research paper on tablet computers

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Research data collection methods: from paper to tablet computers

Affiliation.

1 Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA. [email protected]
PMID: 22692261
DOI: 10.1097/MLR.0b013e318259c1e7

Background: Primary data collection is a critical activity in clinical research. Even with significant advances in technical capabilities, clear benefits of use, and even user preferences for using electronic systems for collecting primary data, paper-based data collection is still common in clinical research settings. However, with recent developments in both clinical research and tablet computer technology, the comparative advantages and disadvantages of data collection methods should be determined.

Objective: To describe case studies using multiple methods of data collection, including next-generation tablets, and consider their various advantages and disadvantages.

Materials and methods: We reviewed 5 modern case studies using primary data collection, using methods ranging from paper to next-generation tablet computers. We performed semistructured telephone interviews with each project, which considered factors relevant to data collection. We address specific issues with workflow, implementation and security for these different methods, and identify differences in implementation that led to different technology considerations for each case study.

Results and discussion: There remain multiple methods for primary data collection, each with its own strengths and weaknesses. Two recent methods are electronic health record templates and next-generation tablet computers. Electronic health record templates can link data directly to medical records, but are notably difficult to use. Current tablet computers are substantially different from previous technologies with regard to user familiarity and software cost. The use of cloud-based storage for tablet computers, however, creates a specific challenge for clinical research that must be considered but can be overcome.

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Commentary: Clinical informatics. E Frisse M. E Frisse M. Med Care. 2012 Jul;50 Suppl:S36-7. doi: 10.1097/MLR.0b013e318257ddeb. Med Care. 2012. PMID: 22692257 No abstract available.

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Research Data Collection Methods

From paper to tablet computers.

Wilcox, Adam B. PhD * ; Gallagher, Kathleen D. MPH * ; Boden-Albala, Bernadette DrPH † ; Bakken, Suzanne R. RN, DNSc *

Departments of * Biomedical Informatics

† Neurology, Columbia University, New York, NY

Supported by a contract from AcademyHealth. Additional funding was provided by R01 HS019853 from the Agency for Healthcare Research and Quality; Title: Washington Heights/Inwood Informatics Infrastructure for Community-Centered Comparative Effectiveness Research (WICER).

The authors declare no conflict of interest.

Reprints: Adam B. Wilcox, PhD, Department of Biomedical Informatics, Columbia University Medical Center, 622 W. 168th Street, VC-5, New York, NY 10032. E-mail: [email protected] .

Background:

Primary data collection is a critical activity in clinical research. Even with significant advances in technical capabilities, clear benefits of use, and even user preferences for using electronic systems for collecting primary data, paper-based data collection is still common in clinical research settings. However, with recent developments in both clinical research and tablet computer technology, the comparative advantages and disadvantages of data collection methods should be determined.

Objective:

To describe case studies using multiple methods of data collection, including next-generation tablets, and consider their various advantages and disadvantages.

Materials and Methods:

We reviewed 5 modern case studies using primary data collection, using methods ranging from paper to next-generation tablet computers. We performed semistructured telephone interviews with each project, which considered factors relevant to data collection. We address specific issues with workflow, implementation and security for these different methods, and identify differences in implementation that led to different technology considerations for each case study.

Results and Discussion:

There remain multiple methods for primary data collection, each with its own strengths and weaknesses. Two recent methods are electronic health record templates and next-generation tablet computers. Electronic health record templates can link data directly to medical records, but are notably difficult to use. Current tablet computers are substantially different from previous technologies with regard to user familiarity and software cost. The use of cloud-based storage for tablet computers, however, creates a specific challenge for clinical research that must be considered but can be overcome.

Primary data collection is a principal component of the clinical research process. Unless analysis is performed only through secondary use of existing data, at some point all clinical research studies will require primary data collection. For years, researchers have sought and used electronic data systems for primary data as electronic systems have demonstrated benefits over paper-based methods. These benefits have been most clearly shown in the storage and management of data. 1–3 Sufficient benefits in data collection have lagged because data collection involves a specific interactive workflow where electronic devices can become cumbersome. However, recent changes in consumer electronic devices, both in functionality and portability, have increased the potential utility of mobile technologies for research data collection. 2,4,5 In this paper, we discuss these changes and their potential impact on the clinical research process, including specific case studies highlighting their use.

BACKGROUND AND SIGNIFICANCE

Data collection is critical to clinical research, and often is a prominent factor in determining the cost and success of a research project. How data are collected has a sizeable impact on how data are managed, and ultimately how the research is performed. Many technologies exist for data collection, ranging from simple paper forms to portable electronic devices. As yet, no data collection method is perfect and each has its own benefits, costs, and risks. A challenge for researchers is matching the capabilities of the different data collection methods to the data collection priorities of the research project.

Shapiro et al 6 discussed issues related to research data collection, and identified factors to consider regarding different data collection methods. 6 They addressed these factors with both paper-based and computer-based data collection methods. Other researchers have also compared paper-based data collection with computer-based approaches; a literature review of controlled trials comparing handheld computers with paper methods found improvements in storage, management, and collection of data, and computers were preferred by users. 1 Although the use of computers for clinical research is increasing, paper-based methods still remain common for clinical research data collection, because of remaining advantages of paper over computer-based approaches. 6,7 Paper is especially useful because of its simplicity, with low initial costs for implementation, expertise, support, equipment, and training. In certain implementations and settings, these benefits can far outweigh the advantages of immediate feedback and monitoring, incorporated logic, and decreased duplicate documentation inherent in computer-based approaches.

Recent developments have made considerations of electronic data collection tools for clinical research more compelling. First, the value of electronic data collection is increasing. Prospective research studies leveraging data from electronic health records (EHRs) have intensified the need for data collected in electronic form, so they can be merged with other electronic data sources. 8–10 Translational research initiatives also require data that can easily be integrated with EHRs. The use of multicenter trials is expanding, and they strongly benefit from electronic data systems that manage the collection and transfer of data. A second significant development is the introduction of a new generation of mobile computing devices, including smartphones and tablets. Although handheld devices and tablet PCs have been used for years, current devices have shown greater adoption, such that the end-user needs for training and the disadvantages to computerized data collection may be significantly reduced. These next-generation tablet devices already represent over 90% of tablet computer use, 11 with estimates of overall consumer use as high as 13%. 12–16 These tablets also have redesigned operating systems focused on smaller devices that use small distributed applications, or “apps,” and increasingly leverage cloud-based storage. These 2 differences, the high acceptance rate and the small apps using cloud storage, represent a significant difference in design and potential implementation from previous tablet technology. To date, while researchers have suggested ways newer tablets could be used 17–21 studies have not addressed the important differences in data storage approaches and connectivity, or even the combination of functions (eg, capture of still and video images in addition to textual and numeric data) of these next-generation tablets that are relevant to primary data collection in clinical research. Although Shapiro et al 6 studied handheld computer forms, these next-generation tablets differ significantly from the evaluated handheld devices in the factors considered.

In this paper, we review 5 projects as case studies using different forms of primary research data collection. Each project uses data extracted from EHRs in addition to primary research data collection. With these projects we examine the different methods of data collection, using the factors considered by Shapiro et al, 6 and specifically consider methods most affected by recent developments in clinical research.

The 5 case studies are the Comparative Outcomes Management with Electronic Data Technology (COMET) Study at Stanford University, the Indiana PROSPECT project as part of the Indiana Network for Patient Care (INPC), the Pediatric Enhanced Registry project at Cincinnati Children’s Hospital Medical Center, the Scalable Architecture for Federated Translational Inquiries Network (SAFTINet) project at the University of Colorado School of Medicine, and the Washington Heights/Inwood Infrastructure for Comparative Effectiveness Research (WICER) project at Columbia University. Each project was recently funded through the Agency for Healthcare Research and Quality as part of its investment to build infrastructure to conduct comparative effectiveness research with electronic and prospective data. The projects therefore represented research studies that required primary data collection, had high value for electronic data, and are recent enough to have considered newer technologies for data collection. In addition, each project collected data from multiple sites or research centers.

For each project, we performed a semistructured interview with a project representative (either the principal investigator or the team expert identified by the principal investigator), specifically related to their primary data collection tasks. The goal of the interview was to create a general qualitative description of their project, while concurrently identifying specific factors relevant to data collection. After acquiring a general description of the primary data collection process and the method used, we asked specific questions about the method with regard to workflow, connectivity, security, and data integration ( Fig. 1 ). Interviews lasted approximately 30–45 minutes. Using the results of the interviews, we then assessed each data collection method using the factors considered by Shapiro et al, 6 specifically ease of use, the experience required to develop the data collection forms, the end-user training needed to use the forms, instrument and distribution cost, instrument flexibility, speed of data entry, accuracy of data entry, potential for data loss, need for technical support, and hardware/software requirements. For each method, we assessed whether that factor was a strength of the method or a weakness, as noted by the project representatives for their specific use case.

