literature on digital literacy, skills and competencies is rich in definitions and classifications, but there is still no consensus on the larger themes and subsumed themes categories. (Heitin, 2016 ). To exemplify, existing inventories of Internet skills suffer from ‘incompleteness and over-simplification, conceptual ambiguity’ (van Deursen et al., 2015 ), and Internet skills are only a part of digital skills. While there is already a plethora of research in this field, this research paper hereby aims to provide a general framework of digital areas and themes that can best describe digital (cap)abilities in the novel context of Industry 4.0 and the accelerated pandemic-triggered digitalisation. The areas and themes can represent the starting point for drafting a contemporary digital literacy framework.

Sousa and Rocha ( 2019 ) explained that there is a stake of digital skills for disruptive digital business, and they connect it to the latest developments, such as the Internet of Things (IoT), cloud technology, big data, artificial intelligence, and robotics. The topic is even more important given the large disparities in digital literacy across regions (Tinmaz et al., 2022 ). More precisely, digital inequalities encompass skills, along with access, usage and self-perceptions. These inequalities need to be addressed, as they are credited with a ‘potential to shape life chances in multiple ways’ (Robinson et al., 2015 ), e.g., academic performance, labour market competitiveness, health, civic and political participation. Steps have been successfully taken to address physical access gaps, but skills gaps are still looming (Van Deursen & Van Dijk, 2010a ). Moreover, digital inequalities have grown larger due to the COVID-19 pandemic, and they influenced the very state of health of the most vulnerable categories of population or their employability in a time when digital skills are required (Baber et al., 2022 ; Beaunoyer, Dupéré & Guitton, 2020 ).

The systematic review the researchers propose is a useful updated instrument of classification and inventory for digital literacy. Considering the latest developments in the economy and in line with current digitalisation needs, digitally literate population may assist policymakers in various fields, e.g., education, administration, healthcare system, and managers of companies and other concerned organisations that need to stay competitive and to employ competitive workforce. Therefore, it is indispensably vital to comprehend the big picture of digital literacy related research.

Literature review

Since the advent of Digital Literacy, scholars have been concerned with identifying and classifying the various (cap)abilities related to its operation. Using the most cited academic papers in this stream of research, several classifications of digital-related literacies, competencies, and skills emerged.

Digital literacies

Digital literacy, which is one of the challenges of integration of technology in academic courses (Blau, Shamir-Inbal & Avdiel, 2020 ), has been defined in the current literature as the competencies and skills required for navigating a fragmented and complex information ecosystem (Eshet, 2004 ). A ‘Digital Literacy Framework’ was designed by Eshet-Alkalai ( 2012 ), comprising six categories: (a) photo-visual thinking (understanding and using visual information); (b) real-time thinking (simultaneously processing a variety of stimuli); (c) information thinking (evaluating and combining information from multiple digital sources); (d) branching thinking (navigating in non-linear hyper-media environments); (e) reproduction thinking (creating outcomes using technological tools by designing new content or remixing existing digital content); (f) social-emotional thinking (understanding and applying cyberspace rules). According to Heitin ( 2016 ), digital literacy groups the following clusters: (a) finding and consuming digital content; (b) creating digital content; (c) communicating or sharing digital content. Hence, the literature describes the digital literacy in many ways by associating a set of various technical and non-technical elements.

• Digital competencies

The Digital Competence Framework for Citizens (DigComp 2.1.), the most recent framework proposed by the European Union, which is currently under review and undergoing an updating process, contains five competency areas: (a) information and data literacy, (b) communication and collaboration, (c) digital content creation, (d) safety, and (e) problem solving (Carretero, Vuorikari & Punie, 2017 ). Digital competency had previously been described in a technical fashion by Ferrari ( 2012 ) as a set comprising information skills, communication skills, content creation skills, safety skills, and problem-solving skills, which later outlined the areas of competence in DigComp 2.1, too.

• Digital skills

Ng ( 2012 ) pointed out the following three categories of digital skills: (a) technological (using technological tools); (b) cognitive (thinking critically when managing information); (c) social (communicating and socialising). A set of Internet skill was suggested by Van Deursen and Van Dijk ( 2009 , 2010b ), which contains: (a) operational skills (basic skills in using internet technology), (b) formal Internet skills (navigation and orientation skills); (c) information Internet skills (fulfilling information needs), and (d) strategic Internet skills (using the internet to reach goals). In 2014, the same authors added communication and content creation skills to the initial framework (van Dijk & van Deursen). Similarly, Helsper and Eynon ( 2013 ) put forward a set of four digital skills: technical, social, critical, and creative skills. Furthermore, van Deursen et al. ( 2015 ) built a set of items and factors to measure Internet skills: operational, information navigation, social, creative, mobile. More recent literature (vaan Laar et al., 2017 ) divides digital skills into seven core categories: technical, information management, communication, collaboration, creativity, critical thinking, and problem solving.

It is worth mentioning that the various methodologies used to classify digital literacy are overlapping or non-exhaustive, which confirms the conceptual ambiguity mentioned by van Deursen et al. ( 2015 ).

• Digital thinking

Thinking skills (along with digital skills) have been acknowledged to be a significant element of digital literacy in the educational process context (Ferrari, 2012 ). In fact, critical thinking, creativity, and innovation are at the very core of DigComp. Information and Communication Technology as a support for thinking is a learning objective in any school curriculum. In the same vein, analytical thinking and interdisciplinary thinking, which help solve problems, are yet other concerns of educators in the Industry 4.0 (Ozkan-Ozen & Kazancoglu, 2021 ).

However, we have recently witnessed a shift of focus from learning how to use information and communication technologies to using it while staying safe in the cyber-environment and being aware of alternative facts. Digital thinking would encompass identifying fake news, misinformation, and echo chambers (Sulzer, 2018 ). Not least important, concern about cybersecurity has grown especially in times of political, social or economic turmoil, such as the elections or the Covid-19 crisis (Sulzer, 2018 ; Puig, Blanco-Anaya & Perez-Maceira, 2021 ).

Ultimately, this systematic review paper focuses on the following major research questions as follows:

Research question 1: What is the yearly distribution of digital literacy related papers?

Research question 2: What are the research methods for digital literacy related papers?

Research question 3: What are the main themes in digital literacy related papers?

Research question 4: What are the concentrated categories (under revealed main themes) in digital literacy related papers?

This study employed the systematic review method where the authors scrutinized the existing literature around the major research question of digital literacy. As Uman ( 2011 ) pointed, in systematic literature review, the findings of the earlier research are examined for the identification of consistent and repetitive themes. The systematic review method differs from literature review with its well managed and highly organized qualitative scrutiny processes where researchers tend to cover less materials from fewer number of databases to write their literature review (Kowalczyk & Truluck, 2013 ; Robinson & Lowe, 2015 ).

Data collection

To address major research objectives, the following five important databases are selected due to their digital literacy focused research dominance: 1. WoS/Clarivate Analytics, 2. Proquest Central; 3. Emerald Management Journals; 4. Jstor Business College Collections; 5. Scopus/Elsevier.

The search was made in the second half of June 2021, in abstract and key words written in English language. We only kept research articles and book chapters (herein referred to as papers). Our purpose was to identify a set of digital literacy areas, or an inventory of such areas and topics. To serve that purpose, systematic review was utilized with the following synonym key words for the search: ‘digital literacy’, ‘digital skills’, ‘digital competence’ and ‘digital fluency’, to find the mainstream literature dealing with the topic. These key words were unfolded as a result of the consultation with the subject matter experts (two board members from Korean Digital Literacy Association and two professors from technology studies department). Below are the four key word combinations used in the search: “Digital literacy AND systematic review”, “Digital skills AND systematic review”, “Digital competence AND systematic review”, and “Digital fluency AND systematic review”.

A sequential systematic search was made in the five databases mentioned above. Thus, from one database to another, duplicate papers were manually excluded in a cascade manner to extract only unique results and to make the research smoother to conduct. At this stage, we kept 47 papers. Further exclusion criteria were applied. Thus, only full-text items written in English were selected, and in doing so, three papers were excluded (no full text available), and one other paper was excluded because it was not written in English, but in Spanish. Therefore, we investigated a total number of 43 papers, as shown in Table 1 . “ Appendix A ” shows the list of these papers with full references.

Data analysis

The 43 papers selected after the application of the inclusion and exclusion criteria, respectively, were reviewed the materials independently by two researchers who were from two different countries. The researchers identified all topics pertaining to digital literacy, as they appeared in the papers. Next, a third researcher independently analysed these findings by excluded duplicates A qualitative content analysis was manually performed by calculating the frequency of major themes in all papers, where the raw data was compared and contrasted (Fraenkel et al., 2012 ). All three reviewers independently list the words and how the context in which they appeared and then the three reviewers collectively decided for how it should be categorized. Lastly, it is vital to remind that literature review of this article was written after the identification of the themes appeared as a result of our qualitative analyses. Therefore, the authors decided to shape the literature review structure based on the themes.

As an answer to the first research question (the yearly distribution of digital literacy related papers), Fig.  1 demonstrates the yearly distribution of digital literacy related papers. It is seen that there is an increasing trend about the digital literacy papers.

Yearly distribution of digital literacy related papers

Research question number two (The research methods for digital literacy related papers) concentrates on what research methods are employed for these digital literacy related papers. As Fig.  2 shows, most of the papers were using the qualitative method. Not stated refers to book chapters.

Research methods used in the reviewed articles

When forty-three articles were analysed for the main themes as in research question number three (The main themes in digital literacy related papers), the overall findings were categorized around four major themes: (i) literacies, (ii) competencies, (iii) skills, and (iv) thinking. Under every major theme, the categories were listed and explained as in research question number four (The concentrated categories (under revealed main themes) in digital literacy related papers).

The authors utilized an overt categorization for the depiction of these major themes. For example, when the ‘creativity’ was labelled as a skill, the authors also categorized it under the ‘skills’ theme. Similarly, when ‘creativity’ was mentioned as a competency, the authors listed it under the ‘competencies’ theme. Therefore, it is possible to recognize the same finding under different major themes.

Major theme 1: literacies

Digital literacy being the major concern of this paper was observed to be blatantly mentioned in five papers out forty-three. One of these articles described digital literacy as the human proficiencies to live, learn and work in the current digital society. In addition to these five articles, two additional papers used the same term as ‘critical digital literacy’ by describing it as a person’s or a society’s accessibility and assessment level interaction with digital technologies to utilize and/or create information. Table 2 summarizes the major categories under ‘Literacies’ major theme.

Computer literacy, media literacy and cultural literacy were the second most common literacy (n = 5). One of the article branches computer literacy as tool (detailing with software and hardware uses) and resource (focusing on information processing capacity of a computer) literacies. Cultural literacy was emphasized as a vital element for functioning in an intercultural team on a digital project.

Disciplinary literacy (n = 4) was referring to utilizing different computer programs (n = 2) or technical gadgets (n = 2) with a specific emphasis on required cognitive, affective and psychomotor skills to be able to work in any digital context (n = 3), serving for the using (n = 2), creating and applying (n = 2) digital literacy in real life.

Data literacy, technology literacy and multiliteracy were the third frequent categories (n = 3). The ‘multiliteracy’ was referring to the innate nature of digital technologies, which have been infused into many aspects of human lives.

Last but not least, Internet literacy, mobile literacy, web literacy, new literacy, personal literacy and research literacy were discussed in forty-three article findings. Web literacy was focusing on being able to connect with people on the web (n = 2), discover the web content (especially the navigation on a hyper-textual platform), and learn web related skills through practical web experiences. Personal literacy was highlighting digital identity management. Research literacy was not only concentrating on conducting scientific research ability but also finding available scholarship online.

Twenty-four other categories are unfolded from the results sections of forty-three articles. Table 3 presents the list of these other literacies where the authors sorted the categories in an ascending alphabetical order without any other sorting criterion. Primarily, search, tagging, filtering and attention literacies were mainly underlining their roles in information processing. Furthermore, social-structural literacy was indicated as the recognition of the social circumstances and generation of information. Another information-related literacy was pointed as publishing literacy, which is the ability to disseminate information via different digital channels.

While above listed personal literacy was referring to digital identity management, network literacy was explained as someone’s social networking ability to manage the digital relationship with other people. Additionally, participatory literacy was defined as the necessary abilities to join an online team working on online content production.

Emerging technology literacy was stipulated as an essential ability to recognize and appreciate the most recent and innovative technologies in along with smart choices related to these technologies. Additionally, the critical literacy was added as an ability to make smart judgements on the cost benefit analysis of these recent technologies.

Last of all, basic, intermediate, and advanced digital assessment literacies were specified for educational institutions that are planning to integrate various digital tools to conduct instructional assessments in their bodies.

