Exploring First-Year Students’ Computer Literacy Through a Two-Step Cluster Analysis Approach

Exploring First-Year Students’ Computer Literacy Through a Two-Step Cluster Analysis Approach | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Exploring First-Year Students’ Computer Literacy Through a Two-Step Cluster Analysis Approach Ekaterina Rzyankina, Robert Noel Prince This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8028679/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Understanding the computer literacy of students entering university is important as the shift towards online teaching, learning and assessment in higher education has become the ‘new normal’ since COVID-19. This new normal makes assumptions about the levels of computer literacy of incoming students. This paper then surveys incoming students in order to ask the following questions: What is the computer literacy of students when they enter higher education? And, how can this research inform the facilitation of students’ online teaching, learning and assessment? The survey research provides valuable insights into the computer literacy levels of students entering a South African University of Technology. Methodologically the two-step cluster analysis, which is a hybrid approach that first uses a distance measure to separate groups and then a probabilistic approach to choose the optimal subgroup model, is used. The significance of the variables (factors), such as general technology use, internet search skills, collaborative technology use, and technology for assignment submission, underscores the importance of these skills in higher education. The two-step process identified three distinct groups (clusters) of students with varying levels of computer literacy among the respondents from two engineering departments. Understanding the computer literacy levels of incoming students can inform strategic planning for integrating technology into educational practices and support services across transdisciplinary. By tailoring educational approaches to match students' existing skills and preferences, this University of Technology specifically, and universities in general, can enhance learning experiences and better prepare students for the demands of the digital age. computer literacy digital literacy COVID-19 impact on computer literacy two-step cluster analysis higher education technology educational practices learning experiences Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction South African universities are beginning to adjust to the fourth industrial revolution (4IR) and Artificial Intelligence (AI). This is evident at the Cape Peninsula University of Technology (CPUT) – the particular site for this research – which is implementing its ‘Vision 2030’, where the aim is to transform the institution into a ‘SMART‘ university. This will provide a space for bringing technology into the classroom to enhance the teaching and learning process. This means that the entire infrastructure will be changed to accommodate the use of smart technology for teaching and learning. However, this aim has been criticised, since some argue that pedagogy should be the driver, and technology merely the accelerator [1]. The technology in South Africa has not been integrated fully into the learning process and many students still have difficulty in studying online. Digital technologies constitute a significant factor in the way in which our day-to-day lives are now distinctly different from what they were 20 years ago. Learning on and through the World Wide Web brings with it an array of new practices that extend, yet challenge, traditional expectations about universities and literate practice [2 - 6]. As in many other countries, there is a digital skills gap in South Africa [7 - 9]. This digital skills gap exists among students as well as educators. In addition, educators and students need to be able to adapt continually to new technology in the digital era. There are various aspects around use and interactivity of e-textbook, which will be discussed in this section. Computer literacy on entering university has become essential for studying in higher education, particularly during and after the COVID-19 pandemic. By computer literacy, we refer to a learner's ability to use computer software and hardware effectively [1-3]. This paper specifically surveyed the computer literacy of learners on entering a university of technology. During the COVID-19 showed that digital gap is much complex in South Africa where the inequality to access and success is high. During this time it highlighted that many students in South Africa do not have access to technology and during the COVID -19 did not access to university infrastructure and technologies. Digital literacy versus computer literacy It is crucial to recognise that digital literacy and computer literacy are related but distinct concepts. Digital literacy extends beyond the basic operational understanding of computers to include a wide range of practices encompassing three core domains of digital competence: technological, cognitive, and social skills. Technological skills refer to the ability to use digital tools effectively; cognitive skills involve critical thinking, information evaluation, and problem-solving; while social skills concern communication and interaction in digital contexts [10]. Expanding on this view, Van Deursen and Van Dijk [11] developed a framework for Internet skills that identified operational, formal, informational, and strategic competencies, capturing the diverse ways individuals navigate online environments. Their subsequent work further incorporated communication and content-creation dimensions [11, 12], acknowledging that digital engagement now requires users to produce and share knowledge as well as consume it. Similarly, [12] identified four key dimensions, ethnical, social, critical, and creative, while later studies [13–16] proposed measurable indicators for evaluating digital performance, such as operational, navigational, creative, and mobile proficiencies. More recent scholarship [15] has categorised these into seven core domains: technical proficiency, information management, communication, collaboration, creativity, critical thinking, and problem-solving. In the context of Artificial Intelligence (AI), thinking skills are increasingly recognised as an integral part of digital literacy [15]. Analytical, interdisciplinary, and systems thinking are now viewed as essential for equipping learners to navigate complex environments and to respond effectively to the demands of Industry 4.0 [13, 15]. The educational focus has therefore shifted from the mechanical use of digital tools to the responsible and ethical application of knowledge in digital spaces. Today, being digitally literate also entails the ability to critically assess online content, identify misinformation and echo chambers, and understand the socio-political implications of digital participation [17, 18]. At the same time, the rising importance of cybersecurity has drawn attention to the protection of data and privacy, particularly during times of uncertainty, such as elections or the COVID-19 pandemic [16]. Computer literacy , in contrast, has historically been linked to access to digital technologies and devices within teaching and learning contexts [4, 8, 10]. As technology has advanced, the meaning of being computer-literate has evolved accordingly. The growing ubiquity of mobile and portable devices has transformed how individuals interact with technology, extending literacy beyond basic technical competence to encompass awareness of how technology shapes society. In contemporary contexts, laptops, tablets, and smartphones function within wireless ecosystems that allow for flexible learning and communication. This transformation means that literacy is no longer limited to skill acquisition but also involves understanding how digital tools influence cognition, relationships, and social structures. Computing technology continues to play a pivotal role in redefining the parameters of computer literacy. Scholars have observed that portable and mobile technologies have become defining tools of the current decade [7, 9]. While traditional personal computers once served as stationary appliances, the integration of wireless connectivity has liberated devices from fixed physical spaces. Portable computing enables users to access and exchange information seamlessly, reflecting a new paradigm of mobility and portability . These qualities, ubiquitous access to information and the capacity to communicate in any place and at any time, symbolise the fluid and interconnected nature of digital literacy in the 21st century [14]. From this perspective, computer literacy has traditionally been concerned with access and operational competence in using technology. Digital literacy, however, extends beyond this to include not only the ability to operate platforms but also the acquisition of higher-order skills such as critical thinking, evaluation, and ethical engagement in digital environments. Background and aims The higher education sector in South Africa is made up of 26 public universities, 50 public Technical and Vocational Education and Training (TVET) colleges, and a range of private institutions. Post-1994, South Africa witnessed a dramatic rise in student numbers, alongside greater racial diversity in higher education [ 16 ]. Many South African students form the largest demographic in these institutions. To promote inclusivity, the South African government trying offers financial aid via the National Student Financial Aid Scheme (NSFAS), administered by the Department of Higher Education and Training. This intervention is assisting many students who have challenges with financial means to fund their education and who do not qualify for other grants or loans [ 19 ]. Another alternative innovation in digital transformation for higher education is the transition to innovative teaching methods, learning spaces, and pedagogical frameworks. Sometimes it can be find resistance from academic staff to change persists as a key barrier. Yet, the COVID-19 crisis revealed a critical insight and open opportunity for technology which can empower educators and improve learning efficiency [ 2 , 12 , 13 ]. Globally, academia is often seen as a stable career path, which can create resistance to new teaching and learning approaches, especially if perceived as jeopardizing job security. As a result, digital transformation initiatives sometimes face reluctance. However, the