Table 1 shows the general characteristics of a primary data collection activity for each project. In each case, data collection was done by a research coordinator, rather than directly by subjects or patients. A description of each project and the context of the primary data collection activity is given below.

The COMET Study at Stanford University is developing an electronic network infrastructure to collect and link prospective data from multiple clinical centers and from various patient and research participant populations. Specifically they are using the network to integrate data from clinical and research centers at 4 academic institutions across the United States to support cross-institution analysis. 22 The COMET project mainly uses web-based forms for primary data collection, using standard desktop or laptop computers during an interview performed with each research participant. Occasionally data are collected using paper-based forms, and then entered directly into the web-based form. A main advantage of the forms is data collection validation, to ensure that all questions were asked and answered appropriately. Other advantages were that the researchers can do rapid quality assurance on the data capture process, because the data are entered into electronic form during or immediately after collection.

The Indiana PROSPECT project expands the INPC by capturing more extensive clinical data including patient outcomes, patient study enrollment, and genomic information. 23 INPC is a state-wide health information exchange network that contains structured and text data for approximately 12 million patients. The Indiana PROSPECT project uses scannable forms and a barcode scanner tool (caTrack) as part of data collection for biological samples to enhance the data already in the INPC. When a sample is drawn, information about subjects and the biological sample are documented in structured form in paper forms that are later scanned by a computer to extract the data. The paper forms also include barcodes so they can be automatically linked with the biological sample. The goal of the scannable forms and barcode scanner was to link the subject identifier to the sample at multiple points of data processing to avoid human errors. Samples are first scanned when blood is drawn, then again when the samples arrive to the lab, and then when the sample is spun and scanned. Data are then transferred from the device to the central server, where data are integrated.

The Pediatric Enhanced Registry project at Cincinnati Children’s Hospital Medical Center creates a patient research registry that links to EHRs in different types of delivery sites. 24 The Pediatric Enhanced Registry involves 30 geographic sites across the United States that are all populating data into the registry. Investigators are using the registry for studies demonstrating the impact of a quality improvement initiative using both the EHR data and registry. Primary data collection for the evaluation is mainly in qualitative interviews, with some questionnaires. This project uses paper forms and audio recording devices for data collection, and information is entered directly or transcribed. Paper was preferred because of the low costs for initial creation of forms, and no technology was seen as flexible enough to fit the workflow of the qualitative interview process. Paper was also portable, which was necessary as interviews were done at the most convenient location for the subject (usually a clinical site or research center, or home). Data collected from the qualitative interviews on paper forms are eventually transferred to electronic form during the analysis stage.

The SAFTINet project at the University of Colorado School of Medicine is creating a distributed health data network to support CER and quality improvement efforts targeted at safety net populations. 25 SAFTINet has created a research partnership with organizations throughout the Intermountain West and United States with diverse clinical and administrative data, and uses this partnership and data to create a learning community for research in safety net populations. Much of the data are from existing clinical sources, using inpatient, outpatient and claims data for research purposes. SAFTINet also collects primary data for patient surveys regarding their current disease status (specifically, asthma control). As most of these surveys are connected to the clinical encounter, a template was created that can be implemented within an EHR, so that data can be entered and automatically connected to the patient/subject. This approach is similar to a web-based form, except that subject and user context is already established through the EHR. Some sites use paper forms for initial data collection and data are then retrospectively entered into the EHR, whereas other sites document directly into the EHR template.

The WICER project at Columbia University contains a research data warehouse that integrates patient-level data, including clinical data from multiple facilities, settings and sites of care, with person-level self-reported information collected through a community survey. 26 WICER uses primary data collection with the community survey, where most of the data are being collected in residents’ homes by community health workers. Individual survey data are linked to data of others in the household, as well as linked with any available individual EHR data. Because of the scale and complexity of the community survey (8000 individuals in 3500 homes interviewed annually; ∼200 discrete response questions with branching logic; 45–75 minutes per survey; 8–10 community health workers), a next-generation tablet computer (Apple iPad2) was selected to support community survey administration. As the survey administrators would be traveling to residents’ homes, portability was especially important and the iPad was judged to be as portable as paper. Other advantages specific to the iPad were relatively modest requirements for user training and technical support, and the ability to purchase third-party software for survey administration and processing. In addition, using the camera or GPS in the device to collect data was being investigated, and was seen as a potential, but unrealized, advantage.

Table 2 contains a comparison of the different data collection approaches used in the projects, highlighting the advantages and disadvantages of the methods. For each factor, we assessed whether the data collection approach was a strength (+) or weakness (−) for the project. Paper is the simplest and most commonly used method, and also was best understood in terms of its strengths and weaknesses. The most significant benefits of paper were its simplicity in development and implementation, but it has disadvantages in obtaining and managing data. Scannable forms maintain some benefits of paper in ease of use, while mitigating some of its disadvantages. Web-based forms are the most flexible method, and forms can be easily adjusted with moderate effort by the form designer. However, they are not as easy to use as paper-based methods, and are generally not portable. EHR form templates are the most secure with data, by linking the information within the clinical record and storing in the EHR database. However, they must be developed and used within the constraints of an EHR, and EHRs are not generally lauded for their ease of use. Although previous versions of tablet computers were difficult to use and required the use of specialized software with significant developer and end-user training, next-generation, consumer-focused tablet computers are much easier to use and have less disadvantages in end-user experience or available software. They have the potential to gain most of the advantages of direct electronic data entry without incurring the user experience disadvantages normally associated with new technologies.

A review of 5 modern research projects has demonstrated that there remain multiple methods of primary data collection, with varying advantages and disadvantages. Paper remains the easiest to use, and in studies with a small number of subjects that require complex data collection, it remains a preferred method. Other common methods attempt to reduce the disadvantages of paper (scannable forms) or leverage the benefits of computers (web-based forms). Recent developments in the need to integrate EHR data or the emergence of consumer-focused devices have produced additional data collection strategies (EHR templates, tablet computers).

Although the main data entry method with the project was the focus of the case study and showed the breadth of approaches, the researchers have also pursuing other data collection approaches. For example, COMET is pursuing direct patient entry for a specific questionnaire, which could be entered either online or at a clinical site with kiosk machines. However, some data collection was seen as not amenable to direct patient entry, especially for the initial interviews. The Pediatric Enhanced Registry project reviewed mobile tools that could integrate notes, patient consent, and audio recordings. These were seen as potentially useful, but the researchers felt during initial implementation that no technology was sufficiently capable or robust to justify replacing paper. With the SAFTINet project, initially considered technologies such as kiosks and handheld devices, but prior experience through other projects identified cost barriers to their implementation.

Next-generation tablet computers are particularly interesting, because they most closely achieve the experience of paper-based methods while remaining computer-based. These devices leverage portable applications and smaller-device operating systems, and can potentially introduce new opportunities in clinical research settings. They also introduce challenges, especially around data security and connectivity with cloud-based storage where the collected data are stored and managed by an external company, and outside the direct control of the researchers. With the WICER project, this was a significant barrier, because the research data included protected health information and could not be stored outside our institutional control—control of the data is both an institutional requirement and regulatory recommendation for research data. 27 We addressed this challenge with a data encryption process controlled by the software. Data were encrypted on the tablet computer before transmission to the cloud server, and data were only decrypted after being retrieved by the research institution. To address connectivity, we used a local application on the device rather than using the tablet computer as a browser for web-based forms. This reduced workflow issues because of connectivity, but also carried the risk of storing information on the device. However, this risk was similar to the risk of losing paper forms. In addition, the device was password protected, and the application had functionality to automatically delete data if the device was identified as missing or stolen.

Although effective mobile technologies are emerging for use in research data collection, the technologies are still nascent, paper-based methods are still preferred in many instances, and few implementations successfully achieve the benefits of electronic data collection while also minimizing its disadvantages. With the WICER study, the number of surveys to be administered by community health workers was sufficiently large that electronic systems were seen as preferable, but previous generation electronic tools were considered too difficult, complicated, and expensive to replace paper. The lower cost of hardware, software, and data transfer for next-generation tablets made them viable. Issues around portability, user training and experience, and ease of use that have made paper preferred over electronic tools were significantly reduced with this newer hardware.