Major theme 2: competencies

The second major theme was revealed as competencies. The authors directly categorized the findings that are specified with the word of competency. Table 4 summarizes the entire category set for the competencies major theme.

The most common category was the ‘digital competence’ (n = 14) where one of the articles points to that category as ‘generic digital competence’ referring to someone’s creativity for multimedia development (video editing was emphasized). Under this broad category, the following sub-categories were associated:

Problem solving (n = 10)

Safety (n = 7)

Information processing (n = 5)

Content creation (n = 5)

Communication (n = 2)

Digital rights (n = 1)

Digital emotional intelligence (n = 1)

Digital teamwork (n = 1)

Big data utilization (n = 1)

Artificial Intelligence utilization (n = 1)

Virtual leadership (n = 1)

Self-disruption (in along with the pace of digitalization) (n = 1)

Like ‘digital competency’, five additional articles especially coined the term as ‘digital competence as a life skill’. Deeper analysis demonstrated the following points: social competences (n = 4), communication in mother tongue (n = 3) and foreign language (n = 2), entrepreneurship (n = 3), civic competence (n = 2), fundamental science (n = 1), technology (n = 1) and mathematics (n = 1) competences, learning to learn (n = 1) and self-initiative (n = 1).

Moreover, competencies were linked to workplace digital competencies in three articles and highlighted as significant for employability (n = 3) and ‘economic engagement’ (n = 3). Digital competencies were also detailed for leisure (n = 2) and communication (n = 2). Furthermore, two articles pointed digital competencies as an inter-cultural competency and one as a cross-cultural competency. Lastly, the ‘digital nativity’ (n = 1) was clarified as someone’s innate competency of being able to feel contented and satisfied with digital technologies.

Major theme 3: skills

The third major observed theme was ‘skills’, which was dominantly gathered around information literacy skills (n = 19) and information and communication technologies skills (n = 18). Table 5 demonstrates the categories with more than one occurrence.

Table 6 summarizes the sub-categories of the two most frequent categories of ‘skills’ major theme. The information literacy skills noticeably concentrate on the steps of information processing; evaluation (n = 6), utilization (n = 4), finding (n = 3), locating (n = 2) information. Moreover, the importance of trial/error process, being a lifelong learner, feeling a need for information and so forth were evidently listed under this sub-category. On the other hand, ICT skills were grouped around cognitive and affective domains. For instance, while technical skills in general and use of social media, coding, multimedia, chat or emailing in specific were reported in cognitive domain, attitude, intention, and belief towards ICT were mentioned as the elements of affective domain.

Communication skills (n = 9) were multi-dimensional for different societies, cultures, and globalized contexts, requiring linguistic skills. Collaboration skills (n = 9) are also recurrently cited with an explicit emphasis for virtual platforms.

‘Ethics for digital environment’ encapsulated ethical use of information (n = 4) and different technologies (n = 2), knowing digital laws (n = 2) and responsibilities (n = 2) in along with digital rights and obligations (n = 1), having digital awareness (n = 1), following digital etiquettes (n = 1), treating other people with respect (n = 1) including no cyber-bullying (n = 1) and no stealing or damaging other people (n = 1).

‘Digital fluency’ involved digital access (n = 2) by using different software and hardware (n = 2) in online platforms (n = 1) or communication tools (n = 1) or within programming environments (n = 1). Digital fluency also underlined following recent technological advancements (n = 1) and knowledge (n = 1) including digital health and wellness (n = 1) dimension.

‘Social intelligence’ related to understanding digital culture (n = 1), the concept of digital exclusion (n = 1) and digital divide (n = 3). ‘Research skills’ were detailed with searching academic information (n = 3) on databases such as Web of Science and Scopus (n = 2) and their citation, summarization, and quotation (n = 2).

‘Digital teaching’ was described as a skill (n = 2) in Table 4 whereas it was also labelled as a competence (n = 1) as shown in Table 3 . Similarly, while learning to learn (n = 1) was coined under competencies in Table 3 , digital learning (n = 2, Table 4 ) and life-long learning (n = 1, Table 5 ) were stated as learning related skills. Moreover, learning was used with the following three terms: learning readiness (n = 1), self-paced learning (n = 1) and learning flexibility (n = 1).

Table 7 shows other categories listed below the ‘skills’ major theme. The list covers not only the software such as GIS, text mining, mapping, or bibliometric analysis programs but also the conceptual skills such as the fourth industrial revolution and information management.

Major theme 4: thinking

The last identified major theme was the different types of ‘thinking’. As Table 8 shows, ‘critical thinking’ was the most frequent thinking category (n = 4). Except computational thinking, the other categories were not detailed.

Computational thinking (n = 3) was associated with the general logic of how a computer works and sub-categorized into the following steps; construction of the problem (n = 3), abstraction (n = 1), disintegration of the problem (n = 2), data collection, (n = 2), data analysis (n = 2), algorithmic design (n = 2), parallelization & iteration (n = 1), automation (n = 1), generalization (n = 1), and evaluation (n = 2).

A transversal analysis of digital literacy categories reveals the following fields of digital literacy application:

Technological advancement (IT, ICT, Industry 4.0, IoT, text mining, GIS, bibliometric analysis, mapping data, technology, AI, big data)

Networking (Internet, web, connectivity, network, safety)

Information (media, news, communication)

Creative-cultural industries (culture, publishing, film, TV, leisure, content creation)

Academia (research, documentation, library)

Citizenship (participation, society, social intelligence, awareness, politics, rights, legal use, ethics)

Education (life skills, problem solving, teaching, learning, education, lifelong learning)

Professional life (work, teamwork, collaboration, economy, commerce, leadership, decision making)

Personal level (critical thinking, evaluation, analytical thinking, innovative thinking)

This systematic review on digital literacy concentrated on forty-three articles from the databases of WoS/Clarivate Analytics, Proquest Central, Emerald Management Journals, Jstor Business College Collections and Scopus/Elsevier. The initial results revealed that there is an increasing trend on digital literacy focused academic papers. Research work in digital literacy is critical in a context of disruptive digital business, and more recently, the pandemic-triggered accelerated digitalisation (Beaunoyer, Dupéré & Guitton, 2020 ; Sousa & Rocha 2019 ). Moreover, most of these papers were employing qualitative research methods. The raw data of these articles were analysed qualitatively using systematic literature review to reveal major themes and categories. Four major themes that appeared are: digital literacy, digital competencies, digital skills and thinking.

Whereas the mainstream literature describes digital literacy as a set of photo-visual, real-time, information, branching, reproduction and social-emotional thinking (Eshet-Alkalai, 2012 ) or as a set of precise specific operations, i.e., finding, consuming, creating, communicating and sharing digital content (Heitin, 2016 ), this study reveals that digital literacy revolves around and is in connection with the concepts of computer literacy, media literacy, cultural literacy or disciplinary literacy. In other words, the present systematic review indicates that digital literacy is far broader than specific tasks, englobing the entire sphere of computer operation and media use in a cultural context.

The digital competence yardstick, DigComp (Carretero, Vuorikari & Punie, 2017 ) suggests that the main digital competencies cover information and data literacy, communication and collaboration, digital content creation, safety, and problem solving. Similarly, the findings of this research place digital competencies in relation to problem solving, safety, information processing, content creation and communication. Therefore, the findings of the systematic literature review are, to a large extent, in line with the existing framework used in the European Union.

The investigation of the main keywords associated with digital skills has revealed that information literacy, ICT, communication, collaboration, digital content creation, research and decision-making skill are the most representative. In a structured way, the existing literature groups these skills in technological, cognitive, and social (Ng, 2012 ) or, more extensively, into operational, formal, information Internet, strategic, communication and content creation (van Dijk & van Deursen, 2014 ). In time, the literature has become richer in frameworks, and prolific authors have improved their results. As such, more recent research (vaan Laar et al., 2017 ) use the following categories: technical, information management, communication, collaboration, creativity, critical thinking, and problem solving.

Whereas digital thinking was observed to be mostly related with critical thinking and computational thinking, DigComp connects it with critical thinking, creativity, and innovation, on the one hand, and researchers highlight fake news, misinformation, cybersecurity, and echo chambers as exponents of digital thinking, on the other hand (Sulzer, 2018 ; Puig, Blanco-Anaya & Perez-Maceira, 2021 ).

This systematic review research study looks ahead to offer an initial step and guideline for the development of a more contemporary digital literacy framework including digital literacy major themes and factors. The researchers provide the following recommendations for both researchers and practitioners.

Recommendations for prospective research

By considering the major qualitative research trend, it seems apparent that more quantitative research-oriented studies are needed. Although it requires more effort and time, mixed method studies will help understand digital literacy holistically.

As digital literacy is an umbrella term for many different technologies, specific case studies need be designed, such as digital literacy for artificial intelligence or digital literacy for drones’ usage.

Digital literacy affects different areas of human lives, such as education, business, health, governance, and so forth. Therefore, different case studies could be carried out for each of these unique dimensions of our lives. For instance, it is worth investigating the role of digital literacy on lifelong learning in particular, and on education in general, as well as the digital upskilling effects on the labour market flexibility.

Further experimental studies on digital literacy are necessary to realize how certain variables (for instance, age, gender, socioeconomic status, cognitive abilities, etc.) affect this concept overtly or covertly. Moreover, the digital divide issue needs to be analysed through the lens of its main determinants.

New bibliometric analysis method can be implemented on digital literacy documents to reveal more information on how these works are related or centred on what major topic. This visual approach will assist to realize the big picture within the digital literacy framework.

Recommendations for practitioners

The digital literacy stakeholders, policymakers in education and managers in private organizations, need to be aware that there are many dimensions and variables regarding the implementation of digital literacy. In that case, stakeholders must comprehend their beneficiaries or the participants more deeply to increase the effect of digital literacy related activities. For example, critical thinking and problem-solving skills and abilities are mentioned to affect digital literacy. Hence, stakeholders have to initially understand whether the participants have enough entry level critical thinking and problem solving.

Development of digital literacy for different groups of people requires more energy, since each group might require a different set of skills, abilities, or competencies. Hence, different subject matter experts, such as technologists, instructional designers, content experts, should join the team.

It is indispensably vital to develop different digital frameworks for different technologies (basic or advanced) or different contexts (different levels of schooling or various industries).

These frameworks should be updated regularly as digital fields are evolving rapidly. Every year, committees should gather around to understand new technological trends and decide whether they should address the changes into their frameworks.

Understanding digital literacy in a thorough manner can enable decision makers to correctly implement and apply policies addressing the digital divide that is reflected onto various aspects of life, e.g., health, employment, education, especially in turbulent times such as the COVID-19 pandemic is.

Lastly, it is also essential to state the study limitations. This study is limited to the analysis of a certain number of papers, obtained from using the selected keywords and databases. Therefore, an extension can be made by adding other keywords and searching other databases.

Availability of data and materials

The authors present the articles used for the study in “ Appendix A ”.

Baber, H., Fanea-Ivanovici, M., Lee, Y. T., & Tinmaz, H. (2022). A bibliometric analysis of digital literacy research and emerging themes pre-during COVID-19 pandemic. Information and Learning Sciences . https://doi.org/10.1108/ILS-10-2021-0090 .

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Blau, I., Shamir-Inbal, T., & Avdiel, O. (2020). How does the pedagogical design of a technology-enhanced collaborative academic course promote digital literacies, self-regulation, and perceived learning of students? The Internet and Higher Education, 45 , 100722. https://doi.org/10.1016/j.iheduc.2019.100722

Carretero, S., Vuorikari, R., & Punie, Y. (2017). DigComp 2.1: The Digital Competence Framework for Citizens with eight proficiency levels and examples of use (No. JRC106281). Joint Research Centre, https://publications.jrc.ec.europa.eu/repository/handle/JRC106281

Eshet, Y. (2004). Digital literacy: A conceptual framework for survival skills in the digital era. Journal of Educational Multimedia and Hypermedia , 13 (1), 93–106, https://www.learntechlib.org/primary/p/4793/

Eshet-Alkalai, Y. (2012). Thinking in the digital era: A revised model for digital literacy. Issues in Informing Science and Information Technology, 9 (2), 267–276. https://doi.org/10.28945/1621

Ferrari, A. (2012). Digital competence in practice: An analysis of frameworks. JCR IPTS, Sevilla. https://ifap.ru/library/book522.pdf

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Ng, W. (2012). Can we teach digital natives digital literacy? Computers & Education, 59 (3), 1065–1078. https://doi.org/10.1016/j.compedu.2012.04.016

Ozkan-Ozen, Y. D., & Kazancoglu, Y. (2021). Analysing workforce development challenges in the Industry 4.0. International Journal of Manpower . https://doi.org/10.1108/IJM-03-2021-0167

Puig, B., Blanco-Anaya, P., & Perez-Maceira, J. J. (2021). “Fake News” or Real Science? Critical thinking to assess information on COVID-19. Frontiers in Education, 6 , 646909. https://doi.org/10.3389/feduc.2021.646909

Robinson, L., Cotten, S. R., Ono, H., Quan-Haase, A., Mesch, G., Chen, W., Schulz, J., Hale, T. M., & Stern, M. J. (2015). Digital inequalities and why they matter. Information, Communication & Society, 18 (5), 569–582. https://doi.org/10.1080/1369118X.2015.1012532

Robinson, P., & Lowe, J. (2015). Literature reviews vs systematic reviews. Australian and New Zealand Journal of Public Health, 39 (2), 103. https://doi.org/10.1111/1753-6405.12393

Sousa, M. J., & Rocha, A. (2019). Skills for disruptive digital business. Journal of Business Research, 94 , 257–263. https://doi.org/10.1016/j.jbusres.2017.12.051

Sulzer, A. (2018). (Re)conceptualizing digital literacies before and after the election of Trump. English Teaching: Practice & Critique, 17 (2), 58–71. https://doi.org/10.1108/ETPC-06-2017-0098

Tinmaz, H., Fanea-Ivanovici, M., & Baber, H. (2022). A snapshot of digital literacy. Library Hi Tech News , (ahead-of-print).