pandemic era proved that well-planned technological integration can enhance educational performance in universities [ 15 , 17 , 20 ]. While some institutions adapted by leveraging the opportunities presented by online learning, it soon became apparent that not all universities could make this transition smoothly [ 21 – 25 ].Traditional and comprehensive universities in South Africa adapted reasonably well, but universities of technology (UoTs) faced major challenges [ 22 – 29 ]. These challenges highlighted the deep-seated inequalities in access to and participation in higher education. This can be explained that in South Africa, most students at UoTs come from marginalised communities and historically disadvantaged backgrounds with limited resources [ 28 , 29 ]. A prevalent issue in South African higher education is the inability of many students to fund their university studies [ 30 – 33 ]. The "Higher Education and Skills in South Africa" report by Statistics South Africa (StatsSA) found that over half of the youth (aged 18–24) cited financial constraints as a barrier to higher education [ 19 ]. This paper aims to contribute to the understanding of the different levels of computer literacy – high, medium, and low – among learners entering university. In this paper, the authors identify levels of digital literacy among students through the use of cluster analysis. This approach is expected to reveal both the challenges students face and the varying levels of digital literacy they possess upon entering university. Gaining such insights will enable faculties and departments to better understand students’ needs, minimise the risk of academic underperformance, and design holistic support structures that foster student success throughout their studies. Research Design Case study This study focused on a single case with embedded units, which involved examining multiple units or objects of analysis within that single case. Selecting the case and setting up the breadth and depth (boundaries) of the case study was done using the following criteria as presented in Fig. 1 . In this study we used single case study (Fig. 1 ). This single case study focused on two embedded units, namely the Departments of Chemical Engineering and Maritime Studies at a South African University of Technology (UoT). The participants were first-year engineering students enrolled in 2021 through the Extended Curriculum Programme (ECP). The ECP provides an access route for students who do not meet the minimum entry requirements, thereby enabling them to pursue engineering qualifications. Through this mechanism, the Department of Higher Education and Training (DHET) seeks to widen opportunities for historically and currently disadvantaged students. Mainstream programmes at UoT, ECP students are given an additional year which is integrated in the curriculum to allow students to have more time with lectures and less subjects per year. The ECP differs from standard academic programs by extending the study period, enabling students to cover the same curriculum content over two years instead of one. Recognizing the financial implications of this additional year, the Department of Higher Education and Training (DHET) provided specific funding for ECP initiatives. This support came with the understanding that instructors teaching these courses would need both deep subject-matter expertise and advanced teaching skills to effectively deliver content and facilitate student comprehension. The one single case study referenced in Fig. 1 focused on first-year students at two departments on the subject Physics. Physics is a subject which many first-year students could find particularly difficult [ 33 ]. If we compare two subjects Mathematics and Physics of the higher failure rate. While Mathematics is frequently viewed as concentrating on formulas and established principles, Physics had a combination and simple on memorisation will be very difficult to solve practical problems. Physics demands the practical application of theoretical concepts to engineering real-life case scenarios. South African students often face significant challenges in this regard, because of unequal access to technological resources and educational materials. As authors we believe that many students who come with previous and current disadvantages background have knowledge in the context were they grow up and applies them in real life which can provide valuable connections to formal Physics instruction and its real-world implementations. It is also important to emphasise that applied Physics involves using physical principles to address engineering challenges, integrating insights from Physics, Mathematics, Engineering, and related sciences in order to design and refine technologies. In this way, Physics can be understood not only as a body of theoretical knowledge but also as a human practice, requiring learners to engage in both abstract reasoning and practical problem-solving in real-world contexts. The first embedded unit examined in this study was the Chemical Engineering Department at the selected UoT. Participants included ECP first-year students and three lecturers who integrated e-textbooks into their teaching of engineering subjects. The second embedded unit was the ECP Nautical Science programme in the Maritime Studies Department, where participants consisted of first-year nautical science students and one physics lecturer. Research sample and method The total sample consisted of 73 respondents. These respondents were registered in two departments: 39 (53.4%) in the Maritime Studies Department and 34 (46.6%) in the Chemical Engineering Department. The data were obtained from an online questionnaire given between March and June, 2021 during COVID-19. We used six independent variables to determine the computer literacy levels of students on entering university. These variables were: Research for information on the Internet for study purposes (BU Search Internet): This variable indicates a student’s ability to effectively use online resources for academic research. This variable in a cluster suggests that students need to be proficient in finding and using online information for their studies. Use the school library’s electronic catalogue to find a book (BU Electronic Catalogue): This variable represents the ability to navigate digital library systems. High predictor importance here would identify students familiar with using digital catalogues to access academic resources. Submit assignments by email (BU Submit Assignments): This variable highlights basic digital communication skills necessary for modern academic settings. Students in a cluster where this variable is significant have experience in using email for academic purposes. Share files with your teachers and classmates using cloud-based storage (e.g. using Google drive, Drop Box, one drive, etc.) (BU Share Files): This variable points to the use of cloud technologies for file sharing and storage. High importance indicates students who are comfortable with using these platforms for collaborative and individual work. Work with classmates on the same document on file (collaboration) when preparing for an assignment or group project (BU Submit Assignments): This variable measures the ability to engage in collaborative work using digital tools. A high significance here suggests students who are adept at using technology for group work. Never used any technology for my learning activities (BU No Technology): This variable inversely describes digital literacy. High importance in a cluster suggests a group of students with minimal or no experience using technology for educational purposes. Respondents were grouped using the two-step cluster analysis with the statistical programme IBM SPSS 29.0. In this case, the two-step cluster analysis grouped respondents in order to describe their levels of computer literacy. This method can use both continuous and categorical variables. However, we only used categorical variables in our study. The benefit of using the two-step cluster analysis methodology in IBM SPSS is that it automatically determines the best possible number of groupings. This methodology is made up of several steps, including cluster quality, optimal cluster number, and distance measure. The cluster quality was measured using Silhouette’s measure. The value of Silhouette’s measures the extent to which an object is similar to its cluster (cohesion) compared to the extent to which it is dissimilar to other clusters (separation). Cluster cohesion gives an indication of the average distance between a sample and all other data points within the same cluster. In contrast, cluster separation gives an indication of the average distance between a sample and all other data points in the nearest cluster [ 34 , 35 ]. Silhouette’s metric ranges from − 1 to + 1. A Silhouette value from − 1.0 to 0.2 identifies the classification as poor, a value from 0.2 to 0.5 as fair classification and a value from 0.5 to 1.0 as good classification. 