However, there are areas where the device portability would not achieve the same advantages, and even be a disadvantage. Most data entry was done by research coordinators, and in only one instance the patients entered data directly. Direct patient data entry has different considerations, and a mobile solution would likely not be appropriate because of device control. Theft risk was a specific consideration against pursuing tablets for the SAFTINet project. In addition, having no or minimal training required is even more critical for patient entry than coordinator entry. This usually requires purpose-specific rather than consumer-focused applications. 8 As a result, patient data entry approaches usually have more complicated software developed, that is more expansive than just questions and answers (eg, including clarifying instructions to help users understand and answer questions). 9

Another potential benefit of next-generation tablets, beyond user familiarity and software costs, is that the tools can combine functions. Many tablets include capabilities for data entry, audio recorders, cameras, and global positioning system receivers. These are all capabilities that have been used for data collection in some form in research studies. By combining the functions together, research coordinators could more easily collect disparate data that could be internally linked. The WICER project links some data with geographic information systems, and the Indiana PROSPECT considered using smartphones for barcode scanning. Although the capability to combine tools is a new benefit without many demonstrated examples, it should be considered a potential advantage.

There were limitations to our case study approach. Although controlled trials and even systematic reviews have been performed studying previous generation tablets, 1,2,4 the use of the next-generation tablets is early. Case studies may be more informative to define the important characteristics for a controlled trial. Another limitation is that each project is in the initial stages of data collection, and the final outcome of each approach was not measured. It will be important to note whether the findings at implementation are still valid after the studies are completed, and to consider if there are differences why they may have occurred. However, there is a need for literature describing main considerations in implementing the newer technologies. With the expanding growth and interest in tablet computers, there is an expected high interest in using these tablets for clinical research studies.

CONCLUSIONS

These case studies demonstrated various approaches for data collection, leveraging technology ranging from paper to current EHRs to next-generation tablet computers. Next-generation tablet computers, which have not been considered in previous studies of data collection methods, most closely approach the ease of use simplicity of paper, while gaining the benefits of computer-based approaches. Although they can introduce risks with data security and connectivity, these risks have been successfully mitigated. As more experience in these tools is gained, even greater benefits from combining device functions may also be significant.

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DOI: 10.2196/resprot.4672
Corpus ID: 16401750

Using Tablet Computers to Increase Patient Engagement With Electronic Personal Health Records: Protocol For a Prospective, Randomized Interventional Study

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Ryan Greysen Mhs Ma +17 authors Originally

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The Reading Brain in the Digital Age: The Science of Paper versus Screens

E-readers and tablets are becoming more popular as such technologies improve, but research suggests that reading on paper still boasts unique advantages

By Ferris Jabr

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In a viral YouTube video from October 2011 a one-year-old girl sweeps her fingers across an iPad's touchscreen, shuffling groups of icons. In the following scenes she appears to pinch, swipe and prod the pages of paper magazines as though they too were screens. When nothing happens, she pushes against her leg, confirming that her finger works just fine—or so a title card would have us believe. The girl's father, Jean-Louis Constanza , presents "A Magazine Is an iPad That Does Not Work" as naturalistic observation—a Jane Goodall among the chimps moment—that reveals a generational transition. "Technology codes our minds," he writes in the video's description. "Magazines are now useless and impossible to understand, for digital natives"—that is, for people who have been interacting with digital technologies from a very early age. Perhaps his daughter really did expect the paper magazines to respond the same way an iPad would. Or maybe she had no expectations at all—maybe she just wanted to touch the magazines. Babies touch everything . Young children who have never seen a tablet like the iPad or an e-reader like the Kindle will still reach out and run their fingers across the pages of a paper book; they will jab at an illustration they like; heck, they will even taste the corner of a book. Today's so-called digital natives still interact with a mix of paper magazines and books, as well as tablets, smartphones and e-readers; using one kind of technology does not preclude them from understanding another. Nevertheless, the video brings into focus an important question: How exactly does the technology we use to read change the way we read? How reading on screens differs from reading on paper is relevant not just to the youngest among us , but to just about everyone who reads—to anyone who routinely switches between working long hours in front of a computer at the office and leisurely reading paper magazines and books at home; to people who have embraced e-readers for their convenience and portability, but admit that for some reason they still prefer reading on paper; and to those who have already vowed to forgo tree pulp entirely. As digital texts and technologies become more prevalent, we gain new and more mobile ways of reading—but are we still reading as attentively and thoroughly? How do our brains respond differently to onscreen text than to words on paper? Should we be worried about dividing our attention between pixels and ink or is the validity of such concerns paper-thin? Since at least the 1980s researchers in many different fields—including psychology, computer engineering, and library and information science—have investigated such questions in more than one hundred published studies. The matter is by no means settled. Before 1992 most studies concluded that people read slower, less accurately and less comprehensively on screens than on paper. Studies published since the early 1990s , however, have produced more inconsistent results: a slight majority has confirmed earlier conclusions, but almost as many have found few significant differences in reading speed or comprehension between paper and screens. And recent surveys suggest that although most people still prefer paper—especially when reading intensively—attitudes are changing as tablets and e-reading technology improve and reading digital books for facts and fun becomes more common. In the U.S., e-books currently make up between 15 and 20 percent of all trade book sales. Even so, evidence from laboratory experiments , polls and consumer reports indicates that modern screens and e-readers fail to adequately recreate certain tactile experiences of reading on paper that many people miss and, more importantly, prevent people from navigating long texts in an intuitive and satisfying way. In turn, such navigational difficulties may subtly inhibit reading comprehension. Compared with paper, screens may also drain more of our mental resources while we are reading and make it a little harder to remember what we read when we are done. A parallel line of research focuses on people's attitudes toward different kinds of media. Whether they realize it or not, many people approach computers and tablets with a state of mind less conducive to learning than the one they bring to paper.

