1 Introduction

The COVID-19 pandemic has increased the importance of the use of ICT in the education sector. Many countries have used online learning to reduce the adverse effects of school closures (World Bank, 2021). The prevalence and use of ICT have increased due to the increase of state-based primary school projects and the decrease in the cost of digital devices compared to the past. Families now have more ICT tools (Meggiolaro, 2018). In this way, many students have much better opportunities to use ICT resources at school and home. The use of ICT in teaching has many advantages. Using more exciting and fun ICT tools increases students’ motivation (Bullock, 2001; Tüzün et al., 2009). Therefore, the effective use of ICT enables more interactive and collaborative learning. It also promotes communication between teachers and students (Koç, 2005; Schulz-Zander et al., 2002). However, students Have some problems associated with using ICT. Intensive use of ICT in various forms, such as leisure or social activities, can distract students from the lesson (Leuven et al., 2007). Thus, the positive effects of ICT use may be reduced, leading to a more unfavourable or insignificant overall effect. It can also lead to problematic use of the Internet and ICT (Mamun et al., 2020). Hinvest and Brosnan (2012) conducted a meta-analysis showing that excessive ICT use can lead to addiction to specific technologies, especially the Internet and video games. According to research conducted by Falck et al. (2018) and Lorena et al. (2017), excessive use of ICT in educational settings may cause students to lose focus from traditional learning activities. Additionally, it has been proposed that teachers may need to invest more time in ensuring that the inclusion of ICT in learning processes is more impactful (Falck et al., 2018). Butoria and Aljabri (2022) stated that using ICT in education is effective in certain situations. Positive results can be obtained by combining computer-assisted learning environments with traditional learning techniques.

1.1 Literature review

The purpose of this section is to conduct a thorough examination of the literature on the application and influence of ICT in the field of education. Prior research has primarily concentrated on the availability of ICT and its impact on students' academic performance. Given that PISA data has a hierarchical structure due to its nature, most studies have taken a variable-centred approach. On the other hand, this research aims to address gaps in the literature by adopting a person-centred approach. Instead of the hierarchical linear modelling (HLM) method preferred in most studies (Bhutoria & Aljabri, 2022; Hu & Yu, 2021; Ertem, 2021), LPA was used in this study. In addition, the effects of ICT motivational covariates on students' ICT use were also examined.

1.1.1 Student-level covariates

Socio-demographic factors

Technology is essential in education (Johnson et al., 2014). To understand this effect, large-scale exams such as PISA have been conducted, and countries have compared their technology use through the ICT Familiarity Questionnaire of PISA (Aryadoust, 2020). Gnambs (2021) stated that there are personal differences in students' ICT literacy, while Scherer et al. (2017) emphasised that gender significantly impacts ICT use. This difference shows that, generally, females are more disadvantaged in ICT use than males. It has been reported that male students are more confident in computer use (Tømte & Hatlevik, 2011; Zhang & Liu, 2016), but recent studies show that the differences in gender-based ICT attitudes are decreasing (Cai et al., 2017; Gnambs, 2021).

In PISA data, socioeconomic status (ESCS) measures students' access to their family's social, cultural and financial capital (Avvisati, 2020), and the findings show that there is a digital divide according to students' social, cultural and economic status (Luu & Freeman, 2011; Scherer & Siddiq, 2019). An analysis of students from 22 developed countries participating in PISA 2012 (Tan & Hew, 2017) found that the lack of ICT resources in schools harms students' academic achievement. ICT accessibility to students at school and home can positively affect their autonomy in ICT use, their interest in ICT use, and academic achievement. High ICT accessibility allows students to learn at their own pace, and students with higher ICT resources can more easily obtain and share additional resources, such as learning materials.

Motivational factors of ICT

In the PISA context, ICT engagement enables students to control their learning through ICT tools on their initiative. This, in turn, leads to an increase in students' intrinsic motivation to use ICT and the development of their ICT knowledge and skills (Park & Weng, 2020). ICT self-efficacy is an individual's confidence level in ICT tools (Yang & Cheng, 2009). According to Fagan et al., individuals with high self-efficacy in ICT use tend to be more proficient in computer use (2004). According to Venkatesh and Davis (2000), these people take a determined approach to using it despite their difficulties. Rohatgi et al. (2016) discovered a Decently weak positive correlation between ICT self-efficacy and ICT use. Tømte and Hatlevik (2011) reported that people with high self-efficacy in advanced ICT operations use it more often for educational and entertainment purposes. Lei et al. (2021) found that students with high ICT proficiency can competently solve problems using various ICT tools, including computers, and thus help them learn.

ICT usage and academic success

Studies have revealed that using ICT has the potential to enhance students' academic achievement (Kong et al., 2022). Although some research has shown that ICT can positively impact students' academic performance (Chiao & Chiu, 2018), others have suggested that this effect may not be substantial (Hu et al., 2018). According to a country-by-country PISA data analysis, moderate ICT use at home and school (i.e., engaging with technology several times a week) is associated with the highest reading scores (Gubbels et al., 2020). In a study that analysed the effects of ICT use in Taiwan using PISA 2018 data, Chiu (2020) found that three types of ICT use (i.e., leisure, education, and school) can lead to secondary returns for course scores, and the effectiveness of ICT use typically levels off beyond a particular threshold. Moreover, Karlsson (2020) discovered that while monthly and weekly computer use can positively affect students' scores, daily computer use can negatively correlate with test scores.