35 Firstly, Silhouette’s value less than 0 indicates that the object is likely assigned to the wrong cluster because it is closer to a neighbouring cluster than its own. Secondly, Silhouette’s value of 0 indicates that the object is close to two neighbouring cluster, meaning it is positioned between them, not strongly belonging to either. Finally, Silhouette’s value of 1 indicates that the object is well assigned to its own cluster and far away from other clusters. In other words, higher Silhouette values indicate better clustering performance, as objects are more appropriately grouped with others in their cluster. The optimal number of clusters is determined based on the lowest Schwarz’s Bayesian Information Criterion (BIC) score which is calculated for each number of clusters within a specific range. The BIC with lower values indicate the optimal number of clusters, and the optimal number of clusters has the lowest BIC value. In addition, we also kept track of the large ratio of BIC changes and the large ratio of distance measures. However, the statistical programme IBM SPSS 29.0 automatically determines the optimal number of clusters without the authors’ decision 36 . The distance measure used in this study is a log-likelihood measure which can be used for mixed categorical and numerical variables. Our study only included categorical variables so the log-likelihood measure was appropriate. For continuous variables the Euclidian algorithm is used [ 35 ]. The two-step cluster analysis employed in this study exemplifies a transdisciplinary methodology by combining statistical techniques with educational theory and cognitive psychology. This allows for a deeper understanding of how various factors – such as prior exposure to technology, socio-economic status, and academic preparedness – intersect to shape students’ computer literacy and their ability to engage fruitfully with the academic programmes of study. Ethical considerations Ethics approval for this study was obtained from the Faculty Research Committee (FRC) at the relevant UoT (ID 16165892) at which this research was carried out. Results Reliability Analysis The Cronbach Alpha Coefficient for this set of items is 0.75 which is within the acceptable range of 0.70 to 1 [ 36 , 37 ] and indicates that the items measure a single construct. The heads of the Chemical Engineering and Maritime Studies departments were informed about the research and granted permission, although they were not participants. All participants, including students and lecturers, were assured that the research posed no risks to individuals, departments, or institutions. Model Summary Table 1 shows how the cluster analysis divided the total sample of 73 respondents, 39 (53.4%) registered in the Maritime Studies Department and 34 (46.6%) registered in the Chemical Engineering Department, into three clusters. The first cluster had 34 respondents, the second cluster had 26 respondents, while the third cluster had the fewest respondents (13). The ratio of the largest cluster to the smallest cluster is 2.62 which is in the ideal range of being less than 3. Table 1 Cluster distribution N % of Total Cluster 1 34 46.6 2 26 35.6 3 13 17.8 Combined 73 100 We applied the two-step cluster analysis methodology to group students’ by their computer literacy on entering university during the COVID-19 pandemic. We used six independent variables: (1) Search for information on the internet for study purposes; (2) Use the school library’s electronic catalogue to find a book; (3) Submit assignments by email; (4) Share files with your teachers and classmates using cloud-based storage (e.g. using Google drive, Drop Box, one drive, etc.); (5) Work with classmates on the same document on file (collaboration) when preparing for an assignment or group project; and (6) Never used any technology for my learning activities. All these variables describe computer literacy on entering university. Figure 3 shows all categorical variables used in the two-step cluster analysis with their predictor importance. The two-step cluster analysis, using the independent variables, divided the total sample into three groups or clusters. Since the aim of this paper to identify the specific computer literacy levels of students on entering university during the COVID-19 pandemic the results is significant. Figure 2 shows that the two-step cluster analysis using the six independent variables has Silhouette’s measure of cohesion and separation of 0.7 (this is well into the good interval) [ 38 ]. These results demonstrate that the computer literacy levels of these groups of students on entering university were significantly different from each other, but respondents in individual groups had similar computer literacy levels on entering university. Clusters The two-step cluster analysis in IBM SPSS 29.0 automatically determined the number of clusters. When the Bayesian Information Criterion (BIC) was computed in the first phase, it resulted in a good initial estimation of the maximum number of clusters (see Table 2 ). Table 2 presents the auto-clustering statistics of the BIC which determined the appropriate number of clusters. The auto-clustering table summarises the process by which the number of groups were chosen. The BIC was computed for each potential group, where smaller values indicate better models. In Table 2 , the large BIC value decreases from 328.408 for two Clusters to 254.367 for three Clusters, suggesting that three clusters would be the most appropriate number of clusters, based on the highest ratio of distance measures. In addition to the BIC, Table 2 demonstrates BIC change, the ratio of BIC changes and the ratio of distance measures. In Table 2 the BIC values were calculated for 15 clusters. In general, it is difficult to interpret a higher number of clusters as it leads to a difficult model. The statistical programme IBM SPSS 29.0 adopts an automatic solution based on a compromise between a large ratio of distance measures and a large ratio ofBIC changes. The optimal number of clusters for this study was determined to be three (ratio of BIC changes = 0.646, ratio of distance measures = 2.236). Table 2 Auto-Clustering Number of Clusters Schwarz's Bayesian Criterion (BIC) BIC Change a Ratio of BIC Changes b Ratio of Distance Measures c 1 446,028 2 328,408 -117,620 1,000 1,409 3 252,367 -76,041 ,646 2,236 4 232,596 -19,771 ,168 1,419 5 226,261 -6,335 ,054 1,387 6 228,881 2,620 -,022 1,451 7 238,686 9,805 -,083 1,609 8 254,524 15,837 -,135 1,030 9 270,648 16,124 -,137 1,323 10 289,119 18,471 -,157 1,031 11 307,811 18,692 -,159 1,304 12 328,147 20,336 -,173 1,080 13 348,886 20,739 -,176 1,028 14 369,763 20,877 -,177 1,274 15 391,687 21,924 -,186 1,377 a. The changes are from the previous number of clusters in the table. b. The ratios of changes are relative to the change for the two cluster solution. c. The ratios of distance measures are based on the current number of clusters against the previous number of clusters. Figure 4 summarises the results of the two-step cluster analysis as: the cluster size; the importance of the input variables (see the scale); and the most numerous groups of respondents, depending on the selected independent variable. Figure 4 reveals the ranking of the input predictors according to within-group importance in each cluster. We found that ‘Never used any technology for my learning activities’ was the most significant factor for cluster 3. For cluster 1, ‘Search for information on the Internet for study purposes’ was the most significant factor and, for cluster 2, ‘Work with classmates on the same document on file (collaboration) when preparing for an assignment or group project’ was the most significant factor. Cluster Comparison Figure 5 shows that the first (light blue) cluster consists of respondents who all ‘Search for information on the Internet for study purposes’ (importance 0.62), did not ‘Work with classmates on the same document on file (collaboration) when preparing for an assignment or group project’ (importance 0.47), did not ‘Submit assignments by email’ (importance 0.43), ‘Never used any technology for my learning activities’ (importance 1.00), did not ‘Use the school library’s electronic catalogue to find a book’ (importance 0.36) and did not ‘Share files with your teachers and classmates using cloud-based storage (e.g. using Google drive, Drop Box, one drive, etc.)’ (importance 0.32). This cluster had the lowest computer literacy level before arriving at university. Figure 5 also shows that the second (red) cluster consisted of respondents 57.7% of whom ‘Work with classmates on the same document on file (collaboration) when preparing for an assignment or group project’ (importance 0.47), 53.8% ‘Submit assignments by email’ (importance 0.43), only 46.2% ‘Use the school library’s electronic catalogue to find a book’ (importance 0.36), only 42.3% ‘Share files with your teachers and classmates using cloud-based storage (e.g. using Google drive, Drop Box, one drive, etc.)’ (importance 0.32), 100% ‘Never used any technology for my learning activities’ (importance 1.00), and 80.8% ‘Search for information on the Internet for study purposes’ (importance 0.62). This cluster had the highest computer literacy level before arriving at university. Figure 5 furthermore shows that the third (dark blue) cluster consisted exclusively of respondents who ‘Never used any technology for my learning activities’ (importance 1.00), 7.7% ‘Search for information on the Internet for study purposes’ (importance 0.62), none who ‘Use the school library’s electronic catalogue to find a book’ (importance 0.47), none who ‘Submit assignments by email’ (importance 0.43), none who ‘Share files with your teachers and classmates using cloud-based storage (e.g. using Google drive, Drop Box, one drive, etc.)’ (importance 0.36), and none who ‘Work with classmates on the same document on file (collaboration) when preparing for an assignment or group project’ (importance 0.32). This cluster can be seen to be the one with the middle level of computer literacy before arriving at university. Discussion Computer literacy is foundational for navigating the digital aspects of higher education teaching, learning and assessment [ 39 , 40 ]. The COVID-19 pandemic underscored the necessity for students to be proficient in using computers for a range of academic activities, including attending virtual classes, completing assignments, and accessing resources. The academic success of students is significantly influenced by their previous technological exposure, functional digital skills, and familiarity with electronic tools. These competencies have become increasingly vital in today's rapidly evolving digital landscape and artificial intelligence revolution. This study employs cluster analysis to classify incoming students into three distinct computer literacy categories (advanced, intermediate, and basic). This classification system enables educational institutions to develop customized support mechanisms aimed at enhancing students' digital capabilities. Early identification of these groups allows universities to implement focused interventions that reduce the risk of academic failure, loss of motivation, or eventual withdrawal. The application of cluster analysis can also guide institutional policies during the pre-admission phase. Such forward-thinking strategies ensure that faculty members and support services receive crucial information to organize appropriate academic resources and infrastructure. Potential interventions might include extended computer lab hours, digital literacy workshops, and training in ethical research practices and academic standards. South Africa's shift from paper-based systems to digital platforms has progressed slowly, hindered by unequal access and students' varied technological backgrounds. For example, the mandatory digital registration process has occasionally created difficulties. The researchers contend that incorporating computer literacy support and cluster analysis from the outset can facilitate this transition, minimize obstacles, and improve academic outcomes