"There is physicality in reading," says developmental psychologist and cognitive scientist Maryanne Wolf of Tufts University, "maybe even more than we want to think about as we lurch into digital reading—as we move forward perhaps with too little reflection. I would like to preserve the absolute best of older forms, but know when to use the new." Navigating textual landscapes Understanding how reading on paper is different from reading on screens requires some explanation of how the brain interprets written language. We often think of reading as a cerebral activity concerned with the abstract—with thoughts and ideas, tone and themes, metaphors and motifs. As far as our brains are concerned, however, text is a tangible part of the physical world we inhabit. In fact, the brain essentially regards letters as physical objects because it does not really have another way of understanding them. As Wolf explains in her book Proust and the Squid , we are not born with brain circuits dedicated to reading. After all, we did not invent writing until relatively recently in our evolutionary history, around the fourth millennium B.C. So the human brain improvises a brand-new circuit for reading by weaving together various regions of neural tissue devoted to other abilities, such as spoken language, motor coordination and vision. Some of these repurposed brain regions are specialized for object recognition —they are networks of neurons that help us instantly distinguish an apple from an orange, for example, yet classify both as fruit. Just as we learn that certain features—roundness, a twiggy stem, smooth skin—characterize an apple, we learn to recognize each letter by its particular arrangement of lines, curves and hollow spaces. Some of the earliest forms of writing, such as Sumerian cuneiform , began as characters shaped like the objects they represented —a person's head, an ear of barley, a fish. Some researchers see traces of these origins in modern alphabets: C as crescent moon, S as snake. Especially intricate characters—such as Chinese hanzi and Japanese kanji —activate motor regions in the brain involved in forming those characters on paper: The brain literally goes through the motions of writing when reading, even if the hands are empty. Researchers recently discovered that the same thing happens in a milder way when some people read cursive. Beyond treating individual letters as physical objects, the human brain may also perceive a text in its entirety as a kind of physical landscape. When we read, we construct a mental representation of the text in which meaning is anchored to structure. The exact nature of such representations remains unclear, but they are likely similar to the mental maps we create of terrain—such as mountains and trails—and of man-made physical spaces, such as apartments and offices. Both anecdotally and in published studies , people report that when trying to locate a particular piece of written information they often remember where in the text it appeared. We might recall that we passed the red farmhouse near the start of the trail before we started climbing uphill through the forest; in a similar way, we remember that we read about Mr. Darcy rebuffing Elizabeth Bennett on the bottom of the left-hand page in one of the earlier chapters. In most cases, paper books have more obvious topography than onscreen text. An open paperback presents a reader with two clearly defined domains—the left and right pages—and a total of eight corners with which to orient oneself. A reader can focus on a single page of a paper book without losing sight of the whole text: one can see where the book begins and ends and where one page is in relation to those borders. One can even feel the thickness of the pages read in one hand and pages to be read in the other. Turning the pages of a paper book is like leaving one footprint after another on the trail—there's a rhythm to it and a visible record of how far one has traveled. All these features not only make text in a paper book easily navigable, they also make it easier to form a coherent mental map of the text. In contrast, most screens, e-readers, smartphones and tablets interfere with intuitive navigation of a text and inhibit people from mapping the journey in their minds. A reader of digital text might scroll through a seamless stream of words, tap forward one page at a time or use the search function to immediately locate a particular phrase—but it is difficult to see any one passage in the context of the entire text. As an analogy, imagine if Google Maps allowed people to navigate street by individual street, as well as to teleport to any specific address, but prevented them from zooming out to see a neighborhood, state or country. Although e-readers like the Kindle and tablets like the iPad re-create pagination—sometimes complete with page numbers, headers and illustrations—the screen only displays a single virtual page: it is there and then it is gone. Instead of hiking the trail yourself, the trees, rocks and moss move past you in flashes with no trace of what came before and no way to see what lies ahead. "The implicit feel of where you are in a physical book turns out to be more important than we realized," says Abigail Sellen of Microsoft Research Cambridge in England and co-author of The Myth of the Paperless Office . "Only when you get an e-book do you start to miss it. I don't think e-book manufacturers have thought enough about how you might visualize where you are in a book." At least a few studies suggest that by limiting the way people navigate texts, screens impair comprehension. In a study published in January 2013 Anne Mangen of the University of Stavanger in Norway and her colleagues asked 72 10th-grade students of similar reading ability to study one narrative and one expository text, each about 1,500 words in length. Half the students read the texts on paper and half read them in pdf files on computers with 15-inch liquid-crystal display (LCD) monitors. Afterward, students completed reading-comprehension tests consisting of multiple-choice and short-answer questions, during which they had access to the texts. Students who read the texts on computers performed a little worse than students who read on paper. Based on observations during the study, Mangen thinks that students reading pdf files had a more difficult time finding particular information when referencing the texts. Volunteers on computers could only scroll or click through the pdfs one section at a time, whereas students reading on paper could hold the text in its entirety in their hands and quickly switch between different pages. Because of their easy navigability, paper books and documents may be better suited to absorption in a text. "The ease with which you can find out the beginning, end and everything inbetween and the constant connection to your path, your progress in the text, might be some way of making it less taxing cognitively, so you have more free capacity for comprehension," Mangen says. Supporting this research, surveys indicate that screens and e-readers interfere with two other important aspects of navigating texts: serendipity and a sense of control. People report that they enjoy flipping to a previous section of a paper book when a sentence surfaces a memory of something they read earlier, for example, or quickly scanning ahead on a whim. People also like to have as much control over a text as possible—to highlight with chemical ink, easily write notes to themselves in the margins as well as deform the paper however they choose. Because of these preferences—and because getting away from multipurpose screens improves concentration—people consistently say that when they really want to dive into a text, they read it on paper. In a 2011 survey of graduate students at National Taiwan University, the majority reported browsing a few paragraphs online before printing out the whole text for more in-depth reading. A 2008 survey of millennials (people born between 1980 and the early 2000s) at Salve Regina University in Rhode Island concluded that, "when it comes to reading a book, even they prefer good, old-fashioned print". And in a 2003 study conducted at the National Autonomous University of Mexico, nearly 80 percent of 687 surveyed students preferred to read text on paper as opposed to on a screen in order to "understand it with clarity". Surveys and consumer reports also suggest that the sensory experiences typically associated with reading—especially tactile experiences—matter to people more than one might assume. Text on a computer, an e-reader and—somewhat ironically—on any touch-screen device is far more intangible than text on paper. Whereas a paper book is made from pages of printed letters fixed in a particular arrangement, the text that appears on a screen is not part of the device's hardware—it is an ephemeral image. When reading a paper book, one can feel the paper and ink and smooth or fold a page with one's fingers; the pages make a distinctive sound when turned; and underlining or highlighting a sentence with ink permanently alters the paper's chemistry. So far, digital texts have not satisfyingly replicated this kind of tactility (although some companies are innovating, at least with keyboards ). Paper books also have an immediately discernible size, shape and weight. We might refer to a hardcover edition of War and Peace as a hefty tome or a paperback Heart of Darkness as a slim volume. In contrast, although a digital text has a length—which is sometimes represented with a scroll or progress bar—it has no obvious shape or thickness. An e-reader always weighs the same, regardless of whether you are reading Proust's magnum opus or one of Hemingway's short stories. Some researchers have found that these discrepancies create enough " haptic dissonance " to dissuade some people from using e-readers. People expect books to look, feel and even smell a certain way; when they do not, reading sometimes becomes less enjoyable or even unpleasant. For others, the convenience of a slim portable e-reader outweighs any attachment they might have to the feel of paper books. Exhaustive reading Although many old and recent studies conclude that people understand what they read on paper more thoroughly than what they read on screens, the differences are often small. Some experiments, however, suggest that researchers should look not just at immediate reading comprehension, but also at long-term memory. In a 2003 study Kate Garland of the University of Leicester and her colleagues asked 50 British college students to read study material from an introductory economics course either on a computer monitor or in a spiral-bound booklet. After 20 minutes of reading Garland and her colleagues quizzed the students with multiple-choice questions. Students scored equally well regardless of the medium, but differed in how they remembered the information. Psychologists distinguish between remembering something—which is to recall a piece of information along with contextual details, such as where, when and how one learned it—and knowing something, which is feeling that something is true without remembering how one learned the information. Generally, remembering is a weaker form of memory that is likely to fade unless it is converted into more stable, long-term memory that is "known" from then on. When taking the quiz, volunteers who had read study material on a monitor relied much more on remembering than on knowing, whereas students who read on paper depended equally on remembering and knowing. Garland and her colleagues think that students who read on paper learned the study material more thoroughly more quickly; they did not have to spend a lot of time searching their minds for information from the text, trying to trigger the right memory—they often just knew the answers. Other researchers have suggested that people comprehend less when they read on a screen because screen-based reading is more physically and mentally taxing than reading on paper. E-ink is easy on the eyes because it reflects ambient light just like a paper book, but computer screens, smartphones and tablets like the iPad shine light directly into people's faces. Depending on the model of the device, glare, pixilation and flickers can also tire the eyes. LCDs are certainly gentler on eyes than their predecessor, cathode-ray tubes (CRT), but prolonged reading on glossy self-illuminated screens can cause eyestrain, headaches and blurred vision. Such symptoms are so common among people who read on screens—affecting around 70 percent of people who work long hours in front of computers—that the American Optometric Association officially recognizes computer vision syndrome . Erik Wästlund of Karlstad University in Sweden has conducted some particularly rigorous research on whether paper or screens demand more physical and cognitive resources. In one of his experiments 72 volunteers completed the Higher Education Entrance Examination READ test—a 30-minute, Swedish-language reading-comprehension exam consisting of multiple-choice questions about five texts averaging 1,000 words each. People who took the test on a computer scored lower and reported higher levels of stress and tiredness than people who completed it on paper. In another set of experiments 82 volunteers completed the READ test on computers, either as a paginated document or as a continuous piece of text. Afterward researchers assessed the students' attention and working memory, which is a collection of mental talents that allow people to temporarily store and manipulate information in their minds. Volunteers had to quickly close a series of pop-up windows, for example, sort virtual cards or remember digits that flashed on a screen. Like many cognitive abilities, working memory is a finite resource that diminishes with exertion. Although people in both groups performed equally well on the READ test, those who had to scroll through the continuous text did not do as well on the attention and working-memory tests. Wästlund thinks that scrolling—which requires a reader to consciously focus on both the text and how they are moving it—drains more mental resources than turning or clicking a page, which are simpler and more automatic gestures. A 2004 study conducted at the University of Central Florida reached similar conclusions. Attitude adjustments An emerging collection of studies emphasizes that in addition to screens possibly taxing people's attention more than paper, people do not always bring as much mental effort to screens in the first place. Subconsciously, many people may think of reading on a computer or tablet as a less serious affair than reading on paper. Based on a detailed 2005 survey of 113 people in northern California, Ziming Liu of San Jose State University concluded that people reading on screens take a lot of shortcuts—they spend more time browsing, scanning and hunting for keywords compared with people reading on paper, and are more likely to read a document once, and only once. When reading on screens, people seem less inclined to engage in what psychologists call metacognitive learning regulation—strategies such as setting specific goals, rereading difficult sections and checking how much one has understood along the way. In a 2011 experiment at the Technion–Israel Institute of Technology, college students took multiple-choice exams about expository texts either on computers or on paper. Researchers limited half the volunteers to a meager seven minutes of study time; the other half could review the text for as long as they liked. When under pressure to read quickly, students using computers and paper performed equally well. When managing their own study time, however, volunteers using paper scored about 10 percentage points higher. Presumably, students using paper approached the exam with a more studious frame of mind than their screen-reading peers, and more effectively directed their attention and working memory. Perhaps, then, any discrepancies in reading comprehension between paper and screens will shrink as people's attitudes continue to change. The star of "A Magazine Is an iPad That Does Not Work" is three-and-a-half years old today and no longer interacts with paper magazines as though they were touchscreens, her father says. Perhaps she and her peers will grow up without the subtle bias against screens that seems to lurk in the minds of older generations. In current research for Microsoft, Sellen has learned that many people do not feel much ownership of e-books because of their impermanence and intangibility: "They think of using an e-book, not owning an e-book," she says. Participants in her studies say that when they really like an electronic book, they go out and get the paper version. This reminds Sellen of people's early opinions of digital music, which she has also studied. Despite initial resistance, people love curating, organizing and sharing digital music today. Attitudes toward e-books may transition in a similar way, especially if e-readers and tablets allow more sharing and social interaction than they currently do. Books on the Kindle can only be loaned once , for example. To date, many engineers, designers and user-interface experts have worked hard to make reading on an e-reader or tablet as close to reading on paper as possible. E-ink resembles chemical ink and the simple layout of the Kindle's screen looks like a page in a paperback. Likewise, Apple's iBooks attempts to simulate the overall aesthetic of paper books, including somewhat realistic page-turning. Jaejeung Kim of KAIST Institute of Information Technology Convergence in South Korea and his colleagues have designed an innovative and unreleased interface that makes iBooks seem primitive. When using their interface, one can see the many individual pages one has read on the left side of the tablet and all the unread pages on the right side, as if holding a paperback in one's hands. A reader can also flip bundles of pages at a time with a flick of a finger. But why, one could ask, are we working so hard to make reading with new technologies like tablets and e-readers so similar to the experience of reading on the very ancient technology that is paper? Why not keep paper and evolve screen-based reading into something else entirely? Screens obviously offer readers experiences that paper cannot. Scrolling may not be the ideal way to navigate a text as long and dense as Moby Dick , but the New York Times , Washington Post , ESPN and other media outlets have created beautiful, highly visual articles that depend entirely on scrolling and could not appear in print in the same way. Some Web comics and infographics turn scrolling into a strength rather than a weakness. Similarly, Robin Sloan has pioneered the tap essay for mobile devices. The immensely popular interactive Scale of the Universe tool could not have been made on paper in any practical way. New e-publishing companies like Atavist offer tablet readers long-form journalism with embedded interactive graphics, maps, timelines, animations and sound tracks. And some writers are pairing up with computer programmers to produce ever more sophisticated interactive fiction and nonfiction in which one's choices determine what one reads, hears and sees next. When it comes to intensively reading long pieces of plain text, paper and ink may still have the advantage. But text is not the only way to read.