Uman, L. S. (2011). Systematic reviews and meta-analyses. Journal of the Canadian Academy of Child and Adolescent Psychiatry, 20 (1), 57–59.

Van Deursen, A. J. A. M., Helsper, E. J., & Eynon, R. (2015). Development and validation of the Internet Skills Scale (ISS). Information, Communication & Society, 19 (6), 804–823. https://doi.org/10.1080/1369118X.2015.1078834

Van Deursen, A. J. A. M., & van Dijk, J. A. G. M. (2009). Using the internet: Skills related problems in users’ online behaviour. Interacting with Computers, 21 , 393–402. https://doi.org/10.1016/j.intcom.2009.06.005

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Tinmaz, H., Lee, YT., Fanea-Ivanovici, M. et al. A systematic review on digital literacy. Smart Learn. Environ. 9 , 21 (2022). https://doi.org/10.1186/s40561-022-00204-y

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For a number of years, education authorities have responded to the importance of school students developing computer literacy by including it as part of the school curriculum, directly as a cross-curriculum capability, and by assessing the extent to which students are computer literate. Computer literacy and related concepts, such as ICT literacy, are defined so as to include both technological expertise and information literacy. Assessments of computer literacy, even though they vary, indicate that there are substantial variations in levels of computer literacy among students in the lower years of secondary school. In technologically developed countries, approximately one half of Year 8 students demonstrate proficiency, or advanced proficiency, in computer literacy, but up to 10% have very limited computer literacy. Assessments of computer literacy can also provide the basis for progression maps that could be used to inform curriculum development. Those progression maps will be more valuable if the frameworks on which they are based become more strongly integrated with each other. In addition, computer literacy appears to be influenced by student background, including familiarity with computers, as well as the emphases placed on it in classrooms and schools and the support provided by ICT in education systems. At present, there is less information about school and classroom influences on computer literacy than there is about student background influences. In the immediate future, the construct of computer literacy may need to accommodate increasingly to changes in software and hardware contexts in which it is manifested.

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Ainley, J. (2018). Students and Their Computer Literacy: Evidence and Curriculum Implications. In: Voogt, J., Knezek, G., Christensen, R., Lai, KW. (eds) Second Handbook of Information Technology in Primary and Secondary Education . Springer International Handbooks of Education. Springer, Cham. https://doi.org/10.1007/978-3-319-71054-9_4

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Students’ Computer Literacy and Academic Performance

This study determined the level of computer literacy and its influence on the academic performance of junior high school students. Specifically, it probed into the students’ attitude toward computers and sought answers to the following: the extent of students’ computer literacy in terms of Word Processing, Spreadsheet, Presentation, and General Computing; their academic performance based on the mean percentage scores during the first and second quarters of the school year 2019-2020; issues and problems encountered by them relative to the extent of their computer literacy; and the solutions that may be suggested by themselves to address the constraints they encountered relative to the extent of their computer literacy. Also, by employing descriptive-correlational analysis, the study examined the significant differences in the extent of students’ computer literacy in said areas when paired according to their attitude toward computers and the significant relationship between their academic performance and the extent of their computer literacy in terms of the identified areas. Generally, the findings of the study revealed that the students needed to enhance the extent of their computer literacy in the areas of word processing, spreadsheet, presentation, and general computing. The results also signified that the greater the extent of their computer literacy in said areas, the higher their academic performance. This implied that classroom intervention activities are imperative to enhance the extent of the students' computer literacy. Thus, teachers should support them by implementing an intervention program that improves students’ level of computer literacy in the specific areas mentioned.

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The objective of this study was to verify the association of psychological factors and body practices with the academic performance of high school students. A sample of 330 students participated, made up of 167 girls and 163 boys. Likert scale instruments were used for collecting information on self-concept, and on general and academic self-efficacy. Time spent on moderate to vigorous physical activity was recorded, and so was participation in several types of body practices, such as sports, dances, martial arts, performing arts, and systematic physical exercises. Academic achievement was referred to from the students' grades on regular subjects. Linear regression analysis was used for verifying the association of independent variables with academic performance. The adjusted regression model explains between 7% and 36% of academic performance variance, whereas Self-Concept explains academic performance on six of the twelve subjects, and the mean of the grades, with Beta values between 0.13 (p = 0.02) for Sociology and 0.28 (p <0.01) for Mathematics. Academic self-efficacy explained performance on eleven subjects and the mean of the grades, with Beta values between 0.21 (p <0.01) for Physical Education and Philosophy, and 0.44 (p <0.01) for Biology. Participation in extracurricular activities involving body practices explained academic performance on six subjects and the mean of the grades, with Beta values between 0.14 (p = 0.02) for Sociology and 0.31 (p <0.01) for Arts. The studied psychological variables and participation in projects concerning body practices during extracurricular activities correlated with academic achievement as to several school subjects, and with the mean of the grades.

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• Digital Readiness Gaps

Americans fall along a spectrum of preparedness when it comes to using tech tools to pursue learning online, and many are not eager or ready to take the plunge

Table of contents.

• 1. The meaning of digital readiness
• 2. The spectrum of digital readiness for e-learning
• 3. Greater digital readiness translates to higher level of use of technology in learning
• Appendix: Detail on digital readiness and other metrics across groups
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For many years concerns about “digital divides” centered primarily on whether people had access to digital technologies. Now, those worried about these issues also focus on the degree to which people succeed or struggle when they use technology to try to navigate their environments, solve problems, and make decisions. A recent Pew Research Center report showed that adoption of technology for adult learning in both personal and job-related activities varies by people’s socio-economic status, their race and ethnicity, and their level of access to home broadband and smartphones. Another report showed that some users are unable to make the internet and mobile devices function adequately for key activities such as looking for jobs .

In this report, we use newly released Pew Research Center survey findings to address a related issue: digital readiness. The new analysis explores the attitudes and behaviors that underpin people’s preparedness and comfort in using digital tools for learning as we measured it in a survey about people’s activities for personal learning .

Specifically, we assess American adults according to five main factors: their confidence in using computers, their facility with getting new technology to work, their use of digital tools for learning, their ability to determine the trustworthiness of online information, and their familiarity with contemporary “education tech” terms. It is important to note that the findings here just cover people’s learning activities in digital spaces and do not address the full range of important things that people can do online or their “readiness” to perform them.

To better understand the way in which different groups of Americans line up when it comes to digital readiness, researchers used a statistical technique called cluster analysis that places people into groups based on similarities in their answers to key questions.

The analysis shows there are several distinct groups of Americans who fall along a spectrum of digital readiness from relatively more prepared to relatively hesitant. Those who tend to be hesitant about embracing technology in learning are below average on the measures of readiness, such as needing help with new electronic gadgets or having difficulty determining whether online information is trustworthy. Those whose profiles indicate a higher level of preparedness for using tech in learning are collectively above average on measures of digital readiness.

Relatively Hesitant – 52% of adults in three distinct groups. This overall cohort is made up of three different clusters of people who are less likely to use digital tools in their learning. This has to do, in part, with the fact that these groups have generally lower levels of involvement with personal learning activities. It is also tied to their professed lower level of digital skills and trust in the online environment.

• A group of 14% of adults make up The Unprepared . This group has both low levels of digital skills and limited trust in online information. The Unprepared rank at the bottom of those who use the internet to pursue learning, and they are the least digitally ready of all the groups.
• We call one small group Traditional Learners, and they make up of 5% of Americans. They are active learners, but use traditional means to pursue their interests. They are less likely to fully engage with digital tools, because they have concerns about the trustworthiness of online information.
• A larger group, The Reluctant, make up 33% of all adults. They have higher levels of digital skills than The Unprepared, but very low levels of awareness of new “education tech” concepts and relatively lower levels of performing personal learning activities of any kind. This is correlated with their general lack of use of the internet in learning.

Relatively more prepared – 48% of adults in two distinct groups. This cohort is made up of two groups who are above average in their likeliness to use online tools for learning.

• A group we call Cautious Clickers comprises 31% of adults. They have tech resources at their disposal, trust and confidence in using the internet, and the educational underpinnings to put digital resources to use for their learning pursuits. But they have not waded into e-learning to the extent the Digitally Ready have and are not as likely to have used the internet for some or all of their learning.
• Finally, there are the Digitally Ready . They make up 17% of adults, and they are active learners and confident in their ability to use digital tools to pursue learning. They are aware of the latest “ed tech” tools and are, relative to others, more likely to use them in the course of their personal learning. The Digitally Ready, in other words, have high demand for learning and use a range of tools to pursue it – including, to an extent significantly greater than the rest of the population, digital outlets such as online courses or extensive online research.

There are several important qualifying notes to sound about this analysis. First, the research focuses on a particular activity – online learning. The findings are not necessarily projectable to people’s capacity (or lack of capacity) to perform health-related web searches, use mobile apps for civic activities, or use smartphones to apply for a job.

Second, while there are numerical descriptions of the groups, there is some fluidity in the boundaries of the groups. Unlike many other statistical techniques, cluster analysis does not require a single “correct” result. Instead, researchers run numerous versions of it (e.g., asking it to produce different numbers of clusters) and judge each result by how analytically practical and substantively meaningful it is. Fortunately, nearly every version produced had a great deal in common with the others, giving us confidence that the pattern of divisions were genuine and that the comparative shares of those who were relatively ready and not ready each constituted about half of Americans.

Third, it is important to note that the findings represent a snapshot of where adults are today in a fairly nascent stage of e-learning in society. The groupings reported here may well change in the coming years as people’s understanding of e-tools grows and as the creators of technology related to e-learning evolve it and attempt to make it more user friendly.

Even allowing for those caveats, the findings add additional context to insights about those who pursue personal learning activities. Although factors such as educational attainment or age might influence whether people use digital tools in learning, other things such as people’s digital skills and their trust in online information may also loom large. These “readiness” factors, separate and apart from demographic ones, are the focus in this report.

The results are also significant in light of Americans’ expressed interest in learning and personal growth. Most Americans said in the Center survey that they like to look for opportunities to grow as people: 58% said this applies to them “very well” and another 31% said it applies to them “somewhat well.” Additionally, as they age, many Americans say they hope to stay active and engaged with the world .

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• Research article
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• Published: 19 June 2006

Computer literacy and attitudes towards e-learning among first year medical students

• Thomas Michael Link 1 &
• Richard Marz 1  

BMC Medical Education volume  6 , Article number:  34 ( 2006 ) Cite this article

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At the Medical University of Vienna, most information for students is available only online. In 2005, an e-learning project was initiated and there are plans to introduce a learning management system. In this study, we estimate the level of students' computer skills, the number of students having difficulty with e-learning, and the number of students opposed to e-learning.

The study was conducted in an introductory course on computer-based and web-based training (CBT/WBT). Students were asked to fill out a questionnaire online that covered a wide range of relevant attitudes and experiences.

While the great majority of students possess sufficient computer skills and acknowledge the advantages of interactive and multimedia-enhanced learning material, a small percentage lacks basic computer skills and/or is very skeptical about e-learning. There is also a consistently significant albeit weak gender difference in available computer infrastructure and Internet access. As for student attitudes toward e-learning, we found that age, computer use, and previous exposure to computers are more important than gender. A sizable number of students, 12% of the total, make little or no use of existing e-learning offerings.