while preparing students for digital careers in an AI-dominated future. Study Limitations One of the limitation of this study is the investigation was limited in scope, focusing exclusively on a single university and two engineering departments. Future studies should encompass multiple universities with various departments and regions and include a wider array of engineering courses. Conclusion The findings indicate that targeted teaching methods, learning strategies, and evaluation approaches can substantially improve student readiness for tertiary education. This research emphasizes the ongoing need to develop students' computer literacy to ensure fair access to success in our progressively digital academic landscape. This study makes multiple important contributions: Firstly, it establishes digital literacy as a crucial determinant of student achievement in our fast-evolving technological environment, where AI integration demands continuous skill development in research, communication, and assessment. Secondly, it presents a pilot study that could inform the creation of inclusive pedagogical approaches for students with varying digital competencies. Thirdly, it validates cluster analysis as an effective diagnostic tool for assessing incoming students' knowledge and preparedness. Lastly, it illustrates how cluster analysis can guide curriculum design and institutional review processes. The authors propose examining in the future the correlation between academic performance and digital literacy to evaluate whether existing systems adequately prepare students for university demands. Given South Africa's persistent inequalities and comparatively limited technological access, this research area holds particular significance. As AI and digital technologies advance at an unprecedented pace, students must cultivate new competencies, including understanding ethical implications and maintaining academic honesty in assessments and coursework. Ethics and Consent Form The protocol was approved by the Faculty of Engineering and the Built Environment (FEBE) Ethics Committee (EiRC) of University of Technology (protocol code 16165892, June 2020) in accordance with the Faculty of Engineering and the Built Environment (FEBE) Ethics Committee guidelines and regulations. Declarations Ethics and Consent Form: The protocol was approved by the Faculty of Engineering and the Built Environment (FEBE) Ethics Committee (EiRC) of University of Technology (protocol code 16165892, June 2020) in accordance with the Faculty of Engineering and the Built Environment (FEBE) Ethics Committee guidelines and regulations. Consent to participate: Informed consent was obtained from all participants involved in the study. Consent to Publish: Not applicable Data availability statement: The data of this study are available from the corresponding author upon reasonable request. Contribution: ER writing introduction, methodology and discussion and conclusion, data collection; RP idea of the paper, data analysis, methodology and discussion, conceptualisation of the study. Funding Declaration: There was no funding applicable in this study. Clinical trial number: Not applicable. References Fataar A. 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Philosophy of mathematics and natural science. Princeton University Press. Mohamed N, Awang SR. The multiple intelligence classification of management graduates using two-step cluster analysis. Malays J Fundam Appl Sci. 2015;11:108–13. Silhouette Coefficient. An overview. Available from: https://www.sciencedirect.com/topics/computer-science/silhouette-coefficient. Supandi A, Saefuddin A, Sulvianti ID. Two-step cluster application to classify villages in Kabupaten Madiun based on village potential data. Xplore J Stat. 2021;10:12–26. Rađenović Ž, Boshkov T. Economic effects of congress tourism: Two-step cluster approach. Challenges Tour Bus Logist 21st Century. 2022;5:185–92. Brace N, Kemp R, Snelgar R. SPSS for psychologists. London: Palgrave Macmillan; 2009. Tkaczynski A. Segmentation using two-step cluster analysis. In: Dietrich T, Rundle-Thiele S, Kubacki K, editors. Segmentation in social marketing. Singapore: Springer; 2017. p. 109–25. Delmond AR, Weber EM, Busch HS. An interdisciplinary assessment of information literacy instruction. J Acad Librariansh. 2024;50(5):102944. South Africa Government. Apply for Financial Assistance from NSFAS. 2022. Available online: https://www.gov.za/services/tertiary-education/apply-financial-assistance-national-student-financial-aid-scheme-nsfas (accessed on 19 February 2022). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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1","display":"","copyAsset":false,"role":"figure","size":135286,"visible":true,"origin":"","legend":"\u003cp\u003eCase study\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8028679/v1/d1336286f254f8e277496bed.png"},{"id":99788555,"identity":"0fdb5596-6a33-453f-8c3c-68f9ad9f8792","added_by":"auto","created_at":"2026-01-08 12:47:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":47140,"visible":true,"origin":"","legend":"\u003cp\u003eModel summary and cluster quality based on Silhouette’s measure of cohesion and separation\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8028679/v1/b40797574d4151e284af944f.png"},{"id":99353674,"identity":"024d40de-bfa1-4ecc-9802-833f6f5aea49","added_by":"auto","created_at":"2026-01-01 13:40:31","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":64296,"visible":true,"origin":"","legend":"\u003cp\u003eSix independent predictors in segmentation in computer literacy before entering university\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8028679/v1/0d411b166f823d250f593b20.png"},{"id":99353678,"identity":"2e8a27b6-fcb4-474b-b98b-dd2807943f66","added_by":"auto","created_at":"2026-01-01 13:40:31","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":111797,"visible":true,"origin":"","legend":"\u003cp\u003eClusters\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8028679/v1/245f46c3454442db3cfd105f.png"},{"id":99789159,"identity":"664c282b-7460-40d0-83f1-3695e46b2a64","added_by":"auto","created_at":"2026-01-08 12:48:53","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":43775,"visible":true,"origin":"","legend":"\u003cp\u003eCluster comparison\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8028679/v1/3ec88a71a1f090ab8d02e982.png"},{"id":102749286,"identity":"7ec985f5-2629-4387-9ad2-72b4408c64f4","added_by":"auto","created_at":"2026-02-16 09:12:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":961869,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8028679/v1/c7c0d2b0-a0eb-4e7a-a5ae-ef4c6a434c2a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring First-Year Students’ Computer Literacy Through a Two-Step Cluster Analysis Approach","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSouth African universities are beginning to adjust to the fourth industrial revolution (4IR) and Artificial Intelligence (AI). This is evident at the Cape Peninsula University of Technology (CPUT) – the particular site for this research – which is implementing its ‘Vision 2030’, where the aim is to transform the institution into a ‘SMART‘ university. This will provide a space for bringing technology into the classroom to enhance the teaching and learning process. This means that the entire infrastructure will be changed to accommodate the use of smart technology for teaching and learning. However, this aim has been criticised, since some argue that pedagogy should be the driver, and technology merely the accelerator [1]. The technology in South Africa has not been integrated fully into the learning process and many students still have difficulty in studying online.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDigital technologies constitute a significant factor in the way in which our day-to-day lives are now distinctly different from what they were 20 years ago. Learning on and through the World Wide Web brings with it an array of new practices that extend, yet challenge, traditional expectations about universities and literate practice [2 - 6].\u003c/p\u003e\n\u003cp\u003eAs in many other countries, there is a digital skills gap in South Africa [7 - 9]. This digital skills gap exists among students as well as educators. In addition, educators and students need to be able to adapt continually to new technology in the digital era. There are various aspects around use and interactivity of e-textbook, which will be discussed in this section.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eComputer literacy on entering university has become essential for studying in higher education, particularly during and after the COVID-19 pandemic. By computer literacy, we refer to a learner's ability to use computer software and hardware effectively [1-3]. This paper specifically surveyed the computer literacy of learners on entering a university of technology.\u003c/p\u003e\n\u003cp\u003eDuring the COVID-19 showed that digital gap is much complex in South Africa where the inequality to access and success is high. During this time it highlighted that many students in South Africa do not have access to technology and during the COVID -19 did not access to university infrastructure and technologies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDigital literacy versus computer literacy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIt is crucial to recognise that\u0026nbsp;\u003cem\u003edigital literacy\u003c/em\u003e\u003cem\u003e\u0026nbsp;and\u0026nbsp;\u003c/em\u003e\u003cem\u003ecomputer literacy\u003c/em\u003e are related but distinct concepts. Digital literacy extends beyond the basic operational understanding of computers to include a wide range of practices encompassing three core domains of digital competence: technological, cognitive, and social skills. Technological skills refer to the ability to use digital tools effectively; cognitive skills involve critical thinking, information evaluation, and problem-solving; while social skills concern communication and interaction in digital contexts [10]. Expanding on this view, Van Deursen and Van Dijk [11] developed a framework for Internet skills that identified operational, formal, informational, and strategic competencies, capturing the diverse ways individuals navigate online environments. Their subsequent work further incorporated communication and content-creation dimensions [11, 12], acknowledging that digital engagement now requires users to produce and share knowledge as well as consume it. Similarly, [12] identified four key dimensions, ethnical, social, critical, and creative, while later studies [13–16] proposed measurable indicators for evaluating digital performance, such as operational, navigational, creative, and mobile proficiencies. More recent scholarship [15] has categorised these into seven core domains: technical proficiency, information management, communication, collaboration, creativity, critical thinking, and problem-solving.