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v.8(3); 2022 Mar

Influence of computers in students’ academic achievement

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Data will be made available on request.

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.

Items	VIF	Weights
CU1	1.257	0.220∗
CU2	1.016	0.724∗∗∗
CU3	1.273	0.477∗

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.

	Independent variable		Dependent variable	Moderator		Findings	Conclusion
a	Educational attitudes (EdA)	→	Computer confidence (CC)	n.a.	0.307	∗∗∗	Supported
b	Educational attitudes (EdA)	→	Academic achievement (AA)	n.a.	-0.002	Non-significant	Not supported
	Stereotype attitudes (SA)	→	Computer confidence (CC)	n.a.	-0.160	∗∗	Supported
a	Enjoyment attitudes (EjA)	→	Computer confidence (CC)	n.a.	0.236	∗∗∗	Supported
b	Enjoyment attitudes (EjA)	→	Academic achievement (AA)	n.a.	-0.162	∗	Not supported
a	Home environment (HE)	→	Computer use (CU)	n.a.	0.421	∗∗∗	Supported
b	Home environment (HE)	→	Academic achievement (AA)	n.a.	0.111	Non-significant	Not supported
c	Home environment (HE)	→	Computer self-efficacy (CS)	n.a.	0.200	∗∗	Supported
a	School environment (SE)	→	Computer use (CU)	n.a.	0.317	∗	Supported
b	School environment (SE)	→	Academic achievement (AA)	n.a	-0.246	∗∗∗	Not supported
a	Interest motivations (IM)	→	Computer use (CU)	n.a.	0.124	∗	Supported
b	Interest motivations (IM)	→	Academic achievement (AA)	n.a.	-0.159	∗	Not supported
c	Interest motivations (IM)	→	Computer self-efficacy (CS)	n.a.	-0.156	∗	Not Supported
a	Employment motivations (EM)	→	Computer self-efficacy (CS)	n.a.	0.217	∗∗	Supported
b	Employment motivations (EM)	→	Academic achievement (AA)	n.a	0.183	∗	Supported
	Computer use (CU)	→	Computer confidence (CC)	n.a.	0.136	∗	Supported
	Computer confidence (CC)	→	Computer self-efficacy (CS)	n.a.	0.476	∗∗∗	Supported
	Computer confidence (CC)	→	Academic achievement (AA)	n.a.	0.109	Non-significant	Not supported
	Computer use (CU)	→	Academic achievement (AA)	n.a	0.257	∗	Supported
	Employment Motivations ∗ Computer self-efficacy	→	Academic achievement (AA)	Computer Self-efficacy	0.108	∗	Supported
	Loneliness (L)	→	Academic achievement (AA)	n.a.	-0.150	∗	Supported
a	School Environment ∗ Family size	→	Academic achievement (AA)	Family size	0.141	∗∗	Supported
b	Computer Use ∗ Family size	→	Academic achievement (AA)	Family size	-0.233	∗∗	Supported
	Parental marital status (MS)	→	Academic achievement (AA)	n.a.	0.073	Non-significant	Not supported
	Mother's education (ME)	→	Academic achievement (AA)	n.a	0.135	∗	Supported

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.

Effect of	Indirect effect (a x b) (t-value)	Direct effect (c) (t-value)	Sign (a x b x c)	Interpretation	Conclusion
HE - > CU - > AA	0.117∗ (2.025)	0.111 (1.560)	+	Full mediation	c supported
SE - > CU - > AA	0.086∗ (2.271)	-0.246 ∗∗∗ (3.958)	+	Complementary mediation	c supported

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

Constructs	Items	Author
Educational attitudes	EdA1 – Computers are fascinating	( )
	EdA2 – A computer is an educational tool
	EdA3 – A computer is an effective learning tool
	EdA4 – One can learn new things from a computer
	EdA5 – You can learn a lot from using a computer
Stereotypes attitudes	SA1 - People who like computers are often not very sociable	( )
	SA2 – People who like computers are usually weird
	SA3 – I would not expect a good athlete to like computers
	SA4 – People who like computers are often squares
Enjoyment attitudes	EjA1 – Working with a computer is a good way to pass the time	( )
	EjA2 – I prefer computer games to other games
	EdA3 – The computer stops me from getting bored
	EdA4 – I use the computer when I have nothing else to do
Home environment	HE1 – I work with a computer at home most of the time	( )
Home environment	HE2 – When I am at home, I am always using a computer	( )
School environment	SE1 – Most of my teachers encourage me to learn with computers
	SE2 – The computer learning facilities at my school are good	( )
	SE3 – I use computers at school a lot	( )
Interest motivations	IM1 – I enjoy using computers	( )
	IM2 – I would take any opportunity to use computers
	IM3 – I am motivated when I use a computer
Employment motivations	EM1 – Computer skills will be helpful for me to get a good job	( )
	EM2 – I will need adequate computer skills for my future work
	EM3 – Computer skills will improve my curriculum
	EM4 – I will need a computer to work in my daily job
Computer use	CU1 – The extent of computer use at school	( )
	CU2 – The frequency of general computer use at home
	CU3 – The frequency of general computer use in school
Computer confidence	CC1 – I feel comfortable working with computers	( )
	CC2 – I find using a computer easy
	CC3 – I learn more rapidly when I use a computer
Computer self-efficacy	CS1 – I can skillfully use a computer to make a report/write an essay.	( )
	CS2 – I can skillfully use a computer to analyse numerical data.
	CS3 – I can easily write a simple program for a computer.
	CS4 – I can skillfully use a computer to organise information.
Loneliness	L1 – How often do you feel that you lack companionship?	( )
	L2 – How often do you feel left out?
	L3 – How often do you feel isolated from others?
Academic achievement	AA1 – Mathematical achievement	( )
	AA2 – Verbal achievement
	AA3 – Remaining subjects
	AA4 – Global achievement in remaining areas.
Family size	FS1: What is your family size?	( )
Parents Marital Status	MS1: What is your parent's marital status?	( )
Mothers' Education	PE1: What is the highest educational level your mother completed	( )
Age	A1: Age	( )
Gender	G1: Gender	( )