Many students would benefit from a basic introduction to computers and to the relevant computer-based resources of the university. Given to the wide range of computer skills among students, a single computer course for all students would not be useful nor would it be accepted. Special measures should be taken to prevent students who lack computer skills from being disadvantaged or from developing computer-hostile attitudes.

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Computer literacy has been a subject of educational research ever since personal computers were introduced to the classroom, either as teaching aids or as tools for self-study. In the 1980s, research on computer literacy focused on the question whether medical students were ready for the foreseeable omnipresence of computers in the future doctors' professional environments [ 1 – 4 ], i.e., whether they possessed the necessary computer skills [ 2 , 5 – 9 ]. The vision of a knowledge-based society saw future economic wealth dependent on people's abilities to deal with the growing information load and to adapt to an ever-changing working environment [ 10 – 13 ]. It was assumed that computers would become ubiquitous tools for managing medical knowledge [ 14 ]. In some medical schools, a privately owned computer was made a requirement for medical students [ 15 , 16 ].

E-Learning, in particular the use of learning management systems (LMSs), introduced a new aspect. Researchers [ 17 ] suggested that some students may lack the necessary skills to use web-based learning platforms effectively and are therefore handicapped. This issue is often discussed in the context of gender differences. The main concern is that female students are at a disadvantage due to different patterns of computer usage, e.g. a less dominant style of discussion in web-based communication [ 18 , 19 ]. These gender differences can be observed in students' computer-related behaviors but also in their attitudes towards computer-based and web-based training (CBT/WBT). In a Danish study, Dørup [ 9 ] reported that among first-year students, 46% of the men were in favor of replacing "traditional teaching with use of computers if possible" while only 22% women agreed with this statement.

In 2004, 80% of Austria's 20–29 year olds had Internet access and 75% of university and high school students used a computer daily [ 20 ]. We can thus assume that, in general, students entering university have good basic computer skills. Studies nevertheless demonstrate that there is a considerable difference in computer use according to students' disciplines. Middendorff [ 21 ] reports that German medical students spend an average of 8 hours per week at the computer (including private activities). This is the lowest value of all disciplines, what makes it difficult to draw conclusions about medical students' computer use from general surveys. Often the degree of "informational fluency" remains at a basic level and students tend to over-estimate their computer skills [ 22 ].

This study examines the level of computer literacy and patterns of computer usage of first-year medical students at the Medical University of Vienna. It was conducted in an introductory course for first-year students on CBT/WBT. The goal of the study was to determine the need for such introductory courses and to provide information that could be used to improve them. A secondary aim was to identify difficulties that may be encountered in implementing a university-wide LMS due to students' lack of computer literacy or low acceptance of e-learning. While multimedia learning programs have been praised for their educational superiority, actual use of these programs has sometimes failed to meet our expectations.

Since autumn 2003, we have required students to take an introductory course on CBT/WBT as a single 90-minute class session. This course is held for first-year students (about 1500 students took it in 2004 and 2005) and second-year students (about 600 students from 2003 to 2005) [ 23 ]. The course serves two main purposes:

To ensure a certain level of computer and information literacy, including online communication skills.

To acquaint students with computer and web-based learning materials.

In 2003 and 2004, students had to review web-based learning programs (e.g. [ 24 ]) and post their statements in a dedicated online forum. In the course for first-year students we used a student-developed platform [ 25 ]. In the course for second-year students, we used Manila [ 26 ] in 2003 and TikiWiki [ 27 ] in 2004 as a collaboration tool. In 2005, we switched to tools that were partly self-developed and less demanding with respect to the server load.

This paper reports on data from an online survey for the 2004 course for first-year students. Participation in the survey was voluntary and anonymous (though students were asked to give their student ID if they wanted to). The tutors were not able to determine who has or has not filled out the questionnaire. Using class time for students to fill out the questionnaire nevertheless ensured a high response rate of 79%.

A total of 1232 questionnaires were completed, 1160 of which remained in the data set after applying some filtering rules in order to eliminate records of uncertain origin. The gender breakdown of respondents was 61% female and 39% male. This corresponds exactly to the gender breakdown of the 1560 students entering the study module (61% female and 39% male). We thus conclude that our sample was representative of the 2004 cohort. Missing values due to non-responses are not included in tables or figures. Differences between the reported counts and the sample size (n = 1160) are thus due to missing responses.

Questionnaire

The questionnaire [ 28 ] (see Additional file 1 ) was designed to collect the following information:

Overall evaluation of the course

Attitudes towards e-learning as well as previous experiences and expectations about the use of CBT/WBT

Computer and Internet usage

Extent of students' private computer infrastructure

Basic demographic data.

In the following, we will focus on students' computer usage and private computer infrastructure as well as their attitudes toward e-learning.

Attitudes towards e-learning (understood as an umbrella concept for learning methods supported by information- and communication technologies (ICT) in general) were determined by the students' agreement or disagreement with several statements about the importance of ICT in medical education. These statements contained items like "Web-based learning programs are able to replace lectures" or "In medical teaching, there is no need for the use of Web-based programs." The students rated their agreement or disagreement on a bi-polar eight-point Likert scale. For the purpose of comparability with Dørup [ 9 ], we recoded their answers into dichotomous variables. As computer use and attitudes towards e-learning were measured on an ordinal scale, we accordingly used Spearman rho to describe the statistical relationship of these variables with other items. For other metric variables Pearson r was used.

Computer infrastructure

Almost all students (94%) have access to a privately owned PC they can use for their studies, which is either owned by the students themselves (74%) or shared with family members or roommates (20%). Only 5% rely primarily on public computer facilities (Table 1 ).

Student-owned PCs are on average 2.3 years old; 92% are newer than 5 years, 87% newer than 4 years. This corresponds to the life span of computers in companies or public administration offices. Only 3.2% of the students have a computer older than 6 years. Male students' PCs (mean ± SD: 2 ± 1.42 years) are newer than those owned by women (2.5 ± 2.05 years). The 95% confidence interval for the difference is 0.33..0.79 years.

Internet access

The great majority of students also have access to the Internet, though the quality of connectivity varies widely; 60% have access via ADSL, cable TV, or LAN (which, however, usually signifies the use of public facilities at the university or elsewhere); 37% have access using a telephone connection (modem or ISDN) (Table 2 ). The type of Internet access differs according to gender (Cramer V = 0.28, p = 0.001). Male students tend to have faster Internet access while older technologies (e.g. modem) are more common among women. The proportion of modem users is twice as high among women (33%) than among men (15%).

Computer use

Types of computer use.

Students are familiar with e-mail and the use of the Internet for information research; 94% of the students communicate via e-mail and 97% use the Internet for information research at least several times per month. While the use of word processors is very common (82% use such a program several times a month), students are less familiar with other program types (Table 3 ).

Very few medical students have experience in Web design or the creation of HTML documents (5% at least weekly) and thus make no use of the Internet for publishing or more sophisticated collaboration purposes. The frequencies of using communication technologies other than e-mail, e.g., chats (21%), forums and bulletin boards (13%), are also low.

One noteworthy detail is the proportion of students who use computers for organizing appointments, to do lists, or making notes: 28% use such a personal organizer software several times per week, which may point to the use of personal digital assistants (PDA) or smart cell phones.

Except for the categories "Word Processor" and "E-mail," male students use the computer significantly more often than women. The strength of this statistical relationship is weak. Spearman rho is highest for the categories "Web-design" (r s = 0.25, p = 0.001), "Games" (r s = 0.23, p = 0.001), "Forums" (r s = 0.21, p = 0.001), and "Spreadsheets" (r s = 0.20, p = 0.001).

Age when using a computer for the first time

Half of all students (50%) used a computer for the first time by the age of 11 (mean 11.2 ± 3.77 SD). By the time they entered university, i.e., before the age of 18, fully 96% of all students had begun to use computers. The average age when students began using computers for the first time is slightly lower for men (10.7 ± 3.40 years) than women (11.5 ± 3.96 years). The 95% confidence interval for this difference is 0.33..1.24 years.

Prior experiences and expectations

Half of the students (49%) report using a computer or Web-based learning program at least once per month. In order to determine how many students have little or no experience with e-learning, we consolidated answers to questions about four different kinds of e-learning programs (information retrieval, downloading scripts, LMS, and CBT/WBT) into one index. Because of the high response rates for "downloading learning material," we defined inexperienced users as those who answered "less often" or "never" to questions about at least three of these kinds of programs. Following this typology, 12% of the students are inexperienced, having used at most one kind of e-learning program at least once per term (Table 4 ).

The majority of students (66%) have already used a computer or Web-based dictionary like the Pschyrembel medical dictionary, which is one of the standard references used by Vienna medical students. Half of them (50%) have used an online image repository at least once and 42% have used some kind of online quiz to test their knowledge (Table 5 ). Other kinds of learning programs, such as those associated with a constructivist approach, are less well known among first-year Vienna students. The results given in Tables 4 and 5 relating to students' use of LMS are inconsistent. This inconsistency arises most likely from the students' lack of understanding of what a LMS is since very few lecturers use this kind of software to support their courses.

About 10% of the students have never used any of the above-mentioned kinds of e-learning programs and 4.4% do not regard any of them as helpful. Those who regard only two or fewer as helpful tend to prefer learning programs that have no "built-in" educational theory, such as encyclopedias (38%), image collections (23%), and quizzes (23%). The number of different kinds of programs that students have experience with and that they consider helpful correlates with Pearson r = 0.32 (p = 0.001) – the more kinds of programs they know, the more kinds they consider useful.

A majority of the students agree (median = 2, interquartile range = 3) that CBT/WBT should be offered as a supplement to lectures and seminars (Figure 1 ). On the other hand, most students disagree with the statement that e-learning should replace these traditional forms of teaching (median = 7, IQR = 4).

Students' agreement or disagreement with statements on the usefulness of e-learning . The x-axis represents the values of an 8-point bi-polar rating scale: 1 = strong agreement, 8 = strong disagreement. The boxes show the quartiles (25% of the distribution) and the median (50% cut).

Men (median = 6) tend to be slightly more in favor of replacing traditional lectures with CBT/WBT than women (median = 7). The strength of this effect is negligible (r s = 0.06, p = 0.041). After recoding to a dichotomous scale (1..4 = pro, 5..8 = contra), 28% of male and 25% of female students can be considered favoring the replacement of traditional teaching methods with e-learning. The gender difference is slightly bigger for the item "Computer or Web-based training should play a more important role" but still hardly noteworthy (r s = 0.16, p = 0.001). In general, the following variables have bigger effects on e-learning-related attitudes than gender per se:

Lack of experience with CBT/WBT

Productive computer and Internet use (e.g. spreadsheets, organizer, word processor, graphics, e-mail, Web design, and information research).

We consolidated statements 2 to 4 in Figure 1 into one index (Cronbach alpha = 0.65; inclusion of the items 1 and 5 leads to a slight decrease in reliability). In a regression model (Table 6 ) that includes the above 3 variables and gender (R 2 adj = 0.15, p = 0.001, SEE = 1.54), gender is not statistically significant (p = 0.41). When the stepwise regression method is used, gender is excluded from the final model.

Computer infrastructure and internet access

A sizable number of students still have Internet access only via dial-up connections using a modem. This mode of Internet access is slow and impedes the use of synchronous communication tools that require one to stay online for a long period of time. Even if the majority of students do have broadband access to the Internet, mandatory e-learning solutions cannot rely on synchronous online communication tools like chats and on extensive video material, e.g. recordings from lectures. Instead, preference should be given to asynchronous online communication tools and textual information along with videos. Asynchronous communication tools also have the advantage that teachers and students do not have to be online at the same time.

Computer use patterns

Only a small number of students have experience with Internet publishing and asynchronous communication tools like BBS or forums. Thus, most of our students are rather passive Internet users and miss out on numerous possibilities of virtual communities and Web-based publishing. The lack of experience with synchronous and asynchronous online communication, with the exception of e-mail, may cause problems when using the collaboration tools included in an LMS [ 29 ].

Attitudes towards e-learning

Most students agree that e-learning could serve as a supplement for lectures and seminars. However, about as many students disagree with the statement that e-learning could replace traditional ways of teaching. In the Danish context, Dørup [ 9 ] reported a slightly greater proportion of first-year medical students in favor of replacing traditional lectures with e-learning (47% men, 22% women). These higher levels of agreement could be explained by the different response scales used but also by the fact that Danish people in general are reported [ 30 ] to be more "digital literate" than Austrians – although this difference cannot be claimed for persons under 24 years of age [ 30 ].

The intensity of computer use and previous experience with CBT/WBT have the greatest effect on students' attitudes towards e-learning. The explanation for this could be a general discomfort with the technology that makes students who lack experience with ICT express themselves cautiously about its use in education [ 31 ]. It could also be explained by the relative novelty of e-learning and students' difficulties in integrating CBT/WBT into their way of learning [ 32 ].