\u003c/p\u003e\n\u003cp\u003eIn the context of Artificial Intelligence (AI),\u0026nbsp;\u003cem\u003ethinking skills\u003c/em\u003e are increasingly recognised as an integral part of digital literacy [15]. Analytical, interdisciplinary, and systems thinking are now viewed as essential for equipping learners to navigate complex environments and to respond effectively to the demands of Industry 4.0 [13, 15]. The educational focus has therefore shifted from the mechanical use of digital tools to the responsible and ethical application of knowledge in digital spaces. Today, being digitally literate also entails the ability to critically assess online content, identify misinformation and echo chambers, and understand the socio-political implications of digital participation [17, 18]. At the same time, the rising importance of cybersecurity has drawn attention to the protection of data and privacy, particularly during times of uncertainty, such as elections or the COVID-19 pandemic [16].\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eComputer literacy\u003c/em\u003e\u003cem\u003e,\u003c/em\u003e in contrast, has historically been linked to access to digital technologies and devices within teaching and learning contexts [4, 8, 10]. As technology has advanced, the meaning of being computer-literate has evolved accordingly. The growing ubiquity of mobile and portable devices has transformed how individuals interact with technology, extending literacy beyond basic technical competence to encompass awareness of how technology shapes society. In contemporary contexts, laptops, tablets, and smartphones function within wireless ecosystems that allow for flexible learning and communication. This transformation means that literacy is no longer limited to skill acquisition but also involves understanding how digital tools influence cognition, relationships, and social structures.\u003c/p\u003e\n\u003cp\u003eComputing technology continues to play a pivotal role in redefining the parameters of computer literacy. Scholars have observed that portable and mobile technologies have become defining tools of the current decade [7, 9]. While traditional personal computers once served as stationary appliances, the integration of wireless connectivity has liberated devices from fixed physical spaces. Portable computing enables users to access and exchange information seamlessly, reflecting a new paradigm of\u0026nbsp;\u003cem\u003emobility\u003c/em\u003e and\u0026nbsp;\u003cem\u003eportability\u003c/em\u003e. These qualities, ubiquitous access to information and the capacity to communicate in any place and at any time, symbolise the fluid and interconnected nature of digital literacy in the 21st century [14].\u003c/p\u003e\n\u003cp\u003eFrom this perspective, computer literacy has traditionally been concerned with access and operational competence in using technology. Digital literacy, however, extends beyond this to include not only the ability to operate platforms but also the acquisition of higher-order skills such as critical thinking, evaluation, and ethical engagement in digital environments.\u003c/p\u003e"},{"header":"Background and aims","content":"\u003cp\u003eThe higher education sector in South Africa is made up of 26 public universities, 50 public Technical and Vocational Education and Training (TVET) colleges, and a range of private institutions. Post-1994, South Africa witnessed a dramatic rise in student numbers, alongside greater racial diversity in higher education [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Many South African students form the largest demographic in these institutions. To promote inclusivity, the South African government trying offers financial aid via the National Student Financial Aid Scheme (NSFAS), administered by the Department of Higher Education and Training. This intervention is assisting many students who have challenges with financial means to fund their education and who do not qualify for other grants or loans [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAnother alternative innovation in digital transformation for higher education is the transition to innovative teaching methods, learning spaces, and pedagogical frameworks. Sometimes it can be find resistance from academic staff to change persists as a key barrier. Yet, the COVID-19 crisis revealed a critical insight and open opportunity for technology which can empower educators and improve learning efficiency [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGlobally, academia is often seen as a stable career path, which can create resistance to new teaching and learning approaches, especially if perceived as jeopardizing job security. As a result, digital transformation initiatives sometimes face reluctance. However, the pandemic era proved that well-planned technological integration can enhance educational performance in universities [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhile some institutions adapted by leveraging the opportunities presented by online learning, it soon became apparent that not all universities could make this transition smoothly [\u003cspan additionalcitationids=\"CR22 CR23 CR24\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].Traditional and comprehensive universities in South Africa adapted reasonably well, but universities of technology (UoTs) faced major challenges [\u003cspan additionalcitationids=\"CR23 CR24 CR25 CR26 CR27 CR28\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. These challenges highlighted the deep-seated inequalities in access to and participation in higher education.\u003c/p\u003e \u003cp\u003eThis can be explained that in South Africa, most students at UoTs come from marginalised communities and historically disadvantaged backgrounds with limited resources [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. A prevalent issue in South African higher education is the inability of many students to fund their university studies [\u003cspan additionalcitationids=\"CR31 CR32\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The \"Higher Education and Skills in South Africa\" report by Statistics South Africa (StatsSA) found that over half of the youth (aged 18\u0026ndash;24) cited financial constraints as a barrier to higher education [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis paper aims to contribute to the understanding of the different levels of computer literacy \u0026ndash; high, medium, and low \u0026ndash; among learners entering university. In this paper, the authors identify levels of digital literacy among students through the use of cluster analysis. This approach is expected to reveal both the challenges students face and the varying levels of digital literacy they possess upon entering university. Gaining such insights will enable faculties and departments to better understand students\u0026rsquo; needs, minimise the risk of academic underperformance, and design holistic support structures that foster student success throughout their studies.\u003c/p\u003e\n\u003ch3\u003eResearch Design\u003c/h3\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eCase study\u003c/h2\u003e \u003cp\u003eThis study focused on a single case with embedded units, which involved examining multiple units or objects of analysis within that single case. Selecting the case and setting up the breadth and depth (boundaries) of the case study was done using the following criteria as presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn this study we used single case study (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This single case study focused on two embedded units, namely the Departments of Chemical Engineering and Maritime Studies at a South African University of Technology (UoT). The participants were first-year engineering students enrolled in 2021 through the Extended Curriculum Programme (ECP). The ECP provides an access route for students who do not meet the minimum entry requirements, thereby enabling them to pursue engineering qualifications. Through this mechanism, the Department of Higher Education and Training (DHET) seeks to widen opportunities for historically and currently disadvantaged students. Mainstream programmes at UoT, ECP students are given an additional year which is integrated in the curriculum to allow students to have more time with lectures and less subjects per year. The ECP differs from standard academic programs by extending the study period, enabling students to cover the same curriculum content over two years instead of one. Recognizing the financial implications of this additional year, the Department of Higher Education and Training (DHET) provided specific funding for ECP initiatives. This support came with the understanding that instructors teaching these courses would need both deep subject-matter expertise and advanced teaching skills to effectively deliver content and facilitate student comprehension.\u003c/p\u003e \u003cp\u003eThe one single case study referenced in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e focused on first-year students at two departments on the subject Physics. Physics is a subject which many first-year students could find particularly difficult [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. If we compare two subjects Mathematics and Physics of the higher failure rate. While Mathematics is frequently viewed as concentrating on formulas and established principles, Physics had a combination and simple on memorisation will be very difficult to solve practical problems. Physics demands the practical application of theoretical concepts to engineering real-life case scenarios. South African students often face significant challenges in this regard, because of unequal access to technological resources and educational materials. As authors we believe that many students who come with previous and current disadvantages background have knowledge in the context were they grow up and applies them in real life which can provide valuable connections to formal Physics instruction and its real-world implementations.