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)

	Mean	SD	CR	EdA	SA	EjA	HE	SE	IM	EM	CU	CC	CS	L	FS	MS	ME
Educational attitudes (EdA)	4.345	0.609	0.880
Stereotypes attitudes (SA)	1.533	0.711	0.881	-0.312
Enjoyment attitudes (EjA)	3.425	0.941	0.849	0.307	0.023
Home environment (HE)	3.325	0.995	0.847	0.383	-0.054	0.313
School environment (SE)	2.559	0.888	0.780	0.176	0.001	0.042	0.246
Interest motivations (IM)	3.837	0.814	0.845	0.481	-0.125	0.473	0.466	0.233
Employment motivations (EM)	4.230	0.716	0.854	0.473	-0.145	0.142	0.360	0.227	0.292
Computer use (CU)	3.557	0.799		0.284	-0.065	0.170	0.557	0.449	0.394	0.353
Computer confidence (CC)	4.113	0.755	0.865	0.468	-0.259	0.349	0.291	0.187	0.494	0.268	0.274
Computer self-efficacy (CS)	3.930	0.779	0.846	0.353	-0.259	0.173	0.344	0.151	0.235	0.371	0.279	0.516
Loneliness (L)	2.596	1.119	0.920	-0.081	0.142	0.155	0.025	-0.055	-0.010	-0.041	-0.093	-0.096	-0.132
Family size (FS)	3.811	1.066	1.000	-0.104	0.065	0.010	0.079	-0.005	-0.042	0.003	-0.009	0.001	0.002	0.014
Marital status (MS)	1.000	0.000	1.000	-0.078	0.072	-0.042	0.027	-0.100	-0.052	0.059	-0.002	0.016	0.003	-0.057	0.152
Mother education (ME)	13.291	4.006	1.000	0.087	-0.009	-0.061	0.002	-0.091	-0.076	0.107	-0.034	0.006	0.131	-0.117	0.025	0.070
Academic achievement (AA)	14.597	2.347	0.921	0.043	-0.092	-0.147	0.170	-0.102	-0.086	0.203	0.190	0.053	0.135	-0.205	0.086	0.194	0.191

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

Appendix C. Outer Loadings and Cross-Loadings

	CC	CS	EjA	HE	SE	EdA	SA	L	EM	IM	AA
CC3		0.466	0.279	0.240	0.163	0.430	-0.253	-0.082	0.296	0.453	0.079
CC4		0.505	0.273	0.190	0.131	0.344	-0.248	-0.147	0.237	0.354	0.110
CC5		0.280	0.331	0.315	0.177	0.394	-0.123	0.010	0.107	0.429	-0.088
CS1	0.367		0.098	0.253	0.085	0.320	-0.204	-0.115	0.305	0.110	0.166
CS2	0.324		0.089	0.184	0.067	0.219	-0.133	-0.083	0.282	0.149	0.052
CS3	0.444		0.186	0.293	0.205	0.208	-0.158	-0.097	0.220	0.188	0.057
CS4	0.416		0.142	0.298	0.092	0.314	-0.277	-0.101	0.317	0.260	0.125
EjA1	0.337	0.175		0.231	0.072	0.315	-0.048	0.137	0.193	0.453	-0.176
EjA2	0.240	0.118		0.259	-0.035	0.199	0.085	0.023	0.075	0.322	-0.116
EjA3	0.228	0.065		0.210	0.033	0.209	0.034	0.171	0.073	0.352	-0.113
EjA4	0.231	0.158		0.272	0.047	0.175	0.033	0.148	0.045	0.271	0.003
HE3	0.241	0.353	0.142		0.211	0.371	-0.125	-0.009	0.392	0.361	0.229
HE4	0.266	0.221	0.443		0.214	0.275	0.060	0.062	0.202	0.457	0.037
SE1	0.143	0.098	0.034	0.268		0.228	-0.035	-0.001	0.286	0.235	-0.050
SE2	0.124	0.195	0.003	0.166		0.158	-0.068	-0.083	0.145	0.060	-0.016
SE3	0.144	0.056	0.051	0.104		0.004	0.095	-0.048	0.063	0.197	-0.151
EdA1	0.436	0.274	0.478	0.325	0.110		-0.180	0.016	0.339	0.530	-0.102
EdA2	0.380	0.304	0.126	0.258	0.147		-0.219	-0.093	0.382	0.312	0.095
EdA3	0.348	0.251	0.155	0.307	0.199		-0.258	-0.144	0.321	0.357	0.050
EdA4	0.289	0.274	0.146	0.310	0.083		-0.316	-0.077	0.392	0.268	0.119
EdA5	0.314	0.252	0.220	0.268	0.135		-0.256	-0.026	0.396	0.337	0.039
SA2	-0.229	-0.221	0.020	-0.007	0.007	-0.206		0.139	-0.055	-0.066	-0.041
SA3	-0.263	-0.209	-0.023	-0.089	-0.029	-0.370		0.110	-0.200	-0.197	-0.095
SA4	-0.096	-0.212	0.168	-0.017	0.010	-0.116		0.105	-0.035	0.061	-0.106
SA5	-0.189	-0.214	0.002	-0.041	0.031	-0.239		0.108	-0.131	-0.103	-0.076
L1	-0.049	-0.125	0.130	-0.002	-0.052	-0.035	0.102		0.004	-0.010	-0.196
L2	-0.143	-0.112	0.169	0.014	-0.042	-0.091	0.148		-0.078	-0.035	-0.162
L3	-0.075	-0.114	0.120	0.054	-0.052	-0.094	0.134		-0.043	0.015	-0.186
EM1	0.261	0.262	0.215	0.262	0.199	0.418	-0.066	-0.065		0.309	0.123
EM2	0.201	0.307	0.071	0.297	0.179	0.361	-0.162	0.012		0.227	0.178
EM3	0.190	0.337	0.052	0.238	0.220	0.370	-0.087	-0.025		0.144	0.160
EM4	0.186	0.223	0.129	0.331	0.089	0.317	-0.134	-0.062		0.253	0.163
IM1	0.452	0.291	0.405	0.428	0.141	0.459	-0.218	-0.095	0.287		-0.001
IM2	0.374	0.122	0.356	0.341	0.231	0.278	0.020	0.095	0.215		-0.144
IM4	0.350	0.108	0.384	0.342	0.219	0.418	-0.056	0.012	0.182		-0.095
AA1	-0.021	0.062	-0.124	0.078	-0.151	-0.008	-0.097	-0.103	0.106	-0.126
AA2	0.054	0.176	-0.138	0.141	-0.148	0.068	-0.050	-0.183	0.177	-0.087
AA3	0.080	0.096	-0.117	0.170	-0.024	0.023	-0.043	-0.182	0.192	-0.038
AA4	0.062	0.124	-0.131	0.188	-0.043	0.056	-0.123	-0.226	0.216	-0.055

Appendix D. Heterotrait-Monotrait Ratio (HTMT)

Constructs	EdA	SA	EjA	HE	SE	IM	EM	CC	CS	L	FS	MS	ME
Educational attitudes (EdA)
Stereotypes attitudes (SA)	0.354
Enjoyment attitudes (EjA)	0.347	0.122
Home environment (HE)	0.508	0.158	0.489
School environment (SE)	0.277	0.139	0.088	0.399
Interest motivations (IM)	0.592	0.202	0.605	0.681	0.360
Employment motivations (EM)	0.594	0.168	0.180	0.496	0.331	0.387
Computer confidence (CC)	0.580	0.294	0.450	0.434	0.285	0.658	0.340
Computer self-efficacy (CS)	0.437	0.326	0.216	0.469	0.272	0.285	0.477	0.657
Loneliness (L)	0.114	0.169	0.207	0.066	0.091	0.109	0.074	0.131	0.160
Family size (FS)	0.109	0.086	0.043	0.096	0.081	0.043	0.075	0.028	0.011	0.015
Maritus Status (MS)	0.079	0.067	0.070	0.031	0.130	0.079	0.065	0.039	0.023	0.061	0.152
Mothers education (ME)	0.105	0.039	0.091	0.035	0.162	0.095	0.118	0.034	0.150	0.123	0.025	0.070
Academic Achievement (AA)	0.121	0.113	0.177	0.202	0.163	0.144	0.242	0.145	0.158	0.228	0.091	0.209	0.202

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Electronics

The Best Tablets

By Wirecutter Staff

After hundreds of hours of research and testing over the past seven years, we’ve concluded that Apple’s 10th-generation iPad —with all the performance and features most people need for watching video, browsing the internet, and staying on top of email and social media feeds—is the best all-around tablet. But we also have recommendations for people who want an Android tablet, a basic ebook reader, or a more powerful tablet for gaming, for handling design and creative tasks, or for replacing a laptop computer.