Most students seem to acknowledge the range of possibilities of new media to enhance their learning experience although they consider CBT/WBT a supplement to rather than a replacement of other learning materials. However, there is also a group of students who are strictly opposed to CBT/WBT (4.4% of the first-year students do not value any of the kinds of programs mentioned above). More disturbing, 24% strongly agree (values 1 and 2 on an 8-point rating scale) with the statement that the Medical University of Vienna could do well without CBT/WBT. When introducing an online LMS or Web-based learning program, special care should be taken not to lose these students because of the choice of a certain learning technology.

In December 2005, we also held a few focus groups with teachers and students on a similar subject. In the course of these discussions it became clear how some characteristics of the new curriculum, especially the emphasis on the MCQ-based year-end examinations, impeded the use of CBT/WBT. In these discussions the students had doubts about the usability and efficiency of e-learning (with regard to costs, handling of ICT, but also learning efficiency) while they still acknowledged the possibilities of ICT support with respect to visualization, simulation, self-quizzing, and fast information retrieval from several sources such as encyclopedias or Web pages.

Gender differences

We were able to identify gender differences for all computer-related variables. In sum, men make more frequent use of computers and have access to better computer infrastructure and faster Internet connections. While this difference is quite consistent over several variables, the strength of the statistical relationship is weak and, with respect to students' attitudes towards e-learning, overshadowed by other variables (e.g. previous exposure to CBT/WBT) that are more important for predicting students' attitudes.

With respect to the implementation of an LMS, the most important difference between men and women is the relatively high number of women still using a slow dial-up connection to the Internet, which could impede the use of synchronous communication tools or multimedia-rich Web applications. Well planned use of e-learning and supportive measures should help to neutralize this difference. Although women have less experience with forums, Gunn [ 19 ] showed that these differences in online communication behavior do not necessarily result in worse examination outcomes.

E-Learning must be appropriate to students' level of computer expertise in order not to become a source of frustration. Courses to develop students' computer skills can improve this situation by influencing students' attitudes and capabilities. Our conclusions with respect to such introductory courses are twofold. Students certainly need some kind of formal introduction to the new ICT for learning purposes. But due to the wide range of previous experience and computer skills, there is no one-size-fits-all course design available. Such a course should either be split into several tracks according to students' different levels of computer literacy [ 33 ], or it should be held only for students with little or no computer experience.

There is, however, the danger that precisely those students who need this course the most will hesitate to attend it voluntarily. It is difficult to say how these students could be persuaded to take such a course despite their skepticism towards ICT and e-learning. One strategy would be to emphasize the practical value for solving everyday problems and obtaining useful information. Once they have learned how computers help them solve recurring problems, they will perhaps develop more computer-friendly attitudes. Another solution could be to make the course compulsory but to make the impact negligible for students with good ICT knowledge. This could be achieved with a Web-based entry test. Students who pass the test would be exempted from having to take the course.

When introducing a campus-wide LMS, one has to take into consideration that some students lack the necessary computer skills or infrastructure to participate effectively in online courses, and that others are strictly opposed to e-learning. Introducing a campus-wide e-learning solution thus poses not only technical and organizational challenges but also calls for a promotional strategy. In the future, we can expect more students to think of computers as standard tools for learning as schools make more use ICT in their classrooms. For example, an "avant-garde" of Vienna medical students already created an online forum [ 34 – 36 ] for informally exchanging information about courses as well as students authored learning materials.

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Acknowledgements

We thank Thomas Benesch for statistical advice. We would also like to thank Jens Dørup, William Fulton, and Sean Marz for critically reading the manuscript and their helpful suggestions.

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RM and TML planned and organized courses [ 23 ] to promote computer literacy among medical students.

TML was responsible for designing the study, implementing the online questionnaire, analyzing the data, writing the first draft, and proofreading the final draft.

RM was responsible for designing the course content, recruiting and training the tutors and supervising all aspects of the course. He revised the article extensively.

Both authors read and approved the final version.

Thomas Michael Link and Richard Marz contributed equally to this work.

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Link, T.M., Marz, R. Computer literacy and attitudes towards e-learning among first year medical students. BMC Med Educ 6 , 34 (2006). https://doi.org/10.1186/1472-6920-6-34

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National Center for Education Statistics

New study on u.s. eighth-grade students’ computer literacy.

In the 21st-century global economy, computer literacy and skills are an important part of an education that prepares students to compete in the workplace. The results of a recent assessment show us how U.S. students compare to some of their international peers in the areas of computer information literacy and computational thinking.

In 2018, the U.S. participated for the first time in the International Computer and Information Literacy Study (ICILS), along with 13 other education systems around the globe. The ICILS is a computer-based international assessment of eighth-grade students that measures outcomes in two domains: computer and information literacy (CIL) [1] and computational thinking (CT). [2] It compares U.S. students’ skills and experiences using technology to those of students in other education systems and provides information on teachers’ experiences, school resources, and other factors that may influence students’ CIL and CT skills.

ICILS is sponsored by the International Association for the Evaluation of Educational Achievement (IEA) and is conducted in the United States by the National Center for Education Statistics (NCES).

The newly released U.S. Results from the 2018 International Computer and Information Literacy Study (ICILS) web report provides information on how U.S. students performed on the assessment compared with students in other education systems and describes students’ and teachers’ experiences with computers.

U.S. Students’ Performance

In 2018, U.S. eighth-grade students’ average score in CIL was higher than the average of participating education systems [3] (figure 1), while the U.S. average score in CT was not measurably different from the average of participating education systems.

¹ Met guidelines for sample participation rates only after replacement schools were included.

² National Defined Population covers 90 to 95 percent of National Target Population.

³ Did not meet the guidelines for a sample participation rate of 85 percent and not included in the international average.

⁴ Nearly met guidelines for sample participation rates after replacement schools were included.

⁵ Data collected at the beginning of the school year.

NOTE: The ICILS computer and information literacy (CIL) scale ranges from 100 to 700. The ICILS 2018 average is the average of all participating education systems meeting international technical standards, with each education system weighted equally. Education systems are ordered by their average CIL scores, from largest to smallest. Italics indicate the benchmarking participants.

SOURCE: International Association for the Evaluation of Educational Achievement (IEA), the International Computer and Information Literacy Study (ICILS), 2018.

Given the importance of students’ home environments in developing CIL and CT skills (Fraillon et al. 2019), students were asked about how many computers (desktop or laptop) they had at home. In the United States, eighth-grade students with two or more computers at home performed better in both CIL and CT than their U.S. peers with fewer computers (figure 2). This pattern was also observed in all participating countries and education systems.

Figure 2. Average computational thinking (CT) scores of eighth-grade students, by student-reported number of computers at home and education system: 2018

NOTE: The ICILS computational thinking (CT) scale ranges from 100 to 700. The number of computers at home includes desktop and laptop computers. Students with fewer than two computers include students reporting having “none” or “one” computer. Students with two or more computers include students reporting having “two” or “three or more” computers. The ICILS 2018 average is the average of all participating education systems meeting international technical standards, with each education system weighted equally. Education systems are ordered by their average scores of students with two or more computers at home, from largest to smallest. Italics indicate the benchmarking participants.

U.S. Students’ Technology Experiences

Among U.S. eighth-grade students, 72 percent reported using the Internet to do research in 2018, and 56 percent reported completing worksheets or exercises using information and communications technology (ICT) [4] every school day or at least once a week. Both of these percentages were higher than the respective ICILS averages (figure 3). The learning activities least frequently reported by U.S eighth-grade students were using coding software to complete assignments (15 percent) and making video or audio productions (13 percent).

Figure 3. Percentage of eighth-grade students who reported using information and communications technology (ICT) every school day or at least once a week, by activity: 2018

*  p  < .05. Significantly different from the U.S. estimate at the .05 level of statistical significance.

¹ Did not meet the guidelines for a sample participation rate of 85 percent and not included in the international average.

NOTE: The ICILS 2018 average is the average of all participating education systems meeting international technical standards, with each education system weighted equally. Activities are ordered by the percentages of U.S. students reporting using information and communications technology (ICT) for the activities, from largest to smallest.

Browse the full U.S. Results from the 2018 International Computer and Information Literacy Study (ICILS) web report to learn more about how U.S. students compare with their international peers in their computer literacy skills and experiences.

By Yan Wang, AIR, and Linda Hamilton, NCES

[1] CIL refers to “an individual's ability to use computers to investigate, create, and communicate in order to participate effectively at home, at school, in the workplace, and in society” (Fraillon et al. 2019).

[2] CT refers to “an individual’s ability to recognize aspects of real-world problems which are appropriate for computational formulation and to evaluate and develop algorithmic solutions to those problems so that the solutions could be operationalized with a computer” (Fraillon et al. 2019). CT was an optional component in 2018. Nine out of 14 ICILS countries participated in CT in 2018.

[3] U.S. results are not included in the ICILS international average because the U.S. school level response rate of 77 percent was below the international requirement for a participation rate of 85 percent.

[4]  Information and communications technology (ICT) can refer to desktop computers, notebook or laptop computers, netbook computers, tablet devices, or smartphones (except when being used for talking and texting).

Fraillon, J., Ainley, J., Schulz, W., Duckworth, D., and Friedman, T. (2019). IEA International Computer and Information Literacy Study 2018: Assessment Framework . Cham, Switzerland: Springer. Retrieved October 7, 2019, from https://link.springer.com/book/10.1007%2F978-3-030-19389-8 .

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• New Study on U.S. Eighth-Grade Students’ Computer Literacy In the 21st-century global economy, computer literacy and skills are an important part of an educati
• International Computer and Information Literacy Study: 2023 Data Collection In April, the National Center for Education Statistics (NCES) will kick off the 2023 International C
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Influence of computers in students’ academic achievement

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With fast-growing technology, schools have to adapt and use technology constantly as a tool to grow. This study aims to understand the influence of computer factors on students' academic achievement. We propose a model on the influence of computer attitudes, computer learning environments, computer learning motivations, computer confidence, computer use, computer self-efficacy, loneliness, mothers' education, parents' marital status and family size on academic achievement (AA). To validate the conceptual model, 286 students aged 16–18 years old answered an online questionnaire. The most important drivers that positively affect AA are computer use, employment motivations, and mothers' education. While enjoyment attitudes, school environment, interest motivations, and loneliness influence AA negatively. Also, family size and computer self-efficacy work as moderators, and computer use works as a mediator between computer learning environments and academic achievement.

Academic achievement; Computers; Family; Learning; Students.

1. Introduction

Countries are constantly facing everchanging economic challenges and social transformations due to globalisation and technology development. Education helps overcome these challenges by developing knowledge and high skills, allowing better opportunities and faster economic progression ( OECD, 2019 ). Computers and information technology have become key to educational institutions worldwide ( Hsu and Huang, 2006 ). With the advantages of the digital era through digital markets, advanced scientific and social networks, there is a growth in innovation, development and employment ( OECD, 2015 ). Education needs to adapt to social changes, students' needs, and technology growth ( OECD, 2019 ), the perfect example of this adaptation is during the recent pandemic. The COVID-19 pandemic (meaning "CO" - corona; "VI" – virus; "D" – disease; "19" - "2019″) started in December 2019 in Wuhan, a province of China. It is caused by a highly contagious virus that has already claimed millions of lives worldwide ( Roy et al., 2020 ). The virus forced schools to close, and since classes had to continue, teachers and students had to adapt, resorting to virtual classes ( Ng and Peggy, 2020 ). However, it impacted academic life in yet unknown dimensions ( Rajkumar, 2020 ).

Digital technology provides access to high-quality learning and consequently allows schools to develop their teaching and learning methods ( Ertmer et al., 2012 ). Nonetheless, access to computers at home or the internet is not equal in every dwelling, and some students have the disadvantage of not having parental support or engagement to learn by themselves online. For these reasons, the pandemic can bestow tremendous advantages in digital education and academic achievement or significant disadvantages, mostly in developing countries. Therefore, access to technology is not enough; fostering a close relationship between families and teachers is essential ( OECD, 2020 ). Technology has been an invaluable tool, and it is being taken under consideration in students' academic achievement, including not only in access to the internet but also the way students use it ( Levine and Donitsa-Schmidt, 1998 ; Torres-Díaz et al., 2016 ; Voogt et al., 2013 ). Schools are expected to have a particular concern regarding integrating computers in classroom teaching ( Schmid and Petko, 2019 ), and technical devices such as computers, laptops, tablets and mobile phones should be included wisely in adolescent education. Through the information gathered, this study was motivated mainly by the atual pandemic context and the important role technology has on the academic achievement.