\u003c/p\u003e \u003cp\u003eIt is also important to emphasise that applied Physics involves using physical principles to address engineering challenges, integrating insights from Physics, Mathematics, Engineering, and related sciences in order to design and refine technologies. In this way, Physics can be understood not only as a body of theoretical knowledge but also as a human practice, requiring learners to engage in both abstract reasoning and practical problem-solving in real-world contexts.\u003c/p\u003e \u003cp\u003eThe first embedded unit examined in this study was the Chemical Engineering Department at the selected UoT. Participants included ECP first-year students and three lecturers who integrated e-textbooks into their teaching of engineering subjects. The second embedded unit was the ECP Nautical Science programme in the Maritime Studies Department, where participants consisted of first-year nautical science students and one physics lecturer.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eResearch sample and method\u003c/h3\u003e\n\u003cp\u003eThe total sample consisted of 73 respondents. These respondents were registered in two departments: 39 (53.4%) in the Maritime Studies Department and 34 (46.6%) in the Chemical Engineering Department. The data were obtained from an online questionnaire given between March and June, 2021 during COVID-19.\u003c/p\u003e \u003cp\u003eWe used six independent variables to determine the computer literacy levels of students on entering university. These variables were:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eResearch for information on the Internet for study purposes (BU Search Internet): This variable indicates a student\u0026rsquo;s ability to effectively use online resources for academic research. This variable in a cluster suggests that students need to be proficient in finding and using online information for their studies.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eUse the school library\u0026rsquo;s electronic catalogue to find a book (BU Electronic Catalogue): This variable represents the ability to navigate digital library systems. High predictor importance here would identify students familiar with using digital catalogues to access academic resources.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eSubmit assignments by email (BU Submit Assignments): This variable highlights basic digital communication skills necessary for modern academic settings. Students in a cluster where this variable is significant have experience in using email for academic purposes.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eShare files with your teachers and classmates using cloud-based storage (e.g. using Google drive, Drop Box, one drive, etc.) (BU Share Files): This variable points to the use of cloud technologies for file sharing and storage. High importance indicates students who are comfortable with using these platforms for collaborative and individual work.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eWork with classmates on the same document on file (collaboration) when preparing for an assignment or group project (BU Submit Assignments): This variable measures the ability to engage in collaborative work using digital tools. A high significance here suggests students who are adept at using technology for group work.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eNever used any technology for my learning activities (BU No Technology): This variable inversely describes digital literacy. High importance in a cluster suggests a group of students with minimal or no experience using technology for educational purposes.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eRespondents were grouped using the two-step cluster analysis with the statistical programme IBM SPSS 29.0. In this case, the two-step cluster analysis grouped respondents in order to describe their levels of computer literacy. This method can use both continuous and categorical variables. However, we only used categorical variables in our study. The benefit of using the two-step cluster analysis methodology in IBM SPSS is that it automatically determines the best possible number of groupings. This methodology is made up of several steps, including cluster quality, optimal cluster number, and distance measure.\u003c/p\u003e \u003cp\u003eThe \u003cem\u003ecluster quality\u003c/em\u003e was measured using Silhouette\u0026rsquo;s measure. The value of Silhouette\u0026rsquo;s measures the extent to which an object is similar to its cluster (cohesion) compared to the extent to which it is dissimilar to other clusters (separation). Cluster cohesion gives an indication of the average distance between a sample and all other data points within the same cluster. In contrast, cluster separation gives an indication of the average distance between a sample and all other data\u003c/p\u003e \u003cp\u003epoints in the nearest cluster [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Silhouette\u0026rsquo;s metric ranges from \u0026minus;\u0026thinsp;1 to +\u0026thinsp;1. A Silhouette value from \u0026minus;\u0026thinsp;1.0 to 0.2 identifies the classification as poor, a value from 0.2 to 0.5 as fair classification and a value from 0.5 to 1.0 as good classification.\u003csup\u003e35\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eFirstly, Silhouette\u0026rsquo;s value less than 0 indicates that the object is likely assigned to the wrong cluster because it is closer to a neighbouring cluster than its own. Secondly, Silhouette\u0026rsquo;s value of 0 indicates that the object is close to two neighbouring cluster, meaning it is positioned between them, not strongly belonging to either. Finally, Silhouette\u0026rsquo;s value of 1 indicates that the object is well assigned to its own cluster and far away from other clusters. In other words, higher Silhouette values indicate better clustering performance, as objects are more appropriately grouped with others in their cluster.\u003c/p\u003e \u003cp\u003eThe \u003cem\u003eoptimal number of clusters\u003c/em\u003e is determined based on the lowest Schwarz\u0026rsquo;s Bayesian Information Criterion (BIC) score which is calculated for each number of clusters within a specific range. The BIC with lower values indicate the optimal number of clusters, and the optimal number of clusters has the lowest BIC value. In addition, we also kept track of the large ratio of BIC changes and the large ratio of distance measures. However, the statistical programme IBM SPSS 29.0 automatically determines the optimal number of clusters without the authors\u0026rsquo; decision\u003csup\u003e36\u003c/sup\u003e .\u003c/p\u003e \u003cp\u003eThe \u003cem\u003edistance measure\u003c/em\u003e used in this study is a log-likelihood measure which can be used for mixed categorical\u003c/p\u003e \u003cp\u003eand numerical variables. Our study only included categorical variables so the log-likelihood measure was appropriate. For continuous variables the Euclidian algorithm is used [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe two-step cluster analysis employed in this study exemplifies a transdisciplinary methodology by combining statistical techniques with educational theory and cognitive psychology. This allows for a deeper understanding of how various factors \u0026ndash; such as prior exposure to technology, socio-economic status, and academic preparedness \u0026ndash; intersect to shape students\u0026rsquo; computer literacy and their ability to engage fruitfully with the academic programmes of study.\u003c/p\u003e\u003cp\u003e\u003cem\u003eEthical considerations\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eEthics approval for this study was obtained from the Faculty Research Committee (FRC) at the relevant UoT (ID 16165892) at which this research was carried out.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eReliability Analysis\u003c/h2\u003e \u003cp\u003eThe Cronbach Alpha Coefficient for this set of items is 0.75 which is within the acceptable range of 0.70 to 1 [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] and indicates that the items measure a single construct. The heads of the Chemical Engineering and Maritime Studies departments were informed about the research and granted permission, although they were not participants. All participants, including students and lecturers, were assured that the research posed no risks to individuals, departments, or institutions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eModel Summary\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows how the cluster analysis divided the total sample of 73 respondents, 39 (53.4%) registered in the Maritime Studies Department and 34 (46.6%) registered in the Chemical Engineering Department, into three clusters. The first cluster had 34 respondents, the second cluster had 26 respondents, while the third cluster had the fewest respondents (13). The ratio of the largest cluster to the smallest cluster is 2.62 which is in the ideal range of being less than 3.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCluster distribution\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e% of Total\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCluster\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCombined\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWe applied the two-step cluster analysis methodology to group students\u0026rsquo; by their computer literacy on entering university during the COVID-19 pandemic. We used six independent variables: (1) Search for information on the internet for study purposes; (2) Use the school library\u0026rsquo;s electronic catalogue to find a book; (3) Submit assignments by email; (4) Share files with your teachers and classmates using cloud-based storage (e.g. using Google drive, Drop Box, one drive, etc.); (5) Work with classmates on the same document on file (collaboration) when preparing for an assignment or group project; and (6) Never used any technology for my learning activities. All these variables describe computer literacy on entering university. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows all categorical variables used in the two-step cluster analysis with their predictor importance.\u003c/p\u003e \u003cp\u003eThe two-step cluster analysis, using the independent variables, divided the total sample into three groups or clusters. Since the aim of this paper to identify the specific computer literacy levels of students on entering university during the COVID-19 pandemic the results is significant. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows that the two-step cluster analysis using the six independent variables has Silhouette\u0026rsquo;s measure of cohesion and separation of 0.7 (this is well into the good interval) [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. These results demonstrate that the computer literacy levels of these groups of students on entering university were significantly different from each other, but respondents in individual groups had similar computer literacy levels on entering university.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eClusters\u003c/h3\u003e\n\u003cp\u003eThe two-step cluster analysis in IBM SPSS 29.0 automatically determined the number of clusters. When the Bayesian Information Criterion (BIC) was computed in the first phase, it resulted in a good initial estimation of the maximum number of clusters (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the auto-clustering statistics of the BIC which determined the appropriate number of clusters. The auto-clustering table summarises the process by which the number of groups were chosen. The BIC was computed for each potential group, where smaller values indicate better models. In Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the large BIC value decreases from 328.408 for two Clusters to 254.367 for three Clusters, suggesting that three clusters would be the most appropriate number of clusters, based on the highest ratio of distance measures. In addition to the BIC, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e demonstrates BIC change, the ratio of BIC changes and the ratio of distance measures. In Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e the BIC values were calculated for 15 clusters. In general, it is difficult to interpret a higher number of clusters as it leads to a difficult model. The statistical programme IBM SPSS 29.0 adopts an automatic solution based on a compromise between a large ratio of distance measures and a large ratio ofBIC changes. The optimal number of clusters for this study was determined to be three (ratio of BIC changes\u0026thinsp;=\u0026thinsp;0.646, ratio of distance measures\u0026thinsp;=\u0026thinsp;2.236).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAuto-Clustering\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of Clusters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSchwarz's Bayesian Criterion (BIC)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBIC Change\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRatio of BIC Changes\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRatio of Distance Measures\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e446,028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e328,408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-117,620\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,409\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e252,367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-76,041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e,646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,236\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e232,596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-19,771\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e,168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,419\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e226,261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-6,335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e,054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,387\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e228,881\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,620\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-,022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,451\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e238,686\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9,805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-,083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,609\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e254,524\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15,837\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-,135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,030\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e270,648\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16,124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-,137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,323\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e289,119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18,471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-,157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e307,811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18,692\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-,159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,304\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e328,147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20,336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-,173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,080\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e348,886\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20,739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-,176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e369,763\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20,877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-,177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,274\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e391,687\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21,924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-,186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,377\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003ea. The changes are from the previous number of clusters in the table.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eb. The ratios of changes are relative to the change for the two cluster solution.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003ec. The ratios of distance measures are based on the current number of clusters against the previous number of clusters.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e summarises the results of the two-step cluster analysis as: the cluster size; the importance of the input variables (see the scale); and the most numerous groups of respondents, depending on the selected independent variable. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e reveals the ranking of the input predictors according to within-group importance in each cluster. We found that \u0026lsquo;Never used any technology for my learning activities\u0026rsquo; was the most significant factor for cluster 3. For cluster 1, \u0026lsquo;Search for information on the Internet for study purposes\u0026rsquo; was the most significant factor and, for cluster 2, \u0026lsquo;Work with classmates on the same document on file (collaboration) when preparing for an assignment or group project\u0026rsquo; was the most significant factor.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eCluster Comparison\u003c/h3\u003e\n\u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows that the first (light blue) cluster consists of respondents who all \u0026lsquo;Search for information on the Internet for study purposes\u0026rsquo; (importance 0.62), did not \u0026lsquo;Work with classmates on the same document on file (collaboration) when preparing for an assignment or group project\u0026rsquo; (importance 0.47), did not \u0026lsquo;Submit assignments by email\u0026rsquo; (importance 0.43), \u0026lsquo;Never used any technology for my learning activities\u0026rsquo; (importance 1.00), did not \u0026lsquo;Use the school library\u0026rsquo;s electronic catalogue to find a book\u0026rsquo; (importance 0.36) and did not \u0026lsquo;Share files with your teachers and classmates using cloud-based storage (e.g. using Google drive, Drop Box, one drive, etc.)\u0026rsquo; (importance 0.32). This cluster had the lowest computer literacy level before arriving at university.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e also shows that the second (red) cluster consisted of respondents 57.7% of whom \u0026lsquo;Work with classmates on the same document on file (collaboration) when preparing for an assignment or group project\u0026rsquo; (importance 0.47), 53.8% \u0026lsquo;Submit assignments by email\u0026rsquo; (importance 0.43), only 46.2% \u0026lsquo;Use the school library\u0026rsquo;s electronic catalogue to find a book\u0026rsquo; (importance 0.36), only 42.3% \u0026lsquo;Share files with your teachers and classmates using cloud-based storage (e.g. using Google drive, Drop Box, one drive, etc.)\u0026rsquo; (importance 0.32), 100% \u0026lsquo;Never used any technology for my learning activities\u0026rsquo; (importance 1.00), and 80.8% \u0026lsquo;Search for information on the Internet for study purposes\u0026rsquo; (importance 0.62). This cluster had the highest computer literacy level before arriving at university.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e furthermore shows that the third (dark blue) cluster consisted exclusively of respondents who \u0026lsquo;Never used any technology for my learning activities\u0026rsquo; (importance 1.00), 7.7% \u0026lsquo;Search for information on the Internet for study purposes\u0026rsquo; (importance 0.62), none who \u0026lsquo;Use the school library\u0026rsquo;s electronic catalogue to find a book\u0026rsquo; (importance 0.47), none who \u0026lsquo;Submit assignments by email\u0026rsquo; (importance 0.43), none who \u0026lsquo;Share files with your teachers and classmates using cloud-based storage (e.g. using Google drive, Drop Box, one drive, etc.)\u0026rsquo; (importance 0.36), and none who \u0026lsquo;Work with classmates on the same document on file (collaboration) when preparing for an assignment or group project\u0026rsquo; (importance 0.32). This cluster can be seen to be the one with the middle level of computer literacy before arriving at university.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eComputer literacy is foundational for navigating the digital aspects of higher education teaching, learning and assessment [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. The COVID-19 pandemic underscored the necessity for students to be proficient in using computers for a range of academic activities, including attending virtual classes, completing assignments, and accessing resources. The academic success of students is significantly influenced by their previous technological exposure, functional digital skills, and familiarity with electronic tools. These competencies have become increasingly vital in today's rapidly evolving digital landscape and artificial intelligence revolution.