The research

The best all-around tablet: apple ipad (10th generation), an upgrade for multitaskers and creatives: apple ipad pro (m4), the best android tablet: google pixel tablet, a budget tablet for media: amazon fire hd 8, the best ebook reader: amazon kindle, the best drawing tablet: huion inspiroy 2 m.

Apple iPad (10th generation)

The best tablet for most people.

The cheapest iPad that Apple sells has a large screen, fast performance, a USB-C port, and plenty of color options to suit the needs of most people.

Buying Options

Who it’s for: You want a great all-around tablet that can handle pretty much any task.

Why we like it: The 10th-gen iPad is the best tablet for most people, thanks to great hardware, an easy-to-use operating system, and a huge library of quality apps, even if you normally use Android on your phone or Windows on your computer. iOS also receives frequent updates—including prompt security updates—which isn’t something you can say of many Android tablets.

Flaws but not dealbreakers: The 10th-gen iPad offers 64 GB or 256 GB of storage, which is either too little or too much for most people. Also, the 10th-gen iPad supports only the 1st-gen and USB-C Apple Pencil models, with only the latter being able to connect to the iPad magnetically. Neither stylus offers pressure sensitivity, which determines how dark your mark is based on how hard you press. Digital illustrators might need a more expensive iPad.

For more on the 10th-generation iPad and how it compares to other iPad models, read our full guide to Apple’s iPad lineup .

An iPad propped in a stand, connected to a silver Magic Keyboard with an Apple Pencil attached to the top.

Apple iPad Pro (11-inch, M4)

For pro-level performance and a vibrant oled screen.

The M4 iPad Pro has Apple’s fastest processor and a fantastic OLED display, and it’s compatible with the newest Apple Pencil and Magic Keyboard case.

Who it’s for: You want the best possible performance for making digital art, taking notes or using productivity apps on the go, or viewing and editing photos and videos.

Why we like it: For serious creative work or as a second device for taking notes and handling quick office tasks, the iPad Pro is the best option. It has a slim, uniform bezel that wraps around the entire screen, making it feel equally natural whether you use it in landscape or portrait orientation—a design choice that makes it stand out from competitors. Its new OLED display gets bright enough for use in direct sunlight and has fantastic contrast in comparison with previous iPads.

Artists and diligent note-takers who buy the new Apple Pencil Pro will also appreciate its “squeeze” feature, where you can lightly squeeze the barrel of the Pencil Pro to pull up a helpful menu of different brushes and tool options. This means you can swap brushes or colors without moving your hand, which makes the Pencil Pro feel more natural to use.

Flaws but not dealbreakers: Apple advertises the iPad Pro as a replacement for a traditional PC, but whether it can serve that purpose depends on what you do, how you work, and what apps you use. In general, iPad Pro keyboard cases and covers aren’t as nice as standalone Bluetooth keyboards or the keyboards on the best laptops. But drawing and photo-editing apps are well suited to touchscreen and Apple Pencil controls, and they benefit from the iPad Pro’s large, color-accurate screen.

For more on the iPad Pro, read our full guide to pro tablets .

Google Pixel Tablet

Best android tablet for most people.

With a vivid screen and a great processor, Google’s tablet is ideal for viewing content, gaming, and browsing the web. The bundled charging dock transforms it into a smart-home hub and is worth the $100 upgrade over the standalone tablet.

Who it’s for: You’re already invested in or partial to Google’s version of Android, and you want an affordable tablet with a good display, excellent performance, and useful smart-home controls.

Why we like it: The Google Pixel Tablet has a bright and vivid 11-inch display and is powerful enough to handle high-end gaming along with multitasking and split-screen apps. It offers our favorite Google features, like hands-free Google Assistant, voice typing, live translation, multi-profile support, and more. The 5,000 mAh battery lasted 12 hours in our testing. The included dock (which is bundled with the tablet for $100 more than the standalone version, but we recommend the bundle) boosts the bass and enables Hub mode, which transforms the Pixel Tablet into a smart-home hub that allows you to control smart-home devices such as smart lights, video doorbells, security cameras, and thermostats.

Flaws but not dealbreakers: If you want a tablet that supports a stylus, your options for the Pixel Tablet are limited; the Lenovo USI Pen 2 and Penoval USI 2.0 styluses are among the few that are compatible. If you want a tablet for drawing or writing, consider seeking out a different option.

Visit our full guide to the best Android tablets to read more about the Pixel Tablet and other Android tablets we’ve tested.

Our pick for best android tablet on a budget, the Amazon Fire HD 8 (12th generation).

Budget pick

Amazon Fire HD 8 (12th generation)

A cheap tablet for streaming media.

The Fire HD 8 has a smaller, lower-resolution screen than the Galaxy Tab S8, but it’s a great cheap tablet for reading or watching video, especially if you get that content from Amazon’s store.

You save $57 (51%)

Who it’s for: You want the cheapest tablet that’s good for reading and watching video, with access to a big library of video, ebooks, and music.

Why we like it: The Amazon Fire HD 8 (12th generation) costs less than $100 and is an excellent value. It lets you stream video from Netflix, Hulu, HBO Max, and other popular services, and you can also read your Kindle ebooks. It offers built-in support for the Alexa voice assistant used by Amazon’s popular Echo devices, which makes ordering products and media from Amazon easier. In addition, Amazon Prime members get access to a selection of no-extra-cost movies, TV shows, and ebooks (though Amazon’s apps for iOS and other Android tablets all work similarly).

Flaws but not dealbreakers: The Fire HD 8 is slower and has a lower-resolution screen than any of our other picks, so text isn’t as crisp—the Kindle Paperwhite is better for reading ebooks—and its performance is optimized for watching videos and reading rather than getting work done. It’s also limited to Amazon’s Android app store, which has a smaller selection of games and apps than the regular Google Play store (which in turn lags behind Apple’s App Store when it comes to great tablet apps). Although it’s possible to install the Google Play store on the Fire HD 8 , doing so requires a workaround, and we don’t recommend it. Unlike our other tablet picks, which offer a solid selection of apps and productivity tools, the Fire HD 8 is best used only as a media-consumption device.

To find out how the Fire HD 8 stacks up against other Android tablets, see our guide to the best Android tablets .

The Amazon Kindle with a page of a book on the screen, sitting on a green background.

Amazon Kindle (2022)

The best e-reader for most people.

Amazon’s most affordable Kindle is also its most portable, with a 6-inch screen that has finally been upgraded with a higher pixel density for sharper text and support for USB-C charging. Access to Amazon’s huge ebook library makes the Kindle the best dedicated device for reading.

Who it’s for: You don’t care about apps or browsing—you just want to read books.

Why we like it: The cheapest Kindle is also the best one. Its 6-inch E Ink screen offers 300 pixels per inch, which makes text sharp and easy to read, and its portable size makes it convenient for toting wherever you go—it even fits in a small purse. Amazon finally switched from Micro-USB to USB-C charging for the entry-level Kindle, so you don’t need to hunt down a special cable to juice it up. Because it lasts weeks on a charge, it’s better than an iPad or Android tablet for reading. And the Kindle comes with 16 GB of storage, which is plenty of room for your library of ebooks.

Flaws but not dealbreakers: The entry-level Kindle isn’t waterproof, so if you plan to read by the pool or in the bathtub, you might want to splurge for the pricier Kindle Paperwhite.

If you’re interested in Amazon’s more expensive Kindles or non-Amazon options, read our full guide to ebook readers .