Over the years, researchers have tried to identify the variables that contribute to academic excellence in an attempt to understand which factors lead to better students' performance ( Valli Jayanthi et al., 2014 ). A vast number of studies have been conducted to identify predictors of academic achievement ( Gonzalez-pienda et al., 2002 ; J. Lee, Shute and Lee, 2010 ; Suárez-álvarez et al., 2014 ) although few have studied computer influences on the prediction of students' academic achievement.

Since there is a need to extend innovations in education ( Admiraal et al., 2017 ), we identified a need to investigate how students' relationships with computers impact their academic performance to understand the real impact of computers on schooling. To the best of our knowledge, some studies address computers' impact on academic achievement, but the data available is not totally enlightening. With the actual context of the pandemic, this subject gains additional importance, comparing technology use and academic achievement (AA) in such a tumultuous time for the world. This study presents three contributions. Firstly , it identifies which the best computer-related determinants to understand AA are through a research model that combines computer-related variables to students' grades. In this way, we identify the factors that lead to better academic achievement, helping schools and parents use them as a strategic advantage. Secondly , it investigates the moderation effect of family size and computer self-efficacy and the mediation effect of computer use between the factors identified and AA. Finally , to understand how the COVID-19 pandemic is influencing students' AA, using the variable loneliness, we explore how forced social isolation affected the use of computers and students' academic achievement in the pandemic period.

A literature review is presented in the next section. Section 3 introduces a theoretical model explaining academic achievement. Section 4 elucidates on the data-collection methods, followed by the results in Section 5 . The results are discussed in Section 6 , and conclusions are outlined in the final section.

2. Literature review and hypotheses

2.1. computer attitudes.

Attitudes and perceptions play a pivotal role in learning behaviours. Some researchers tested a model based on the concept of the attitude-behaviour theory, which argues that beliefs lead to attitudes, and attitudes are an essential factor to predict behaviour ( Levine and Donitsa-Schmidt, 1998 ). They predicted that computer use leads to more computer confidence and positive attitudes towards computers, and these elements influence each other. The computer attitudes refer to the opinion of students about: the stereotypes of those who use the computer the most – stereotypes; the use of computers for education purposes – educational; and about the use of the computer for fun – enjoyment. In their view, student achievement is a reflection of their behaviour in school. Even with the change of technology over time, recent studies support their theory that positive computer attitudes and positive computer confidence continue to lead to better outcomes ( Lee et al., 2019 ). Stereotypes associated with computers are usually on gender, proving the idea that women have less computer knowledge than men ( Punter et al., 2017 ). However, there are no results on how other stereotypes, such as the lack of computer use by athletes', or even if the concept of people who use computers are considered nerds, negatively affects the confidence of those who use computers.

Regarding the attitudes of enjoyment and educational use of computers, there is no consensus in the literature. Some researchers found a positive association between students' academic achievement and computer use for interactive social media and video gaming, as well as for educational purposes ( Bowers and Berland, 2013 ; Tang and Patrick, 2018 ), although other researchers have found that students who play more videogames have worse results in school ( Bae and Wickrama, 2015 ), some previous studies suggest that the technology intervention has a positive effect on students' attitudes toward the use of computers for educational purposes ( Gibson et al., 2014 ). Others show concerns on the effects of technology and social media use on students' outcomes and confirm that students who have lower grades spend more time using computers for fun ( Bae and Wickrama, 2015 ; Tang and Patrick, 2018 ), others find no evidence that using computers for fun causes higher or lower achievement ( Hamiyet, 2015 ). Milani et al. (2019) demonstrated that using computers with moderate levels of video gaming may improve student achievement because it increases visual-spatial skills ( Milani et al., 2019 ) when complemented with educational use such as homework, extracurricular activities, and reading ( Bowers and Berland, 2013 ). Regarding the effect on computer confidence, we expect students to feel confident about using computers when using them for school ( Claro et al., 2012 ) and even more when using them for recreational purposes. Taking this background into account, we propose the following hypotheses.

Educational attitudes have a positive effect on computer confidence.

Educational attitudes have a positive effect on academic achievement.

Stereotype attitudes have a negative effect on computer confidence.

Enjoyment attitudes have a positive effect on computer confidence.

Enjoyment attitudes have a negative effect on academic achievement.

2.2. Learning environments and motivations

The environment where students learn can affect their attitudes ( Hsu and Huang, 2006 ). Studies have found that students achieve higher grades when they have a computer at home ( Fairlie, 2012 ; Fairlie et al., 2010 ) and use it daily to facilitate their school work ( Gu and Xu, 2019 ), suggesting that home computers improve educational outcomes and computer skills, leading to more efficient use of computers ( Fairlie and London, 2012 ). Many researchers pointed to a positive impact of computer use in schools on students' educational outcomes ( Bayrak and Bayram, 2010 ; Murillo-Zamorano et al., 2019 ; Xiao and Sun, 2021 ). The integration of computers in the classroom positively influences the interaction between students and increases learning and teaching ( Murillo-Zamorano et al., 2019 ). Experimental class manipulations using a computer in class were tested over the years, with positive results: students' academic achievement increases when a computer assists them in learning ( Bayrak and Bayram, 2010 ). However, most students show dissatisfaction with the learning environment of schools ( Hsu and Huang, 2006 ). So, we propose that home and school environments positively influence computer use in general and student achievement particularly, as hypothesised below.

Home environments have a positive effect on computer use.

Home environments have a positive effect on academic achievement.

Computer use mediates the effect of home environment on academic achievement

School environments have a positive effect on computer use.

School environments have a positive effect on academic achievement

Computer use mediates the effect of school environment on academic achievement

Regarding motivations, several types of motivations have already been studied to predict academic achievement, and the best predictor so far is associated with interest. If the student is interested, he will engage in the activity independently, and there is also evidence that interest motivations directly affect reading achievements ( Habók et al., 2020 ). When analysing students' motivations for using computers, studies show that using computers at school and for schoolwork results in higher motivation when studying and positively impacts academic achievement ( Partovi and Razavi, 2019 ). Likewise, when the students' perceptions of learning motivations are improved, there is an increasing computer use by the students and, as a result, it enhances their computer self-efficacy - perceived skill on the use ( Rohatgi et al., 2016 ) - indirectly ( Hsu and Huang, 2006 ). Therefore, in order to increase computer self-efficacy, students need to use computers more frequently. Previous results indicate that interest motivations positively affect computer use and computer self-efficacy, predicting that when student interests in computers are higher, student computer self-efficacy increases. Students are also motivated by employment and recognise that computer abilities can help them get a good job ( Hsu and Huang, 2006 ). This factor can be predicted by self-efficacy because it defines the confidence and ability on achieving success ( Serge et al., 2018 ). A study showed that learners who are more engaged and motivated use more technology for their learning purposes, most likely for individual learning than for collaborative tasks ( Lee et al., 2019 ). Regarding the use of technology, students who use it more are more motivated to do it and have better grades ( Higgins, Huscroft-D’Angelo and Crawford, 2019 ), and students who are motivated by attaining better grades tend to use e-learning more ( Dunn and Kennedy, 2019 ). In line with the literature, we expect the confirmation of the presented hypotheses.

Interest motivations have a positive effect on computer use.

Interest motivations have a positive effect on academic achievement.

Interest motivations have a positive effect on computer self-efficacy.

Employment motivations have a positive effect on computer self-efficacy.

Employment motivations have a positive effect on academic achievement.

2.3. Computer confidence, computer use & computer self-efficacy

Hands-on experience with technology is the most important factor in increasing students' confidence while using it and consequently increasing their perceived computer self-efficacy ( Hatlevik and Bjarnø, 2021 ). Students with access to a computer are more involved and interested in their classwork ( Gibson et al., 2014 ). Higher commitment to school, curiosity, and positivism can help students develop motivation and interest in school subjects, leading to higher self-efficacy and consequently better academic achievement ( Stajkovic et al., 2018 ).

Computer use has a positive effect on computer confidence.

Computer confidence has a positive effect on computer self-efficacy.

Computer confidence has a positive effect on academic achievement.

Computer use has a positive effect on academic achievement.

We know from previous literature that employment motivations positively influence academic achievement, and computer self-efficacy is also a significant influence factor on employment ( Serge et al., 2018 ) to explain academic achievement, so we believe that computer self-efficacy can moderate this relation by proposing H14 .

Computer self-efficacy moderates the effect of employment motivations on academic achievement.

2.4. Loneliness

Due to the coronavirus pandemic, schools were closed to slow down the virus transmission as a control measure, affecting half of the students globally ( Viner et al., 2020 ). Schools were forced to adapt during coronavirus outbreaks since campus classes were suspended, and online platforms have been exploited to conduct virtual classes ( Ng and Peggy, 2020 ). Ng and Peggy (2020) states that virtual classes can improve students' learning outcomes if all students are self-disciplined. However, self-isolation may affect people's mental health ( Roy et al., 2020 ), primarily impacting adolescents, influencing their behaviours and achievement in academic pursuits. Interaction with others is a pivotal factor for academic performance since students who engage with colleagues and teachers tend to have more academic success than those who study by themselves ( Torres-Díaz et al., 2016 ). Loneliness or social isolation is linked to anxiety and self-esteem ( Helm et al., 2020 ), leading to unhealthy smartphone use ( Shen and Wang, 2019 ) and sedentary behaviours ( Werneck et al., 2019 ), motivating us to posit the following.

Loneliness has a negative effect on academic achievement.

2.5. Family and students' factors

Technology use is linked to additional factors that influence adolescents' academic outcomes such as family socioeconomic factors – in particular, parents' occupation, marital status ( Abosede and Akintola, 2016 ; Asendorpf and Conner, 2012 ), parents' educational level ( Chesters and Daly, 2017 ) and family size - and student socio-emotional factors - such as relationship with colleagues, student motivation and anxiety ( Balogun et al., 2017 ). Family involvement and closeness to younger progeny have positive impacts on their achievements ( Fang, 2020 ), so we believe that the relation between using computers in a school environment on academic achievement, verified above, may change depending on the family size. Also, we know from the previous results that computer use has increased with the pandemic due to online classes, and family context has a significant impact on home computer use, so we predict a moderation effect on the relation between computer use and academic achievement. The psychological status of parents, mostly their marital status and economic status, has a powerful association with the family environment and consequently on their child's educational attainments ( Poon, 2020 ). We predict there is a positive impact of mothers' education on academic achievement since the maternal figure is the most relevant for children ( Abosede and Akintola, 2016 ). Expecting that the higher the level of education of mothers, the better the students result at school, also, we predict that parents being married have a positive influence on students' results, H15 and H16 .

Family size moderates the school environment on academic achievement.

Family size moderates computer use on academic achievement.

Parents marital status has a positive effect on academic achievement.

Mothers' education has a positive effect on academic achievement.

According to their age and gender, students' grades can differ independently of their family characteristics: female students tend to achieve higher scores than male students ( Valli Jayanthi et al., 2014 ) and older students showed lower grades compared to younger students ( Chowa et al., 2015 ). Some of these factors are not of primary interest for this study. Nevertheless, it is crucial to include them in the research to control for bias since they influence the association between the use of technology and adolescents' outcomes ( Tang and Patrick, 2018 ). We have therefore used age and gender as a control variable on our research model.

2.6. Conceptual model

Figure 1 illustrates our proposed model. We focus our research on computers and their influence on academic achievement. The drivers shown in the research model emerged from the literature above. We first gathered information and identified the main factors that influence academic achievement through computer use, and from the most significant constructs relating to computers and academic achievement, we examined and analysed their viability on the study. From the computers' context, the most significant constructs found were computer attitudes (educational attitudes, enjoyable attitudes, stereotypes attitudes), computer use, computer confidence ( Levine and Donitsa-Schmidt, 1998 ), computer self-efficacy, learning environments (home environment, school environment) and learning motivations (interest motivations, employment motivations) ( Hsu and Huang, 2006 ). We identified loneliness as the most relevant construct from the pandemic context considering its impact on academic achievement ( Helm et al., 2020 ). We identified mothers' education, marital status, and family size as the most relevant influencers from the family context. Finally, with our central construct, academic achievement, we are trying to understand how it is impacted by computers, the pandemic and family factors from students' points of view. So, the proposed model tries to predict AA through students' computer attitudes, learning environments, learning motivations, computer confidence, computer use, computer self-efficacy and loneliness, adding sociodemographic data related to students and their families - parents' marital status, mothers' education and family size, where the latter only works as a moderator, including two additional control variables, age and gender. This model integrates several constructs on the literature relevant to the study of computers influence on academic achievement since is essential to fortify and unify the knowledge in this investigation field. As explained above, the model merges two existing models ( Hsu and Huang, 2006 ; Levine and Donitsa-Schmidt, 1998 ), allowing us to update the previous results and test new hypothesis. Additionally, the integration of the covid pandemic context brings a different and important analysis of today's reality.