Borgonovi & Pokropek's, 2021 research suggests that students who use intermediate levels of ICT either in school or outside of it have the highest reading scores. However, Leuven et al., 2007 noted that students who use ICT intensively in various ways, including leisure or social activities, tend to get distracted from their learning. In 2021, Gómez-Fernandez and Mediavilla found that better academic performance in reading and science is linked to using ICT for leisure activities at home while using ICT for school activities at home hurts academic performance in mathematics, science, and reading. Overuse of ICT in schools has been shown to reduce academic performance in mathematics, science, and reading. However, Fuchs & Woessmann's, 2004 analysis of PISA 2000 data from all participating countries determined that computer use positively affects the educational process, and schools with more computers per student tend to score higher in mathematics and science. Kubiatko and Vlckova analysed the PISA 2006 data for the Czech Republic in 2010. They found that students who used ICT effectively in education were more academically successful than those who used it in non-educational activities. Finally, Alderete & Formichella's, 2016 study of the effects of the "Equal Connection" Program in Argentina, using PISA data, concluded that providing student laptops improves academic performance.

Despite some studies not finding a connection between academic performance and ICT usage, Aypay (2010) found that using computers did not affect mathematics, science, or reading scores, according to PISA-2006 data for Turkey. Biagi and Loi (2013) noted that using computers extensively positively influenced PISA exam results. Their research suggested that students who frequently played games on computers achieved higher academic success, while those who used computers regularly for academic purposes experienced a decline in academic performance. Based on data from the International Mathematics and Science Study (TIMSS), Falck et al. (2018) found that students who used computers to search for information were more successful academically. Still, the authors determined that using computers in classrooms did not affect student achievement. Computer use at home did not affect the educational process, as Fairlie and Robinson (2013) and Beuermann et al. (2015) found in an experiment involving portable computers in Peru. Various studies utilising PISA data have suggested that computer use at home contributed to higher academic achievement (Fairlie et al., 2010; Notten & Kraaykamp, 2009; Schmitt & Wadsworth, 2006). Using computers at home had more significant effects than using them in school, according to Spiezia (2011). Agasisti and his colleagues (2020) found that excessive computer usage for homework at home resulted in lower academic scores across all subjects in OECD countries. Previous research indicates that incorporating ICT in learning, social interaction, or leisure activities can increase students' autonomy and interest in technology (Burbat, 2016; Honarzad & Rassaei, 2019; Liu et al., 2018). By integrating technology into their schoolwork and daily routine, students can improve their ability to use it efficiently in their free time, ultimately improving their learning outcomes. Therefore, students' ICT usage, autonomy, and interest in technology can significantly influence their academic success. Zhang and Liu's (2016) study, which analysed five years of PISA data, showed a positive correlation between the number of computers connected to the internet and academic achievement.

School level covariates

Due to the COVID-19 pandemic, schools worldwide have switched to online education instead of face-to-face education. This situation has increased the necessity of digital technology, especially for schools (Kong et al., 2022). However, in the face of this global emergency, schools lack digital device infrastructure (Kim et al., 2021; Zhang & Liu, 2016) and encounter unexpected complex challenges, such as the limited capacity of teachers to use digital devices (Bozkus, 2021). At the same time, outdated technological equipment is shown to be a significant obstacle to implementing ICT in educational activities (Owston, 2003). However, research shows that schools in many countries have significantly improved ICT technology (Fraillon et al., 2014; OECD, 2015a, b). More than 66% of secondary school students attend schools with educational activities unaffected by the lack of computers. However, the rest of them are educated in schools that do not have internet connections or are of poor quality (OECD, 2013b). A school's digital access and support significantly affect teachers' technology use and integration (Liu et al., 2016). Saal et al. (2021) reported that computer availability and frequency of use in mathematics lessons positively correlate with students' mathematics achievement. On the other hand, Woessmann and Fuchs (2004) found an inverse U relationship between internet use in schools and students' mathematics and reading performance. There may be differences in school size and ICT use between private and public schools. Private schools offer students more opportunities for success in education and school administration (Fuchs & Wößmann, 2008) this indicates that they have more ICT resources. Previous studies have identified the differences between private and public schools, including the use of ICT and access to the Internet (Ferraro, 2018). The academic success of the school type and the use of ICT are influenced by many factors, including the socioeconomic characteristics of students and schools in OECD countries (OECD, 2016). The ratio of computers per student in schools is increasing in many countries (for developing economies), except for wealthy countries (OECD, 2015a, b). For example, Román Carrasco and Murillo Torrecilla (2012) stated in their study conducted in 16 Latin American countries that students studying in schools with more than ten computers are more successful than students studying in schools with fewer than ten computers.

1.1.2 Problems experienced in the use of ICT throughout the world and Turkey

There are many challenges related to using ICT globally, including in Turkey. Several fundamental problems can be identified:

  1. 1.

    Digital Divide: The digital divide refers to the disparity between individuals with access to ICT and those without access. Factors such as income, education, and infrastructure contribute to this divide. As a result, specific segments of the population may be deprived of the opportunities provided by ICT, which can lead to increased inequality between different groups.

  2. 2.

    Foreign suppliers in Turkey are encountering growing obstacles because of the prevailing digital services tax (DST), local content requirements, and limitations enforced on social media networks. Additionally, the absence of technical resources and support impedes the incorporation of ICT into education (Turgut & Aslan, 2021).

  3. 3.

    Insufficient ICT Provision in Education: Although the use of ICT by teachers in Turkey is close to the EU average, the level of ICT provision in schools remains insufficient. This limitation may hinder ICT's potential benefits in the education sector.

  4. 4.

    ICT Competence of Teachers: The integration of ICT into education is influenced by teachers' ICT and pedagogical competencies. Improving teachers' skills and knowledge through high-quality in-service ICT training is imperative.

  5. 5.

    Students' ICT Competencies: Students' ICT competencies also play an essential role in integrating ICT into learning environments in Turkey (Turgut & Aslan, 2021).

  6. 6.

    Scarcity of Educational Resources: The scarcity of educational materials supporting ICT use in learning environments is widespread in Turkey (Europan Schoolnet, 2012).

  7. 7.