Huion Inspiroy 2 M

The best drawing tablet for most people.

Offering a smooth drawing experience and plenty of space and hotkeys, the Huion Inspiroy 2 M is a great drawing tablet for all but the most demanding professionals.

Who it’s for: If you’re an artist, a drawing tablet is a good way to create images in Adobe Photoshop, Corel Painter, or Celsys Clip Studio Paint Pro. Drawing tablets are also excellent tools for working with 3D modeling programs and other situations where using a stylus makes sense.

Why we like it: The 12-by-7-inch Huion Inspiroy 2 M offers a lot of space to sketch on, and drawing on it is comfortable, but even so, it doesn’t take up too much space on a desk. The included wireless stylus provides excellent tracking with no perceivable latency, is comfortable to hold for extended periods, and has two function buttons. The Inspiroy 2 M also has plenty of hotkeys for you to program as you like, along with a dial and a pen holder with replacement nibs.

Flaws but not dealbreakers: Its design and construction are solid, but it’s still just a slab of (mostly) plastic. It also lacks wireless support.

You can find great drawing tablets for almost every situation and budget, and we have more information in our full guide to drawing tablets .

Meet your guide

Wirecutter Staff

The Best Drawing Tablets

by Arthur Gies

Drawing tablets are nearly indispensable for creating art on a PC or laptop, and models such as the Huion Inspiroy 2 M are great for beginners and veteran artists alike.

An iPad in a Logitech Slim Folio case sitting next to a stack of books and a smartphone.

Getting Work Done on an iPad

by Ivy Liscomb

You can do a surprising amount of work on an iPad with the right gear. These are the best accessories for turning your iPad into a mobile work space.

Two iPads in cases sitting next to each other.

The Best Pro Tablets

by Dave Gershgorn

If you’re looking to replace or supplement your laptop with a tablet, you have great options—but you also have some tough choices ahead of you.

A kid using an Amazon Fire HD 8 Kids Edition tablet.

The Best Tablet for Kids

by Ryan Whitwam and Andrew Cunningham

The best tablet for your kid is the old one you aren’t using anymore. If you’re buying new, Apple’s 9th-generation iPad has the best app selection.

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Computer Science > Machine Learning

Title: automated text scoring in the age of generative ai for the gpu-poor.

Abstract: Current research on generative language models (GLMs) for automated text scoring (ATS) has focused almost exclusively on querying proprietary models via Application Programming Interfaces (APIs). Yet such practices raise issues around transparency and security, and these methods offer little in the way of efficiency or customizability. With the recent proliferation of smaller, open-source models, there is the option to explore GLMs with computers equipped with modest, consumer-grade hardware, that is, for the "GPU poor." In this study, we analyze the performance and efficiency of open-source, small-scale GLMs for ATS. Results show that GLMs can be fine-tuned to achieve adequate, though not state-of-the-art, performance. In addition to ATS, we take small steps towards analyzing models' capacity for generating feedback by prompting GLMs to explain their scores. Model-generated feedback shows promise, but requires more rigorous evaluation focused on targeted use cases.

Comments:	21 pages, 1 figure
Subjects:	Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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School of Electrical and Computer Engineering

College of engineering, laboratory for emerging devices and circuits team wins best paper award for ai computing memory research.

The team, led by ECE professor Shimeng Yu, analyzed different combinations of settings for emerging non-volatile memory (eNVM) technologies in hopes of improving AI hardware efficiency and power.

Georgia Tech School of Electrical and Computer Engineering (ECE) professor Shimeng Yu and his team at the Laboratory for Emerging Devices and Circuits won the Association for Computer Memory (ACM) Transactions on Design Automation of Electronic Systems (TODAES) 2024 Best Paper Award.

The prestigious award recognizes the best paper published in the TODAES, the ACM's flagship publications in the area of electronic design automation (EDA).

Yu accepted the award at the 61st Design Automation Conference in San Francisco, Calif. in June.

This is the second consecutive year Yu’s team won an award for research printed in a flagship publication in the area of EDA, and the third year in a row research from ECE has received such an honor.

The paper titled, “ Hardware-aware quantization/mapping strategies for compute-in-memory accelerators, ” analyzed different combinations of settings for emerging non-volatile memory (eNVM) technologies.

This new technology is important for mixed-signal Compute-in-Memory (CIM) accelerators, which are very energy efficient, thus making them crucial for artificial intelligence hardware design, which are notoriously resource intensive platforms.

Ultimately, the research found the right configuration and settings can significantly improve output and efficiency. Yu’s team was able to achieve an increase in processing speed by up to 60 percent, while doubling the energy efficiency and reducing overall hardware size by up to 25 percent.

The findings provide design guidelines to engineers who continue to research eNVM and CIM technology.

Yu co-authored the paper with ECE Ph.D. graduates Shanshi Huang and Hongwu Jiang, who are now both assistant professors in Hong Kong University of Science and Technology.

Yu’s lab won the IEEE’s Donald O. Pederson Best Paper Award in 2023 for their research on an end-to-end benchmark framework to evaluate state-of-the-art CIM accelerators. The award honors the best paper in the IEEE’s Transactions on Computer-Aided Design of Integrated Circuits and Systems, the flagship journal of the IEEE Council on Electronic Design Automation.

In 2022, ECE Professor Sung Kyu Lim and his research team won the Donald O. Pederson Best Paper Award for their paper on a physical design tool named Compact-2D that automatically builds high-density and commercial-quality monolithic three-dimensional integrated circuits.

Yu also recently received a 2023 Intel Outstanding Research Award for his work on a chip that will help quantify uncertainty that is beyond the capabilities of existing binary computing systems, and improve computing robustness.

Zachary Winiecki

[email protected]

GRADUATE APTITUDE TEST IN ENGINEERING 2025

अभियांत्रिकी स्नातक अभिक्षमता परीक्षा २०२५, organising institute: indian institute of technology roorkee.

GATE 2025 TEST PAPERS & SYLLABUS

GATE 2025 will be conducted for 30 test papers. The following table shows the list of papers with codes. Please click the Paper/Code to download the syllabus.

A candidate is allowed to appear either in ONE or UP TO TWO test papers. Please see the page Two-Paper Combination for more details.

GATE Test Paper	Code	GATE Test Paper	Code

The test papers will be in English. Each GATE 2025 paper is for a total of 100 marks, General Aptitude (GA) is common for all papers (15 marks), and the rest of the paper covers the respective test paper syllabus (85 marks). Click here for detailed pattern of the question papers .

XE Paper Sections	Code	XH Paper Sections	Code	XL Paper Sections	Code
Engineering Mathematics (Compulsory) (15 marks)	A	Reasoning and Comprehension (Compulsory) (25 marks)	B1	Chemistry (Compulsory) (25 marks)	P
Any TWO optional Sections		Any ONE optional Section		Any TWO optional Sections
(2x35 = 70 marks)		(60 marks)		(2x30 = 60 marks)
Fluid Mechanics	B	Economics	C1	Biochemistry	Q
Materials Science	C	English	C2	Botany	R
Solid Mechanics	D	Linguistics	C3	Microbiology	S
Thermodynamics	E	Philosophy	C4	Zoology	T
Polymer Science and Engineering	F	Psychology	C5	Food Technology	U
Food Technology	G	Sociology	C6
Atmospheric and Oceanic Sciences	H

Multi-sessional papers: Candidate will be assigned to appear only in one of the sessions for the papers running in multiple sessions.

Computer Science and Information Technology (CS) and Civil Engineering (CE) will be conducted as multi-session papers in GATE 2025. More precisely, they will be two-session papers. This means that the candidates will be assigned to one of the sessions only — either the forenoon session or the afternoon session. The question papers will be different for each session. Test papers are held in multiple sessions when the candidate count is so high that they cannot all appear for the test in the same session. The scores of the candidates will be normalized according to the normalization formula given in Section 13.2 of the Information Brochure.

Candidates must familiarize themselves with the paper code as it is required both during application and examination.

Each candidate should fill ONLY ONE application. If they wish to appear in second paper (from the two-paper combination), they can add respective paper in their original application. In case of Multiple applications, only one will be accepted and remaining applications will be rejected without any refund for the paid fee.

IISc Bengaluru

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IIT Kharagpur

IIT Roorkee

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GATE 2025
The test papers will be in English. Each GATE 2025 paper is for a total of 100 marks, General Aptitude (GA) is common for all papers (15 marks), and the rest of the paper covers the respective test paper syllabus (85 marks). Click here for detailed pattern of the question papers.
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