\u003c/p\u003e \u003cp\u003eThis study employs cluster analysis to classify incoming students into three distinct computer literacy categories (advanced, intermediate, and basic). This classification system enables educational institutions to develop customized support mechanisms aimed at enhancing students' digital capabilities. Early identification of these groups allows universities to implement focused interventions that reduce the risk of academic failure, loss of motivation, or eventual withdrawal.\u003c/p\u003e \u003cp\u003eThe application of cluster analysis can also guide institutional policies during the pre-admission phase. Such forward-thinking strategies ensure that faculty members and support services receive crucial information to organize appropriate academic resources and infrastructure. Potential interventions might include extended computer lab hours, digital literacy workshops, and training in ethical research practices and academic standards.\u003c/p\u003e \u003cp\u003eSouth Africa's shift from paper-based systems to digital platforms has progressed slowly, hindered by unequal access and students' varied technological backgrounds. For example, the mandatory digital registration process has occasionally created difficulties. The researchers contend that incorporating computer literacy support and cluster analysis from the outset can facilitate this transition, minimize obstacles, and improve academic outcomes while preparing students for digital careers in an AI-dominated future.\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStudy Limitations\u003c/h2\u003e \u003cp\u003eOne of the limitation of this study is the investigation was limited in scope, focusing exclusively on a single university and two engineering departments. Future studies should encompass multiple universities with various departments and regions and include a wider array of engineering courses.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe findings indicate that targeted teaching methods, learning strategies, and evaluation approaches can substantially improve student readiness for tertiary education. This research emphasizes the ongoing need to develop students' computer literacy to ensure fair access to success in our progressively digital academic landscape.\u003c/p\u003e \u003cp\u003eThis study makes multiple important contributions: Firstly, it establishes digital literacy as a crucial determinant of student achievement in our fast-evolving technological environment, where AI integration demands continuous skill development in research, communication, and assessment. Secondly, it presents a pilot study that could inform the creation of inclusive pedagogical approaches for students with varying digital competencies. Thirdly, it validates cluster analysis as an effective diagnostic tool for assessing incoming students' knowledge and preparedness. Lastly, it illustrates how cluster analysis can guide curriculum design and institutional review processes.\u003c/p\u003e \u003cp\u003eThe authors propose examining in the future the correlation between academic performance and digital literacy to evaluate whether existing systems adequately prepare students for university demands.\u003c/p\u003e \u003cp\u003eGiven South Africa's persistent inequalities and comparatively limited technological access, this research area holds particular significance. As AI and digital technologies advance at an unprecedented pace, students must cultivate new competencies, including understanding ethical implications and maintaining academic honesty in assessments and coursework.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthics and Consent Form\u003c/strong\u003e \u003cp\u003e The protocol was approved by the Faculty of Engineering and the Built Environment (FEBE) Ethics Committee (EiRC) of University of Technology (protocol code 16165892, June 2020) in accordance with the Faculty of Engineering and the Built Environment (FEBE) Ethics Committee guidelines and regulations.\u003c/p\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics and Consent Form:\u0026nbsp;\u003c/strong\u003eThe protocol was approved by the Faculty of Engineering and the Built Environment (FEBE) Ethics Committee (EiRC) of University of Technology (protocol code 16165892, June 2020) in accordance with the Faculty of Engineering and the Built Environment (FEBE) Ethics Committee guidelines and regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate:\u0026nbsp;\u003c/strong\u003eInformed consent was obtained from all participants involved in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish:\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement:\u0026nbsp;\u003c/strong\u003eThe data of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContribution:\u0026nbsp;\u003c/strong\u003eER writing introduction, methodology and discussion and conclusion, data collection; RP idea of the paper, data analysis, methodology and discussion, conceptualisation of the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Declaration:\u0026nbsp;\u003c/strong\u003eThere was no funding applicable in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFataar A. Placing students at the centre of the decolonizing education imperative: Engaging the (mis)recognition struggles of students at the post-apartheid university. Educ Stud. 2018;54(6):595\u0026ndash;608.\u003c/li\u003e\n\u003cli\u003eWoldegiorgis ET. Mitigating the digital divide in the South African higher education system in the face of the COVID-19 pandemic. Perspect Educ. 2022;40(3):197\u0026ndash;211.\u003c/li\u003e\n\u003cli\u003eDalvit L. Mobile communication and urban/rural flows in a South African marginalised community. Am Behav Sci. 2023;67(7):913\u0026ndash;25.\u003c/li\u003e\n\u003cli\u003eMurray MC, P\u0026eacute;rez J. Unraveling the digital literacy paradox: How higher education fails at the fourth literacy. Issues Informing Sci Inf Technol. 2014;11:85\u0026ndash;100.\u003c/li\u003e\n\u003cli\u003eWu D. Digital literacy: Evolution, evaluation, and enhancement. In: International Conference on Blended Learning. 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Available from: https://www.sciencedirect.com/topics/computer-science/silhouette-coefficient.\u003c/li\u003e\n\u003cli\u003eSupandi A, Saefuddin A, Sulvianti ID. Two-step cluster application to classify villages in Kabupaten Madiun based on village potential data. Xplore J Stat. 2021;10:12\u0026ndash;26.\u003c/li\u003e\n\u003cli\u003eRađenović Ž, Boshkov T. Economic effects of congress tourism: Two-step cluster approach. Challenges Tour Bus Logist 21st Century. 2022;5:185\u0026ndash;92.\u003c/li\u003e\n\u003cli\u003eBrace N, Kemp R, Snelgar R. SPSS for psychologists. London: Palgrave Macmillan; 2009.\u003c/li\u003e\n\u003cli\u003eTkaczynski A. Segmentation using two-step cluster analysis. In: Dietrich T, Rundle-Thiele S, Kubacki K, editors. Segmentation in social marketing. Singapore: Springer; 2017. p. 109\u0026ndash;25.\u003c/li\u003e\n\u003cli\u003eDelmond AR, Weber EM, Busch HS. An interdisciplinary assessment of information literacy instruction. J Acad Librariansh. 2024;50(5):102944.\u003c/li\u003e\n\u003cli\u003eSouth Africa Government. Apply for Financial Assistance from NSFAS. 2022. Available online: https://www.gov.za/services/tertiary-education/apply-financial-assistance-national-student-financial-aid-scheme-nsfas (accessed on 19 February 2022).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"computer literacy, digital literacy, COVID-19 impact on computer literacy, two-step cluster analysis, higher education, technology, educational practices, learning experiences","lastPublishedDoi":"10.21203/rs.3.rs-8028679/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8028679/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUnderstanding the computer literacy of students entering university is important as the shift towards online teaching, learning and assessment in higher education has become the \u0026lsquo;new normal\u0026rsquo; since COVID-19. This new normal makes assumptions about the levels of computer literacy of incoming students. This paper then surveys incoming students in order to ask the following questions: What is the computer literacy of students when they enter higher education? And, how can this research inform the facilitation of students\u0026rsquo; online teaching, learning and assessment? The survey research provides valuable insights into the computer literacy levels of students entering a South African University of Technology. Methodologically the two-step cluster analysis, which is a hybrid approach that first uses a distance measure to separate groups and then a probabilistic approach to choose the optimal subgroup model, is used. The significance of the variables (factors), such as general technology use, internet search skills, collaborative technology use, and technology for assignment submission, underscores the importance of these skills in higher education. The two-step process identified three distinct groups (clusters) of students with varying levels of computer literacy among the respondents from two engineering departments. Understanding the computer literacy levels of incoming students can inform strategic planning for integrating technology into educational practices and support services across transdisciplinary. By tailoring educational approaches to match students' existing skills and preferences, this University of Technology specifically, and universities in general, can enhance learning experiences and better prepare students for the demands of the digital age.\u003c/p\u003e","manuscriptTitle":"Exploring First-Year Students’ Computer Literacy Through a Two-Step Cluster Analysis Approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-01 13:40:24","doi":"10.21203/rs.3.rs-8028679/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e356fd66-d700-44bb-9339-19bbc5814c53","owner":[],"postedDate":"January 1st, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-16T08:11:19+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-01 13:40:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8028679","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8028679","identity":"rs-8028679","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}