    Management and Policy Concerns: The attitudes of school administrators and the quality of ICT policies can significantly affect the integration of ICT into education (Europan Schoolnet, 2012).

  8. 8.

    Cyber Security: Turkey's high risk of cyber threats increases the importance of cyber security in the ICT sector (Turgut & Aslan, 2021).

To overcome these challenges, it is essential to improve the infrastructure, provide better access to ICT resources, increase the ICT competencies of teachers and students, and develop comprehensive and harmonised ICT policies (European Schoolnet, 2012).

1.1.3 The importance of the study

The use of ICT in the field of education has gained significant importance, especially in the context of the pandemic. The unprecedented challenges posed by the pandemic have brought the effective use of ICT tools and educational resources into the spotlight. The accessibility of technological resources and their impact on subsequent student achievement is a notable focus of the research. Understanding the differences between students becomes very important in developing more comprehensive and efficient educational strategies. The article is essential in closing the digital gap in education and ensuring equal opportunities for all students. Examining the effects of ICT-related motivational factors such as interest, competence, social interaction and autonomy, as well as the effects of students on the frequency of technology use, can facilitate the creation of strategies needed to increase student motivation. Therefore, the research will significantly contribute to the literature as it examines the accessibility of ICT resources and their effects on student achievement at the student and school levels with covariates. The results of this research can provide teachers and administrators with a roadmap for ICT access and usage strategies to determine the size of the digital gap in education. The fact that Turkey is a developing country in the digital field with a large student population may help understand this issue (Akyüz, 2014). It provides essential information for the development of educational policies and practices related to the use of technology in education, especially in the context of Turkey. It provides an opportunity for comparison to other researchers on the same topic.

1.1.4 Research questions

  1. 1.1.4.1.

    What are the characteristics of Turkish students' profiles based on ICT use?

  2. 1.1.4.2.

    The Effect of student-level covariates on ICT Use Profiles (Gender, ESCS, ICT Motivation, ICT resources and Academic Achievements: Students' ICT-related use patterns are significantly influenced by their past characteristics, such as gender and the educational level achieved by their parents. It has been observed that males are more likely to fall into this category of advanced ICT users (Karakoyun & Başaran, 2021). Also, since students who exhibit more ICT participation and motivation tend to use technology more extensively for educational purposes, motivational factors play a vital role (Simõeset al., 2021). In addition, since advanced ICT users tend to show lower scores in mathematics, reading and science assessments, academic achievements have also been associated with ICT use (Karakoyun & Başaran, 2021).

  3. 1.1.4.3.

    The Effect of school-level covariates on ICT Usage Profiles: The ICT usage profiles of Turkish students, private-public schools, the availability of computers for educational purposes, the number of computers connected to the Internet, and teacher-educational material shortages may be influenced by various factors at the school level.

As a result, the characteristics of ICT usage profiles among Turkish students are shaped by many covariates, including individual background characteristics, motivation factors, academic achievements, and factors at the school level. The article examines the relationship of these covariates on ICT profiles.

The research model is given in Figure 1.

Fig. 1
figure 1

Model of the study.

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2 Method

2.1 PISA data

The PISA evaluates the competence and skills required for 15-year-old students to participate effectively in modern society, and this evaluation is conducted every three years. This assessment is designed for students about to complete their compulsory education. In 2018, 612,004 students from 82 countries participated in PISA. This study used PISA 2018 Turkey data, which included 6890 students (49.1% female, 50.9% male) from 186 schools.

Abbreviations and explanations of the indicators that form the basis of the research model and covariates at the student-school level are given below.

2.2 ICT use (indicator variables)

PISA has devised a questionnaire on ICT that relies on students' level of ICT familiarity, perceived ICT proficiency, and frequency of ICT use. Five indices related to ICT were derived using the item response theory (IRT) of PISA 2018. In this study, all five indices were utilised to identify factors associated with using ICT. The first factor is.

  1. 2.2.1.

    The HOMESCH index measures students' use of technology outside of school for academic purposes. An example item from HOMESCH is "Browsing the Internet for schoolwork."

  2. 2.2.2.

    The USESCH index pertains to students' overall usage of ICT in school and is obtained through a 10-question survey (IC011). For instance, a USESCH index might encompass activities like accessing, uploading, or downloading materials from the school's website.

  3. 2.2.3.

    The ENTUSE index expresses students' use of ICT in their spare time outside of school and is derived from a 13-item scale (IC008) that can be given as an example of "surfing the Internet for fun".

  4. 2.2.4.

    The ICTCLASS index is derived from a nine-item scale (IC150) and focuses on how much time students spend using topic-related ICT in lessons.

  5. 2.2.5.

    The ICTOUTSIDE index is derived from a similar scale (IC151) and is related to the time students devote to using ICT related to the subject outside the lessons. (OECD, 2017).

2.3 Student level covariates

  1. 2.3.1.

    Gender

    Gender was coded as a dummy variable as 1 = female and 2 = male.

  2. 2.3.2.

    Interest in ICT (INTICT)

    The INTICT index was used to determine students' interest in ICT. INTICT was calculated based on the IRT scaling of six questions related to students' ICT usage experiences (for example, "I forget the time when using digital devices"). The answers are "strongly disagree", "disagree", "agree", and "strongly agree". The INTICT value is standardised as 0 with a mean of 0 and a standard deviation of 1.

  3. 2.3.3.

    Perceived competence in ICT (COMPICT)

    The COMPICT index was used to determine students' perceived competence in using ICT. COMPICT was calculated based on a five-item IRT scale of students' experiences with ICT (for example, "I feel comfortable using digital devices that I am not very familiar with"). The answers are "strongly disagree", "disagree", "agree", and "strongly agree". COMPICT is standardised with a mean of 0 and a standard deviation 1.

  4. 2.3.4.