Conceptual model.

3.1. Participants and procedure

For this study, we developed a questionnaire for students enrolled in public high schools. The survey, with an estimated completion time of 8 min was sent by e-mail to several schools in Portugal to achieve more diversity within the collected answers. The participants consented to the use of their information as long as it was anonymous and confidential. The questionnaire was answered online and comprised 26 closed questions (please, see Appendix A ) inquiring about computer attitudes, motivations, use at home and school, frequency of use, students' grade average from 0 to 20 marks, and sociodemographic information. With this data, we can compare and analyse the impact of their type of use and opinion about computers on their achievement in school. The study's target population were 16 to 18-year-old adolescents in the 10 th , 11 th and 12 th grades at secondary schools. This range of students allowed us to surround a group of people with similar maturity and identical needs in digital use. We chose to study public school students because teaching methods in private schools are quite different, as are the type of students and families who choose private schools. Also, most students in Portugal study at public schools, and it seems more coherent to study only public education since it is more accessible to address. According to the Ethics Committee of NOVA IMS and MagIC Research Center regulations, this project was considered to meet the requirements, being considered approved.

A pilot test with 30 answers allowed us to comprehend the viability of some survey questions and their order, and afterwards, when evaluating the model, the strength of constructs led us to drop a few items due to the lack of importance and correlations within them. The pilot test allowed us to improve the questionnaire to facilitate answering and adapt the research model initially built. After the complete collection of data, we considered only student responses 100% completed, amounting to 286 valid responses, from a total of 465 answers. We had 98 boys and 188 girls among the respondents, with an average age of 17 years old, with an average global grade of 15 points (on a scale from 0 to 20). Students' academic achievement was measured through students' average grades - on reading, mathematics and global average grade. Computer use was measured through a scale range from 1 (never) to 5 (every day) to measure the frequency of use. A 3-item loneliness scale was used to assess the loneliness construct ( Hughes et al., 2004 ) based on the UCLA Loneliness Scale ( Russel, 1996 ). This scale has been used in several studies recently ( Helm et al., 2020 ; Liu et al., 2020 ; Shen and Wang, 2019 ) to study loneliness as a consequence of the coronavirus. The remaining items, apart from the demographic variables (age, gender, marital status, mothers' education, family size), were measured through a scale range from 1 (strongly disagree) to 5 (strongly agree).

4. Analysis and results

We used structural equation modelling (SEM) to test the relations estimated in our theoretical model and its effects ( Marsh et al., 2004 ). Consequently, we applied partial least squares (PLS), a method used to develop theories in explanatory research. The use of PLS-SEM is to maximise the explained variance in the dependent constructs and evaluate data quality, knowing that it is a method that works better on bigger sample sizes and larger complexity with less restrictive assumptions on data (Joe F Hair et al., 2014 ). We used the partial least squares method as the recommended two-step approach that first tests the reliability and validity of the measurement model and then assesses the structural model ( Anderson and Gerbing, 1988 ).

4.1. Measurement model

Measurement models measure the relation between the latent variables and their indicators for both reflective and formative constructs. In this study, all constructs are reflective except computer use, which is formative.

The internal consistency, convergent validity and discriminatory validity must be verified to assess the reflective measurement model. The composite reliability (CR), shown in Appendix B, is higher than 0.7 in all constructs, reflecting internal consistency ( Mcintosh et al., 2014 ). Also, by analysing the loadings of the items, which are all higher than 0,6, we can conclude there is indicator reliability. To demonstrate convergent validity, we verify the average variance extracted (AVE) values of constructs, and they are all higher than 0.5 (please see Appendix B), confirming there is convergent validity ( Sarstedt et al., 2017 ). To analyse discriminant validity, we implemented three methods - the Fornell-Larcker criterion, the loadings and cross-loadings analysis, and the heterotrait-monotrait ratio (HTMT) methodology. The Fornell-Larcker criterion supports that the AVE square root of each construct should be higher than the correlation between constructs ( Fornell and Larcker, 1981 ), which Appendix B can confirm. The second criteria support that the loadings should be higher than the respective cross-loadings (Joseph F Hair et al., 2014 ), which is observed in Appendix C. The HTMT method sustains that the HTMT values should be lower than 0.9 (Joseph F Hair et al., 2017 ; Sarstedt et al., 2017 ), confirmed by Appendix D. Thus, all the constructs have discriminant validity.

In order to assess the validity of the formative construct computer use, we assessed the model for multicollinearity using (variance inflation factor) VIF. Table 1 shows the VIF values are all under 5 (Joseph F Hair et al., 2017 ), as the threshold indicates it should be, so the model does not have multicollinearity problems. In terms of significance, the three items are statistically significant (p < 0.05), as Table 1 confirms, concluding that the formative construct is reliable.

Table 1

Formative measurement model evaluation.

Note: ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001.

We can conclude that both reflective and formative constructs present a good measurement model. For this reason, we can move to the structural model.

4.2. Structural model

To estimate the structural model, first, we assessed the VIF to check the model for multicollinearity issues. The VIF values are below the threshold of 5 ( Sarstedt et al., 2017 ), so the model does not have multicollinearity problems. To evaluate the statistical significance of the path coefficients, we did a bootstrap with 5000 resamples. Results from the model are presented in Figure 2 .

Conceptual model results.

The model explains 30.5% of computer confidence. Educational attitudes (β = 0.307, p < 0.001), stereotype attitudes (β = - 0.160, p < 0.01), enjoyment attitudes (β = 0.236, p < 0.001) and computer use (β = 0.136, p < 0.05) are statistically significant in explaining computer confidence, confirming hypotheses H1 a, H2 , H3 a and H8 . The explained variation of computer use is 42,5%. The results show that home environment (β = 0.421, p < 0.001), school environment (β = 0.317, p < 0.05) and interest motivations (β = 0.124, p < 0.05) are statistically significant and have a positive influence on computer use, thus hypotheses H4 a, H5 a and H6 a are supported. The model explains 35.8% of computer self-efficacy. The home environment construct (β = 0.200, p < 0.01), interest motivations (β = - 0.156, p < 0.05), and employment motivations (β = 0.217, p < 0.01) are statistically significant however, home environment and employment motivation show a positive influence on computer self-efficacy, supporting hypotheses H4 c, H7 a and interest motivations show a negative influence on computer self-efficacy where we expected a positive influence, rejecting H6 c.

The model explains 31.1% of students' academic achievement. Enjoyment attitudes (β = - 0.162, p < 0.05), employment motivations (β = 0.183, p < 0.05), computer use (β = 0.257, p < 0.05), loneliness (β = - 0.150, p < 0.05) and mother's education (β = 0.135, p < 0.05) are statistically significant in explaining academic achievement, supporting the hypotheses, H3 b, H7 b, H11 , H13 and H16 . We reject respective hypotheses H5 b and H6 b respectively, despite school environment (β = - 0.246, p < 0.001) and interest motivations (β = - 0.159, p < 0.05), being statistically significant, because we suggested that school environment and interest motivations would positively influence academic achievement, and the results observe a negative influence. Educational attitudes (β = -0.003, p > 0.05), home environment (β = 0.100, p > 0.05), computer confidence (0.105, p > 0.05) and parental marital status (β = 0.067, p > 0.05) show a non-significant effect on explaining academic achievement, rejecting H1 b, H4 b, H10 and H15 . The moderation effect of computer self-efficacy in employment motivations (β = 0.108, p < 0.05) is statistically significant, supporting H12 . The moderation effect of family size on school environment (β = 0.141, p < 0.05) and on computer use (β = - 0.233, p < 0.01) is statistically significant, supporting H14 a and H14 b.

Table 2 summarises the research hypotheses results. We can conclude that 17 of the 25 proposed hypotheses were supported.

Table 2

Research hypotheses results.

Notes: n.a. - not applicable; ∗ significant at p < 0.05; ∗∗ significant at p < 0.01; ∗∗∗ significant at p < 0.001.

5. Discussion

This research model contributes to and extends the literature review on computers and academic achievement. This study relates academic achievement with loneliness, family and computer-related variables such as computer confidence, computer self-efficacy, computer attitudes, computer learning motivations and computer learning environments.

The results show that educational and enjoyment computer attitudes positively influence computer confidence, while stereotype attitudes negatively influence it. We expected this negative relation regarding stereotypes since there are the same results regarding stereotypes on gender and age ( Punter et al., 2017 ), although similar results concerning stereotypes on computer users have not yet been found. As for the influence of attitudes on academic achievement, educational computer attitudes do not have a statistically significant relationship with academic achievement. On the other hand, enjoyable computer attitudes have a significant negative impact on academic achievement, which leads us to conclude that there is no relation between computers as an educational tool and academic achievement. In fact, apart from some specific high school vocational courses oriented to computing skills, most classes happen in a classic lecture setting and rely mostly on textbook manuals as learning tools, which can help explain the results regarding educational computer attitude. However, using computers for recreational purposes negatively influences students' academic achievement, as similar results have already been observed - students who play more video games have a lower achievement ( Tang and Patrick, 2018 ). Two possible reasons can explain this phenomenon. First, because young adults are so engaged and skilled with technology use for game playing and social media that they do not make the best use of these skills for academic purposes, for instance ( Gurung and Rutledge, 2014 ) and second, because excessive use and multitasking can lead to distractions and lack of time to study ( Rashid and Asghar, 2016 ).

The construct computer use, measured as the frequency of use, positively impacts computer confidence and academic achievement. Thus, the greater the use of computers, the more confident students are while using them, and so the more use of the computer, the better the performance achieved. Several other studies contradict the negative influence verified between school environment and academic achievement ( Bayrak and Bayram, 2010 ; Carle et al., 2009 ; Murillo-Zamorano et al., 2019 ). However, this can be explained by the rapid development of computer technology and the massive use of computers at home compared to the lack of use at school due to schools' technology being obsolete, and students preferring the home environment.

The results demonstrate that computer use works as a full mediator for home environment and academic achievement since there is no relation between home environment and academic achievement, contrary to another study ( Fairlie et al., 2010 ). However, with computer use as a mediator, we suggest that the home environment influences academic achievement when computer use increases since there is a positive relation between home environment and computer use ( Hsu and Huang, 2006 ), i.e., students who use a computer at home have better results. Also, computer use works as a partial mediator for the school environment and academic achievement. Hence, we suggest that, although the use of computers at school already directly (but negatively) influences students' performance, computer use mediates this relation positively. This effect is likely due to the fact that even though there is an effort to implement digital transformation in the education sector, there is still a lack of computers at schools: most students do not have easy access to computers in school (high schools in Portugal have an average 4.2 students per computer), but those who use them benefit on their grades. These results allow us to confirm our second contribution, the investigation of the mediation effect of computer use between the factors identified and academic achievement. The mediation results are shown in Table 3 .

Table 3

Hypotheses testing on mediation.

Note: ∗ |t|> 1.96 and p-value = 0.05.; ∗∗ |t| > 2.57 and p-value = 0.01; ∗∗∗ |t| > 3.291 and p-value = 0.001.

Regarding motivations, interest motivation impacts computer use positively, as concluded by other similar findings ( Rohatgi et al., 2016 ), i.e. the more interested students are in computers, the more they use them. Nonetheless, it negatively influences academic achievement and computer self-efficacy, concluding that the bigger the interest motivation, the more the use of computers but the lower the achievement and the computer self-efficacy. These two negative relations are quite controversial compared to the literature. However, it may mean that the more interest in computers, the more use for recreational purposes, negatively impacting academic achievement ( Rashid and Asghar, 2016 ). The more interest students have in computers, the more knowledge of using the devices, and the perceived efficacy starts to decrease. Thus further research is needed to draw any conclusions on this.

Computer confidence has a strong positive effect on computer self-efficacy, meaning that the perceived computer self-efficacy increases when the confidence in the device is higher, as stated in similar findings ( Hatlevik and Bjarnø, 2021 ). Although, we cannot conclude there is a relation between computer confidence and academic achievement. All the previous results allow us to reflect on the influence that the computer-related variables studied have on the student performance, contributing with data for future research and confirming our first contribution of the study.

The loneliness construct, used as a measure of coronavirus effects, negatively influenced academic achievement, as expected. While students were in lockdown having remote classes, without any presential contact with their school, teachers, and colleagues, the feeling of loneliness and isolation negatively impacted their performance indeed, as observed in our results. These results confirm our contribution to understanding how the COVID-19 pandemic influences students’ academic achievement. Recent studies found negative impacts of loneliness ( Roy et al., 2020 ) on students, demonstrating the importance of cooperating with colleagues ( Torres-Díaz et al., 2016 ). However, there are yet no results of the direct impact of loneliness deriving from the pandemic on academic achievement.