    Perceived ICT autonomy (AUTICT)

    The evaluation metric "ICT autonomy" measures students' level of control and independence while using ICT tools. This metric is based on five queries: "I can install new software on my own." In OECD countries, AUTICT has an average of zero and a standard deviation of one, comprising five questions. The available answers include "strongly disagree," "disagree," "agree," and "strongly agree."

  5. 2.3.5.

    ICT use in social interaction (SOIAICT)

    Social media use was assessed with five questions. It measures the degree to which students communicate and interact with others using ICT tools (For example, "I like to meet friends and play computer and video games with them"). The COMPACT has created five questions with an average of zero and a standard deviation of 1 in OECD countries. The answers “strongly disagree”, “disagree”, “agree”, and “strongly agree” are shaped like this.

  6. 2.3.6.

    ICT resources (ICTRES)

    The index is based on students' answers to whether they have the following features at home: educational software, internet connection, mobile phone with internet access, computers, tablet computers, and e-book readers.

  7. 2.3.7.

    Academic success

    The PISA exam uses ten plausible values to assess students' reading, math, and science achievement scores. These values are derived from the test items' posterior distribution and IRT scaling. They represent the potential scores of all students with similar characteristics and item response patterns. The average of ten reasonable values (PV1MATH to PV10MATH) is calculated to determine the math score. Similarly, the reading and science scores are determined using (PV1READ—PV10READ) and (PV1SCIE—PV10SCIE), respectively.

  8. 2.3.8.

    ESCS

    The ESCS index was used to indicate the socioeconomic status of the students. He used three indices to calculate the ESCS value: The parents' highest occupational status, parental education level (in years), and family wealth.

2.4 Student level covariates

  1. 2.4.1.

    School type (SC013Q01TA)

    School type was coded as a dummy variable with 1 = private School and 2 = Public School.

  2. 2.4.2.

    Number of Computers Per Student (RATCMP1)

    This index defines the ratio of students in a school to available computers. If the index is less than 1, there are more students than the computer, and if the value is greater than 1, there are fewer students than the computer.

  3. 2.4.3.

    Number of Internet-Connected Computers Per Student (RATCMP2)

    This index defines the ratio of students in a school to computers connected to the Internet. If the index is less than 1, there are more students than the computer connected to the Internet, and if the value is greater than 1, there are fewer students than the computer connected to the Internet.

  4. 2.4.4.

    Lack of educational material (EDUSHORT)

    The EDUSHORT index is based on school principals' views on how educational materials in their schools affect their teaching activities. School principals were asked to report the positive or negative effects of the lack of educational materials on their teaching activities. The index means zero among OECD countries, and the standard deviation is one. Positive values indicate that scarcity of educational resources hinders educational activities. Negative values indicate the lack of educational material does not somehow affect learning activities.

  5. 2.4.5.

    Education Staff Shortage (STAFFSHORT)

    The STAFFSHORT index is based on school principals' responses to the presence of human resources in their schools. It has been tried to determine whether the education given in the schools is affected by the lack of teachers and auxiliary personnel. The index means zero in OECD countries, and the standard deviation is one. Positive values indicate that the lack of teachers negatively affects teaching activities. Negative values indicate that the lack of teachers hinders teaching activities less.

2.5 Weighting

Even though students are selected randomly for the PISA sample, the likelihood of being chosen varies. As a result, survey weights are required to accurately determine the number of students in the PISA population that the sample represents. Student weights (W_FSTUWT) and school weights (W_SCHGRNRABWT) were both used in the analysis. This was done to ensure that the sample accurately represented the PISA population.

2.6 Analytical approach

MLPA and MLRA are two analytical techniques widely used in educational research at school and student levels to explore the linkages between student qualifications, school-level factors, and academic outcomes based on the PISA-2018 dataset. The multilevel modelling approach helps researchers examine how individual and contextual factors interact with student achievements while explaining the hierarchical nature of the data associated with students within schools. Person-centred latent profile analysis (LPA) approaches may be more appropriate than variable-centred approaches to identify ICT usage patterns. Person-centred methods can overcome many methodological assumptions and limitations of variable-centred approaches that assume the relationships between variables are the same across individuals in a population or sample, making it challenging to identify unobserved subgroups. A person-centred LPA approach can identify unobserved subgroups (e.g., latent profiles) within a population and group students with similar response patterns. Additionally, LPA does not require the assumption of normal distribution for the data. In the analysis, single-level LPA models were developed before creating the alternative MLPA models. The single-level LPA model helps to illustrate the difference between single and multi-level LPAs. Two alternative methods were used for single-level LPA: the first used unequal means and variances between profiles, and the second used only unequal means. In line with general practice, the covariances between observed variables were fixed to zero according to the LPA assumptions instead of being freely estimated. In the MLPA model, however, the Level-2 variations in the Level-1 profiles and the variances associated with the variations in the profile size were estimated freely at Level-2. Level-1 profiles were freely predicted in Level-2 latent profile covariates.

3 Findings

Emphasis is placed on the importance of considering ICC (intraclass correlation coefficients) when conducting a multilevel analysis of hierarchical data (here, between schools and within students) because it helps to identify the rate of variance attributable to different levels (Cohen, 1988). A significant ICC indicates significant variation between groups, and the analysis should consider higher-level units.

The correlations of ICC and student-school level indicators are given in Table 1. As seen in Table 1, ICC was significant for each of the five ICT usage variables, with significant differences between Level 2 units (i.e. schools). This result reveals that there are differences in the use of ICT not only among students but also between schools.

Table 1 The correlations of ICC and indicators at the student and school levels
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3.1 Model estimates

Mplus 8.1 programs were used in the analysis. The MLR (maximum likelihood parameter estimates) algorithm in Mplus was used for model estimates. Since the MLR algorithm gives better results in analysis, it is preferred to be used in mixture models. At the same time, this algorithm is relatively sensitive to missing data. A set of 1,000 initial values was used in the analysis. It was repeated 50 times to obtain the optimal result. If the log probability value is not repeated in the analysis or there is a convergence problem, the initial value should be increased.

3.1.1 Single level LPA

For the first research question, a single-level LPA analysis was conducted in the first stage to determine the ICT usage profiles of Turkish students. This analysis produced LPA models with unequal variances and LPA models with freely estimated means only.

Research has proven that LPAs that incorporate unequal variances produce more precise and accurate outcomes than models that only estimate means (Diallo et al., 2016; Peugh & Fan, 2013). Models with equal and unequal variances ranging from 1 to 5 were estimated to determine the optimal number of profiles. Various well-known model fit indices, such as Akaïke's information criterion (AIC), Bayesian Information Criteria (BIC), sample size adjusted BIC (SABIC), Lo, Mendell and Rubin's likelihood ratio test (LMR), and entropy (Morin, 2015), were assessed.

It is also essential to remember that when determining the appropriate number of profiles, the theoretical appropriateness and meaning of the selected profile solution should be considered (Foti et al., 2012). While the BIC value of the 3-profile model with equal variances was s 91,002.860, the BIC value of the model with unequal variances was 87,012.030. The best solution was the 3-profile model with unequal variances (Table 2).

Table 2 Fit indices of profiles
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The elbow plot was studied for one to five solution profiles (Fig. 2). When the two-profile and three-profile models were examined, it was seen that the BIC value of the three-profile model decreased compared to the two-profile model. Convergence problems were experienced after the four profile models. Therefore, the LMR value is not given. Although the four-profile model's BIC, AIC and SABIC values are lower than the three-profile model, it was not preferred because it gave insufficient entropy value. It was decided that the most suitable model was the three-profile solution. Figures 3  (standardised scores) and 4 (raw scores) show the three-profile solution.

Fig. 2
figure 2

Elbow plot of fit indices of profiles

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Fig. 3
figure 3

Raw indicator scores of three profile solutions with unequal variances for Level 1

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Fig. 4
figure 4

Standardised indicator scores of the three-profile solution with unequal variances for Level-1

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3.2 Profile features

After Single Level LPA, three different profiles were defined according to the type and frequency of ICT use by students.

  • Profile 1 (Low-level ICT Users): 19.06% of the students are in this profile. This profile has the lowest indicators ENTUSE (M = -1.034), HOMESCH (M =—0.846), USESCH(M = -1.119), ICTCLASS(M = -0.444), ICTOUTSIDE(M = -0.914). The lowest indicator of ICT use is the use of ICT in school for learning.

  • Profile 2 (Intermediate-level ICT Users): 71.10% of the students are in this profile. It is the most significant profile. Indicators in this profile have means values ENTUSE (M = -0.032), HOMESCH (M = 0.223), USESCH (M = -0.048), ICTCLASS (M = 0.414), and ICTOUTSIDE (M = 0.187). The lowest indicator of ICT use is the use of ICT in school for learning.

  • Profile 3 (Advanced-Level ICT Users): 9.84% of students are in this profile. It is the smallest profile. This profile has the highest indicators ENTUSE (M = 1.062), HOMESCH (M = 1.176), USESCH (M = 0.677), ICTCLASS (M = 0.346), ICTOUTSIDE (M = 0.351). The highest indicator is using ICT for home learning (school-related activities).

Before the 2nd and 3rd research questions, a multilevel analysis of variance was performed to decide whether a two-level analysis, at Level 1 (student) and Level 2 (school), is necessary.

In the MLPA analysis, the variations of Level-1 profiles on Level-2 were first tested. This is done by taking the initial values of the single-level LPA' and fixing zero. In this way, Level-1 profiles are kept constant. Random effect MLPA results showed that students' ICT usage profiles differed between schools (variance estimate = 0.742, SE = 0.291, p = 0.011 and variance estimate = 0.701, SE = 0.188, p = 0.000).

Level-2 profiles were then estimated based on the relative frequency of Level-1 profiles. Level-2 hidden profile solutions were compared using the BIC criterion (Finch & French, 2014). The corresponding values for one to three-profile solutions are 88,107,691, 87,439,979 and 87,196,125. Although three profile levels-2 and three solutions levels-1 were the best solutions, this model was not preferred because some profiles were assigned less than 5% (0.02% (n = 2)). As a result, two profile level-2 and two and three solution level 1 were deemed appropriate. These profiles are graphically illustrated in Fig. 5.

Fig. 5
figure 5

Level 2 profile according to the relative frequency of Level 1 profiles

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The results show that the first Level-2 covert profile is representative of schools where most of their students (72.89%) are involved. These schools are labelled as " ICT high resources schools". The Second Level -2 covert profile represented schools with 27.11% of all students. For this reason, the latent profile-2 is labelled as " ICT low resources schools". The distribution of profiles by schools is given in Fig. 5.

When Fig. 5 is examined, 1.82% of advanced ICT users are in " ICT resources low schools", and 9% of advanced ICT users are in " ICT high resources schools".

The fact that multilevel analysis of variance gives meaningful results reveals the necessity of a two-level analysis. Based on the 2 and 3 research questions, an MLRA was conducted to determine the effect of student- and school-level covariates on the profiles defined according to the types and frequencies of ICT use (Table 3).

Table 3 Multi-level multinomial logistics regression analysis results
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Profile-3 was used as the reference group to elucidate covariates in the analysis. Covariates with a p-value of ≤ 0.05 were considered significant. When Profile-3 and Profile-1 were compared, it was seen that ESCS, mathematics, reading and science covariates did not predict profile memberships. It was observed that males (ODD = 0.367) and students with high ICT resources were less likely to be in ICTRES(ODD = 0.327) profile-1. Students with high ICT motivation scores such as INTICT (ODD = 0.480), COMPICT (ODD = 0.635), AUTICT(ODD = 0.761) and SOIAICT(ODD = 0.441) are less likely to be in this class. When profile-3 and profile-2 are compared, it is seen that males (ODD = 0.346) and students with high ICT resources ICTRES(ODD = 0.639) are less likely to be in profile-2. Similarly, students with high ICT motivation scores such as INTICT (ODD = 0.671), AUTICT(ODD = 0.842) and SOIAICT(ODD = 0.623) are less likely to be on Profile-2.