There are three moderation hypotheses using family size and computer self-efficacy. From the family size moderator, we can conclude that family size influences the relation between school environment and academic achievement. In Figure 3 , we can see that when the family size decreases, the negative impact the school environment has on academic achievement increases, suggesting that the smaller the family, the students tend to have worse grades when studying in a school environment. Regarding family size in the relation between computer use and academic achievement, shown in Figure 4 , when the family size decreases, computer use is more important to explain academic achievement because when the family is small, students need to use the computer more to achieve better results. Relating to the computer self-efficacy moderator, in Figure 5 , it impacts the relationship between employment motivations and academic achievement positively, meaning that the better students perceive their computer self-efficacy, the stronger positive impact employment motivation has on academic achievement. This effect can be explained due to the increase of technological jobs: students who feel more capable in their computer skills (with a higher computer self-efficacy) and are more motivated to pursue a technological career have higher academic achievement. These results allow us to confirm our second contribution, the investigation of the moderation effect family size and computer self-efficacy.

Structural model (variance-based technique) for academic achievement.

In this study, we found that marital status does not have any effect on academic achievement, but mothers' education has a positive impact on students' achievement, reinforcing the literature ( Abosede and Akintola, 2016 ).

5.1. Practical implications

Academic achievement is a widely topic studied because there is an ongoing concern for understanding the factors that lead to better academic achievements. Since students practically depend on computers for school nowadays, we tried to relate the most studied computer variables in the literature with academic achievement, expecting results that answer the gaps identified in the literature. To our knowledge, no study has yet provided a conclusion on the influence of loneliness provoked by the COVID-19 pandemic on academic achievement, neither of interest and employment motivations on AA. Moreover, there is no consensus in the literature on the influence of the use of computers for fun and academic performance. We can contribute to the literature with the answers to these questions: students who feel lonely have worse academic achievement, students motivated by an interest in computers have worse academic achievement and students motivated by the expectation of having a good job have better grades. Also, enjoyable computer attitudes negatively influence academic achievement, so the students who find the computer a good tool for recreational purposes have worse grades.

Contrary to the literature, we found that computer confidence does not influence academic achievement; apart from this, we concur with the available results published by other researchers. There are clear positive implications on using computers in education, and consequently, in students' outcomes. Therefore, teachers and parents should encourage using computers in adolescents' education to improve their school performance and future.

5.2. Limitations and further research

The present study has some limitations that point to future research directions on the role of students' academic achievement and its predictors. First, the data collected does not have sufficient diversity in country dispersity and gender balance since most participants were girls hailing from Portugal. Also, better results can be obtained with a more significant sample. Secondly, the fact that we are going through a pandemic forced schools and students to attend classes online, which on the one hand, is an advantage because it provides the opportunity to study loneliness deriving from the pandemic. On the other hand, it could bias the students' answers to the questionnaire and the subsequent results because their opinion on computers could have changed during home-schooling compared to the usual previous schooling method since the literature is related to regular presential school attendance.

In further research, other factors regarding loneliness should be studied to understand the impact of coronavirus on students' lives better, comparing pre-pandemic and pandemic daily computer usage. Other factors such as addiction to technology should be analysed.

6. Conclusions

This study proposes a theoretical model on the influence of several computer factors on the academic achievement of high school students. The results, in general, empirically support the literature in similar findings. The proposed conceptual model explains 31.1% of academic achievement. We found that students who use computers for recreational purposes or feel that a computer is a tool to "pass the time" or play games are those who have the worst grades. We can conclude this through the negative relation between enjoyment attitudes and academic achievement. Nevertheless, there is no relation between students who perceive computers as an educational tool and their academic achievement. We believe this conclusion results from how teenagers use their computers and smartphones excessively, not prioritising the use for school, leading to the observed results. Our results also show that there are still stereotypes about who uses computers most. Respondents believe that peers who play sports do not have the same likelihood of using computers excessively, and those that frequently use computers are not sociable. This mindset leads to less confidence in computers.

A significant conclusion was found regarding the computer use environment, though the mediation effect of computer use. When students use the computer at home, they need to use it frequently to influence their academic achievement, but when students use the computer at school, it will influence their academic achievement positively independently of the frequency of use. However, the frequency of computer use itself influences academic achievement. As we expected, the feelings of loneliness associated with the coronavirus negatively influence students' academic achievement, an important new conclusion in the literature. The moderation effect on family size allows us to conclude that students with a smaller family tend to have worse grades when studying in a school environment and need to use computers more to have better school results than those in larger families. Moreover, the moderation effect on computer self-efficacy lets us conclude that students who perceive better computer self-efficacy, have better grades and academic achievement is influenced by employment motivation.

Declarations

Author contribution statement.

Sofia Simões: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.

Tiago Oliveira: Conceived and designed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.

Catarina Nunes Analyzed and interpreted the data; Wrote the paper.

Funding statement

This work was supported by FCT (Fundação para a Ciência e a Tecnologia) under project DSAIPA/DS/0032/2018 (DS4AA).

Data availability statement

Declaration of interests statement.

The authors declare no conflict of interest.

Additional information

No additional information is available for this paper.

Appendix A. Constructs table

Notes: 1, 2, 3, 4, 5, 6, 7, 9, 10 Range scale from 1 (Strongly Disagree) to 5 (Strongly Agree); 8 Range scale from 1 (Never) to 5 (Everyday); 11 Ordinal Scale (Hardly ever, some of the time, often); 12 Ratio scale from 0 to 20 (number); 13 Nominal scale (number); 14 Nominal scale (married, divorced, in a domestic partnership, widowed, other); 15 Ordinal scale (less than high school, high school or equivalent, bachelor's degree, master's degree, doctorate, other); 16 Ratio scale (number); 17 Nominal scale (male, female).

Appendix B. Descriptive statistics, correlation, composite reliability (CR), and average variance extracted (AVE)

Note: Values in diagonal (bold) are the AVE square root.

Appendix C. Outer Loadings and Cross-Loadings

Appendix d. heterotrait-monotrait ratio (htmt).

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Why writing by hand beats typing for thinking and learning

Jonathan Lambert

If you're like many digitally savvy Americans, it has likely been a while since you've spent much time writing by hand.

The laborious process of tracing out our thoughts, letter by letter, on the page is becoming a relic of the past in our screen-dominated world, where text messages and thumb-typed grocery lists have replaced handwritten letters and sticky notes. Electronic keyboards offer obvious efficiency benefits that have undoubtedly boosted our productivity — imagine having to write all your emails longhand.

To keep up, many schools are introducing computers as early as preschool, meaning some kids may learn the basics of typing before writing by hand.

But giving up this slower, more tactile way of expressing ourselves may come at a significant cost, according to a growing body of research that's uncovering the surprising cognitive benefits of taking pen to paper, or even stylus to iPad — for both children and adults.

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In kids, studies show that tracing out ABCs, as opposed to typing them, leads to better and longer-lasting recognition and understanding of letters. Writing by hand also improves memory and recall of words, laying down the foundations of literacy and learning. In adults, taking notes by hand during a lecture, instead of typing, can lead to better conceptual understanding of material.

"There's actually some very important things going on during the embodied experience of writing by hand," says Ramesh Balasubramaniam , a neuroscientist at the University of California, Merced. "It has important cognitive benefits."

While those benefits have long been recognized by some (for instance, many authors, including Jennifer Egan and Neil Gaiman , draft their stories by hand to stoke creativity), scientists have only recently started investigating why writing by hand has these effects.

A slew of recent brain imaging research suggests handwriting's power stems from the relative complexity of the process and how it forces different brain systems to work together to reproduce the shapes of letters in our heads onto the page.

Your brain on handwriting

Both handwriting and typing involve moving our hands and fingers to create words on a page. But handwriting, it turns out, requires a lot more fine-tuned coordination between the motor and visual systems. This seems to more deeply engage the brain in ways that support learning.

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"Handwriting is probably among the most complex motor skills that the brain is capable of," says Marieke Longcamp , a cognitive neuroscientist at Aix-Marseille Université.

Gripping a pen nimbly enough to write is a complicated task, as it requires your brain to continuously monitor the pressure that each finger exerts on the pen. Then, your motor system has to delicately modify that pressure to re-create each letter of the words in your head on the page.

"Your fingers have to each do something different to produce a recognizable letter," says Sophia Vinci-Booher , an educational neuroscientist at Vanderbilt University. Adding to the complexity, your visual system must continuously process that letter as it's formed. With each stroke, your brain compares the unfolding script with mental models of the letters and words, making adjustments to fingers in real time to create the letters' shapes, says Vinci-Booher.

That's not true for typing.

To type "tap" your fingers don't have to trace out the form of the letters — they just make three relatively simple and uniform movements. In comparison, it takes a lot more brainpower, as well as cross-talk between brain areas, to write than type.

Recent brain imaging studies bolster this idea. A study published in January found that when students write by hand, brain areas involved in motor and visual information processing " sync up " with areas crucial to memory formation, firing at frequencies associated with learning.

"We don't see that [synchronized activity] in typewriting at all," says Audrey van der Meer , a psychologist and study co-author at the Norwegian University of Science and Technology. She suggests that writing by hand is a neurobiologically richer process and that this richness may confer some cognitive benefits.

Other experts agree. "There seems to be something fundamental about engaging your body to produce these shapes," says Robert Wiley , a cognitive psychologist at the University of North Carolina, Greensboro. "It lets you make associations between your body and what you're seeing and hearing," he says, which might give the mind more footholds for accessing a given concept or idea.

Those extra footholds are especially important for learning in kids, but they may give adults a leg up too. Wiley and others worry that ditching handwriting for typing could have serious consequences for how we all learn and think.

What might be lost as handwriting wanes

The clearest consequence of screens and keyboards replacing pen and paper might be on kids' ability to learn the building blocks of literacy — letters.

"Letter recognition in early childhood is actually one of the best predictors of later reading and math attainment," says Vinci-Booher. Her work suggests the process of learning to write letters by hand is crucial for learning to read them.

"When kids write letters, they're just messy," she says. As kids practice writing "A," each iteration is different, and that variability helps solidify their conceptual understanding of the letter.

Research suggests kids learn to recognize letters better when seeing variable handwritten examples, compared with uniform typed examples.

This helps develop areas of the brain used during reading in older children and adults, Vinci-Booher found.

"This could be one of the ways that early experiences actually translate to long-term life outcomes," she says. "These visually demanding, fine motor actions bake in neural communication patterns that are really important for learning later on."

Ditching handwriting instruction could mean that those skills don't get developed as well, which could impair kids' ability to learn down the road.

"If young children are not receiving any handwriting training, which is very good brain stimulation, then their brains simply won't reach their full potential," says van der Meer. "It's scary to think of the potential consequences."

Many states are trying to avoid these risks by mandating cursive instruction. This year, California started requiring elementary school students to learn cursive , and similar bills are moving through state legislatures in several states, including Indiana, Kentucky, South Carolina and Wisconsin. (So far, evidence suggests that it's the writing by hand that matters, not whether it's print or cursive.)

Slowing down and processing information

For adults, one of the main benefits of writing by hand is that it simply forces us to slow down.

During a meeting or lecture, it's possible to type what you're hearing verbatim. But often, "you're not actually processing that information — you're just typing in the blind," says van der Meer. "If you take notes by hand, you can't write everything down," she says.

The relative slowness of the medium forces you to process the information, writing key words or phrases and using drawing or arrows to work through ideas, she says. "You make the information your own," she says, which helps it stick in the brain.

Such connections and integration are still possible when typing, but they need to be made more intentionally. And sometimes, efficiency wins out. "When you're writing a long essay, it's obviously much more practical to use a keyboard," says van der Meer.

Still, given our long history of using our hands to mark meaning in the world, some scientists worry about the more diffuse consequences of offloading our thinking to computers.

"We're foisting a lot of our knowledge, extending our cognition, to other devices, so it's only natural that we've started using these other agents to do our writing for us," says Balasubramaniam.

It's possible that this might free up our minds to do other kinds of hard thinking, he says. Or we might be sacrificing a fundamental process that's crucial for the kinds of immersive cognitive experiences that enable us to learn and think at our full potential.

Balasubramaniam stresses, however, that we don't have to ditch digital tools to harness the power of handwriting. So far, research suggests that scribbling with a stylus on a screen activates the same brain pathways as etching ink on paper. It's the movement that counts, he says, not its final form.

Jonathan Lambert is a Washington, D.C.-based freelance journalist who covers science, health and policy.

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