At the school level (Level-2), it is seen that none of the SC013Q01TA, RATCMP2, EDUSHORT, or STAFFSHORT variables predict Profile-1 and Profile-2 profile memberships. Students studying in schools with a high number of computers per student have been assigned fewer to Profile-1 (standardised estimate = -1.474, SE = 0.625, p < 0.05) and Profile-2 (standardised estimate = -0.893, SE = -2.232, p < . 0.05).

4 Discussion

This study aimed to discover the patterns of ICT usage among Turkish students who took the 2018 PISA exam, both homogeneous and heterogeneous. The study was based on the frequency and types of ICT usage and revealed three different profiles, indicating that Turkish students use ICT for varying purposes and at different frequencies. Previous studies on LPA had focused on students' use of ICT for entertainment and school purposes, but these studies did not consider motivational variables related to ICT usage. Tømte and Hatlevik (2011) created six ICT user profiles by combining ICT use with entertainment and school purposes, based on analysing 2006 PISA data of Norwegian and Finnish students. They found that Norwegian students' self-efficacy varied across ICT user profiles. Using 2018 PISA data, Xiao and Sun (2022) identified two student profiles based on ICT usage in different settings for different purposes for 4,838 students in the USA. Similarly, Kim and Kim (2023) aimed to determine the ICT familiarity profiles of students from high-performing countries in the 2018 PISA. They identified four profiles for Estonia, Hong Kong, Ireland, New Zealand, Poland and Sweden, three for Finland and Korea, two for Macau and five for Singapore. Similarly, Karakoyun and Başaran (2022) identified four profiles based on Turkish students' ICT use in PISA-2018 at home and school. When the profiles were analysed, it was seen that 71.10% of Turkish students (n = 4678) used ICT at a moderate level. When the purposes of ICT use were analysed, it was found that it was least used for learning purposes at school and mainly for school activities at home. The proportion of high ICT users in the sample was the lowest (9.84%; n = 647), and it was observed that students in this profile mostly used ICT for school activities at home and spent less time in lessons. Low ICT users constitute 19.06% (n = 1254) of the students. Like other developing countries, the low ICT use in schools in Turkey may be due to a lack of access to ICT resources or low levels of ICT integration in classrooms. The "Increasing Opportunities in Education and Improving Technology" (FATIH) project, which is presented as a large-scale reform movement aiming to integrate education with technology in Turkey, can be cited as an example (Kaya & Yılayaz, 2013). The main aim of the FATIH project is to appeal to more senses by using the latest ICT in teaching and learning environments. Although projects such as FATIH provide teachers with smart boards and internet support, it can be said that students in Turkish schools do not use ICT actively and sufficiently. A recent study revealed that students aged 15 in OECD countries spent more than one hour online daily, outside school, between 2012 and 2018. The study also found that students spent an average of three hours online on weekdays and approximately 3.5 h a day on weekends in 2018, indicating that they use digital technology more frequently at home than at school. This finding also concludes that Turkish students primarily use ICT at home for school-related activities. Further, the study suggests that using digital technologies depends on individual effort, and it employed a person-centred approach rather than a variable-centred one. Additionally, studies by Murphy and Beggs (2003) and Rohatgi and Throndsen (2015) also support the notion that students use ICT more frequently at home than at school.

The MLRA technique was employed to examine the potential impact of various factors, including students' background, motivation, academic performance, and school-level variables, on their ICT profile. The analysis incorporated both student and school-level variables. One noteworthy finding was that males use ICT more frequently than females, indicating a significant gender difference. This result is consistent with Xiao and Sun's (2021) study, which explored eight countries, including Estonia, Hong Kong, Ireland, New Zealand, Poland, Sweden, Finland, and Korea. Kim and Kim (2023) also discovered that ICT familiarity is high in all countries except Korea, and male students use ICT more frequently than female students. However, no gender-based difference was reported in Korea since most students had low ICT familiarity. Becker (2022) has shown in his studies on gender differences that males have an advantage in ICT use compared to females. It was also proposed that there is a higher likelihood of male students using ICT more frequently during their free time and subsequently utilising it for educational purposes. These findings are consistent with other research that indicates males tend to use ICT more frequently than females (Drabowicz, 2014; OECD, 2015a, b). On the other hand, Siddiq and Scherer (2019) have used a meta-analysis to examine students' ICT literacy performance by gender. They found that females have more positive and higher ICT literacy than males. The different results from various studies suggest that ICT use or ICT familiarity may differ from ICT literacy, and gender-based comparisons may also yield different results. Therefore, gender roles and expectations may affect male and female students' ICT use differently.

Another finding of this study is that ESCS does not meaningfully predict profile membership. This finding shows that ESCS has no significant relationship with students' ICT usage profiles. Although some studies suggest a correlation between ESCS and ICT use, other studies have demonstrated that this correlation is either weak or insignificant. For example, in LCA studies covering eight countries, Kim and Kim (2023) found that students with low ESCS also have low ICT familiarity. In their research, Liao et al. (2016) discovered that there is a direct correlation between an individual's familiarity with ICT and their level of usage. However, Scherer and Siddiq (2019) found that there is only a weak relationship between Decss and ICT usage and that this relationship has no socioeconomic impact on ICT usage. In different studies, it has been found that students with higher ESCS use ICT more often for entertainment and school activities than students with lower ESCS (Scherer et al., 2017).

Furthermore, in this study, students with more ICT resources (ICTRES) used ICT more frequently. Differences in the availability of ICT resources can result in varying levels of ICT usage among students. Students with limited access to ICT resources may be unable to participate in ICT activities and acquire the necessary skills (Harris et al., 2017; OECD, 2018). This finding is consistent with the conclusion that secondary school students' ICT use is proportional to the availability of ICT resources. The relationship between ICT motivation variables and student profiles was estimated in detail. As a result, INTICT (interest in ICT), COMPACT (perceived ICT competence), AUTICT (perceived ICT autonomy) and SOIAICT (social use of ICT) significantly predicted profile membership. Students in Profile-1 and Profile-2 had lower mean scores for INTICT, COMPACT, AUTICT and SOIAICT than students in Profile-3. Positive attitudes, competencies, skills and social interaction are the most important determinants of students' ICT use. Tømte and Hatlevik (2011) and Scherer et al. (2017) confirmed that student profiles can be identified based on ICT engagement variables. Students who frequently use ICT at home for leisure and academic activities were found to have higher ICT competence. These students should be encouraged to use ICT more accurately and effectively. In other words, the frequency or quantity of ICT use may not be necessary. Purposeful and non-conscious ICT use may not enable students to achieve the expected gains. These results are consistent with studies showing that the quality of ICT use is proportional to conscious use rather than quantity (Lee & Wu, 2012; Lei et al., 2021; Petko et al., 2017).

After conducting research, it has been discovered that there is no link between the latent profiles of students and their academic performance in reading, math, and science. This outcome is similar to previous studies that failed to establish a connection between academic achievement scores and using information and communication technology (ICT) (Aypay, 2010; Fairlie & Robinson, 2013). However, it contradicts other studies that have found a correlation between the two (Bowman et al., 2010; Lee & Wu, 2012; Xiao & Sun, 2021). The research has also highlighted that Turkish students tend to use ICT devices more outside of school, which aligns with other studies that suggest using ICT for activities outside of school can have a negative or no impact on academic achievement (Román Carrasco & Murillo Torrecilla, 2012; Kunina-Habenicht & Goldhammer, 2020). Similarly, Agasisti et al. (2020), using data from 15 European countries from PISA 2012, identified the consequences of students' ICT use at home on their academic performance. In most countries, students frequently use computers for homework, which reduces academic achievement scores in all subjects. High frequency of ICT use for entertainment outside of school can put students at an academic disadvantage compared to their peers (Petko et al., 2017; Rodrigues & Biagi, 2017). Overusing ICT gadgets may lead to a high potential for distraction that adolescent students may be unable to handle.

An investigation was carried out by Ananiadou and Claro in 2009 to study the impact of ICT use on the 21st-century skills of students in OECD countries. Based on PISA data, the study discovered a positive correlation between science, maths, and reading skills and ICT use. However, it was observed that this relationship was limited to regular and practical use, and excessive use could have a detrimental effect on students' academic performance. In their study, Biagi and Loi (2013) used econometric methods to determine the association between learning outcomes and ICT use. The results showed that students' ICT skills had a beneficial impact on their science, maths, and reading skills. Cengiz and Ercan (2019) conducted a study on primary school 7th-grade students in Turkey, which found that using ICT improved math and science achievement.

Based on Research Question-3, considering the data's nested nature, a multilevel variance analysis was conducted to determine whether school-level variables create differences in ICT usage profiles and whether Level-1 (student) profiles change at Level-2 (school). The analysis yielded two variance components. This result proves that profiles differ from school to school.

As a result of the two-level profile analysis, two school profiles were identified. These schools were named " ICT high resources schools " and " ICT low resources schools ". Most students who use ICT at the intermediate level (45.8%) and advanced level (9%) are educated in schools with high ICT use. Intermediate (22.45%) and advanced (1.82%) ICT users are clustered in schools with low ICT resources. This shows the importance of the digital capacity of schools in ICT use.

The concentration of students who frequently use ICT in educational institutions with significant ICT capability is an essential issue in contemporary education. This phenomenon can be attributed to several factors. Initially, educational institutions with significant ICT capability often provide a more favourable environment for students to use these technologies and thus attract students who are predisposed to ICT use (Agbo, 2015; Faisal et al., 2022). Such educational institutions typically have superior infrastructure, including fast internet connections and more advanced hardware, which can enrich the educational experience for students adept at using ICT (Timotheou et al., 2022). Second, integrating ICT into education has been associated with increased student performance. For example, it has been found that the frequency of students participating in online entertainment outside of school positively affects their performance in courses such as science and mathematics (Courtney et al., 2022). As a result, students who use ICT may be interested in institutions with high ICT capacity due to the potential academic benefits. However, it is essential to recognise that the concentration of students with ICT skills in certain educational institutions may create digital inequality, and students studying at educational institutions with weaker ICT skills may not have the same opportunities to develop ICT skills. This could potentially exacerbate inequality in education. Although the concentration of ICT students in institutions with high ICT skills can improve these students' educational experience, institutions must provide equal opportunities for all students to develop ICT skills, regardless of their ICT skills. For ICT capacity (Timotheou et al., 2022). As a result of the variance analysis, Level 2 variables were included in the MLRA. The difference between schools could not be explained by the variables of private school or public school (SC013Q01TA), the ratio of the number of computers connected to the Internet to the number of students (RATCMP2), educational materials (EDUSHORT) and lack of staff (STAFFSHORT). This situation can only be explained by the number of computers per student (RATCMP1). The number of computers per student may affect the availability and frequency of ICT students use. Research conducted in primary and secondary schools in Turkey (Bulunuz et al., 2014; Aşıcı & Altınkurt, 2016) have shown that more computers per student have a positive effect on students' ICT usage and access. A study by Tamim and Grant (2013) found that the number of computers per student in US schools affects students' ICT skills. This study found that students in schools with more computer users had higher IT skills. Zawacki-Richter and Anderson (2014) investigated the relationship between computer use and ICT use by school students in Germany. Researchers have found that students use ICT as the number of computers per student increases. The Decoupling between private and public schools could not explain the level difference. As a result of this study, Kim et al. (2021) found that private school students use information technology and more. Still, there is no significant difference in ICT use between private schools. Dec. The literature shows that private schools with better access to ICT resources are generally more likely to use ICT. First, data from the International Teaching and Learning Survey (TALIS) shows that private schools are more advanced in the use of technology than public schools. According to these data, private schools offer more accessible access to technology, and teachers use more technology (OECD, 2020). A meta-analysis reported that private schools outperform public schools in the use of technology (Dumont et al., 2010). This study also shows that private schools are more willing to use technology and encourage their students to use technology more often. Finally, international student achievement studies show private schools are more successful in using technology than public schools (OECD, 2019). Although the difference between private and public schools is insignificant in this study, the literature generally shows that private schools' use of technology positively affects student achievement.

Among the level-2 covariates, educational materials (EDUSHORT) and staff shortages (STAFFSHORT) could not explain the difference in variance between levels. In contrast to this finding, Göktaş et al. (2013) ranked the significant barriers to ICT use in the classroom (post-2000) as follows: lack of in-service training, lack of technical support, lack of equipment, lack of ICT/skills, lack of appropriate software, hardware, lack of appropriate administrative support and time. UNESCO (2019) report states that the lack of training materials and personnel in schools in Turkey limits students' use of ICT. Kılıç (2019) emphasises the need for adequate training materials and qualified staff for successful school ICT integration. Similarly, Leung and Wong (2017) emphasise the need for adequate educational materials and qualified personnel for students' digital literacy.

5 Conclusion and implications

The study found that most Turkish students use ICT moderately in school lessons and mostly at home. The PISA-2018 exam was conducted before the COVID-19 pandemic, which caused the use of ICT for educational purposes at home to become more critical. Many countries have tried to address the adverse effects of school closures by utilising online learning (World Bank, 2021). Removing the obligation to attend school during the COVID-19 pandemic has increased the importance of ICT use at home. ICT use is related to students' learning opportunities. Inequalities in ICT access, ICT skills and familiarity with ICT use for educational purposes directly affect ICT use (Van Deursen & Helsper, 2015). One of the conclusions from this study is that students need to be supported to familiarise themselves with the use of ICT for educational purposes. This can be supported by a more vital inclusion of ICT in schools.

The high accessibility of frequent ICT use outside and at school can distract many students and lower their achievement levels. Even if they use ICT for relevant purposes at school, they may be distracted. This may reduce their academic performance in science and maths courses that require focus and concentration (Hu et al., 21).

One of the essential results obtained is that students with high attitudes towards ICT also have high interest in ICT, perceived ICT competence, perceived autonomy in ICT use and frequency of using ICT for social interaction. Previous research has shown that three innate psychological needs (competence, autonomy and social connectedness) (Deci & Ryan, 2000) predict students' ability to fulfil related tasks (Krapp, 2002; Minnaert, 2007). In other words, students with high ICT psychological need satisfaction are more willing to fulfil ICT tasks.

However, the study revealed that students' ICT use did not significantly affect their academic achievement. Therefore, it is strongly recommended that teachers, schools, administrators and policymakers pay much more attention to identifying how best to improve students' educational practices and learning outcomes. Investing more in equipping schools with ICTs and incentivising them to increase the use of ICTs in teaching may not always lead to positive results. Srijamdee and Pholphirul (2020) stated that governments should be encouraged to direct universities to K-12 schools and provide guidance to students and their parents for the most effective use of ICT. Such strategies lead to more effective outcomes in ICT utilisation. National and local education authorities should invest more in ICT infrastructure and create a social environment for fair sharing of high-quality ICT resources. Furthermore, various measures should be taken to bridge the digital divide between rural and urban schools. The spread of ICT resources to rural areas should be accelerated. The gap between urban and rural areas should be narrowed to increase ICT adoption among schools, teachers and students. Furthermore, teachers should assign challenging ICT-based learning tasks to develop students' ICT usage habits, interest in ICT and positive ICT psychological attitudes. When the role of gender was examined, the analysis revealed that male students use ICT more than female students. The study found that students with high ICTRES used ICT more, but ESCS had no effect. At the school level, the school type (public and private) variables, RATCMP2, EDUSHORT and STAFFSHORT, had no significant effect. On the other hand, it was found that students in schools with more computers per student used ICT more.

One of the main findings of the research is that two school profiles were identified at level 2. These schools were named " ICT high resources schools " and " ICT high resources schools ". While most students with low and medium ICT use are educated in schools with low ICT use, most students with high ICT use are educated in schools with high ICT use.

It is seen that ICT usage is low in schools in Turkey. Various suggestions were made to teachers for students to use ICT more in schools:

  • Make Students Learn More About ICT: You can improve students' attitudes towards using ICT by increasing their knowledge. For this purpose, you can prepare materials to provide students with more information about ICT.

  • Improve Students' ICT Skills: Improving students' ICT skills can help them to use ICT more often. For this purpose, you can offer materials or training courses to improve learners' ICT skills.

  • Explain the Benefits of Using ICT to Learners: Explaining the benefits of using ICT to learners can help them to use ICT more often. For this purpose, you can prepare materials explaining the impact of ICT use on students' education, work and personal lives.

  • Personalize Students' ICT Use: Customizing students' ICT use according to their interests or needs can help them use ICT more often. For example, you can give students more freedom to use ICT for collaboration or projects.

  • Build Learners' Confidence: Making students more confident in using ICT can help them use ICT more often. To this end, you should praise students' ICT skills, forgive their mistakes and celebrate their successes.